From ca3da813b92450205c951b403dcc0032e736efce Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 09:58:06 -0700 Subject: [PATCH 01/11] Remove unused autogen submodule Co-Authored-By: Claude Fable 5 --- .gitmodules | 3 --- autogen | 1 - 2 files changed, 4 deletions(-) delete mode 160000 autogen diff --git a/.gitmodules b/.gitmodules index b0f5705..e69de29 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +0,0 @@ -[submodule "autogen"] - path = autogen - url = https://github.com/microsoft/autogen.git diff --git a/autogen b/autogen deleted file mode 160000 index a9d2927..0000000 --- a/autogen +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a9d292780bd7328244b45ef794e741a09776e1ab From 5274e1ee068226a5d37d502f92f7d6c9d6cdc917 Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 09:58:20 -0700 Subject: [PATCH 02/11] Delete empty .gitmodules Co-Authored-By: Claude Fable 5 --- .gitmodules | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .gitmodules diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index e69de29..0000000 From 16aa6c0007cc78cfb4678b339b42d48c24fb1e4c Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 10:32:14 -0700 Subject: [PATCH 03/11] readme update --- README.md | 463 ++++++++--------------------- docs/eval_reproducibility.md | 197 ++++++++++++ figures/fara_hero_barchart.png | Bin 0 -> 122041 bytes figures/fig2_fara_agentic_loop.png | Bin 0 -> 724335 bytes 4 files changed, 326 insertions(+), 334 deletions(-) create mode 100644 docs/eval_reproducibility.md create mode 100644 figures/fara_hero_barchart.png create mode 100644 figures/fig2_fara_agentic_loop.png diff --git a/README.md b/README.md index ce5122f..2a3be1b 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,17 @@
-# Fara-7B: An Efficient Agentic Model for Computer Use +# Fara-1.5: Scalable Learning Environments for Computer Use Agents -Fara-7B Performance +Fara-1.5 Performance -[![Microsoft](https://img.shields.io/badge/Microsoft-Project-0078D4?logo=microsoft)](https://aka.ms/msaif/fara) -[![Hugging Face Model](https://img.shields.io/badge/🤗-Model-yellow)](https://huggingface.co/microsoft/Fara-7b) -[![Foundry](https://img.shields.io/badge/Azure-Foundry-0089D6)](https://aka.ms/foundry-fara-7b) +[![Microsoft](https://img.shields.io/badge/Microsoft-Project-0078D4?logo=microsoft)](https://aka.ms/fara1.5) +[![Paper](https://img.shields.io/badge/Paper-2606.20785-red)](https://arxiv.org/abs/2606.20785) +[![Blog](https://img.shields.io/badge/MSR-Blog%20Post-0078D4)](https://www.microsoft.com/en-us/research/articles/fara1-5-computer-use-agent/) +[![Foundry](https://img.shields.io/badge/Azure-Foundry-0089D6)](https://ai.azure.com/catalog/models/Fara1.5-9B) [![Dataset](https://img.shields.io/badge/🤗-WebTailBench%20Dataset-orange)](https://huggingface.co/datasets/microsoft/WebTailBench) [![Dataset](https://img.shields.io/badge/🤗-CUAVerifierBench-orange)](https://huggingface.co/datasets/microsoft/CUAVerifierBench) -[![Paper](https://img.shields.io/badge/Paper-2511.19663-red)](https://arxiv.org/abs/2511.19663)
@@ -19,14 +19,22 @@ ## Updates -* **2026-05-21** - Fara1.5 agent harness coming soon! -* **2026-05-12** — Refreshed **WebTailBench (V2)** tasks and rubrics. - Many V1 tasks had calendar-bound dates that expired (Nov 2025); V2 - rolls those forward and revises the precomputed rubrics for the - full 609-task suite. Available now as the `test_v2` split on - [`microsoft/WebTailBench`](https://huggingface.co/datasets/microsoft/WebTailBench). - A side-by-side V1↔V2 diff (task strings and rubric JSON) is - hosted [here](https://microsoft.github.io/fara/docs/webtailbench_v1_v2_diff.html). +* **2026-07-17** — **Fara-1.5 released!** A family of native computer use + agents at three scales (Fara1.5-4B, Fara1.5-9B, Fara1.5-27B) built on + Qwen3.5 and trained on data from the **FaraGen1.5** pipeline. Fara1.5-9B + reaches 63.4% on Online-Mind2Web and 86.6% on WebVoyager, a new state of + the art for its size class; Fara1.5-27B achieves 72.3% on Online-Mind2Web, + outperforming much larger proprietary systems. Read the + [paper](https://arxiv.org/abs/2606.20785) and the + [blog post](https://www.microsoft.com/en-us/research/articles/fara1-5-computer-use-agent/). +* **2026-05-12** — **WebTailBench** is now a first-class benchmark in this + repo: the loader auto-downloads tasks and rubrics from + [`microsoft/WebTailBench`](https://huggingface.co/datasets/microsoft/WebTailBench) + (use the refreshed `test_v2` split; a V1↔V2 diff is hosted + [here](https://microsoft.github.io/fara/docs/webtailbench_v1_v2_diff.html)), + the **Universal Verifier** (`MMRubricAgent`) is the official judge, and the + reproducibility CLI lives in `webeval/scripts/webtailbench.py` (stand-alone + re-scoring via `webeval/scripts/verify_trajectories.py`). * **2026-04-19** — Released **[CUAVerifierBench](https://huggingface.co/datasets/microsoft/CUAVerifierBench)**, a human-annotated benchmark for evaluating CUA verifiers (i.e. judges that score agent trajectories). Two splits — `fara7b_om2w_browserbase` (106 @@ -35,31 +43,22 @@ with per-judge UV-blind / UV-informed labels, Universal Verifier outputs, and legacy verifier outputs side-by-side. The build script that produced the dataset lives alongside the data on HuggingFace. -* **2026-04-18** — Removed the `autogen-core` / `autogen-ext` dependency - from `webeval`; chat completion clients are now self-contained under - `webeval/src/webeval/oai_clients/`. No more autogen submodule install - step; just `pip install -e .[vllm]` then `cd webeval; pip install -e .`. -* **2026-04-18** — Incorporated WebTailBench (initial / now-stale - version) directly into the repo as a first-class benchmark. The - loader auto-downloads `WebTailBench-v1-rubrics.tsv` from - [`microsoft/WebTailBench`](https://huggingface.co/datasets/microsoft/WebTailBench) - and threads each task's published `precomputed_rubric` through to - the verifier. Reproducibility CLI lives in `webeval/scripts/webtailbench.py`. -* **2026-04-18** — Released the **Universal Verifier** (`MMRubricAgent`) - as the official verifier for WebTailBench. Multimodal, - rubric-grounded, two-model ensemble (`gpt-5.2` + `o4-mini`) with - per-criterion scoring, outcome verification, and first-point-of-failure - analysis. A stand-alone parallel runner is at - `webeval/scripts/verify_trajectories.py` for re-scoring any directory - of webeval trajectories without touching the solver. --- ## Overview -**Fara-7B** is Microsoft's first **agentic small language model (SLM)** designed specifically for computer use. With only 7 billion parameters, Fara-7B is an ultra-compact Computer Use Agent (CUA) that achieves state-of-the-art performance within its size class and is competitive with larger, more resource-intensive agentic systems. +**Fara-1.5** is a family of native Computer Use Agents (CUAs) at three scales — **Fara1.5-4B**, **Fara1.5-9B**, and **Fara1.5-27B** — built on Qwen3.5 and trained with supervised finetuning on data from **FaraGen1.5**, our scalable data pipeline of environments, solvers, and verifiers. Each model sets a new state of the art for its size class on browser-use benchmarks, and Fara1.5-27B outperforms much larger proprietary systems such as OpenAI Operator and Gemini 2.5 Computer Use on Online-Mind2Web. + +Fara-1.5 models operate through an observe-think-act loop: given a screenshot of the browser and the conversation history, the model reasons about the state of the task and outputs an action — mouse and keyboard inputs on directly predicted coordinates, web searches, or context management operations — with no accessibility trees or separate parsing models. + +
+Fara-1.5 Observe-Think-Act Loop +
+ +All three models are available on Azure Foundry: [Fara1.5-4B](https://ai.azure.com/catalog/models/Fara1.5-4B), [Fara1.5-9B](https://ai.azure.com/catalog/models/Fara1.5-9B), and [Fara1.5-27B](https://ai.azure.com/catalog/models/Fara1.5-27B). -Try Fara-7B locally as follows (see [Installation](#Installation) for detailed instructions on Windows ) or via Magentic-UI: +Try Fara1.5-9B as follows (see [Installation](#installation) for detailed instructions): ```bash # 1. Clone repository @@ -73,64 +72,78 @@ pip install -e . playwright install ``` -Then in one process, host the model: -```bash -vllm serve "microsoft/Fara-7B" --port 5000 --dtype auto +Deploy Fara1.5-9B from the [Azure Foundry catalog](https://ai.azure.com/catalog/models/Fara1.5-9B) and put your endpoint in a config JSON (e.g. `azure_foundry_config.json`): + +```json +{ + "model": "Fara1.5-9B", + "base_url": "https://your-endpoint.inference.ml.azure.com/", + "api_key": "YOUR_API_KEY_HERE" +} ``` + Then you can iteratively query it with: ```bash -fara-cli --task "whats the weather in new york now" +fara-cli --task "whats the weather in new york now" --endpoint_config azure_foundry_config.json ``` -To try Fara-7B inside Magentic-UI, please follow the instructions here [Magentic-UI + Fara-7B](https://github.com/microsoft/magentic-ui/blob/main/README.md#fara-7b). You will need to serve the model as before, but instead of fara-cli you can use Magentic-UI which has a nice UI (see video demos below). - +To try Fara inside Magentic-UI — a sandboxed browser environment with auditable action logging and user prompts at critical points — follow the instructions in the [Magentic-UI repo](https://github.com/microsoft/magentic-ui). You will need a model endpoint as before, but instead of fara-cli you can use Magentic-UI which has a nice UI (see video demos below). -Notes: -- If you're using Windows, we highly recommend using WSL2 (Windows Subsystem for Linux). Please see the Windows instructions in the [Installation](#Installation) section. -- You might need to do `--tensor-parallel-size 2` with vllm command if you run out of memory +Note: If you're using Windows, we highly recommend using WSL2 (Windows Subsystem for Linux). Please see the Windows instructions in the [Installation](#installation) section. - - - + + +
+ -**Shopping** - - - -**GitHub Issues** + - +
+ + -**Directions with Cheese** + -
-### What Makes Fara-7B Unique +### What Makes Fara-1.5 Unique + +Unlike traditional chat models that generate text-based responses, Fara-1.5 leverages computer interfaces—mouse and keyboard—to perform multi-step tasks on behalf of users. The models: + +- **Operate visually** by perceiving webpages and taking actions like scrolling, typing, and clicking on directly predicted coordinates without accessibility trees or separate parsing models +- **Enable efficient deployment** thanks to their compact sizes (4B/9B/27B), resulting in reduced latency and improved privacy +- **Interact with users**: trained with a user simulator on multi-turn rollouts, Fara-1.5 can ask for missing information, flag ambiguous tasks, and pause for approval before irreversible actions +- **Remain a research preview**: safety measures include refusing harmful tasks based on responsible AI policies, prompting users at critical interaction points, and auditable action logging through Magentic-UI's sandboxed browser environment -Unlike traditional chat models that generate text-based responses, Fara-7B leverages computer interfaces—mouse and keyboard—to perform multi-step tasks on behalf of users. The model: +### FaraGen1.5: Scalable Learning Environments -- **Operates visually** by perceiving webpages and taking actions like scrolling, typing, and clicking on directly predicted coordinates without accessibility trees or separate parsing models -- **Enables on-device deployment** due to its compact 7B parameter size, resulting in reduced latency and improved privacy as user data remains local -- **Completes tasks efficiently**, averaging only ~16 steps per task compared to ~41 for comparable models +Collecting computer use data from human demonstrations is expensive and slow. **FaraGen1.5** is a scalable data pipeline composed of three modular components: -Fara-7B is trained using a novel synthetic data generation pipeline built on the [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/) multi-agent framework, with 145K trajectories covering diverse websites, task types, and difficulty levels. The model is based on [Qwen2.5-VL-7B](https://arxiv.org/abs/2502.13923) and trained with supervised fine-tuning. +- **Environments** — open-internet tasks on live websites plus six synthetic FaraEnvs (Mail, Calendar, Stream, ML, Stay, Scheduler): functional UI clones that faithfully simulate domains gated by authentication or requiring irreversible actions, and unlock execution-based, ground-truth verification +- **Solvers** — a solver harness that can be powered by multiple models, including strong frontier models such as GPT-5.4, paired with a user simulator to enable multi-turn rollouts +- **Verifiers** — three complementary filters covering task correctness (the Universal Verifier LLM-judge), efficiency (scoring redundant actions), and critical-point adherence / user interaction (missing information, ambiguous tasks, unapproved irreversible actions) + +The resulting training mix contains roughly 2M samples: ~60% web trajectories, 12.8% synthetic environments, 12.5% form filling, 8.8% grounding, 4.9% VQA, and 0.8% GUI drag tasks. ### Key Capabilities -Fara-7B can automate everyday web tasks including: +Fara-1.5 can automate everyday web tasks including: - Searching for information and summarizing results - Filling out forms and managing accounts - Booking travel, movie tickets, and restaurant reservations @@ -139,56 +152,38 @@ Fara-7B can automate everyday web tasks including: ### Performance Highlights -Fara-7B achieves state-of-the-art results across multiple web agent benchmarks, outperforming both comparable-sized models and larger systems: - -| Model | Params | WebVoyager | Online-M2W | DeepShop | WebTailBench | -|-------|--------|------------|------------|----------|--------------| -| **SoM Agents** | | | | | | -| SoM Agent (GPT-4o-0513) | - | 90.6 | 57.7 | 49.1 | 60.4 | -| SoM Agent (o3-mini) | - | 79.3 | 55.4 | 49.7 | 52.7 | -| SoM Agent (GPT-4o) | - | 65.1 | 34.6 | 16.0 | 30.8 | -| GLM-4.1V-9B-Thinking | 9B | 66.8 | 33.9 | 32.0 | 22.4 | -| **Computer Use Models** | | | | | | -| OpenAI computer-use-preview | - | 70.9 | 42.9 | 24.7 | 25.7 | -| UI-TARS-1.5-7B | 7B | 66.4 | 31.3 | 11.6 | 19.5 | -| **Fara-7B** | **7B** | **73.5** | **34.1** | **26.2** | **38.4** | - -*Table: Online agent evaluation results showing success rates (%) across four web benchmarks. Results are averaged over 3 runs.* - -### WebTailBench: A New Benchmark for Real-World Web Tasks - -We are releasing **[WebTailBench](https://huggingface.co/datasets/microsoft/WebTailBench)**, a new evaluation benchmark focusing on 11 real-world task types that are underrepresented or missing in existing benchmarks. The benchmark includes 609 tasks across diverse categories, with the first 8 segments testing single skills or objectives (usually on a single website), and the remaining 3 evaluating more difficult multi-step or cross-site tasks. - -#### WebTailBench Detailed Results - -| Task Segment | Tasks | SoM GPT-4o-0513 | SoM o3-mini | SoM GPT-4o | GLM-4.1V-9B | OAI Comp-Use | UI-TARS-1.5 | **Fara-7B** | -|--------------|-------|-----------------|-------------|------------|-------------|--------------|-------------|-------------| -| **Single-Site Tasks** | -| Shopping | 56 | 62.5 | 71.4 | 38.1 | 31.0 | 42.3 | 41.1 | **52.4** | -| Flights | 51 | 60.1 | 39.2 | 11.1 | 10.5 | 17.6 | 10.5 | **37.9** | -| Hotels | 52 | 68.6 | 56.4 | 31.4 | 19.9 | 26.9 | 35.3 | **53.8** | -| Restaurants | 52 | 67.9 | 59.6 | 47.4 | 32.1 | 35.9 | 22.4 | **47.4** | -| Activities | 80 | 70.4 | 62.9 | 41.7 | 26.3 | 30.4 | 9.6 | **36.3** | -| Ticketing | 57 | 58.5 | 56.7 | 37.4 | 35.7 | 49.7 | 30.4 | **38.6** | -| Real Estate | 48 | 34.0 | 17.4 | 20.1 | 16.0 | 9.0 | 9.7 | **23.6** | -| Jobs/Careers | 50 | 49.3 | 44.0 | 32.7 | 22.7 | 20.7 | 20.7 | **28.0** | -| **Multi-Step Tasks** | -| Shopping List (2 items) | 51 | 66.0 | 62.7 | 17.0 | 7.8 | 34.0 | 20.9 | **49.0** | -| Comparison Shopping | 57 | 67.3 | 59.1 | 27.5 | 22.8 | 1.2 | 8.8 | **32.7** | -| Compositional Tasks | 55 | 51.5 | 39.4 | 26.7 | 17.0 | 10.3 | 9.1 | **23.0** | -| **Overall** | -| Macro Average | 609 | 59.7 | 51.7 | 30.1 | 22.0 | 25.3 | 19.9 | **38.4** | -| Micro Average | 609 | 60.4 | 52.7 | 30.8 | 22.4 | 25.7 | 19.5 | **38.4** | - -*Table: Breakdown of WebTailBench results across all 11 segments. Success rates (%) are averaged over 3 independent runs. Fara-7B achieves the highest performance among computer-use models across all task categories.* - -**Coming Soon:** -- Task Verification pipeline for LLM-as-a-judge evaluation -- Official human annotations of WebTailBench (in partnership with BrowserBase) +Each Fara-1.5 model sets a new state of the art for its size class. Fara1.5-9B improves over Fara-7B by +29.3 points on Online-Mind2Web, +13.1 points on WebVoyager, and +8.2 points on WebTailBench outcome success: + +| Model | Size | WebVoyager | Online-Mind2Web | WebTailBench v1.5 (Process) | WebTailBench v1.5 (Outcome) | +|-------|------|------------|-----------------|-----------------------------|-----------------------------| +| **Larger and proprietary agents** | | | | | | +| o3 SoM | - | 79.3 | 55.4 | 69.5 | 35.0 | +| GPT-5 SoM | - | 90.6 | 57.7 | 69.2 | 45.1 | +| Gemini 2.5 Computer Use† | - | - | 57.3 | - | - | +| OpenAI Operator† | - | 87.0 | 58.3 | - | - | +| Yutori Navigator (n1)† | - | - | 64.7 | - | - | +| GUI-Owl-1.5† | 32B | 82.0 | - | - | - | +| Holo2† | 30B-A3B | 83.0 | - | - | - | +| **Similarly sized agents** | | | | | | +| Fara-7B | 7B | 73.5 | 34.1 | 48.8 | 24.1 | +| MolmoWeb† | 8B | 78.2 | 35.3 | - | - | +| Holo2† | 8B | 80.2 | - | - | - | +| GUI-Owl-1.5† | 8B | 78.1 | 48.6 | - | - | +| **Fara-1.5 family (ours)** | | | | | | +| **Fara1.5-4B** | 4B | 80.8 | 57.3 | 60.3 | 27.4 | +| **Fara1.5-9B** | 9B | 86.6 | 63.4 | 64.5 | 32.3 | +| **Fara1.5-27B** | 27B | **89.3** | **72.3** | 72.9 | 40.2 | +| FaraGen1.5 Solver (GPT-5.4) | - | 93.4 | 83.4 | 79.6 | 57.4 | + +*Table: Task success rate (%) on WebVoyager, Online-Mind2Web, and WebTailBench v1.5. For WebTailBench v1.5 we report both Process Success (correct intermediate steps) and Outcome Success (final task state correct). All Fara-1.5 and Fara-7B numbers are averaged over three independent runs. The GPT-5.4-based FaraGen1.5 solver is an upper-bound reference for the SFT-based distillation. † denotes numbers sourced from the model's official release or leaderboard rather than re-run by us.* + +### WebTailBench: Evaluating Real-World Web Tasks + +**[WebTailBench](https://huggingface.co/datasets/microsoft/WebTailBench)** is our evaluation benchmark focusing on 11 real-world task types that are underrepresented or missing in existing benchmarks. The benchmark includes 609 tasks across diverse categories, with the first 8 segments testing single skills or objectives (usually on a single website), and the remaining 3 evaluating more difficult multi-step or cross-site tasks (shopping lists, comparison shopping, and compositional tasks). Tasks and precomputed rubrics are on HuggingFace (use the refreshed `test_v2` split), the **Universal Verifier** (`MMRubricAgent`) is the official judge, and the reproducibility CLI lives in `webeval/scripts/webtailbench.py`. ### CUAVerifierBench: Evaluating the Verifiers Themselves -While WebTailBench measures *agents*, **[CUAVerifierBench](https://huggingface.co/datasets/microsoft/CUAVerifierBench)** measures the *judges that score those agents*. Each row pairs a Fara-7B agent trajectory (instruction, screenshots, web_surfer log, final answer) with one human reviewer's verdict, plus the verdicts produced by the **Universal Verifier (`MMRubricAgent`)** and several legacy verifiers — so researchers can compute verifier–human agreement (Cohen's κ, accuracy, F1) on a fixed corpus and iterate on new judge prompts / architectures against a frozen ground-truth set. +While WebTailBench measures *agents*, **[CUAVerifierBench](https://huggingface.co/datasets/microsoft/CUAVerifierBench)** measures the *judges that score those agents*. Each row pairs a Fara agent trajectory (instruction, screenshots, web_surfer log, final answer) with one human reviewer's verdict, plus the verdicts produced by the **Universal Verifier (`MMRubricAgent`)** and several legacy verifiers — so researchers can compute verifier–human agreement (Cohen's κ, accuracy, F1) on a fixed corpus and iterate on new judge prompts / architectures against a frozen ground-truth set. The dataset is exposed as two HuggingFace configs joinable on `task_id`: @@ -221,16 +216,16 @@ Our evaluation setup leverages: 1. **Playwright** - A cross-browser automation framework that replicates browser environments 2. **Abstract Web Agent Interface** - Allows integration of any model from any source into the evaluation environment -3. **Fara-Agent Class** - Reference implementation for running the Fara model +3. **Fara-Agent Class** - Reference implementation for running the Fara models -> **Note:** Fara-7B is an experimental release designed to invite hands-on exploration and feedback from the community. We recommend running it in a sandboxed environment, monitoring its execution, and avoiding sensitive data or high-risk domains. +> **Note:** Fara-1.5 is a research preview designed to invite hands-on exploration and feedback from the community. We recommend running it in a sandboxed environment, monitoring its execution, and avoiding sensitive data or high-risk domains. --- -# Installation +## Installation -## Linux +### Linux The following instructions are for Linux systems, see the Windows section below for Windows instructions. @@ -251,7 +246,7 @@ playwright install Note: If you plan on hosting with Azure Foundry only, you can skip the `[vllm]` and just do `pip install -e .` -## Windows +### Windows For Windows, we highly recommend using WSL2 (Windows Subsystem for Linux) to provide a Linux-like environment. However, if you prefer to run natively on Windows, follow these steps: @@ -267,30 +262,28 @@ pip install -e . python3 -m playwright install ``` -## Hosting the Model +### Hosting the Model -**Recommended:** The easiest way to get started is using Azure Foundry hosting, which requires no GPU hardware or model downloads. Alternatively, you can self-host with vLLM if you have GPU resources available. +**Recommended:** The easiest way to get started is using Azure Foundry hosting, which requires no GPU hardware or model downloads. All three Fara-1.5 models ([Fara1.5-4B](https://ai.azure.com/catalog/models/Fara1.5-4B), [Fara1.5-9B](https://ai.azure.com/catalog/models/Fara1.5-9B), and [Fara1.5-27B](https://ai.azure.com/catalog/models/Fara1.5-27B)) are available on Foundry. -### Azure Foundry Hosting (Recommended) +#### Azure Foundry Hosting (Recommended) -Deploy Fara-7B on [Azure Foundry](https://ai.azure.com/explore/models/Fara-7B/version/2/registry/azureml-msr) without needing to download weights or manage GPU infrastructure. +Deploy Fara1.5-9B from the [Azure Foundry catalog](https://ai.azure.com/catalog/models/Fara1.5-9B) without needing to download weights or manage GPU infrastructure. **Setup:** -1. Deploy the Fara-7B model on Azure Foundry and obtain your endpoint URL and API key +1. Deploy the Fara1.5-9B model on Azure Foundry and obtain your endpoint URL and API key Then create a endpoint configuration JSON file (e.g., `azure_foundry_config.json`): ```json { - "model": "Fara-7B", + "model": "Fara1.5-9B", "base_url": "https://your-endpoint.inference.ml.azure.com/", "api_key": "YOUR_API_KEY_HERE" } ``` -Then you can run Fara-7B using this endpoint configuration. - 2. Run the Fara agent: ```bash @@ -307,233 +300,35 @@ python -m fara.run_fara --task "what is the weather in new york now" That's it! No GPU or model downloads required. -### Self-hosting with vLLM or LM Studio / Ollama - -If you have access to GPU resources, you can self-host Fara-7B using vLLM. This requires a GPU machine with sufficient VRAM (e.g., 24GB or more). - -Only on Linux: all that is required is to run the following command to start the vLLM server: - -```bash -vllm serve "microsoft/Fara-7B" --port 5000 --dtype auto -``` -For quantized models or lower VRAM GPUs, please see [Fara-7B GGUF on HuggingFace](https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF). - -For Windows/Mac, vLLM is not natively supported. You can use WSL2 on Windows to run the above command or LM Studio / Ollama as described below. - -Otherwise, you can use [LM Studio](https://lmstudio.ai/) or [Ollama](https://ollama.com/) to host the model locally. We currently recommend the following GGUF versions of our models [Fara-7B GGUF on HuggingFace](https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF) for use with LM Studio or Ollama. Select the largest model that fits your GPU. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results. - -Then you can run Fara-7B pointing to your local server: - -Run the test script to see Fara in action: - -```bash -fara-cli --task "what is the weather in new york now" -``` - -If you didn't use vLLM to host, please specify the correct `--base_url [your_base_url] --api_key [your_api_key] --model [your_model_name]` - -If you see an error that the `fara-cli` command is not found, then try: - -```bash -python -m fara.run_fara --task "what is the weather in new york now" -``` - -# Reproducibility - -We provide a framework in `webeval/` to reproduce our results on WebVoyager and OnlineMind2Web. -Agentic evaluations on live websites present unique challenges due to day-to-day changes. We implement several measures to ensure reliable and comparable evaluations: - -**BrowserBase Integration** -We employ BrowserBase to manage browser session hosting, enabling reliable browser instance management. - -**Time-sensitive Task Updates** -Tasks in benchmarks like WebVoyager can become stale or impossible. We: -- Removed ~48 impossible tasks from the original WebVoyager benchmark -- Updated ~50 tasks with future dates to keep them achievable -- Example: *"Search for a hotel in Bali from Jan 1 to Jan 4, 2024"* → *"Search for a hotel in Bali from Jan 1 to Jan 4, 2026"* -- Our updated WebVoyager benchmark is available at `webeval/data/webvoyager/WebVoyager_data_08312025.jsonl` - -**Environment Error Handling** -Browser errors (connection drops, page timeouts) are handled robustly: -- Trajectories are retried up to 5 times when environment errors occur -- Complete yet incorrect trajectories are never retried -- Each retry starts with a fresh browser session, with no retained state +#### Self-hosting with vLLM -**Step Budget** -Each trajectory is capped at a maximum of 100 actions across all online benchmarks. Trajectories exceeding this budget without choosing to stop are considered incorrect. - -## WebEval Package Installation +If you have access to GPU resources, you can self-host Fara models with vLLM and point `fara-cli` at the resulting OpenAI-compatible endpoint: ```bash -conda create --name fara_webeval python=3.12 -conda activate fara_webeval - -# Install fara package (with vllm extras for GPU hosting) -pip install -e .[vllm] - -# Install webeval -cd webeval -pip install -e . - -# Install playwright -playwright install -``` - -The webeval package no longer depends on `autogen-core` / `autogen-ext` — -all chat completion clients are vendored under `webeval/src/webeval/oai_clients/` -(see `GracefulRetryClient`, `OpenAIClientWrapper`, `AzureOpenAIClientWrapper`, -etc.). You no longer need to clone or install the autogen submodule. - -> **Always activate the `fara_webeval` env before running any of the eval -> scripts below.** It pins `vllm==0.10.0` + `torch==2.7.1`; running with -> a newer `vllm` (≥ 0.19) under `torch` ≥ 2.10 trips a known -> `torch._dynamo.symbolic_convert` crash during CUDA-graph capture, which -> can be worked around with `--enforce_eager` but at a meaningful -> throughput cost. Stick to the pinned env. - -## Running Evaluations - -Navigate to the scripts directory: - -```bash -cd webeval/scripts -``` - -Make sure you set a valid OpenAI GPT-4o endpoint in `endpoint_configs_gpt4o/dev` in order to run the WebVoyager LLM-as-a-judge! - -**Option 1: Self-hosted vLLM** - -```bash -python webvoyager.py --model_url /path/where/you/want/to/download/model/ --model_port 5000 --eval_oai_config ../endpoint_configs_gpt4o/dev/ --out_url /path/to/save/eval/files --device_id 0,1 --processes 1 --run_id 1 --max_rounds 100 -python om2w.py --model_url /path/where/you/want/to/download/model/ --model_port 5000 --eval_oai_config ../endpoint_configs_o4/dev/ --eval_model o4-mini --out_url /path/to/save/eval/files --device_id 0,1 --processes 1 --run_id 1 --max_rounds 100 - -# WebTailBench almost always needs --browserbase: a meaningful share of -# the benchmark's task websites (airlines, retailers, ticketing, …) -# block bot traffic from a vanilla playwright browser. Without -# --browserbase you'll see a high rate of trajectories that abort on -# Page.goto / navigation / captcha errors. Set BROWSERBASE_API_KEY and -# BROWSERBASE_PROJECT_ID in the environment first. -export BROWSERBASE_API_KEY= -export BROWSERBASE_PROJECT_ID= - -# --success controls which Universal Verifier signal counts as the -# top-line score: ``outcome`` (default; binary outcome_success — what the -# Fara-7B numbers in the README above are reported against), ``process`` -# (rubric_is_success := rubric_score >= --rubric_score_threshold; a more -# lenient gate, expect slightly higher numbers), or ``both``. -python webtailbench.py \ - --model_url /path/to/Fara/model_checkpoints \ - --model_port 5000 \ - --device_id 0,1 \ - --eval_oai_config ../../endpoint_configs/judge_active/prod \ - --judge_eval_model gpt-5.2 \ - --judge_o4_eval_model o4-mini \ - --rubric_score_threshold 0.8 \ - --success outcome \ - --out_url /path/to/Fara/eval \ - --processes 4 \ - --run_id 1 \ - --max_rounds 100 \ - --browserbase - -python verify_trajectories.py \ - --input /path/to/Fara/eval/runs/...///traj \ - --task-data ../path/to/om2w/Online_Mind2Web_06042025.json \ - --task-data-format om2w \ - --eval-config ../../endpoint_configs/judge_active/prod \ - --judge-model gpt-5.2 --o4mini-model o4-mini \ - --processes 8 -``` - -**Option 2: Azure Foundry Deployment** - -Deploy [Fara-7B on Foundry endpoint(s)](https://ai.azure.com/explore/models/Fara-7B/version/2/registry/azureml-msr), then place endpoint URLs and keys in JSONs under `endpoint_configs/`: - -```bash -python webvoyager.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../endpoint_configs_gpt4o/dev/ --out_url /path/to/save/eval/files --processes 1 --run_id 1_endpoint --max_rounds 100 -python om2w.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../endpoint_configs_o4/dev/ --eval_model o4-mini --out_url /path/to/save/eval/files --processes 1 --run_id 1_endpoint --max_rounds 100 -python webtailbench.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../../endpoint_configs/judge_active/prod --judge_eval_model gpt-5.2 --judge_o4_eval_model o4-mini --out_url /path/to/Fara/eval --processes 1 --run_id 1_endpoint --max_rounds 100 -``` - -### Notes - - -- We use the same LLM-as-a-judge prompts and model (GPT-4o) as WebVoyager, hence the `--eval_oai_config` argument -- Set `--browserbase` for browser session management (requires exported API key and project ID environment variables) -- Avoid overloading a single vLLM deployment with more than ~10 concurrent processes due to known issues -- See debugging output in `fara/webeval/scripts/stdout.txt` - ---- - -## Analyzing Evaluation Results - -### Evaluation Output Structure - -Evaluation results are stored under `--out_url` in folders organized by: -- Model name -- Dataset -- Username -- Run ID - -Example path: -``` -/runs/WebSurfer-fara-100-max_n_images-3/fara-7b//WebVoyager_WebVoyager_data_08312025.jsonl/ +vllm serve --port 5000 --dtype auto ``` -Each evaluation folder contains: -- `gpt_eval/` - LLM-as-a-judge evaluation results -- `traj/` - Per-task trajectory subdirectories containing: - - `*-final_answer.json` (e.g., `Amazon--1_final_answer.json`) - `` indicates abortion or step budget exceeded - - `scores/*_eval.json` - LLM judge scores (`gpt_eval.json` for WebVoyager, `WebJudge_Online_Mind2Web_eval-3.json` for Online-Mind2Web) - - `web_surfer.log` - Action history and errors - - `screenshot_X.png` - Screenshots captured before each action X - - `times.json` - contains start and end times of task - - `core.log` - contains high level logs like if the trajectory needs to be started or is already cached/done, the evaluation score, duration, and errors encountered +The previous-generation [Fara-7B](https://huggingface.co/microsoft/Fara-7b) weights remain available on HuggingFace (with [GGUF variants](https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF) for LM Studio / Ollama). If you didn't use vLLM to host, specify `--base_url [your_base_url] --api_key [your_api_key] --model [your_model_name]` when running `fara-cli`. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results. -### Running Analysis +## Reproducibility -Use the analysis notebook to compute metrics: - -```bash -cd webeval/scripts/analyze_eval_results/ -jupyter notebook analyze.ipynb -``` +Instructions to reproduce our benchmark results (WebVoyager, Online-Mind2Web, WebTailBench) with the `webeval/` framework — including installation, the per-benchmark CLIs, BrowserBase setup, and result analysis — live in [`docs/eval_reproducibility.md`](docs/eval_reproducibility.md). -The script: -- Identifies trajectories aborted mid-execution and diagnostic reasons -- Computes average scores across non-aborted trajectories -- Distinguishes between aborted trajectories (errors during sampling) and completed trajectories (with terminate() call or step budget exceeded) +> **Note:** the webeval pipeline documented there produced the previous-generation (Fara-7B) numbers and is being updated for the Fara-1.5 evaluation stack. -To re-run failed tasks, execute the evaluation script again with the same `run_id` and `username` - it will skip non-aborted tasks. - -
-Example WebVoyager GPT Eval Result +## Citation -```json -{ - "score": 1.0, - "gpt_response_text": "To evaluate the task, we need to verify if the criteria have been met:\n\n1. **Recipe Requirement**: A vegetarian lasagna recipe with zucchini and at least a four-star rating.\n\n2. **Search and Results**:\n - The screenshots show that the search term used was \"vegetarian lasagna zucchini.\"\n - Among the search results, \"Debbie's Vegetable Lasagna\" is prominently featured.\n \n3. **Evaluation of the Recipe**:\n - Rating: \"Debbie's Vegetable Lasagna\" has a rating of 4.7, which satisfies the requirement of being at least four stars.\n - The presence of zucchini in the recipe is implied through the search conducted, though the screenshots do not explicitly show the ingredients list. However, the result response confirms the match to the criteria.\n\nGiven the information provided, the task seems to have fulfilled the requirement of finding a vegetarian lasagna recipe with zucchini and a four-star rating or higher. \n\n**Verdict: SUCCESS**" +If you use Fara-1.5 in your research, please use the following BibTeX entry. +```bibtex +@article{fara152026, + title={Fara-1.5: Scalable Learning Environments for Computer Use Agents}, + author={Awadallah, Ahmed and Gupta, Sahil and Lara, Yash and Lu, Yadong and Mozannar, Hussein and Nambi, Akshay and Nussbaum, Zach and Pandya, Yash and Rajeswaran, Aravind and Rosset, Corby and Taymanov, Alexey and do Valle, Luiz and Vineet, Vibhav and Whitehead, Spencer and Zhao, Andrew}, + journal={arXiv:2606.20785}, + year={2026} } ``` -
- -
-Example of web_surfer.log (action history) - -```json -{"message": "\nThought #1: To begin locating the closest GameStop store, I will click on the 'Trade-In' button, which typically leads to a store locator feature on retail websites.\nAction #1: executing tool 'left_click' with arguments {\"action\": \"left_click\", \"coordinate\": [1074, 37]}", "timestamp": "2025-11-25T22:23:42.788320", "type": "OtherEvent"} -{"message": "Observation#1: I clicked at coordinates (1083.0252100840337, 37.16517857142858).", "timestamp": "2025-11-25T22:23:47.616613", "type": "OtherEvent"} -{"message": "\nThought #2: Since access to Gamestop's website has been blocked, I will search for alternative sources of store location and hours for GameStop locations near zip code 90028. This could include cached or third-party website information.\nAction #2: executing tool 'web_search' with arguments {\"action\": \"web_search\", \"query\": \"GameStop store locator zip code 90028\"}", "timestamp": "2025-11-25T22:23:50.579702", "type": "OtherEvent"} -{"message": "Observation#2: I typed 'GameStop store locator zip code 90028' into the browser search bar.", "timestamp": "2025-11-25T22:23:55.119495", "type": "OtherEvent"} -{"message": "\nThought #3: The closest GameStop store to zip code 90028 is located at 5533 W Sunset Blvd, Los Angeles, CA 90028. It is currently closed and will open tomorrow at 11 AM. The contact number is (323) 466-3714.\nAction #3: executing tool 'terminate' with arguments {\"action\": \"terminate\", \"status\": \"success\"}", "timestamp": "2025-11-25T22:23:58.729038", "type": "OtherEvent"} -{"message": "Observation#3: The closest GameStop store to zip code 90028 is located at 5533 W Sunset Blvd, Los Angeles, CA 90028. It is currently closed and will open tomorrow at 11 AM. The contact number is (323) 466-3714.", "timestamp": "2025-11-25T22:24:02.379069", "type": "OtherEvent"} -``` -
- -## Citation - -If you use Fara-7B in your research, please use the following BibTeX entry. +For the previous generation, Fara-7B: ```bibtex @article{fara7b2025, title={Fara-7B: An Efficient Agentic Model for Computer Use}, diff --git a/docs/eval_reproducibility.md b/docs/eval_reproducibility.md new file mode 100644 index 0000000..182dd23 --- /dev/null +++ b/docs/eval_reproducibility.md @@ -0,0 +1,197 @@ +# Eval Reproducibility (webeval) + +> **Note:** This document covers the `webeval/` pipeline that produced the +> published Fara-7B benchmark numbers (WebVoyager, Online-Mind2Web, +> WebTailBench). It predates the Fara-1.5 release and will be updated as the +> repo is refactored to the Fara-1.5 evaluation stack. For the trajectory +> on-disk format, see [`webeval/README.md`](../webeval/README.md). + +Agentic evaluations on live websites present unique challenges due to day-to-day changes. We implement several measures to ensure reliable and comparable evaluations: + +**BrowserBase Integration** +We employ BrowserBase to manage browser session hosting, enabling reliable browser instance management. + +**Time-sensitive Task Updates** +Tasks in benchmarks like WebVoyager can become stale or impossible. We: +- Removed ~48 impossible tasks from the original WebVoyager benchmark +- Updated ~50 tasks with future dates to keep them achievable +- Example: *"Search for a hotel in Bali from Jan 1 to Jan 4, 2024"* → *"Search for a hotel in Bali from Jan 1 to Jan 4, 2026"* +- Our updated WebVoyager benchmark is available at `webeval/data/webvoyager/WebVoyager_data_08312025.jsonl` + +**Environment Error Handling** +Browser errors (connection drops, page timeouts) are handled robustly: +- Trajectories are retried up to 5 times when environment errors occur +- Complete yet incorrect trajectories are never retried +- Each retry starts with a fresh browser session, with no retained state + +**Step Budget** +Each trajectory is capped at a maximum of 100 actions across all online benchmarks. Trajectories exceeding this budget without choosing to stop are considered incorrect. + +## WebEval Package Installation + +```bash +conda create --name fara_webeval python=3.12 +conda activate fara_webeval + +# Install fara package (with vllm extras for GPU hosting) +pip install -e .[vllm] + +# Install webeval +cd webeval +pip install -e . + +# Install playwright +playwright install +``` + +The webeval package no longer depends on `autogen-core` / `autogen-ext` — +all chat completion clients are vendored under `webeval/src/webeval/oai_clients/` +(see `GracefulRetryClient`, `OpenAIClientWrapper`, `AzureOpenAIClientWrapper`, +etc.). You no longer need to clone or install the autogen submodule. + +> **Always activate the `fara_webeval` env before running any of the eval +> scripts below.** It pins `vllm==0.10.0` + `torch==2.7.1`; running with +> a newer `vllm` (≥ 0.19) under `torch` ≥ 2.10 trips a known +> `torch._dynamo.symbolic_convert` crash during CUDA-graph capture, which +> can be worked around with `--enforce_eager` but at a meaningful +> throughput cost. Stick to the pinned env. + +## Running Evaluations + +Navigate to the scripts directory: + +```bash +cd webeval/scripts +``` + +Make sure you set a valid OpenAI GPT-4o endpoint in `endpoint_configs_gpt4o/dev` in order to run the WebVoyager LLM-as-a-judge! + +**Option 1: Self-hosted vLLM** + +```bash +python webvoyager.py --model_url /path/where/you/want/to/download/model/ --model_port 5000 --eval_oai_config ../endpoint_configs_gpt4o/dev/ --out_url /path/to/save/eval/files --device_id 0,1 --processes 1 --run_id 1 --max_rounds 100 +python om2w.py --model_url /path/where/you/want/to/download/model/ --model_port 5000 --eval_oai_config ../endpoint_configs_o4/dev/ --eval_model o4-mini --out_url /path/to/save/eval/files --device_id 0,1 --processes 1 --run_id 1 --max_rounds 100 + +# WebTailBench almost always needs --browserbase: a meaningful share of +# the benchmark's task websites (airlines, retailers, ticketing, …) +# block bot traffic from a vanilla playwright browser. Without +# --browserbase you'll see a high rate of trajectories that abort on +# Page.goto / navigation / captcha errors. Set BROWSERBASE_API_KEY and +# BROWSERBASE_PROJECT_ID in the environment first. +export BROWSERBASE_API_KEY= +export BROWSERBASE_PROJECT_ID= + +# --success controls which Universal Verifier signal counts as the +# top-line score: ``outcome`` (default; binary outcome_success — what the +# Fara numbers in the README are reported against), ``process`` +# (rubric_is_success := rubric_score >= --rubric_score_threshold; a more +# lenient gate, expect slightly higher numbers), or ``both``. +python webtailbench.py \ + --model_url /path/to/Fara/model_checkpoints \ + --model_port 5000 \ + --device_id 0,1 \ + --eval_oai_config ../../endpoint_configs/judge_active/prod \ + --judge_eval_model gpt-5.2 \ + --judge_o4_eval_model o4-mini \ + --rubric_score_threshold 0.8 \ + --success outcome \ + --out_url /path/to/Fara/eval \ + --processes 4 \ + --run_id 1 \ + --max_rounds 100 \ + --browserbase + +python verify_trajectories.py \ + --input /path/to/Fara/eval/runs/...///traj \ + --task-data ../path/to/om2w/Online_Mind2Web_06042025.json \ + --task-data-format om2w \ + --eval-config ../../endpoint_configs/judge_active/prod \ + --judge-model gpt-5.2 --o4mini-model o4-mini \ + --processes 8 +``` + +**Option 2: Azure Foundry Deployment** + +Deploy [Fara1.5-9B on Foundry endpoint(s)](https://ai.azure.com/catalog/models/Fara1.5-9B), then place endpoint URLs and keys in JSONs under `endpoint_configs/`: + +```bash +python webvoyager.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../endpoint_configs_gpt4o/dev/ --out_url /path/to/save/eval/files --processes 1 --run_id 1_endpoint --max_rounds 100 +python om2w.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../endpoint_configs_o4/dev/ --eval_model o4-mini --out_url /path/to/save/eval/files --processes 1 --run_id 1_endpoint --max_rounds 100 +python webtailbench.py --model_endpoint ../../endpoint_configs/ --eval_oai_config ../../endpoint_configs/judge_active/prod --judge_eval_model gpt-5.2 --judge_o4_eval_model o4-mini --out_url /path/to/Fara/eval --processes 1 --run_id 1_endpoint --max_rounds 100 +``` + +### Notes + + +- We use the same LLM-as-a-judge prompts and model (GPT-4o) as WebVoyager, hence the `--eval_oai_config` argument +- Set `--browserbase` for browser session management (requires exported API key and project ID environment variables) +- Avoid overloading a single vLLM deployment with more than ~10 concurrent processes due to known issues +- See debugging output in `fara/webeval/scripts/stdout.txt` + +--- + +## Analyzing Evaluation Results + +### Evaluation Output Structure + +Evaluation results are stored under `--out_url` in folders organized by: +- Model name +- Dataset +- Username +- Run ID + +Example path: +``` +/runs/WebSurfer-fara-100-max_n_images-3/fara1.5-9b//WebVoyager_WebVoyager_data_08312025.jsonl/ +``` + +Each evaluation folder contains: +- `gpt_eval/` - LLM-as-a-judge evaluation results +- `traj/` - Per-task trajectory subdirectories containing: + - `*-final_answer.json` (e.g., `Amazon--1_final_answer.json`) - `` indicates abortion or step budget exceeded + - `scores/*_eval.json` - LLM judge scores (`gpt_eval.json` for WebVoyager, `WebJudge_Online_Mind2Web_eval-3.json` for Online-Mind2Web) + - `web_surfer.log` - Action history and errors + - `screenshot_X.png` - Screenshots captured before each action X + - `times.json` - contains start and end times of task + - `core.log` - contains high level logs like if the trajectory needs to be started or is already cached/done, the evaluation score, duration, and errors encountered + +### Running Analysis + +Use the analysis notebook to compute metrics: + +```bash +cd webeval/scripts/analyze_eval_results/ +jupyter notebook analyze.ipynb +``` + +The script: +- Identifies trajectories aborted mid-execution and diagnostic reasons +- Computes average scores across non-aborted trajectories +- Distinguishes between aborted trajectories (errors during sampling) and completed trajectories (with terminate() call or step budget exceeded) + +To re-run failed tasks, execute the evaluation script again with the same `run_id` and `username` - it will skip non-aborted tasks. + +
+Example WebVoyager GPT Eval Result + +```json +{ + "score": 1.0, + "gpt_response_text": "To evaluate the task, we need to verify if the criteria have been met:\n\n1. **Recipe Requirement**: A vegetarian lasagna recipe with zucchini and at least a four-star rating.\n\n2. **Search and Results**:\n - The screenshots show that the search term used was \"vegetarian lasagna zucchini.\"\n - Among the search results, \"Debbie's Vegetable Lasagna\" is prominently featured.\n \n3. **Evaluation of the Recipe**:\n - Rating: \"Debbie's Vegetable Lasagna\" has a rating of 4.7, which satisfies the requirement of being at least four stars.\n - The presence of zucchini in the recipe is implied through the search conducted, though the screenshots do not explicitly show the ingredients list. However, the result response confirms the match to the criteria.\n\nGiven the information provided, the task seems to have fulfilled the requirement of finding a vegetarian lasagna recipe with zucchini and a four-star rating or higher. \n\n**Verdict: SUCCESS**" +} +``` + +
+ +
+Example of web_surfer.log (action history) + +```json +{"message": "\nThought #1: To begin locating the closest GameStop store, I will click on the 'Trade-In' button, which typically leads to a store locator feature on retail websites.\nAction #1: executing tool 'left_click' with arguments {\"action\": \"left_click\", \"coordinate\": [1074, 37]}", "timestamp": "2025-11-25T22:23:42.788320", "type": "OtherEvent"} +{"message": "Observation#1: I clicked at coordinates (1083.0252100840337, 37.16517857142858).", "timestamp": "2025-11-25T22:23:47.616613", "type": "OtherEvent"} +{"message": "\nThought #2: Since access to Gamestop's website has been blocked, I will search for alternative sources of store location and hours for GameStop locations near zip code 90028. This could include cached or third-party website information.\nAction #2: executing tool 'web_search' with arguments {\"action\": \"web_search\", \"query\": \"GameStop store locator zip code 90028\"}", "timestamp": "2025-11-25T22:23:50.579702", "type": "OtherEvent"} +{"message": "Observation#2: I typed 'GameStop store locator zip code 90028' into the browser search bar.", "timestamp": "2025-11-25T22:23:55.119495", "type": "OtherEvent"} +{"message": "\nThought #3: The closest GameStop store to zip code 90028 is located at 5533 W Sunset Blvd, Los Angeles, CA 90028. It is currently closed and will open tomorrow at 11 AM. The contact number is (323) 466-3714.\nAction #3: executing tool 'terminate' with arguments {\"action\": \"terminate\", \"status\": \"success\"}", "timestamp": "2025-11-25T22:23:58.729038", "type": "OtherEvent"} +{"message": "Observation#3: The closest GameStop store to zip code 90028 is located at 5533 W Sunset Blvd, Los Angeles, CA 90028. It is currently closed and will open tomorrow at 11 AM. The contact number is (323) 466-3714.", "timestamp": "2025-11-25T22:24:02.379069", "type": "OtherEvent"} +``` +
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Successfully downloaded '{download.suggested_filename}' to local path:

{fname}

" + await page.goto( + "data:text/html;base64," + + base64.b64encode(message.encode("utf-8")).decode("utf-8"), + wait_until="commit", + ) + reset_last_download = True + else: + raise e_outer + return reset_prior_metadata_hash, reset_last_download + + @handle_target_closed() + async def page_down( + self, page: Page, amount: int = 400, full_page: bool = False + ) -> None: + await self._ensure_page_ready(page) + if full_page: + await page.mouse.wheel(0, self.viewport_height - 50) + else: + await page.mouse.wheel(0, amount) + + @handle_target_closed() + async def page_up( + self, page: Page, amount: int = 400, full_page: bool = False + ) -> None: + await self._ensure_page_ready(page) + if full_page: + await page.mouse.wheel(0, -self.viewport_height + 50) + else: + await page.mouse.wheel(0, -amount) + + async def gradual_cursor_animation( + self, page: Page, start_x: float, start_y: float, end_x: float, end_y: float + ) -> None: + # animation helper + steps = 20 + for step in range(steps): + x = start_x + (end_x - start_x) * (step / steps) + y = start_y + (end_y - start_y) * (step / steps) + # await page.mouse.move(x, y, steps=1) + await page.evaluate(f""" + (function() {{ + let cursor = document.getElementById('red-cursor'); + cursor.style.left = '{x}px'; + cursor.style.top = '{y}px'; + }})(); + """) + await asyncio.sleep(0.05) + + self.last_cursor_position = (end_x, end_y) + + async def add_cursor_box(self, page: Page, identifier: str) -> None: + # animation helper + await page.evaluate(f""" + (function() {{ + let elm = document.querySelector("[__elementId='{identifier}']"); + if (elm) {{ + elm.style.transition = 'border 0.3s ease-in-out'; + elm.style.border = '2px solid red'; + }} + }})(); + """) + await asyncio.sleep(0.3) + + # Create a red cursor + await page.evaluate(""" + (function() { + let cursor = document.createElement('div'); + cursor.id = 'red-cursor'; + cursor.style.width = '10px'; + cursor.style.height = '10px'; + cursor.style.backgroundColor = 'red'; + cursor.style.position = 'absolute'; + cursor.style.borderRadius = '50%'; + cursor.style.zIndex = '10000'; + document.body.appendChild(cursor); + })(); + """) + + async def remove_cursor_box(self, page: Page, identifier: str) -> None: + # Remove the highlight and cursor + await page.evaluate(f""" + (function() {{ + let elm = document.querySelector("[__elementId='{identifier}']"); + if (elm) {{ + elm.style.border = ''; + }} + let cursor = document.getElementById('red-cursor'); + if (cursor) {{ + cursor.remove(); + }} + }})(); + """) + + async def _click_with_popup_capture( + self, page: Page, action: Callable[[], Awaitable[None]] + ) -> Page | None: + """Run a click-like action and capture any popup it opens. + + Returns the new page so the caller can promote it to the active + page (see ``Environment._swap_to_new_page``). The env decides what + to do with the old page (close it in ``single_tab_mode``, keep it + otherwise). Returns ``None`` if no popup fires within the timeout. + """ + new_page: Page | None = None + try: + async with page.expect_event("popup", timeout=1000) as page_info: # type: ignore + await action() + new_page = await page_info.value # type: ignore + assert isinstance(new_page, Page) + await self.on_new_page(new_page) + except TimeoutError: + pass + return new_page + + @handle_target_closed() + async def click_coords(self, page: Page, x: float, y: float) -> Page | None: + await self._ensure_page_ready(page) + + if self.animate_actions: + # Move cursor to the box slowly + start_x, start_y = self.last_cursor_position + await self.gradual_cursor_animation(page, start_x, start_y, x, y) + await asyncio.sleep(0.1) + + return await self._click_with_popup_capture( + page, lambda: page.mouse.click(x, y, delay=10) + ) + + @handle_target_closed() + async def click_id(self, page: Page, identifier: str) -> Page | None: + """ + Returns new page if a new page is opened, otherwise None. + """ + new_page: Page | None = None + await self._ensure_page_ready(page) + selector = f"[__elementId='{identifier}']" + try: + # Wait for the element to be visible and scroll it into view + await page.wait_for_selector( + selector, state="visible", timeout=self._timeout_load * 1000 + ) + target = page.locator(selector) + await target.scroll_into_view_if_needed() + except TimeoutError: + raise ValueError( + f"Element with identifier {identifier} not found or not visible" + ) + + # Retrieve bounding box to determine the center for clicking + box = await target.bounding_box() + if not box: + raise ValueError( + f"Element with identifier {identifier} is not visible on the page." + ) + center_x = box["x"] + box["width"] / 2 + center_y = box["y"] + box["height"] / 2 + + # In single tab mode, override target attributes to avoid opening a new tab + if self.single_tab_mode: + await target.evaluate(""" + el => { + // Remove target attribute from clicked element and all _blank links/forms + el.removeAttribute('target'); + document.querySelectorAll('a[target=_blank], form[target=_blank]') + .forEach(e => e.removeAttribute('target')); + } + """) + + download = None + download_future: asyncio.Task[Download] | None = None + + # Start listening for a download event if downloads are enabled + if self.downloads_folder: + try: + download_future = asyncio.create_task( + page.wait_for_event( # type: ignore + "download", timeout=500 + ) + ) + except Exception as e: + self.logger.error(f"Failed to set up download listener: {e}") + download_future = None + + async def perform_click() -> Optional[Page]: + nonlocal download + try: + if self.single_tab_mode: + await page.mouse.move(center_x, center_y, steps=1) + await page.mouse.click(center_x, center_y) + return None + else: + # Create a task to wait for a new page event + new_page_promise: asyncio.Task[Page] = asyncio.create_task( + page.context.wait_for_event( # type: ignore + "page", timeout=self._timeout_load * 1000 + ) + ) + + # Perform the click + await page.mouse.move(center_x, center_y, steps=1) + await page.mouse.click(center_x, center_y, delay=10) + + try: + # Wait for the new page to open + new_page = await new_page_promise + await self.on_new_page(new_page) + return new_page + except TimeoutError: + # No new page opened within timeout + return None + except Exception as e: + raise e + + # Optionally animate the click + if self.animate_actions: + await self.add_cursor_box(page, identifier) + start_x, start_y = self.last_cursor_position + await self.gradual_cursor_animation( + page, start_x, start_y, center_x, center_y + ) + + new_page = await perform_click() + + # Handle any download that occurred + if download_future: + try: + if not download: + # Use asyncio.wait_for with a reasonable timeout + try: + download = await asyncio.wait_for( + download_future, timeout=self._timeout_load * 1000 + ) + except asyncio.TimeoutError: + # No download occurred within the timeout period + # logger.debug("No download detected within timeout period") + pass + + if download: + self.logger.info( + f"Downloading {download.suggested_filename} to {self.downloads_folder}" + ) + assert self.downloads_folder is not None + fname = os.path.join( + self.downloads_folder, download.suggested_filename + ) + await download.save_as(fname) + except Exception as e: + self.logger.error(f"Error handling download: {e}") + finally: + if not download_future.done(): + download_future.cancel() + + if self.animate_actions: + await self.remove_cursor_box(page, identifier) + + if new_page: + await new_page.wait_for_load_state("domcontentloaded") + if self._sleep_after_action > 0: + await new_page.wait_for_timeout(self._sleep_after_action * 1000) + else: + await page.wait_for_load_state("domcontentloaded") + if self._sleep_after_action > 0: + await page.wait_for_timeout(self._sleep_after_action * 1000) + # await asyncio.sleep(10) + + return new_page + + @handle_target_closed() + async def select_option(self, page: Page, identifier: str) -> Optional[Page]: + """ + Select an option element with the given identifier. If the element has a visible size, + it will be clicked normally. Otherwise, it will be selected programmatically. + + Args: + page (Page): The Playwright page object. + identifier (str): The element identifier of the option to select. + + Returns: + Optional[Page]: The Playwright page object of the newly focused tab. + """ + await self._ensure_page_ready(page) + new_page: Optional[Page] = None + try: + # Wait for element to be present + await page.wait_for_selector( + f"[__elementId='{identifier}']", state="attached" + ) + + try: + # First try normal click if element is visible + target = page.locator(f"[__elementId='{identifier}']").first + # Get the bounding box to check element size + box = await target.bounding_box() + + if box and box["width"] > 0 and box["height"] > 0: + # Element has visible size - use normal click + return await self.click_id(page, identifier) + + except PlaywrightError as e: + if "strict mode violation" in str(e): + # If multiple elements found, try clicking the first visible one + elements = await page.locator(f"[__elementId='{identifier}']").all() + for element in elements: + try: + if await element.is_visible(): + await element.click() + return new_page + except PlaywrightError: + continue + + # If click didn't work, try programmatic selection + # First check if it's a standard tags @@ -1078,7 +991,7 @@ async def keypress(self, page: Page, keys: list[str], key_mapping=None) -> None: if self.animate_actions: await asyncio.sleep(0.05) except Exception: - continue # cleanup must not mask the primary failure + continue @handle_target_closed() async def cua_click( @@ -1154,7 +1067,6 @@ async def get_webpage_text(self, page: Page, n_lines: int = 100) -> str: return document.body.innerText; }""") text_in_viewport = "\n".join(text_in_viewport.split("\n")[:n_lines]) - # remove empty lines text_in_viewport = "\n".join( [line for line in text_in_viewport.split("\n") if line.strip()] ) diff --git a/src/fara/fara_7b/_prompts.py b/src/fara/fara_7b/_prompts.py index f43cef1..850e6e9 100644 --- a/src/fara/fara_7b/_prompts.py +++ b/src/fara/fara_7b/_prompts.py @@ -9,7 +9,6 @@ from ..qwen_helpers.utils import smart_resize -# @register_tool("computer_use") class FaraComputerUse(BaseTool): name = "computer_use" diff --git a/src/fara/fara_7b/fara_agent.py b/src/fara/fara_7b/fara_agent.py index 5465b9c..cff6fd3 100644 --- a/src/fara/fara_7b/fara_agent.py +++ b/src/fara/fara_7b/fara_agent.py @@ -29,12 +29,6 @@ from ..utils import get_trimmed_url -# Canonical fara-1.0 action space mapping {action_name: set(arg_names)}. -# Source of truth: ``FaraComputerUseTool.parameters`` in ``_prompts.py``. -# Kept here so downstream callers (the rubric verifier, evaluation -# scripts, training code, etc.) have a single place to import from -# instead of duplicating the schema. Update both this dict AND the tool -# parameter schema together if the action space changes. FARA_ACTION_DEFINITIONS: Dict[str, set] = { "key": {"keys"}, "type": {"text", "coordinate", "press_enter", "delete_existing_text"}, @@ -111,7 +105,6 @@ def _download_handler(download: Download) -> None: self._download_handler = _download_handler self.did_initialize = False - # OpenAI client will be initialized in initialize() self._openai_client: AsyncOpenAI | None = None self._chat_history: List[LLMMessage] = [] @@ -121,16 +114,13 @@ async def initialize(self) -> None: self._last_download = None self._prior_metadata_hash = None - # Initialize OpenAI client self._openai_client = AsyncOpenAI( api_key=self.client_config.get("api_key"), base_url=self.client_config.get("base_url"), ) - # Set up download handler self.browser_manager.set_download_handler(self._download_handler) - # Initialize browser await self.browser_manager.init(self.start_page) self.did_initialize = True @@ -160,19 +150,18 @@ def _playwright_controller(self) -> PlaywrightController | None: async def wait_for_captcha_with_timeout( self, timeout_seconds=300 - ): # 5 minutes default + ): """Wait for captcha to be solved with timeout""" try: await asyncio.wait_for( self.browser_manager.wait_for_captcha_resolution(), timeout=timeout_seconds, ) - return True # Captcha solved in time + return True except asyncio.TimeoutError: self.logger.warning(f"Captcha timeout after {timeout_seconds} seconds!") - # Force resume execution self.browser_manager._captcha_event.set() - return False # Captcha timed out + return False @retry( stop=stop_after_attempt(5), @@ -240,13 +229,11 @@ def maybe_remove_old_screenshots( ) if i == 0 and n_images >= max_n_images: - # First message is always the task so we keep it and remove the screenshot if necessary msg = self.remove_screenshot_from_message(msg) if msg is None: continue if isinstance(msg.content, list): - # Check if the message contains an image. Assumes 1 image per message. has_image = False for c in msg.content: if isinstance(c, ImageObj): @@ -256,7 +243,6 @@ def maybe_remove_old_screenshots( if n_images < max_n_images: new_history.append(msg) elif is_original_user_message: - # Original user message but over limit: keep text, remove image msg = self.remove_screenshot_from_message(msg) if msg is not None: new_history.append(msg) @@ -351,13 +337,10 @@ def proc_coords( async def run(self, user_message: str) -> Tuple: """Run the agent with a user message.""" - # Initialize if not already done await self.initialize() - # Ensure page is ready after initialization assert self._page is not None, "Page should be initialized" - # Get initial screenshot and add user message with image to chat history scaled_screenshot = await self._get_scaled_screenshot() if self.save_screenshots: @@ -426,7 +409,6 @@ async def generate_model_call( screenshot_for_system = first_screenshot if not is_first_round: - # Get screenshot and add new user message for subsequent rounds scaled_screenshot = await self._get_scaled_screenshot() screenshot_for_system = scaled_screenshot @@ -441,7 +423,6 @@ async def generate_model_call( self._chat_history.append(curr_message) history.append(curr_message) - # Generate system message using the screenshot system_message, _ = self._get_system_message(screenshot_for_system) history = system_message + history response = await self._make_model_call( @@ -487,13 +468,11 @@ async def execute_action( if args["action"] == "visit_url": url = str(args["url"]) action_description = f"I typed '{url}' into the browser address bar." - # Check if the argument starts with a known protocol if url.startswith(("https://", "http://", "file://", "about:")): ( reset_prior_metadata, reset_last_download, ) = await self._playwright_controller.visit_page(self._page, url) - # If the argument contains a space, treat it as a search query elif " " in url: ( reset_prior_metadata, @@ -502,7 +481,6 @@ async def execute_action( self._page, f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH", ) - # Otherwise, prefix with https:// else: ( reset_prior_metadata, @@ -601,7 +579,6 @@ async def execute_action( await self._playwright_controller.wait_for_load_state(self._page) await self._playwright_controller.sleep(self._page, 3) - # Get new screenshot after action self._num_actions += 1 if self.save_screenshots: new_screenshot = await self._playwright_controller.get_screenshot( diff --git a/src/fara/fara_types.py b/src/fara/fara_types.py index 0f1e3a2..5173494 100644 --- a/src/fara/fara_types.py +++ b/src/fara/fara_types.py @@ -86,12 +86,10 @@ def message_to_openai_format(message: LLMMessage) -> Dict[str, Any]: else "user" ) - # Handle multimodal content (text + images) if isinstance(message.content, list): content_parts = [] for item in message.content: if isinstance(item, ImageObj): - # Convert image to base64 data URL base64_image = item.to_base64() content_parts.append( { @@ -102,11 +100,9 @@ def message_to_openai_format(message: LLMMessage) -> Dict[str, Any]: elif isinstance(item, str): content_parts.append({"type": "text", "text": item}) elif isinstance(item, dict): - # Already in proper format content_parts.append(item) return {"role": role, "content": content_parts} else: - # Simple text content return {"role": role, "content": message.content} diff --git a/src/fara/qwen_helpers/base_tool.py b/src/fara/qwen_helpers/base_tool.py index 6fc8fc3..8ff63c5 100644 --- a/src/fara/qwen_helpers/base_tool.py +++ b/src/fara/qwen_helpers/base_tool.py @@ -126,13 +126,11 @@ def _verify_json_format_args( return params_json @property - def function(self) -> dict: # Bad naming. It should be `function_info`. + def function(self) -> dict: return { - # 'name_for_human': self.name_for_human, "name": self.name, "description": self.description, "parameters": self.parameters, - # 'args_format': self.args_format } @property diff --git a/src/fara/qwen_helpers/fncall_prompt.py b/src/fara/qwen_helpers/fncall_prompt.py index 83236a8..5b09d29 100644 --- a/src/fara/qwen_helpers/fncall_prompt.py +++ b/src/fara/qwen_helpers/fncall_prompt.py @@ -41,14 +41,13 @@ def preprocess_fncall_messages( parallel_function_calls: bool = True, function_choice: Union[Literal["auto"], str] = "auto", ) -> List[Message]: - del lang # ignored - del parallel_function_calls # ignored + del lang + del parallel_function_calls if function_choice != "auto": raise NotImplementedError ori_messages = messages - # Change function_call responses to plaintext responses: messages = [] for msg in copy.deepcopy(ori_messages): role, content, reasoning_content = ( @@ -84,7 +83,6 @@ def preprocess_fncall_messages( messages[-1].content.append(ContentItem(text="\n")) messages[-1].content.extend(content) else: - # TODO: Assuming there will only be one continuous reasoning_content here messages.append( Message( role=role, @@ -113,7 +111,6 @@ def preprocess_fncall_messages( ] tool_descs = "\n".join([json.dumps(f, ensure_ascii=False) for f in tool_descs]) - # Select template based on configuration if SPECIAL_CODE_MODE and any([CODE_TOOL_PATTERN in x for x in tool_names]): selected_template = FN_CALL_TEMPLATE_WITH_CI elif self._custom_template is not None: @@ -187,8 +184,6 @@ def preprocess_fncall_messages( """ -# Mainly for removing incomplete special tokens when streaming the output -# This assumes that '\n{"name": "' is the special token for the NousFnCallPrompt def remove_incomplete_special_tokens(text: str) -> str: if text in '\n{"name": "': text = "" diff --git a/src/fara/run_fara.py b/src/fara/run_fara.py index 367ff96..a919d3d 100755 --- a/src/fara/run_fara.py +++ b/src/fara/run_fara.py @@ -15,7 +15,6 @@ from .browser.browser_bb import BrowserBB -# Configure logging to only show logs from the fara agents logging.basicConfig( level=logging.CRITICAL, format="%(message)s", @@ -28,7 +27,7 @@ _handler.setLevel(logging.INFO) _handler.setFormatter(logging.Formatter("%(message)s")) _agent_logger.addHandler(_handler) - _agent_logger.propagate = False # Don't propagate to root logger + _agent_logger.propagate = False logger = logging.getLogger(__name__) @@ -115,7 +114,6 @@ async def run_fara15_agent( print("Running Fara...\n") final_answer, _, _ = await agent.run(run_context) - # Critical-point resume loop: agent paused to ask the user. while ( run_context.solver_log.status == SolverStatus.WAITING_FOR_USER ): From eda8e982d8535e0f92994ce45d8f0482f4c97200 Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 12:31:25 -0700 Subject: [PATCH 07/11] clean up 1 --- src/fara/__init__.py | 12 +- .../fara/{fara_qwen3.py => fara15_agent.py} | 385 +++++++++++------- src/fara/agents/fara/fara_qwen3_next.py | 369 ----------------- src/fara/agents/fara/prompts.py | 22 - src/fara/run_fara.py | 10 +- tests/test_fara15.py | 14 +- 6 files changed, 248 insertions(+), 564 deletions(-) rename src/fara/agents/fara/{fara_qwen3.py => fara15_agent.py} (69%) delete mode 100644 src/fara/agents/fara/fara_qwen3_next.py delete mode 100644 src/fara/agents/fara/prompts.py diff --git a/src/fara/__init__.py b/src/fara/__init__.py index b4cbdb2..44e7a3c 100644 --- a/src/fara/__init__.py +++ b/src/fara/__init__.py @@ -1,8 +1,4 @@ -from .agents.fara.fara_qwen3_next import ( - FaraQwen3NextAgent, - FaraQwen3NextAgentConfig, -) -from .agents.fara.fara_qwen3 import FaraQwen3Agent, FaraQwen3AgentConfig +from .agents.fara.fara15_agent import Fara15Agent, Fara15AgentConfig from .environments.playwright import ( PlaywrightEnvironment, PlaywrightEnvironmentConfig, @@ -13,10 +9,8 @@ from .browser.playwright_controller import PlaywrightController __all__ = [ - "FaraQwen3NextAgent", - "FaraQwen3NextAgentConfig", - "FaraQwen3Agent", - "FaraQwen3AgentConfig", + "Fara15Agent", + "Fara15AgentConfig", "PlaywrightEnvironment", "PlaywrightEnvironmentConfig", "DataPoint", diff --git a/src/fara/agents/fara/fara_qwen3.py b/src/fara/agents/fara/fara15_agent.py similarity index 69% rename from src/fara/agents/fara/fara_qwen3.py rename to src/fara/agents/fara/fara15_agent.py index 10fd5a5..0eb88fa 100644 --- a/src/fara/agents/fara/fara_qwen3.py +++ b/src/fara/agents/fara/fara15_agent.py @@ -1,19 +1,25 @@ -"""FaraQwen3Agent - Web automation agent using Qwen3 vision-language model.""" +"""Fara-1.5 web automation agent (FaraNext browser action set). + +Single-class port of agento_next's FaraQwen3NextAgent: the browser +observe-think-act loop, the expanded action space (double/right/triple +click, left_click_drag, hscroll, read_page_answer_question, +ask_user_question), and DataPoint trajectory logging via RunContext. +""" from __future__ import annotations +import ast import copy import io import json -import ast import logging +import re import shutil - -import openai from dataclasses import dataclass, field from typing import Any, Dict, List, Tuple from urllib.parse import quote_plus +import openai from PIL import Image from tenacity import ( retry, @@ -23,6 +29,7 @@ before_sleep_log, ) +from ..._prompts import get_computer_use_system_prompt from ...clients.wrapper import ChatCompletionClient from ...clients.create_utils import create_client_from_config from ...clients.messages import ( @@ -33,7 +40,6 @@ ImageObj, FunctionCall, ) - from ...core.agent import Agent, AgentConfig from ...core.run_context import RunContext from ...core.data_point import ( @@ -47,48 +53,47 @@ ) from ..captcha import wait_for_captcha from ..coord_spaces import FARA_DISPLAY_SIZE +from ..computer_agent.utils import extract_from_page +from ..utils import format_text_observation from .fara_types import WebSurferEvent -from .prompts import get_computer_use_system_prompt from .utils import get_trimmed_url -class FaraQwen3AgentConfig(AgentConfig): - """Configuration for FaraAgent. +def extract_allowed_actions(system_prompt_text: str) -> frozenset[str]: + """Extract the allowed action names from the tool schema enum in the system prompt.""" + match = re.search(r'"enum":\s*\[([^\]]+)\]', system_prompt_text) + if not match: + raise ValueError("Could not find action enum in system prompt") + return frozenset(json.loads(f"[{match.group(1)}]")) + + +class Fara15AgentConfig(AgentConfig): + """Configuration for Fara15Agent. Notable fields: extra_create_args: Extra kwargs merged on top of ``temperature: 0`` and - forwarded to ``chat.completions.create``. vLLM-only keys are routed - via ``extra_body``. - auto_user_reply: When True, ``ask_user_question`` does NOT halt - the run — a fixed dummy user response is injected and the agent - continues. For eval / no-user-simulator settings only. Default False - preserves ``task_workflow.py``'s ``WAITING_FOR_USER`` semantics for - data_gen. - captcha_timeout_limit: Number of consecutive captcha-wait timeouts - tolerated within a single run before the per-step captcha-wait - gate is auto-disabled for the rest of the run. Set to ``0`` to - skip the gate entirely from the start (appropriate without a - Browserbase cloud session, which is what triggers the captcha - solver). Defaults to ``2``. - raise_on_captcha_timeout: When True, restore the legacy behavior - of raising ``RuntimeError`` on the first captcha-wait timeout. - When False (default), the agent logs a warning and moves past - the timed-out captcha, falling back to ``captcha_timeout_limit`` - to auto-disable the gate after repeated timeouts. + forwarded to ``chat.completions.create``. + auto_user_reply: When True, ``ask_user_question`` does NOT halt the run — + a fixed dummy user response is injected and the agent continues (for + eval / no-user-simulator settings only). + captcha_timeout_limit: Consecutive captcha-wait timeouts tolerated in a + single run before the per-step captcha gate is auto-disabled. ``0`` + skips the gate from the start (appropriate without a Browserbase + session, which is what triggers the captcha solver). + raise_on_captcha_timeout: When True, raise on the first captcha-wait + timeout instead of moving past the unsolved captcha. """ - name: str = "fara" + name: str = "fara_next" client_config: dict[str, Any] | None = None - start_page: str = "about:blank" max_rounds: int = 10 max_n_images: int = 3 + max_observation_chars: int = 1000 fn_call_template: str = "fara-qwen3vl" - identity: str | None = "fara_qwen3vl" + identity: str | None = "fara_qwen35" critical_points: str | None = "fara-1.5" - computer_use_mode: str = "aurora" + computer_use_mode: str = "fara_next_browser" save_screenshots: bool = False - animate_actions: bool = False - single_tab_mode: bool = True include_input_text_key_args: bool = True viewport_width: int = 1440 viewport_height: int = 900 @@ -104,8 +109,8 @@ class FaraQwen3AgentConfig(AgentConfig): @dataclass -class FaraQwen3AgentState: - """Mutable state for FaraQwen3Agent that persists across resume calls.""" +class Fara15AgentState: + """Mutable state that persists across resume calls.""" chat_history: list[LLMMessage] = field(default_factory=list) facts: list[str] = field(default_factory=list) @@ -115,31 +120,33 @@ class FaraQwen3AgentState: mlm_height: int = 0 -class FaraQwen3Agent(Agent): - """Web automation agent using vision-language models.""" +class Fara15Agent(Agent): + """Web automation agent with the FaraNext browser action set.""" - DEFAULT_START_PAGE = "https://www.bing.com/" DISPLAY_SIZE = FARA_DISPLAY_SIZE PATCH_SIZE = 16 MERGE_SIZE = 2 USER_MESSAGE = "Here is the next screenshot. Think about what to do next." MAX_URL_LENGTH = 100 + _TEXT_OBSERVATION_ACTIONS: frozenset[str] = frozenset({"read_page_answer_question"}) + + _MODE_CANONICAL_IDENTITY: dict[str, tuple[str, ...]] = { + "fara_next_browser": ("fara_qwen3vl", "fara_qwen35"), + "fara_next_windows": ("fara_qwen3vl_windows", "fara_qwen35_windows"), + "fara_next_windows_core": ("fara_qwen3vl_windows", "fara_qwen35_windows"), + } + def __init__( - self, config: FaraQwen3AgentConfig | dict[str, Any] | None = None, **kwargs: Any + self, config: Fara15AgentConfig | dict[str, Any] | None = None, **kwargs: Any ): super().__init__(config, **kwargs) - self.config: FaraQwen3AgentConfig - if type(self) is FaraQwen3Agent: - assert ( - self.config.computer_use_mode == "aurora" - ), f"FaraQwen3Agent is aurora-v2 era; got computer_use_mode={self.config.computer_use_mode!r}" + self.config: Fara15AgentConfig self.logger = logging.getLogger(__name__) self._client: ChatCompletionClient | None = None - self._state: FaraQwen3AgentState | None = None + self._state: Fara15AgentState | None = None self._pending_observation: str = "" - self._os_type: str = "linux" - self._output_dir = None + self._allowed_actions: frozenset[str] = frozenset() self._captcha_timeouts: int = 0 self._captcha_disabled: bool = self.config.captcha_timeout_limit <= 0 @@ -153,13 +160,13 @@ def mlm_processor_im_cfg(self) -> dict[str, int]: } @classmethod - def _get_config_class(cls) -> type[FaraQwen3AgentConfig]: - return FaraQwen3AgentConfig + def _get_config_class(cls) -> type[Fara15AgentConfig]: + return Fara15AgentConfig async def initialize(self, run_context: RunContext) -> None: - """Initialize the agent.""" + """Initialize the agent for a run.""" await super().initialize(run_context) - self._state = FaraQwen3AgentState( + self._state = Fara15AgentState( mlm_width=self.config.viewport_width, mlm_height=self.config.viewport_height, ) @@ -171,19 +178,39 @@ async def initialize(self, run_context: RunContext) -> None: else: raise ValueError("Either client or client_config must be provided") + valid_modes = set(self._MODE_CANONICAL_IDENTITY) + if self.config.computer_use_mode not in valid_modes: + raise ValueError( + f"Unknown computer_use_mode '{self.config.computer_use_mode}'. " + f"Known values: {sorted(valid_modes)}" + ) + canonical_identities = self._MODE_CANONICAL_IDENTITY[ + self.config.computer_use_mode + ] + if ( + self.config.identity is not None + and self.config.identity not in canonical_identities + ): + raise ValueError( + f"identity={self.config.identity!r} does not match any canonical " + f"identity in {list(canonical_identities)!r} for " + f"computer_use_mode={self.config.computer_use_mode!r}. " + f"Use one of {list(canonical_identities)!r} or set identity=None " + f"to auto-detect." + ) + async def run( self, run_context: RunContext, input: Any = None ) -> Tuple[str, List, List]: """Run the agent on a task. - Supports resuming after WAITING_FOR_USER: if ``_state.chat_history`` - is already populated the fresh-start init is skipped and the user - response is appended to the conversation. + Supports resuming after WAITING_FOR_USER: if ``_state.chat_history`` is + already populated the fresh-start init is skipped and the user response + is appended to the conversation. """ env = run_context.environment + traj = run_context.solver_log - self._os_type = getattr(env, "os_type", "linux") - self._output_dir = run_context.output_dir pending_user_response = "" if self._state.chat_history: @@ -337,10 +364,9 @@ async def close(self, run_context: RunContext) -> None: await super().close(run_context) def _get_final_answer(self, thoughts: str, action_description: str) -> str: - return thoughts + return action_description def _get_observation_prefix(self) -> str: - """Return text to prepend to the next user message. Override in subclasses.""" obs = self._pending_observation self._pending_observation = "" return obs @@ -401,11 +427,15 @@ def _get_system_message( ) system_message = [] + full_text = "" for msg in system_prompt_info["conversation"]: tmp_content = "" for content in msg["content"]: tmp_content += content["text"] system_message.append(SystemMessage(content=tmp_content)) + full_text += tmp_content + + self._allowed_actions = extract_allowed_actions(full_text) return system_message, scaled_screenshot @@ -464,6 +494,16 @@ def proc_coords( [tgt_x, tgt_y], im_w, im_h, og_im_w, og_im_h ) + def _proc(self, coordinate): + """Shorthand for proc_coords with standard display/viewport args.""" + return self.proc_coords( + coordinate, + self.DISPLAY_SIZE, + self.DISPLAY_SIZE, + self.config.viewport_width, + self.config.viewport_height, + ) + @retry( retry=retry_if_not_exception_type(openai.BadRequestError), stop=stop_after_attempt(5), @@ -479,9 +519,8 @@ async def _make_model_call( """Make a model call using the client.""" if self._client is None: raise ValueError( - "No client configured for FaraAgent. Call initialize() first." + "No client configured for Fara15Agent. Call initialize() first." ) - result = await self._client.create( messages=history, extra_create_args=extra_create_args or {}, @@ -491,8 +530,7 @@ async def _make_model_call( def remove_screenshot_from_message(self, msg: LLMMessage) -> LLMMessage | None: """Remove the screenshot from the message content.""" if isinstance(msg.content, list): - new_content = [c for c in msg.content if not isinstance(c, ImageObj)] - msg.content = new_content + msg.content = [c for c in msg.content if not isinstance(c, ImageObj)] return msg elif isinstance(msg.content, ImageObj): return None @@ -501,7 +539,8 @@ def remove_screenshot_from_message(self, msg: LLMMessage) -> LLMMessage | None: def maybe_remove_old_screenshots( self, history: List[LLMMessage], includes_current: bool = False ) -> List[LLMMessage]: - """Remove old screenshots from the chat history.""" + """Remove old screenshots from the chat history, keeping the most recent + ``max_n_images``. Original task and user-response messages keep their text.""" if self.config.max_n_images <= 0: return history @@ -569,8 +608,7 @@ def _msg_has_image(msg: LLMMessage) -> bool: def _fit_images_to_budget(self, history: List[LLMMessage]) -> List[LLMMessage]: """Drop oldest screenshots (keeping the most recent) until the estimated - prompt is within ``image_budget_token_cap``. Returns a new list; the - original messages in ``chat_history`` are left unchanged.""" + prompt is within ``image_budget_token_cap``. Returns a new list.""" cap = self.config.image_budget_token_cap if cap <= 0 or self._estimate_prompt_tokens(history) <= cap: return history @@ -642,15 +680,29 @@ async def _generate_model_call( async def _execute_action( self, env, function_call: list[FunctionCall] ) -> tuple[bool, str]: - """Execute an action on the environment.""" + """Execute an action. Only actions in the current schema are allowed.""" name = function_call[0].name args = function_call[0].arguments - action_description = "" + action_type = args.get("action", "") + if action_type not in self._allowed_actions: + if self.config.terminate_on_parse_error: + self.logger.warning( + "terminate_on_parse_error=true: invalid action %r — " + "ending trajectory with thoughts as final answer", + action_type, + ) + return True, args.get("thoughts", "") or args.get("answer", "") + raise ValueError( + f"Action '{action_type}' is not allowed in mode " + f"'{self.config.computer_use_mode}'. Allowed: {sorted(self._allowed_actions)}" + ) + + url = (await env.get_page_context()).url self.logger.debug( WebSurferEvent( - source="FaraAgent", - url=(await env.get_page_context()).url, + source="Fara15Agent", + url=url, action=name, arguments=args, message=f"{name}( {json.dumps(args)} )", @@ -658,106 +710,135 @@ async def _execute_action( ) if "coordinate" in args: - args["coordinate"] = self.proc_coords( - args["coordinate"], - self.DISPLAY_SIZE, - self.DISPLAY_SIZE, - self.config.viewport_width, - self.config.viewport_height, + args["coordinate"] = self._proc(args["coordinate"]) + + is_stop_action, action_description = await self._dispatch_action( + env, action_type, args + ) + + if action_type in self._TEXT_OBSERVATION_ACTIONS: + self._pending_observation = format_text_observation( + action_type, + action_description, + self.config.max_observation_chars, ) - is_stop_action = False - action_type = args["action"] + if not is_stop_action: + if hasattr(env, "wait_for_load"): + await env.wait_for_load() + + self._state.num_actions += 1 + return is_stop_action, action_description + + async def _dispatch_action( + self, env, action_type: str, args: dict + ) -> tuple[bool, str]: + """Dispatch an action using the environment's canonical methods.""" + if action_type == "left_click": + tgt_x, tgt_y = args["coordinate"] + await env.left_click(tgt_x, tgt_y) + return False, f"I clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "key": + keys = args.get("keys", []) + await env.key(keys) + return False, f"I pressed the following keys: {keys}" - if action_type == "visit_url": - url = str(args["url"]) - action_description = f"I typed '{url}' into the browser address bar." + elif action_type == "mouse_move": + tgt_x, tgt_y = args["coordinate"] + await env.mouse_move(tgt_x, tgt_y) + return False, f"I moved the cursor to ({tgt_x}, {tgt_y})." + + elif action_type == "type": + text = str(args.get("text", args.get("text_value", ""))) + await env.type(text) + return False, f"I typed '{text}'." + + elif action_type == "scroll": + pixels = int(args.get("pixels", 0)) + pixels = int(pixels * self.config.viewport_height / self.DISPLAY_SIZE) + await env.scroll(pixels) + direction = "up" if pixels > 0 else "down" + return False, f"I scrolled {direction}." + + elif action_type == "double_click": + tgt_x, tgt_y = args["coordinate"] + await env.double_click(tgt_x, tgt_y) + return False, f"I double-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "right_click": + tgt_x, tgt_y = args["coordinate"] + await env.right_click(tgt_x, tgt_y) + return False, f"I right-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "triple_click": + tgt_x, tgt_y = args["coordinate"] + await env.triple_click(tgt_x, tgt_y) + return False, f"I triple-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "left_click_drag": + tgt_x, tgt_y = args["coordinate"] + await env.left_click_drag(tgt_x, tgt_y) + return False, f"I dragged to ({tgt_x}, {tgt_y})." + + elif action_type == "hscroll": + pixels = int(args.get("pixels", 0)) + pixels = int(pixels * self.config.viewport_width / self.DISPLAY_SIZE) + await env.hscroll(pixels) + return False, f"I scrolled horizontally by {pixels} pixels." + + elif action_type == "visit_url": + url = args.get("url", "") if url.startswith(("https://", "http://", "file://", "about:")): - await env.goto(url) + target = url elif " " in url: - await env.goto( - f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH" - ) + target = f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH" else: - await env.goto("https://" + url) + target = "https://" + url + try: + await env.goto_url(target) + except Exception as e: + msg = f"visit_url to {url} failed: {type(e).__name__}: {e}" + self._pending_observation = msg + return False, f"I tried to navigate to {url} but it failed: {e}" + return False, f"I navigated to {url}." elif action_type == "history_back": - action_description = "I clicked the browser back button." - await env.back() + await env.go_back() + return False, "I clicked the browser back button." elif action_type == "web_search": - query = args.get("query") - action_description = f"I typed '{query}' into the browser search bar." - encoded_query = quote_plus(query) - await env.goto(f"https://www.bing.com/search?q={encoded_query}&FORM=QBLH") - - elif action_type == "scroll": - pixels = int(args.get("pixels", 0)) - if pixels > 0: - action_description = "I scrolled up one page in the browser." - await env.scroll_up() - elif pixels < 0: - action_description = "I scrolled down one page in the browser." - await env.scroll_down() - - elif action_type in ("keypress", "key"): - keys = args.get("keys", []) - action_description = f"I pressed the following keys: {keys}" - await env.keypress(keys) - - elif action_type in ("hover", "mouse_move"): - if "coordinate" in args: - tgt_x, tgt_y = args["coordinate"] - await env.hover(tgt_x, tgt_y) - - elif action_type in ("sleep", "wait"): - duration = args.get("duration", args.get("time", 3.0)) - action_description = ( - "I am waiting a short period of time before taking further action." + query = args.get("query", "") + await env.goto_url( + f"https://www.bing.com/search?q={quote_plus(query)}&FORM=QBLH" ) - await env.wait(duration) + return False, f"I searched for '{query}'." - elif action_type in ("click", "left_click"): - if "coordinate" in args: - tgt_x, tgt_y = args["coordinate"] - action_description = f"I clicked at coordinates ({tgt_x}, {tgt_y})." - await env.click(tgt_x, tgt_y) - - elif action_type in ("input_text", "type"): - text_value = args.get("text", args.get("text_value")) - if text_value is None: - raise ValueError( - "input_text/type action requires 'text' or 'text_value' argument" - ) - text_value = str(text_value) - action_description = f"I typed '{text_value}'." - press_enter = args.get("press_enter", True) - delete_existing = args.get("delete_existing_text", False) - - if "coordinate" in args: - tgt_x, tgt_y = args["coordinate"] - await env.type_text( - tgt_x, - tgt_y, - text_value, - press_enter=press_enter, - clear_first=delete_existing, - ) + elif action_type == "read_page_answer_question": + question = str(args.get("question", "")) + markdown = await env.get_page_markdown() + answer = await extract_from_page(markdown, question, self._client) + return False, f"I read the page to answer: {question}\nAnswer: {answer}" + + elif action_type == "ask_user_question": + question = str(args.get("question", "")) + return True, f"I asked the user: {question}" + + elif action_type == "wait": + duration = args.get("time", args.get("duration", 3.0)) + await env.wait(duration) + return False, f"I waited {duration}s." elif action_type == "pause_and_memorize_fact": - fact = str(args.get("fact")) + fact = str(args.get("fact", "")) self._state.facts.append(fact) - action_description = f"I memorized the following fact: {fact}" + return False, f"I memorized the following fact: {fact}" - elif action_type in ("stop", "terminate"): - action_description = args.get("thoughts", "Task terminated") - is_stop_action = True + elif action_type == "terminate": + answer = args.get("answer") + if answer is None: + raise ValueError("terminate action requires 'answer' argument") + return True, answer else: raise ValueError(f"Unknown action: {action_type}") - - if not is_stop_action: - await env.wait_for_load() - - self._state.num_actions += 1 - return is_stop_action, action_description diff --git a/src/fara/agents/fara/fara_qwen3_next.py b/src/fara/agents/fara/fara_qwen3_next.py deleted file mode 100644 index 802821a..0000000 --- a/src/fara/agents/fara/fara_qwen3_next.py +++ /dev/null @@ -1,369 +0,0 @@ -"""FaraQwen3NextAgent - Extends FaraQwen3Agent with expanded tool sets.""" - -from __future__ import annotations - -import json -import re -from typing import Any -from urllib.parse import quote_plus - -from ...clients.messages import FunctionCall, ImageObj, SystemMessage, UserMessage - -from ...core.run_context import RunContext -from .fara_qwen3 import FaraQwen3Agent, FaraQwen3AgentConfig -from .fara_types import WebSurferEvent -from .prompts import get_computer_use_system_prompt, TOOL_SET_TO_MODE -from ..computer_agent.utils import extract_from_page -from ..utils import ( - format_text_observation, - save_and_truncate_command_output, - wrap_command_with_tee, -) - - -def extract_allowed_actions(system_prompt_text: str) -> frozenset[str]: - """Extract the allowed action names from the tool schema enum in the system prompt.""" - match = re.search(r'"enum":\s*\[([^\]]+)\]', system_prompt_text) - if not match: - raise ValueError("Could not find action enum in system prompt") - return frozenset(json.loads(f"[{match.group(1)}]")) - - -class FaraQwen3NextAgentConfig(FaraQwen3AgentConfig): - """Configuration for FaraQwen3NextAgent.""" - - name: str = "fara_next" - max_observation_chars: int = 1000 - computer_use_mode: str = "fara_next_browser" - identity: str | None = "fara_qwen3vl" - critical_points: str | None = "fara-1.5" - - -class FaraQwen3NextAgent(FaraQwen3Agent): - """Web/desktop automation agent with expanded tool sets (FaraNext). - - Extends FaraQwen3Agent with: - - Dynamic tool mode selection (browser vs windows) via config.computer_use_mode - - New GUI actions: double_click, right_click, triple_click, left_click_drag, hscroll - - New non-GUI actions: read_page_answer_question, ask_user_question, run_command - - Cursor position tracking for left_click_drag - - press_enter defaults to False (model should use key(["Enter"]) explicitly) - """ - - _TEXT_OBSERVATION_ACTIONS: frozenset[str] = frozenset( - { - "run_command", - "read_page_answer_question", - } - ) - - _MODE_CANONICAL_IDENTITY: dict[str, tuple[str, ...]] = { - "fara_next_browser": ("fara_qwen3vl", "fara_qwen35"), - "fara_next_windows": ("fara_qwen3vl_windows", "fara_qwen35_windows"), - "fara_next_windows_core": ("fara_qwen3vl_windows", "fara_qwen35_windows"), - } - - def __init__( - self, - config: FaraQwen3NextAgentConfig | dict[str, Any] | None = None, - **kwargs: Any, - ): - super().__init__(config, **kwargs) - self.config: FaraQwen3NextAgentConfig - self._allowed_actions: frozenset[str] = frozenset() - self._cursor_x: float = 0.0 - self._cursor_y: float = 0.0 - self._pending_observation: str = "" - - @classmethod - def _get_config_class(cls) -> type[FaraQwen3NextAgentConfig]: - return FaraQwen3NextAgentConfig - - async def initialize(self, run_context: RunContext) -> None: - await super().initialize(run_context) - valid_modes = set(TOOL_SET_TO_MODE.values()) - if self.config.computer_use_mode not in valid_modes: - raise ValueError( - f"Unknown computer_use_mode '{self.config.computer_use_mode}'. " - f"Known values: {sorted(valid_modes)}" - ) - canonical_identities = self._MODE_CANONICAL_IDENTITY[ - self.config.computer_use_mode - ] - if ( - self.config.identity is not None - and self.config.identity not in canonical_identities - ): - raise ValueError( - f"identity={self.config.identity!r} does not match any canonical " - f"identity in {list(canonical_identities)!r} for " - f"computer_use_mode={self.config.computer_use_mode!r}. " - f"Use one of {list(canonical_identities)!r} or set identity=None " - f"to auto-detect." - ) - - def _get_system_message(self, screenshot): - system_prompt_info = get_computer_use_system_prompt( - screenshot, - self.mlm_processor_im_cfg, - mode=self.config.computer_use_mode, - include_input_text_key_args=self.config.include_input_text_key_args, - fn_call_template=self.config.fn_call_template, - randomize=False, - display_size=self.DISPLAY_SIZE, - identity=self.config.identity, - critical_points=self.config.critical_points, - ) - self._state.mlm_width, self._state.mlm_height = system_prompt_info["im_size"] - scaled_screenshot = screenshot.resize( - (self._state.mlm_width, self._state.mlm_height) - ) - - system_message = [] - full_text = "" - for msg in system_prompt_info["conversation"]: - tmp_content = "" - for content in msg["content"]: - tmp_content += content["text"] - system_message.append(SystemMessage(content=tmp_content)) - full_text += tmp_content - - self._allowed_actions = extract_allowed_actions(full_text) - - return system_message, scaled_screenshot - - def _get_final_answer(self, thoughts: str, action_description: str) -> str: - return action_description - - def _get_observation_prefix(self) -> str: - obs = self._pending_observation - self._pending_observation = "" - return obs - - def maybe_remove_old_screenshots(self, history, includes_current=False): - """Override to also strip observation text from old messages in windows mode.""" - result = super().maybe_remove_old_screenshots(history, includes_current) - if "windows" not in self.config.computer_use_mode: - return result - - for msg in result: - if not isinstance(msg, UserMessage): - continue - if (msg.metadata or {}).get("is_original", False): - continue - if isinstance(msg.content, list): - has_image = any(isinstance(c, ImageObj) for c in msg.content) - if not has_image: - msg.content = [ - self.USER_MESSAGE if isinstance(c, str) else c - for c in msg.content - ] - - return result - - async def _execute_action( - self, env, function_call: list[FunctionCall] - ) -> tuple[bool, str]: - """Execute an action. Only actions in the current schema are allowed.""" - name = function_call[0].name - args = function_call[0].arguments - action_type = args.get("action", "") - - if action_type not in self._allowed_actions: - if self.config.terminate_on_parse_error: - self.logger.warning( - "terminate_on_parse_error=true: invalid action %r — " - "ending trajectory with thoughts as final answer", - action_type, - ) - return True, args.get("thoughts", "") or args.get("answer", "") - raise ValueError( - f"Action '{action_type}' is not allowed in mode " - f"'{self.config.computer_use_mode}'. Allowed: {sorted(self._allowed_actions)}" - ) - - url = (await env.get_page_context()).url - self.logger.debug( - WebSurferEvent( - source="FaraQwen3NextAgent", - url=url, - action=name, - arguments=args, - message=f"{name}( {json.dumps(args)} )", - ) - ) - - if "coordinate" in args: - args["coordinate"] = self._proc(args["coordinate"]) - - is_stop_action, action_description = await self._dispatch_action( - env, action_type, args - ) - - if action_type in self._TEXT_OBSERVATION_ACTIONS: - self._pending_observation = format_text_observation( - action_type, - action_description, - self.config.max_observation_chars, - ) - - if not is_stop_action: - if hasattr(env, "wait_for_load"): - await env.wait_for_load() - - self._state.num_actions += 1 - return is_stop_action, action_description - - async def _dispatch_action( - self, env, action_type: str, args: dict - ) -> tuple[bool, str]: - """Dispatch action using ComputerEnvironment canonical methods.""" - - - if action_type == "left_click": - tgt_x, tgt_y = args["coordinate"] - await env.left_click(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I clicked at coordinates ({tgt_x}, {tgt_y})." - - elif action_type == "key": - keys = args.get("keys", []) - await env.key(keys) - return False, f"I pressed the following keys: {keys}" - - elif action_type == "mouse_move": - tgt_x, tgt_y = args["coordinate"] - await env.mouse_move(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I moved the cursor to ({tgt_x}, {tgt_y})." - - elif action_type == "type": - text = str(args.get("text", args.get("text_value", ""))) - await env.type(text) - return False, f"I typed '{text}'." - - elif action_type == "scroll": - pixels = int(args.get("pixels", 0)) - pixels = int(pixels * self.config.viewport_height / self.DISPLAY_SIZE) - await env.scroll(pixels) - direction = "up" if pixels > 0 else "down" - return False, f"I scrolled {direction}." - - elif action_type == "double_click": - tgt_x, tgt_y = args["coordinate"] - await env.double_click(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I double-clicked at coordinates ({tgt_x}, {tgt_y})." - - elif action_type == "right_click": - tgt_x, tgt_y = args["coordinate"] - await env.right_click(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I right-clicked at coordinates ({tgt_x}, {tgt_y})." - - elif action_type == "triple_click": - tgt_x, tgt_y = args["coordinate"] - await env.triple_click(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I triple-clicked at coordinates ({tgt_x}, {tgt_y})." - - elif action_type == "left_click_drag": - tgt_x, tgt_y = args["coordinate"] - await env.left_click_drag(tgt_x, tgt_y) - self._cursor_x, self._cursor_y = tgt_x, tgt_y - return False, f"I dragged to ({tgt_x}, {tgt_y})." - - elif action_type == "hscroll": - pixels = int(args.get("pixels", 0)) - pixels = int(pixels * self.config.viewport_width / self.DISPLAY_SIZE) - await env.hscroll(pixels) - return False, f"I scrolled horizontally by {pixels} pixels." - - - elif action_type == "visit_url": - url = args.get("url", "") - if url.startswith(("https://", "http://", "file://", "about:")): - target = url - elif " " in url: - target = f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH" - else: - target = "https://" + url - try: - await env.goto_url(target) - except Exception as e: - msg = f"visit_url to {url} failed: {type(e).__name__}: {e}" - self._pending_observation = msg - return False, f"I tried to navigate to {url} but it failed: {e}" - return False, f"I navigated to {url}." - - elif action_type == "history_back": - await env.go_back() - return False, "I clicked the browser back button." - - elif action_type == "web_search": - query = args.get("query", "") - await env.goto_url( - f"https://www.bing.com/search?q={quote_plus(query)}&FORM=QBLH" - ) - return False, f"I searched for '{query}'." - - elif action_type == "read_page_answer_question": - question = str(args.get("question", "")) - markdown = await env.get_page_markdown() - answer = await extract_from_page(markdown, question, self._client) - return False, f"I read the page to answer: {question}\nAnswer: {answer}" - - - elif action_type == "run_command": - command = str(args.get("command", "")) - step = self._state.current_step - cmd = wrap_command_with_tee(command, step, self._os_type) - result = await env.execute(cmd) - if isinstance(result, dict): - stdout = result.get("stdout", "") - stderr = result.get("stderr", "") - result = f"{stdout}\n{stderr}".strip() if stderr else stdout - else: - result = str(result) - result = save_and_truncate_command_output( - result, - self._output_dir, - step, - max_chars=self.config.max_observation_chars, - os_type=self._os_type, - ) - return False, f"I executed command: {command}\nOutput: {result}" - - - elif action_type == "ask_user_question": - question = str(args.get("question", "")) - return True, f"I asked the user: {question}" - - elif action_type == "wait": - duration = args.get("time", args.get("duration", 3.0)) - await env.wait(duration) - return False, f"I waited {duration}s." - - elif action_type == "pause_and_memorize_fact": - fact = str(args.get("fact", "")) - self._state.facts.append(fact) - return False, f"I memorized the following fact: {fact}" - - elif action_type == "terminate": - answer = args.get("answer") - if answer is None: - raise ValueError("terminate action requires 'answer' argument") - return True, answer - - else: - raise ValueError(f"Unknown action: {action_type}") - - def _proc(self, coordinate): - """Shorthand for proc_coords with standard display/viewport args.""" - return self.proc_coords( - coordinate, - self.DISPLAY_SIZE, - self.DISPLAY_SIZE, - self.config.viewport_width, - self.config.viewport_height, - ) diff --git a/src/fara/agents/fara/prompts.py b/src/fara/agents/fara/prompts.py deleted file mode 100644 index e64f2e3..0000000 --- a/src/fara/agents/fara/prompts.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Fara prompt entry points. - -``get_computer_use_system_prompt`` is re-exported from this repo's -``fara._prompts`` (which ships the FaraNext browser tool schema, the model -identities, the critical-points block, and the fn-call template). Only the -browser tool set is supported here. -""" - -from ..._prompts import get_computer_use_system_prompt - -__all__ = ["get_computer_use_system_prompt", "TOOL_SET_TO_MODE"] - -TOOL_SET_TO_MODE = { - "BROWSER_TOOLS_CORE": "fara_next_browser", - "GPT54_BROWSER_TOOLS_CORE": "fara_next_browser", - "BROWSER_TOOLS_WITH_READ_PAGE": "fara_next_browser", - "GPT54_BROWSER_TOOLS_WITH_READ_PAGE": "fara_next_browser", - "WINDOWS_TOOLS_CORE": "fara_next_windows_core", - "WINDOWS_TOOLS_WITH_RUN_COMMAND": "fara_next_windows", - "GPT54_WINDOWS_TOOLS_CORE": "fara_next_windows_core", - "GPT54_WINDOWS_TOOLS_WITH_RUN_COMMAND": "fara_next_windows", -} diff --git a/src/fara/run_fara.py b/src/fara/run_fara.py index a919d3d..4b9745c 100755 --- a/src/fara/run_fara.py +++ b/src/fara/run_fara.py @@ -7,7 +7,7 @@ from pathlib import Path import json -from .agents.fara.fara_qwen3_next import FaraQwen3NextAgent, FaraQwen3NextAgentConfig +from .agents.fara.fara15_agent import Fara15Agent, Fara15AgentConfig from .environments.playwright import PlaywrightEnvironment from .core.run_context import RunContext from .core.data_point import Task, SolverStatus, UserMessage, UserMessageType @@ -20,7 +20,7 @@ format="%(message)s", ) -for _logger_name in ("fara.agents.fara.fara_qwen3", "fara.fara_7b.fara_agent"): +for _logger_name in ("fara.agents.fara.fara15_agent", "fara.fara_7b.fara_agent"): _agent_logger = logging.getLogger(_logger_name) _agent_logger.setLevel(logging.INFO) _handler = logging.StreamHandler() @@ -55,7 +55,7 @@ async def run_fara15_agent( max_rounds: int = 100, use_browser_base: bool = False, ) -> None: - """Interactive loop for the Fara-1.5 agent (FaraQwen3NextAgent).""" + """Interactive loop for the Fara-1.5 agent.""" output_root = Path(output_folder or "./fara_runs") print("Initializing Browser...") @@ -71,8 +71,8 @@ async def run_fara15_agent( await env.initialize() print("Browser Running... Starting Fara-1.5 Agent...") - agent = FaraQwen3NextAgent( - FaraQwen3NextAgentConfig( + agent = Fara15Agent( + Fara15AgentConfig( client_config=endpoint_config, max_rounds=max_rounds, identity="fara_qwen35", diff --git a/tests/test_fara15.py b/tests/test_fara15.py index eabdf6c..661551f 100644 --- a/tests/test_fara15.py +++ b/tests/test_fara15.py @@ -1,4 +1,4 @@ -"""Tests for the Fara-1.5 agent (vendored FaraQwen3NextAgent). +"""Tests for the Fara-1.5 agent (Fara15Agent). Covers the two things the port must get exactly right: the system prompt the model sees (identity + critical points + tool schema), and the @@ -10,9 +10,9 @@ from PIL import Image -from fara import DataPoint, FaraQwen3NextAgent, Task -from fara.agents.fara.fara_qwen3_next import ( - FaraQwen3NextAgentConfig, +from fara import DataPoint, Fara15Agent, Task +from fara.agents.fara.fara15_agent import ( + Fara15AgentConfig, extract_allowed_actions, ) from fara._prompts import ( @@ -49,8 +49,8 @@ def _make_agent(identity="fara_qwen35"): - agent = FaraQwen3NextAgent( - FaraQwen3NextAgentConfig( + agent = Fara15Agent( + Fara15AgentConfig( client_config={"model": "m", "base_url": "u", "api_key": "k"}, identity=identity, ) @@ -122,7 +122,7 @@ def test_proc_coords_scales_display_space_to_viewport(): def test_parse_thoughts_and_action(): - agent = FaraQwen3NextAgent.__new__(FaraQwen3NextAgent) + agent = Fara15Agent.__new__(Fara15Agent) agent.logger = logging.getLogger("test") action_json = json.dumps( { From 5c8247a5f2b5fe313d05804e90f8567fc5d33f0f Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 12:52:27 -0700 Subject: [PATCH 08/11] minor --- src/fara/agents/fara/fara15_agent.py | 5 +---- src/fara/agents/fara/utils.py | 9 --------- src/fara/agents/utils.py | 11 +++++++++++ 3 files changed, 12 insertions(+), 13 deletions(-) delete mode 100644 src/fara/agents/fara/utils.py diff --git a/src/fara/agents/fara/fara15_agent.py b/src/fara/agents/fara/fara15_agent.py index 0eb88fa..193a486 100644 --- a/src/fara/agents/fara/fara15_agent.py +++ b/src/fara/agents/fara/fara15_agent.py @@ -54,9 +54,8 @@ from ..captcha import wait_for_captcha from ..coord_spaces import FARA_DISPLAY_SIZE from ..computer_agent.utils import extract_from_page -from ..utils import format_text_observation +from ..utils import format_text_observation, get_trimmed_url from .fara_types import WebSurferEvent -from .utils import get_trimmed_url def extract_allowed_actions(system_prompt_text: str) -> frozenset[str]: @@ -114,7 +113,6 @@ class Fara15AgentState: chat_history: list[LLMMessage] = field(default_factory=list) facts: list[str] = field(default_factory=list) - num_actions: int = 0 current_step: int = 0 mlm_width: int = 0 mlm_height: int = 0 @@ -727,7 +725,6 @@ async def _execute_action( if hasattr(env, "wait_for_load"): await env.wait_for_load() - self._state.num_actions += 1 return is_stop_action, action_description async def _dispatch_action( diff --git a/src/fara/agents/fara/utils.py b/src/fara/agents/fara/utils.py deleted file mode 100644 index 7ebe614..0000000 --- a/src/fara/agents/fara/utils.py +++ /dev/null @@ -1,9 +0,0 @@ -def strip_url_query(url): - return url.split("?", 1)[0] - - -def get_trimmed_url(url, max_len): - trimmed_url = strip_url_query(url) - if len(trimmed_url) > max_len: - trimmed_url = trimmed_url[:max_len] + " ..." - return trimmed_url diff --git a/src/fara/agents/utils.py b/src/fara/agents/utils.py index a1503fa..13a302c 100644 --- a/src/fara/agents/utils.py +++ b/src/fara/agents/utils.py @@ -133,3 +133,14 @@ def build_screen_text( if context_summary: text = f"{text}\n\n{context_summary}" return text + + +def strip_url_query(url: str) -> str: + return url.split("?", 1)[0] + + +def get_trimmed_url(url: str, max_len: int) -> str: + trimmed_url = strip_url_query(url) + if len(trimmed_url) > max_len: + trimmed_url = trimmed_url[:max_len] + " ..." + return trimmed_url From 879afdd82c8102aff55dfbbf0a42b64ba2ba6071 Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 12:59:27 -0700 Subject: [PATCH 09/11] more cleanup --- src/fara/__init__.py | 2 - src/fara/{ => agents/fara}/_prompts.py | 6 +- src/fara/agents/fara/fara15_agent.py | 2 +- src/fara/{ => fara_7b}/browser/__init__.py | 0 src/fara/{ => fara_7b}/browser/browser_bb.py | 0 src/fara/{ => fara_7b}/browser/page_script.js | 0 .../browser/playwright_controller.py | 0 src/fara/fara_7b/fara_agent.py | 6 +- src/fara/{ => fara_7b}/fara_types.py | 0 src/fara/{ => fara_7b}/utils.py | 0 src/fara/run_fara.py | 2 +- src/fara/version.py | 2 +- src/fara/vllm/__init__.py | 0 src/fara/vllm/az_vllm.py | 334 - src/fara/vllm/requirements.txt | 7 - src/fara/vllm/vllm_facade.py | 111 - tests/test_fara15.py | 4 +- uv.lock | 5925 +++++++++++++++++ 18 files changed, 5936 insertions(+), 465 deletions(-) rename src/fara/{ => agents/fara}/_prompts.py (98%) rename src/fara/{ => fara_7b}/browser/__init__.py (100%) rename src/fara/{ => fara_7b}/browser/browser_bb.py (100%) rename src/fara/{ => fara_7b}/browser/page_script.js (100%) rename src/fara/{ => fara_7b}/browser/playwright_controller.py (100%) rename src/fara/{ => fara_7b}/fara_types.py (100%) rename src/fara/{ => fara_7b}/utils.py (100%) delete mode 100644 src/fara/vllm/__init__.py delete mode 100644 src/fara/vllm/az_vllm.py delete mode 100644 src/fara/vllm/requirements.txt delete mode 100644 src/fara/vllm/vllm_facade.py create mode 100644 uv.lock diff --git a/src/fara/__init__.py b/src/fara/__init__.py index 44e7a3c..4324d2f 100644 --- a/src/fara/__init__.py +++ b/src/fara/__init__.py @@ -6,7 +6,6 @@ from .core.data_point import DataPoint, Task from .core.run_context import RunContext from .fara_7b import FARA_ACTION_DEFINITIONS, FaraAgent -from .browser.playwright_controller import PlaywrightController __all__ = [ "Fara15Agent", @@ -18,5 +17,4 @@ "RunContext", "FARA_ACTION_DEFINITIONS", "FaraAgent", - "PlaywrightController", ] diff --git a/src/fara/_prompts.py b/src/fara/agents/fara/_prompts.py similarity index 98% rename from src/fara/_prompts.py rename to src/fara/agents/fara/_prompts.py index 4526b5b..e354494 100644 --- a/src/fara/_prompts.py +++ b/src/fara/agents/fara/_prompts.py @@ -6,9 +6,9 @@ from typing import Union -from .qwen_helpers.base_tool import BaseTool -from .qwen_helpers.fncall_prompt import NousFnCallPrompt -from .qwen_helpers.utils import smart_resize +from ...qwen_helpers.base_tool import BaseTool +from ...qwen_helpers.fncall_prompt import NousFnCallPrompt +from ...qwen_helpers.utils import smart_resize FARA_QWEN35_IDENTITY = """\ You are Fara, a computer use agent (CUA) specialized for web browsers. \ diff --git a/src/fara/agents/fara/fara15_agent.py b/src/fara/agents/fara/fara15_agent.py index 193a486..c7c1632 100644 --- a/src/fara/agents/fara/fara15_agent.py +++ b/src/fara/agents/fara/fara15_agent.py @@ -29,7 +29,7 @@ before_sleep_log, ) -from ..._prompts import get_computer_use_system_prompt +from ._prompts import get_computer_use_system_prompt from ...clients.wrapper import ChatCompletionClient from ...clients.create_utils import create_client_from_config from ...clients.messages import ( diff --git a/src/fara/browser/__init__.py b/src/fara/fara_7b/browser/__init__.py similarity index 100% rename from src/fara/browser/__init__.py rename to src/fara/fara_7b/browser/__init__.py diff --git a/src/fara/browser/browser_bb.py b/src/fara/fara_7b/browser/browser_bb.py similarity index 100% rename from src/fara/browser/browser_bb.py rename to src/fara/fara_7b/browser/browser_bb.py diff --git a/src/fara/browser/page_script.js b/src/fara/fara_7b/browser/page_script.js similarity index 100% rename from src/fara/browser/page_script.js rename to src/fara/fara_7b/browser/page_script.js diff --git a/src/fara/browser/playwright_controller.py b/src/fara/fara_7b/browser/playwright_controller.py similarity index 100% rename from src/fara/browser/playwright_controller.py rename to src/fara/fara_7b/browser/playwright_controller.py diff --git a/src/fara/fara_7b/fara_agent.py b/src/fara/fara_7b/fara_agent.py index cff6fd3..62fbc85 100644 --- a/src/fara/fara_7b/fara_agent.py +++ b/src/fara/fara_7b/fara_agent.py @@ -13,9 +13,9 @@ from playwright.async_api import Download from playwright.async_api import BrowserContext import asyncio -from ..browser.playwright_controller import PlaywrightController +from .browser.playwright_controller import PlaywrightController from ._prompts import get_computer_use_system_prompt -from ..fara_types import ( +from .fara_types import ( LLMMessage, SystemMessage, UserMessage, @@ -26,7 +26,7 @@ message_to_openai_format, WebSurferEvent, ) -from ..utils import get_trimmed_url +from .utils import get_trimmed_url FARA_ACTION_DEFINITIONS: Dict[str, set] = { diff --git a/src/fara/fara_types.py b/src/fara/fara_7b/fara_types.py similarity index 100% rename from src/fara/fara_types.py rename to src/fara/fara_7b/fara_types.py diff --git a/src/fara/utils.py b/src/fara/fara_7b/utils.py similarity index 100% rename from src/fara/utils.py rename to src/fara/fara_7b/utils.py diff --git a/src/fara/run_fara.py b/src/fara/run_fara.py index 4b9745c..18509ee 100755 --- a/src/fara/run_fara.py +++ b/src/fara/run_fara.py @@ -12,7 +12,7 @@ from .core.run_context import RunContext from .core.data_point import Task, SolverStatus, UserMessage, UserMessageType from .fara_7b import FaraAgent -from .browser.browser_bb import BrowserBB +from .fara_7b.browser.browser_bb import BrowserBB logging.basicConfig( diff --git a/src/fara/version.py b/src/fara/version.py index 6b51f65..1f50994 100644 --- a/src/fara/version.py +++ b/src/fara/version.py @@ -1,3 +1,3 @@ -VERSION = "0.1.0" +VERSION = "0.2.0" __version__ = VERSION APP_NAME = "Fara" diff --git a/src/fara/vllm/__init__.py b/src/fara/vllm/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/fara/vllm/az_vllm.py b/src/fara/vllm/az_vllm.py deleted file mode 100644 index 55affac..0000000 --- a/src/fara/vllm/az_vllm.py +++ /dev/null @@ -1,334 +0,0 @@ -from fastapi import FastAPI, Request, Response -from contextlib import asynccontextmanager -import httpx -import argparse -import hashlib -import os -import shutil -import subprocess -from pathlib import Path -from typing import Optional -import logging - -from .vllm_facade import VLLM, Status - -try: - # Optional Azure-blob mount helpers; only used when ``--cache`` resolves - # an Azure blob URL. Public installs that don't need that codepath can - # leave the dependency unset. - import importlib as _importlib - _azcp = _importlib.import_module("." + "azcp", package="az" + "tool") - AzFolder = _azcp.AzFolder - LocalFolder = _azcp.LocalFolder -except ImportError: - AzFolder = None - LocalFolder = None - -try: - from huggingface_hub import snapshot_download -except ImportError: - snapshot_download = None - -# Hardcoded HuggingFace model ID for automatic download -DEFAULT_HF_MODEL_ID = "microsoft/Fara-7B" - - -def _is_azure_blob_url(model_path: str) -> bool: - return ( - model_path.startswith(("https://", "http://")) - and "blob.core.windows.net" in model_path - ) - - -def _download_model_from_hf( - output_dir: Path, model_id: str = DEFAULT_HF_MODEL_ID -) -> str: - """Download model from HuggingFace Hub if not already present.""" - if snapshot_download is None: - raise ImportError( - "huggingface_hub is required for automatic model download. " - "Install it with: pip install huggingface_hub" - ) - - output_dir.mkdir(parents=True, exist_ok=True) - - logging.info(f"Downloading {model_id} from HuggingFace to {output_dir}") - logging.info("This may take a while depending on your internet connection...") - - try: - snapshot_download( - repo_id=model_id, - local_dir=str(output_dir), - local_dir_use_symlinks=False, - ) - logging.info(f"Successfully downloaded model to {output_dir}") - return str(output_dir.resolve()) - except Exception as e: - logging.error(f"Error downloading model: {e}") - logging.error("If you're getting authentication errors, you may need to:") - logging.error(" 1. Install huggingface-cli: pip install -U huggingface_hub") - logging.error(" 2. Login: huggingface-cli login") - logging.error(" 3. Or set HF_TOKEN environment variable") - raise - - -def _extract_model_name(model_url: str) -> str: - """Extract model name from URL for consistent naming.""" - url_parts = model_url.rstrip("/").split("/") - return url_parts[-1] if url_parts else model_url - - -def _cache_model(model_url: str) -> str: - if AzFolder is None: - raise RuntimeError( - "Azure-blob support not available. Either install the optional " - "Azure mount helper (provides ``AzFolder``) or run without --cache." - ) - - cache_root = Path(args.cache_dir or os.path.expanduser("~/.cache/vllm_models")) - cache_root.mkdir(parents=True, exist_ok=True) - - model_name = _extract_model_name(model_url) - cache_key = f"{model_name}_{hashlib.md5(model_url.encode()).hexdigest()[:8]}" - cached_model_path = cache_root / cache_key - - if cached_model_path.exists() and cached_model_path.is_dir(): - return str(cached_model_path) - - model_az = AzFolder.from_uri(model_url) - context = model_az.mount() - context.mount() - copy_error: Optional[Exception] = None - try: - if cached_model_path.exists(): - shutil.rmtree(cached_model_path) - shutil.copytree(context.path, cached_model_path) - except OSError as exc: - copy_error = exc - finally: - context.unmount() - - if copy_error is None: - return str(cached_model_path) - - if LocalFolder is None: - raise copy_error - - if cached_model_path.exists(): - shutil.rmtree(cached_model_path) - cached_model_path.mkdir(parents=True, exist_ok=True) - try: - model_az.copy(LocalFolder(cached_model_path, create=False)) - except subprocess.CalledProcessError as exc: - raise RuntimeError("Failed to copy model using azcopy") from exc - - return str(cached_model_path) - - -def _prepare_cached_model(model_url: str) -> str: - if _is_azure_blob_url(model_url): - return _cache_model(model_url) - - model_path = Path(model_url).expanduser() - if not model_path.exists(): - raise FileNotFoundError(f"Local model directory not found: {model_url}") - return str(model_path.resolve()) - - -class AzVllm: - def __init__( - self, - model_url, - port, - device_id, - max_n_images, - dtype="auto", - enforce_eager=False, - use_external_endpoint=False, - ): - self.model_az = None - self.local_model_path = None - self.vllm = None - self.context = None - self.device_id = device_id - self.max_n_images = max_n_images - self.dtype = dtype - self.enforce_eager = enforce_eager - self.use_external_endpoint = use_external_endpoint - if model_url and not use_external_endpoint: - # Check if model_url is an Azure blob URL or a local directory - if _is_azure_blob_url(model_url): - self.model_az = AzFolder.from_uri(model_url) - else: - # It's a local directory - model_path = Path(model_url).expanduser() - if not model_path.exists(): - # Auto-download from HuggingFace if path doesn't exist - logging.warning(f"Local model directory not found: {model_url}") - logging.info( - f"Attempting to download {DEFAULT_HF_MODEL_ID} from HuggingFace..." - ) - self.local_model_path = _download_model_from_hf(model_path) - else: - self.local_model_path = str(model_path.resolve()) - self.port = port - - def __enter__(self): - # No-op if using external endpoint - if self.use_external_endpoint: - print("Using external endpoint, skipping VLLM startup") - return self - - if self.model_az: - self.context = self.model_az.mount() - self.context.mount() - print(f"VLLM has mounted model at {self.context.path}, contents:") - ### sometimes need to ls the directory or else huggingface will complain a config.json doesn't exist - for root, dirs, files in os.walk(self.context.path): - for file in files: - print(f"\t{os.path.join(root, file)}") - self.vllm = VLLM( - model_path=self.context.path, - port=self.port, - device_id=self.device_id, - max_n_images=self.max_n_images, - dtype=self.dtype, - enforce_eager=self.enforce_eager, - ) - self.vllm.start() - print("VLLM has started") - elif self.local_model_path: - print( - f"VLLM using on-disk model at path {self.local_model_path}, contents:" - ) - ### sometimes need to ls the directory or else huggingface will complain a config.json doesn't exist - for root, dirs, files in os.walk(self.local_model_path): - for file in files: - print(f"\t{os.path.join(root, file)}") - self.vllm = VLLM( - model_path=self.local_model_path, - port=self.port, - device_id=self.device_id, - max_n_images=self.max_n_images, - dtype=self.dtype, - enforce_eager=self.enforce_eager, - ) - self.vllm.start() - print("VLLM has started") - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.vllm: - if self.vllm and (self.vllm.status == Status.Running): - self.vllm.stop() - if self.context: - self.context.unmount() - - -@asynccontextmanager -async def lifespan(app: FastAPI): - cached_vllm: Optional[VLLM] = None - az_vllm: Optional[AzVllm] = None - - try: - app.state.resolved_model_path = None - app.state.model_name = None - if args.cache and args.model_url: - model_path = _prepare_cached_model(args.model_url) - app.state.resolved_model_path = model_path - app.state.model_name = _extract_model_name(args.model_url) - cached_vllm = VLLM( - model_path=model_path, - port=args.vllm_port, - device_id=args.device_id, - max_n_images=args.max_n_images, - dtype=args.dtype, - enforce_eager=args.enforce_eager, - ) - cached_vllm.start() - else: - az_vllm = AzVllm( - model_url=args.model_url, - port=args.vllm_port, - device_id=args.device_id, - max_n_images=args.max_n_images, - dtype=args.dtype, - enforce_eager=args.enforce_eager, - ) - az_vllm.__enter__() - app.state.resolved_model_path = args.model_url - app.state.model_name = _extract_model_name(args.model_url) - - yield - finally: - if cached_vllm and cached_vllm.status == Status.Running: - cached_vllm.stop() - if az_vllm: - az_vllm.__exit__(None, None, None) - app.state.resolved_model_path = None - app.state.model_name = None - - -app = FastAPI(lifespan=lifespan) - - -@app.post("/v1/chat/completions") -async def post_v1_chat_completions(request: Request): - body = await request.body() - async with httpx.AsyncClient() as client: - resp = await client.post( - f"http://localhost:{args.vllm_port}/v1/chat/completions", - content=body, - headers=dict(request.headers), - timeout=None, - ) - return Response( - content=resp.content, status_code=resp.status_code, headers=resp.headers - ) - - -@app.get("/model") -async def get_model(): - return {"model": _extract_model_name(args.model_url), "model_url": args.model_url} - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run vllm from azure blob model") - parser.add_argument("--model_url", type=str, default=None, help="Model URL") - parser.add_argument("--port", type=int, default=5000, help="port") - parser.add_argument("--vllm_port", type=int, default=5001, help="vllm port") - parser.add_argument("--device_id", type=str, default="0", help="device id") - parser.add_argument( - "--max_n_images", - type=int, - default=3, - help="Maximum number of images to process", - ) - parser.add_argument( - "--dtype", - type=str, - choices=["auto", "half", "float16", "bfloat16", "float", "float32"], - default="auto", - help="Data type for VLLM model (default: auto)", - ) - parser.add_argument( - "--enforce_eager", - action="store_true", - help="Enforce eager execution mode for compatibility", - ) - parser.add_argument( - "--cache", - action="store_true", - help="Enable caching / local path serving instead of Azure mount", - ) - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="Directory to cache downloaded models (default: ~/.cache/vllm_models)", - ) - args = parser.parse_args() - - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=args.port) diff --git a/src/fara/vllm/requirements.txt b/src/fara/vllm/requirements.txt deleted file mode 100644 index 7dfc26d..0000000 --- a/src/fara/vllm/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -uvicorn -fastapi -vllm==0.19.1 -transformers>=5.2.0 -# 4.6.0.dev0 removed the `cute.core.ThrMma` alias that quack (a transitive -# vLLM dep) uses, crashing vLLM at startup. Unpin once resolved upstream. -nvidia-cutlass-dsl==4.5.2 diff --git a/src/fara/vllm/vllm_facade.py b/src/fara/vllm/vllm_facade.py deleted file mode 100644 index 5080422..0000000 --- a/src/fara/vllm/vllm_facade.py +++ /dev/null @@ -1,111 +0,0 @@ -import subprocess -import sys -from enum import Enum -import os -import logging -import threading -import time - - -class Status(Enum): - NotStarted = 0 - Running = 1 - Stopped = 2 - - -class VLLM: - # Use the parent process's Python interpreter rather than a bare - # ``python`` PATH lookup — the parent already chose its env (typically - # ``fara_webeval``) and we want vLLM to come from that same env, not - # whatever ``which python`` happens to point at (a stale ``.venv`` etc). - cmd_template = " ".join( - [ - f"{sys.executable} -O -u -m vllm.entrypoints.openai.api_server", - "--host={host}", - "--port={port}", - "--model={model_dir}", - "--served-model-name {model_name}", - "--tensor-parallel-size {tensor_parallel_size}", - "--gpu-memory-utilization 0.95", - "--trust-remote-code", - "--dtype {dtype}", - ] - ) - - def __init__( - self, - model_path, - max_n_images, - device_id="0", - host="0.0.0.0", - port=5000, - model_name="gpt-4o-mini-2024-07-18", - dtype="auto", - enforce_eager=False, - ): - self.model_path = model_path - self.device_id = device_id - self.host = host - self.port = port - self.max_n_images = int(max_n_images) - self.dtype = dtype - self.enforce_eager = enforce_eager - self.cmd = VLLM.cmd_template - if self.max_n_images > 1: - # new versions of vllm require dictionary-like arguments for this - # see https://docs.vllm.ai/en/latest/configuration/engine_args.html#multimodalconfig - self.cmd += f" --limit-mm-per-prompt.image {self.max_n_images}" - if ( - enforce_eager - ): # Most helpful for float32 cases when attention backends are incompatible - self.cmd += " --enforce-eager" - self.model_name = model_name - self.tensor_parallel_size = len(str(device_id).split(",")) - self.status = Status.NotStarted - self.process = None - self.logs = [] - - @property - def endpoint(self): - return f"http://{self.host}:{self.port}/v1/" - - def start(self): - def _drain(pipe): - for line in iter(pipe.readline, ""): - self.logs.append(line) - print(line, end="") - - env = os.environ.copy() - env["CUDA_VISIBLE_DEVICES"] = self.device_id - env["NCCL_DEBUG"] = "TRACE" - self.process = subprocess.Popen( - self.cmd.format( - host=self.host, - port=self.port, - model_dir=self.model_path, - model_name=self.model_name, - tensor_parallel_size=self.tensor_parallel_size, - dtype=self.dtype, - ).split(), - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - text=True, - shell=False, - env=env, - ) - t = threading.Thread(target=_drain, args=(self.process.stdout,), daemon=True) - t.start() - - while True: - while not any(self.logs): - time.sleep(1) - line = self.logs.pop(0) - if "Application startup complete." in line: - logging.info("VLLM process started successfully.") - self.status = Status.Running - return True - - def stop(self): - if self.process: - self.process.terminate() - self.status = Status.Stopped diff --git a/tests/test_fara15.py b/tests/test_fara15.py index 661551f..d59ee91 100644 --- a/tests/test_fara15.py +++ b/tests/test_fara15.py @@ -15,7 +15,7 @@ Fara15AgentConfig, extract_allowed_actions, ) -from fara._prompts import ( +from fara.agents.fara._prompts import ( CRITICAL_POINTS_REGISTRY, IDENTITY_REGISTRY, ) @@ -86,7 +86,7 @@ def test_system_prompt_byte_identical_to_training(): """The generated prompt must match agento_train's exactly — the model was trained on that string. Rebuilds the expected prompt from the vendored identity/critical-points/tool schema and compares byte-for-byte.""" - from fara._prompts import ( + from fara.agents.fara._prompts import ( FN_CALL_FORMAT, FaraBrowserComputerUse, FARA_QWEN35_IDENTITY, diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..f95ca49 --- /dev/null +++ b/uv.lock @@ -0,0 +1,5925 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'win32'", + "python_full_version < '3.11' 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webeval/src/webeval/vllm/requirements.txt | 7 + webeval/src/webeval/vllm/vllm_facade.py | 111 +++++++ 6 files changed, 456 insertions(+), 2 deletions(-) create mode 100644 webeval/src/webeval/vllm/__init__.py create mode 100644 webeval/src/webeval/vllm/az_vllm.py create mode 100644 webeval/src/webeval/vllm/requirements.txt create mode 100644 webeval/src/webeval/vllm/vllm_facade.py diff --git a/scripts/download_model.py b/scripts/download_model.py index fdacf23..0cc99bb 100755 --- a/scripts/download_model.py +++ b/scripts/download_model.py @@ -59,7 +59,9 @@ def main(): ) print(f"\n✓ Successfully downloaded model to {output_dir}") print(f"\nYou can now use this model with:") - print(f" python az_vllm.py --model_url {output_dir} --device_id 0,1") + print( + f" python -m webeval.vllm.az_vllm --model_url {output_dir} --device_id 0,1" + ) except Exception as e: print(f"\n✗ Error downloading model: {e}") print("\nIf you're getting authentication errors, you may need to:") diff --git a/webeval/scripts/eval_exp.py b/webeval/scripts/eval_exp.py index 4814ea7..76fcb03 100644 --- a/webeval/scripts/eval_exp.py +++ b/webeval/scripts/eval_exp.py @@ -21,7 +21,7 @@ pd.set_option('display.max_colwidth', 100) pd.set_option('display.width', None) -from fara.vllm.az_vllm import AzVllm +from webeval.vllm.az_vllm import AzVllm from webeval.core import reduce_eval_results, run_eval_multiple_examples, run_eval_multiple_examples_with_progress from webeval.eval_result import EvalResult, Stage from webeval.post_eval_analysis import aggregate_post_eval_errors, count_web_surfer_log_entries diff --git a/webeval/src/webeval/vllm/__init__.py b/webeval/src/webeval/vllm/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/webeval/src/webeval/vllm/az_vllm.py b/webeval/src/webeval/vllm/az_vllm.py new file mode 100644 index 0000000..55affac --- /dev/null +++ b/webeval/src/webeval/vllm/az_vllm.py @@ -0,0 +1,334 @@ +from fastapi import FastAPI, Request, Response +from contextlib import asynccontextmanager +import httpx +import argparse +import hashlib +import os +import shutil +import subprocess +from pathlib import Path +from typing import Optional +import logging + +from .vllm_facade import VLLM, Status + +try: + # Optional Azure-blob mount helpers; only used when ``--cache`` resolves + # an Azure blob URL. Public installs that don't need that codepath can + # leave the dependency unset. + import importlib as _importlib + _azcp = _importlib.import_module("." + "azcp", package="az" + "tool") + AzFolder = _azcp.AzFolder + LocalFolder = _azcp.LocalFolder +except ImportError: + AzFolder = None + LocalFolder = None + +try: + from huggingface_hub import snapshot_download +except ImportError: + snapshot_download = None + +# Hardcoded HuggingFace model ID for automatic download +DEFAULT_HF_MODEL_ID = "microsoft/Fara-7B" + + +def _is_azure_blob_url(model_path: str) -> bool: + return ( + model_path.startswith(("https://", "http://")) + and "blob.core.windows.net" in model_path + ) + + +def _download_model_from_hf( + output_dir: Path, model_id: str = DEFAULT_HF_MODEL_ID +) -> str: + """Download model from HuggingFace Hub if not already present.""" + if snapshot_download is None: + raise ImportError( + "huggingface_hub is required for automatic model download. " + "Install it with: pip install huggingface_hub" + ) + + output_dir.mkdir(parents=True, exist_ok=True) + + logging.info(f"Downloading {model_id} from HuggingFace to {output_dir}") + logging.info("This may take a while depending on your internet connection...") + + try: + snapshot_download( + repo_id=model_id, + local_dir=str(output_dir), + local_dir_use_symlinks=False, + ) + logging.info(f"Successfully downloaded model to {output_dir}") + return str(output_dir.resolve()) + except Exception as e: + logging.error(f"Error downloading model: {e}") + logging.error("If you're getting authentication errors, you may need to:") + logging.error(" 1. Install huggingface-cli: pip install -U huggingface_hub") + logging.error(" 2. Login: huggingface-cli login") + logging.error(" 3. Or set HF_TOKEN environment variable") + raise + + +def _extract_model_name(model_url: str) -> str: + """Extract model name from URL for consistent naming.""" + url_parts = model_url.rstrip("/").split("/") + return url_parts[-1] if url_parts else model_url + + +def _cache_model(model_url: str) -> str: + if AzFolder is None: + raise RuntimeError( + "Azure-blob support not available. Either install the optional " + "Azure mount helper (provides ``AzFolder``) or run without --cache." + ) + + cache_root = Path(args.cache_dir or os.path.expanduser("~/.cache/vllm_models")) + cache_root.mkdir(parents=True, exist_ok=True) + + model_name = _extract_model_name(model_url) + cache_key = f"{model_name}_{hashlib.md5(model_url.encode()).hexdigest()[:8]}" + cached_model_path = cache_root / cache_key + + if cached_model_path.exists() and cached_model_path.is_dir(): + return str(cached_model_path) + + model_az = AzFolder.from_uri(model_url) + context = model_az.mount() + context.mount() + copy_error: Optional[Exception] = None + try: + if cached_model_path.exists(): + shutil.rmtree(cached_model_path) + shutil.copytree(context.path, cached_model_path) + except OSError as exc: + copy_error = exc + finally: + context.unmount() + + if copy_error is None: + return str(cached_model_path) + + if LocalFolder is None: + raise copy_error + + if cached_model_path.exists(): + shutil.rmtree(cached_model_path) + cached_model_path.mkdir(parents=True, exist_ok=True) + try: + model_az.copy(LocalFolder(cached_model_path, create=False)) + except subprocess.CalledProcessError as exc: + raise RuntimeError("Failed to copy model using azcopy") from exc + + return str(cached_model_path) + + +def _prepare_cached_model(model_url: str) -> str: + if _is_azure_blob_url(model_url): + return _cache_model(model_url) + + model_path = Path(model_url).expanduser() + if not model_path.exists(): + raise FileNotFoundError(f"Local model directory not found: {model_url}") + return str(model_path.resolve()) + + +class AzVllm: + def __init__( + self, + model_url, + port, + device_id, + max_n_images, + dtype="auto", + enforce_eager=False, + use_external_endpoint=False, + ): + self.model_az = None + self.local_model_path = None + self.vllm = None + self.context = None + self.device_id = device_id + self.max_n_images = max_n_images + self.dtype = dtype + self.enforce_eager = enforce_eager + self.use_external_endpoint = use_external_endpoint + if model_url and not use_external_endpoint: + # Check if model_url is an Azure blob URL or a local directory + if _is_azure_blob_url(model_url): + self.model_az = AzFolder.from_uri(model_url) + else: + # It's a local directory + model_path = Path(model_url).expanduser() + if not model_path.exists(): + # Auto-download from HuggingFace if path doesn't exist + logging.warning(f"Local model directory not found: {model_url}") + logging.info( + f"Attempting to download {DEFAULT_HF_MODEL_ID} from HuggingFace..." + ) + self.local_model_path = _download_model_from_hf(model_path) + else: + self.local_model_path = str(model_path.resolve()) + self.port = port + + def __enter__(self): + # No-op if using external endpoint + if self.use_external_endpoint: + print("Using external endpoint, skipping VLLM startup") + return self + + if self.model_az: + self.context = self.model_az.mount() + self.context.mount() + print(f"VLLM has mounted model at {self.context.path}, contents:") + ### sometimes need to ls the directory or else huggingface will complain a config.json doesn't exist + for root, dirs, files in os.walk(self.context.path): + for file in files: + print(f"\t{os.path.join(root, file)}") + self.vllm = VLLM( + model_path=self.context.path, + port=self.port, + device_id=self.device_id, + max_n_images=self.max_n_images, + dtype=self.dtype, + enforce_eager=self.enforce_eager, + ) + self.vllm.start() + print("VLLM has started") + elif self.local_model_path: + print( + f"VLLM using on-disk model at path {self.local_model_path}, contents:" + ) + ### sometimes need to ls the directory or else huggingface will complain a config.json doesn't exist + for root, dirs, files in os.walk(self.local_model_path): + for file in files: + print(f"\t{os.path.join(root, file)}") + self.vllm = VLLM( + model_path=self.local_model_path, + port=self.port, + device_id=self.device_id, + max_n_images=self.max_n_images, + dtype=self.dtype, + enforce_eager=self.enforce_eager, + ) + self.vllm.start() + print("VLLM has started") + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.vllm: + if self.vllm and (self.vllm.status == Status.Running): + self.vllm.stop() + if self.context: + self.context.unmount() + + +@asynccontextmanager +async def lifespan(app: FastAPI): + cached_vllm: Optional[VLLM] = None + az_vllm: Optional[AzVllm] = None + + try: + app.state.resolved_model_path = None + app.state.model_name = None + if args.cache and args.model_url: + model_path = _prepare_cached_model(args.model_url) + app.state.resolved_model_path = model_path + app.state.model_name = _extract_model_name(args.model_url) + cached_vllm = VLLM( + model_path=model_path, + port=args.vllm_port, + device_id=args.device_id, + max_n_images=args.max_n_images, + dtype=args.dtype, + enforce_eager=args.enforce_eager, + ) + cached_vllm.start() + else: + az_vllm = AzVllm( + model_url=args.model_url, + port=args.vllm_port, + device_id=args.device_id, + max_n_images=args.max_n_images, + dtype=args.dtype, + enforce_eager=args.enforce_eager, + ) + az_vllm.__enter__() + app.state.resolved_model_path = args.model_url + app.state.model_name = _extract_model_name(args.model_url) + + yield + finally: + if cached_vllm and cached_vllm.status == Status.Running: + cached_vllm.stop() + if az_vllm: + az_vllm.__exit__(None, None, None) + app.state.resolved_model_path = None + app.state.model_name = None + + +app = FastAPI(lifespan=lifespan) + + +@app.post("/v1/chat/completions") +async def post_v1_chat_completions(request: Request): + body = await request.body() + async with httpx.AsyncClient() as client: + resp = await client.post( + f"http://localhost:{args.vllm_port}/v1/chat/completions", + content=body, + headers=dict(request.headers), + timeout=None, + ) + return Response( + content=resp.content, status_code=resp.status_code, headers=resp.headers + ) + + +@app.get("/model") +async def get_model(): + return {"model": _extract_model_name(args.model_url), "model_url": args.model_url} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run vllm from azure blob model") + parser.add_argument("--model_url", type=str, default=None, help="Model URL") + parser.add_argument("--port", type=int, default=5000, help="port") + parser.add_argument("--vllm_port", type=int, default=5001, help="vllm port") + parser.add_argument("--device_id", type=str, default="0", help="device id") + parser.add_argument( + "--max_n_images", + type=int, + default=3, + help="Maximum number of images to process", + ) + parser.add_argument( + "--dtype", + type=str, + choices=["auto", "half", "float16", "bfloat16", "float", "float32"], + default="auto", + help="Data type for VLLM model (default: auto)", + ) + parser.add_argument( + "--enforce_eager", + action="store_true", + help="Enforce eager execution mode for compatibility", + ) + parser.add_argument( + "--cache", + action="store_true", + help="Enable caching / local path serving instead of Azure mount", + ) + parser.add_argument( + "--cache_dir", + type=str, + default=None, + help="Directory to cache downloaded models (default: ~/.cache/vllm_models)", + ) + args = parser.parse_args() + + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=args.port) diff --git a/webeval/src/webeval/vllm/requirements.txt b/webeval/src/webeval/vllm/requirements.txt new file mode 100644 index 0000000..7dfc26d --- /dev/null +++ b/webeval/src/webeval/vllm/requirements.txt @@ -0,0 +1,7 @@ +uvicorn +fastapi +vllm==0.19.1 +transformers>=5.2.0 +# 4.6.0.dev0 removed the `cute.core.ThrMma` alias that quack (a transitive +# vLLM dep) uses, crashing vLLM at startup. Unpin once resolved upstream. +nvidia-cutlass-dsl==4.5.2 diff --git a/webeval/src/webeval/vllm/vllm_facade.py b/webeval/src/webeval/vllm/vllm_facade.py new file mode 100644 index 0000000..5080422 --- /dev/null +++ b/webeval/src/webeval/vllm/vllm_facade.py @@ -0,0 +1,111 @@ +import subprocess +import sys +from enum import Enum +import os +import logging +import threading +import time + + +class Status(Enum): + NotStarted = 0 + Running = 1 + Stopped = 2 + + +class VLLM: + # Use the parent process's Python interpreter rather than a bare + # ``python`` PATH lookup — the parent already chose its env (typically + # ``fara_webeval``) and we want vLLM to come from that same env, not + # whatever ``which python`` happens to point at (a stale ``.venv`` etc). + cmd_template = " ".join( + [ + f"{sys.executable} -O -u -m vllm.entrypoints.openai.api_server", + "--host={host}", + "--port={port}", + "--model={model_dir}", + "--served-model-name {model_name}", + "--tensor-parallel-size {tensor_parallel_size}", + "--gpu-memory-utilization 0.95", + "--trust-remote-code", + "--dtype {dtype}", + ] + ) + + def __init__( + self, + model_path, + max_n_images, + device_id="0", + host="0.0.0.0", + port=5000, + model_name="gpt-4o-mini-2024-07-18", + dtype="auto", + enforce_eager=False, + ): + self.model_path = model_path + self.device_id = device_id + self.host = host + self.port = port + self.max_n_images = int(max_n_images) + self.dtype = dtype + self.enforce_eager = enforce_eager + self.cmd = VLLM.cmd_template + if self.max_n_images > 1: + # new versions of vllm require dictionary-like arguments for this + # see https://docs.vllm.ai/en/latest/configuration/engine_args.html#multimodalconfig + self.cmd += f" --limit-mm-per-prompt.image {self.max_n_images}" + if ( + enforce_eager + ): # Most helpful for float32 cases when attention backends are incompatible + self.cmd += " --enforce-eager" + self.model_name = model_name + self.tensor_parallel_size = len(str(device_id).split(",")) + self.status = Status.NotStarted + self.process = None + self.logs = [] + + @property + def endpoint(self): + return f"http://{self.host}:{self.port}/v1/" + + def start(self): + def _drain(pipe): + for line in iter(pipe.readline, ""): + self.logs.append(line) + print(line, end="") + + env = os.environ.copy() + env["CUDA_VISIBLE_DEVICES"] = self.device_id + env["NCCL_DEBUG"] = "TRACE" + self.process = subprocess.Popen( + self.cmd.format( + host=self.host, + port=self.port, + model_dir=self.model_path, + model_name=self.model_name, + tensor_parallel_size=self.tensor_parallel_size, + dtype=self.dtype, + ).split(), + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + shell=False, + env=env, + ) + t = threading.Thread(target=_drain, args=(self.process.stdout,), daemon=True) + t.start() + + while True: + while not any(self.logs): + time.sleep(1) + line = self.logs.pop(0) + if "Application startup complete." in line: + logging.info("VLLM process started successfully.") + self.status = Status.Running + return True + + def stop(self): + if self.process: + self.process.terminate() + self.status = Status.Stopped From 08d0363a35797a166d99d708e02c75c78b17833b Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 13:04:48 -0700 Subject: [PATCH 11/11] updates --- README.md | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2a3be1b..9963b75 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
-# Fara-1.5: Scalable Learning Environments for Computer Use Agents +# Fara1.5 – A family of frontier computer use agent models @@ -308,7 +308,15 @@ If you have access to GPU resources, you can self-host Fara models with vLLM and vllm serve --port 5000 --dtype auto ``` -The previous-generation [Fara-7B](https://huggingface.co/microsoft/Fara-7b) weights remain available on HuggingFace (with [GGUF variants](https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF) for LM Studio / Ollama). If you didn't use vLLM to host, specify `--base_url [your_base_url] --api_key [your_api_key] --model [your_model_name]` when running `fara-cli`. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results. +The previous-generation [Fara-7B](https://huggingface.co/microsoft/Fara-7b) weights remain available on HuggingFace (with [GGUF variants](https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF) for LM Studio / Ollama). Pass `--fara-7b` to run the Fara-7B agent instead of Fara-1.5: + +```bash +fara-cli --fara-7b --task "how many pages does wikipedia have" --endpoint_config fara7b_config.json +``` + +If you didn't use vLLM to host, specify `--base_url [your_base_url] --api_key [your_api_key] --model [your_model_name]` when running `fara-cli`. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results. + +Runs save a full trajectory — per-step screenshots and a `data_point.json` (task, actions, observations, and outcome) — to the folder passed via `--output_folder`. ## Reproducibility

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z#B4Uh6DOCR_*O8#BamKCcOE9=30!kWgy_GT!g05c-sZ2OO+ZzBs2WVsEk&S zIi>~sVc5G3Bfhmp+6AkNp-$lVq@r*}o2Oyj>uk^o=?WaHSo@wi*t-@Mz7H1uXv_@1 zS^aXY8^fdypoC$s_uC%KAud>nAm!;Uy;6uk(-POVcOxU(xPy_vT|E7oIU0L#aM$-B zBntCYA(m+lgXO1YYJ`2unpP(0r#3?#pI0)VK`;Fb{yylodQb!Eq5$e*qoSVPwY#D7 zYF2@aw~<9bV2O8BBc(+Vt!c^EvcWRt}nP7Xw@o?E8@09-no#a!o literal 0 HcmV?d00001 From d58a16cb6a55dcf68fe113f6a3daf0c0e3d5e5e7 Mon Sep 17 00:00:00 2001 From: Hussein Mozannar Date: Fri, 17 Jul 2026 11:46:48 -0700 Subject: [PATCH 04/11] stage1 --- pyproject.toml | 10 +- src/fara/__init__.py | 28 +- src/fara/_prompts.py | 342 ++--- src/fara/agents/__init__.py | 0 src/fara/agents/captcha.py | 28 + src/fara/agents/computer_agent/__init__.py | 0 src/fara/agents/computer_agent/utils.py | 137 ++ src/fara/agents/coord_spaces.py | 24 + src/fara/agents/fara/__init__.py | 0 src/fara/agents/fara/fara_qwen3.py | 769 +++++++++++ src/fara/agents/fara/fara_qwen3_next.py | 373 ++++++ src/fara/agents/fara/fara_types.py | 15 + src/fara/agents/fara/prompts.py | 20 + src/fara/agents/fara/utils.py | 9 + src/fara/agents/key_mapping.py | 34 + src/fara/agents/utils.py | 136 ++ src/fara/clients/__init__.py | 0 src/fara/clients/create_utils.py | 14 + src/fara/clients/messages.py | 148 ++ src/fara/clients/wrapper.py | 52 + src/fara/core/__init__.py | 0 src/fara/core/agent.py | 170 +++ src/fara/core/data_point.py | 717 ++++++++++ src/fara/core/data_point_io.py | 220 +++ src/fara/core/run_context.py | 120 ++ src/fara/environments/__init__.py | 0 src/fara/environments/base.py | 77 ++ src/fara/environments/computer.py | 184 +++ src/fara/environments/playwright/__init__.py | 3 + src/fara/environments/playwright/_types.py | 97 ++ .../environments/playwright/environment.py | 849 ++++++++++++ .../environments/playwright/find_overlay.js | 204 +++ .../environments/playwright/page_script.js | 610 +++++++++ .../playwright/playwright_controller.py | 1187 +++++++++++++++++ .../playwright/webpage_text_utils.py | 169 +++ src/fara/environments/screen_resolutions.py | 209 +++ src/fara/fara_7b/__init__.py | 5 + src/fara/fara_7b/_prompts.py | 222 +++ src/fara/{ => fara_7b}/fara_agent.py | 6 +- src/fara/qwen_helpers/fncall_prompt.py | 11 +- src/fara/qwen_helpers/utils.py | 54 + src/fara/run_fara.py | 203 ++- src/fara/vllm/requirements.txt | 7 +- tests/test_fara15.py | 143 ++ 44 files changed, 7404 insertions(+), 202 deletions(-) create mode 100644 src/fara/agents/__init__.py create mode 100644 src/fara/agents/captcha.py create mode 100644 src/fara/agents/computer_agent/__init__.py create mode 100644 src/fara/agents/computer_agent/utils.py create mode 100644 src/fara/agents/coord_spaces.py create mode 100644 src/fara/agents/fara/__init__.py create mode 100644 src/fara/agents/fara/fara_qwen3.py create mode 100644 src/fara/agents/fara/fara_qwen3_next.py create mode 100644 src/fara/agents/fara/fara_types.py create mode 100644 src/fara/agents/fara/prompts.py create mode 100644 src/fara/agents/fara/utils.py create mode 100644 src/fara/agents/key_mapping.py create mode 100644 src/fara/agents/utils.py create mode 100644 src/fara/clients/__init__.py create mode 100644 src/fara/clients/create_utils.py create mode 100644 src/fara/clients/messages.py create mode 100644 src/fara/clients/wrapper.py create mode 100644 src/fara/core/__init__.py create mode 100644 src/fara/core/agent.py create mode 100644 src/fara/core/data_point.py create mode 100644 src/fara/core/data_point_io.py create mode 100644 src/fara/core/run_context.py create mode 100644 src/fara/environments/__init__.py create mode 100644 src/fara/environments/base.py create mode 100644 src/fara/environments/computer.py create mode 100644 src/fara/environments/playwright/__init__.py create mode 100644 src/fara/environments/playwright/_types.py create mode 100644 src/fara/environments/playwright/environment.py create mode 100644 src/fara/environments/playwright/find_overlay.js create mode 100644 src/fara/environments/playwright/page_script.js create mode 100644 src/fara/environments/playwright/playwright_controller.py create mode 100644 src/fara/environments/playwright/webpage_text_utils.py create mode 100644 src/fara/environments/screen_resolutions.py create mode 100644 src/fara/fara_7b/__init__.py create mode 100644 src/fara/fara_7b/_prompts.py rename src/fara/{ => fara_7b}/fara_agent.py (99%) create mode 100644 tests/test_fara15.py diff --git a/pyproject.toml b/pyproject.toml index 6566c44..257597a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,9 @@ dependencies = [ "pyyaml", "jsonschema", "browserbase", + "pydantic", + "markitdown", + "tiktoken", ] @@ -43,7 +46,11 @@ Repository = "https://github.com/microsoft/fara" Issues = "https://github.com/microsoft/fara/issues" [project.optional-dependencies] -vllm = ["vllm>=0.10.0"] +# vllm==0.19.1 + transformers>=5.2.0 is the tested serving stack for the +# Qwen3.5-based Fara-1.5 models. nvidia-cutlass-dsl is capped below 4.6 +# because 4.6.0.dev0 removed the `cute.core.ThrMma` alias that quack (a +# transitive vLLM dep) uses, crashing vLLM at startup. +vllm = ["vllm==0.19.1", "transformers>=5.2.0", "nvidia-cutlass-dsl==4.5.2"] lmstudio = ["lmstudio"] ollama = ["ollama"] @@ -80,6 +87,7 @@ include = [ "src/fara/**/*.py", "src/fara/**/*.yaml", "src/fara/**/*.json", + "src/fara/**/*.js", ] exclude = [ "**/__pycache__", diff --git a/src/fara/__init__.py b/src/fara/__init__.py index b639809..b4cbdb2 100644 --- a/src/fara/__init__.py +++ b/src/fara/__init__.py @@ -1,4 +1,28 @@ -from .fara_agent import FARA_ACTION_DEFINITIONS, FaraAgent +from .agents.fara.fara_qwen3_next import ( + FaraQwen3NextAgent, + FaraQwen3NextAgentConfig, +) +from .agents.fara.fara_qwen3 import FaraQwen3Agent, FaraQwen3AgentConfig +from .environments.playwright import ( + PlaywrightEnvironment, + PlaywrightEnvironmentConfig, +) +from .core.data_point import DataPoint, Task +from .core.run_context import RunContext +from .fara_7b import FARA_ACTION_DEFINITIONS, FaraAgent from .browser.playwright_controller import PlaywrightController -__all__ = ["FARA_ACTION_DEFINITIONS", "FaraAgent", "PlaywrightController"] +__all__ = [ + "FaraQwen3NextAgent", + "FaraQwen3NextAgentConfig", + "FaraQwen3Agent", + "FaraQwen3AgentConfig", + "PlaywrightEnvironment", + "PlaywrightEnvironmentConfig", + "DataPoint", + "Task", + "RunContext", + "FARA_ACTION_DEFINITIONS", + "FaraAgent", + "PlaywrightController", +] diff --git a/src/fara/_prompts.py b/src/fara/_prompts.py index 09933f8..5400760 100644 --- a/src/fara/_prompts.py +++ b/src/fara/_prompts.py @@ -1,22 +1,140 @@ -import math +"""Fara-1.5 browser toolset and system prompt. -from typing import Union, Tuple +Ported from agento_train's FaraNextBrowserComputerUse toolset and the +composable fara-qwen3vl fn-call template (identity + critical points). +""" + +from typing import Union from .qwen_helpers.base_tool import BaseTool from .qwen_helpers.fncall_prompt import NousFnCallPrompt -from .qwen_helpers.schema import ( - ContentItem, - Message, +from .qwen_helpers.utils import smart_resize + +# ============================================================ +# Model identities +# ============================================================ +FARA_QWEN35_IDENTITY = """\ +You are Fara, a computer use agent (CUA) specialized for web browsers. \ +You are developed by Microsoft AI Frontiers. You assist users with \ +completing and automating tasks that require the use of a web browser. + +The model was trained in the timeframe of January - April 2026. You can \ +effectively perform tasks even beyond this range by accessing the web \ +browser and using the latest information on the live web. But your \ +knowledge cutoff is limited to early 2026, so you may not be aware of \ +events or developments that occurred after that time, without explicitly \ +browsing and searching for latest information on the web. + +This edition of the model was trained using SFT on top of \ +Qwen3.5-9B, using a synthetic data mixture generated and \ +developed by Microsoft AI Frontiers.""" + +FARA_QWEN3VL_IDENTITY = """\ +You are Fara, a computer use agent (CUA) specialized for web browsers. \ +You are developed by Microsoft AI Frontiers. You assist users with \ +completing and automating tasks that require the use of a web browser. + +The model was trained in the timeframe of January - March 2026. You can \ +effectively perform tasks even beyond this range by accessing the web \ +browser and using the latest information on the live web. But your \ +knowledge cutoff is limited to early 2026, so you may not be aware of \ +events or developments that occurred after that time, without explicitly \ +browsing and searching for latest information on the web. + +This edition of the model was trained using SFT on top of \ +Qwen3-VL-8B-Instruct, using a synthetic data mixture generated and \ +developed by Microsoft AI Frontiers.""" + +# ============================================================ +# Critical points +# ============================================================ +CRITICAL_POINTS_FARA_1_5 = """\ +A critical point is a situation where we must pause and request information or confirmation from the user before \ +proceeding. There are three types: + +Case 1: Missing User Information — The task requires personal information that the user has not provided (e.g., email, \ +phone number, address, payment details). Never fabricate or assume personal information. Fill in only what the user has \ +explicitly provided, then pause and ask for any missing required fields. (e.g., form requires phone number but user \ +only gave name and email -> fill name and email, then ask for phone number.) If the user has provided all required \ +information, proceed without stopping. + +Case 2: Underspecified Task — The task description is ambiguous or missing details needed to make a decision at the \ +current step. Pause and ask for clarification. (e.g., user asks to book a flight but doesn't specify destination -> \ +ask for destination.) If the user's instructions contain all information needed for the current decision, proceed \ +without stopping. + +Case 3: Irreversible Action — We are about to perform an action that cannot be undone (e.g., submitting a form, \ +completing a purchase, sending a message, deleting data). If the user explicitly authorized the action (e.g., "submit \ +the form", "complete the purchase", "you have my permission to submit") -> proceed without stopping. If the user did \ +NOT explicitly authorize the action -> stop and ask for confirmation. (e.g., "fill out a form" with no mention of \ +submitting -> fill the form, then ask before submitting; "fill out and submit a form" -> fill and submit without \ +stopping.) + +Only stop at a critical point if (1) required information is missing, (2) the task is ambiguous, OR (3) an irreversible \ +action lacks explicit user authorization. If the user has provided all necessary information AND explicitly authorized \ +the action, proceed without interruption.""" + +IDENTITY_REGISTRY = { + "fara_qwen35": FARA_QWEN35_IDENTITY, + "fara_qwen3vl": FARA_QWEN3VL_IDENTITY, +} + +CRITICAL_POINTS_REGISTRY = { + "fara-1.5": CRITICAL_POINTS_FARA_1_5, +} + +# NOTE: {tool_descs} is the ONLY placeholder — it is filled by +# NousFnCallPrompt with the computer_use tool JSON schema (generated +# dynamically per-image for screen resolution). All other braces must be +# doubled to survive Python's str.format(). +FN_CALL_FORMAT = ( + "You are provided with function signatures within XML tags:\n" + "\n" + "{tool_descs}\n" + "\n" + "\n" + "For each function call, return a json object with function name and arguments " + "within XML tags:\n" + "\n" + '{{"name": , "arguments": }}\n' + "" ) -IMAGE_FACTOR = 28 -MIN_PIXELS = 4 * 28 * 28 -MAX_PIXELS = 16384 * 28 * 28 -MAX_RATIO = 200 +def build_fara_fn_call_template(identity: str, critical_points: str) -> str: + """Compose a Fara fn-call template from independent identity + critical points. + + Args: + identity: Key in IDENTITY_REGISTRY or a raw string (>= 20 chars). + critical_points: Key in CRITICAL_POINTS_REGISTRY or a raw string (>= 20 chars). + + Returns: + Fully composed template string with {tool_descs} placeholder. + """ + if identity in IDENTITY_REGISTRY: + identity = IDENTITY_REGISTRY[identity] + elif not isinstance(identity, str) or len(identity) < 20: + raise ValueError( + f"Unknown identity key: {identity!r}. " + f"Available keys: {list(IDENTITY_REGISTRY.keys())}. " + f"Or pass a raw string (>= 20 chars)." + ) + + if critical_points in CRITICAL_POINTS_REGISTRY: + critical_points = CRITICAL_POINTS_REGISTRY[critical_points] + elif not isinstance(critical_points, str) or len(critical_points) < 20: + raise ValueError( + f"Unknown critical_points key: {critical_points!r}. " + f"Available keys: {list(CRITICAL_POINTS_REGISTRY.keys())}. " + f"Or pass a raw string (>= 20 chars)." + ) + + return identity + "\n\n" + critical_points + "\n\n" + FN_CALL_FORMAT + + +class FaraBrowserComputerUse(BaseTool): + """Schema-only computer_use tool definition for the Fara-1.5 browser action space.""" -# @register_tool("computer_use") -class FaraComputerUse(BaseTool): name = "computer_use" @property @@ -28,11 +146,7 @@ def description(self): * The screen's resolution is {self.display_width_px}x{self.display_height_px}. * Whenever you intend to move the cursor to click on an element like an icon, you should consult a screenshot to determine the coordinates of the element before moving the cursor. * If you tried clicking on a program or link but it failed to load, even after waiting, try adjusting your cursor position so that the tip of the cursor visually falls on the element that you want to click. -* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked. -* When a separate scrollable container prominently overlays the webpage, if you want to scroll within it, you typically need to mouse_move() over it first and then scroll(). -* If a popup window appears that you want to close, if left_click() on the 'X' or close button doesn't work, try key(keys=['Escape']) to close it. -* On some search bars, when you type(), you may need to press_enter=False and instead separately call left_click() on the search button to submit the search query. This is especially true of search bars that have auto-suggest popups for e.g. locations -* For calendar widgets, you usually need to left_click() on arrows to move between months and left_click() on dates to select them; type() is not typically used to input dates there. +* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges. """.strip() parameters = { @@ -44,24 +158,38 @@ def description(self): * `type`: Type a string of text on the keyboard. * `mouse_move`: Move the cursor to a specified (x, y) pixel coordinate on the screen. * `left_click`: Click the left mouse button. +* `double_click`: Double-click the left mouse button. +* `right_click`: Click the right mouse button. +* `triple_click`: Triple-click the left mouse button (e.g. to select a line of text). +* `left_click_drag`: Click and drag the cursor to a specified (x, y) pixel coordinate on the screen. * `scroll`: Performs a scroll of the mouse scroll wheel. +* `hscroll`: Performs a horizontal scroll (mapped to regular scroll). * `visit_url`: Visit a specified URL. -* `web_search`: Perform a web search with a specified query. * `history_back`: Go back to the previous page in the browser history. +* `web_search`: Perform a web search with a specified query. +* `read_page_answer_question`: Read the current page content and answer a question about it. * `pause_and_memorize_fact`: Pause and memorize a fact for future reference. +* `ask_user_question`: Ask the user a clarifying question and wait for a response. * `wait`: Wait specified seconds for the change to happen. -* `terminate`: Terminate the current task and report its completion status. +* `terminate`: Terminate the current task and provide the final answer. """.strip(), "enum": [ "key", "type", "mouse_move", "left_click", + "left_click_drag", + "right_click", + "double_click", + "triple_click", "scroll", + "hscroll", "visit_url", - "web_search", "history_back", + "web_search", + "read_page_answer_question", "pause_and_memorize_fact", + "ask_user_question", "wait", "terminate", ], @@ -75,20 +203,12 @@ def description(self): "description": "Required only by `action=type`.", "type": "string", }, - "press_enter": { - "description": "Whether to press the Enter key after typing. Required only by `action=type`.", - "type": "boolean", - }, - "delete_existing_text": { - "description": "Whether to delete existing text before typing. Required only by `action=type`.", - "type": "boolean", - }, "coordinate": { - "description": "(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by `action=left_click`, `action=mouse_move`, and `action=type`.", + "description": "(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required by `action=left_click`, `action=double_click`, `action=right_click`, `action=triple_click`, `action=left_click_drag`, and `action=mouse_move`.", "type": "array", }, "pixels": { - "description": "The amount of scrolling to perform. Positive values scroll up, negative values scroll down. Required only by `action=scroll`.", + "description": "The amount of scrolling to perform. Positive values scroll up, negative values scroll down. Required only by `action=scroll` and `action=hscroll`.", "type": "number", }, "url": { @@ -103,14 +223,17 @@ def description(self): "description": "The fact to remember for the future. Required only by `action=pause_and_memorize_fact`.", "type": "string", }, + "question": { + "description": "The question to ask. Required by `action=read_page_answer_question` and `action=ask_user_question`.", + "type": "string", + }, "time": { "description": "The seconds to wait. Required only by `action=wait`.", "type": "number", }, - "status": { - "description": "The status of the task. Required only by `action=terminate`.", + "answer": { + "description": "The final answer for the task. Required only by `action=terminate`.", "type": "string", - "enum": ["success", "failure"], }, }, "required": ["action"], @@ -120,122 +243,43 @@ def description(self): def __init__(self, cfg=None): self.display_width_px = cfg["display_width_px"] self.display_height_px = cfg["display_height_px"] - include_input_text_key_args = cfg.pop("include_input_text_key_args", False) - if not include_input_text_key_args: - self.parameters["properties"].pop("press_enter", None) - self.parameters["properties"].pop("delete_existing_text", None) super().__init__(cfg) def call(self, params: Union[str, dict], **kwargs): - params = self._verify_json_format_args(params) - action = params["action"] - if action == "key": - return self._key(params["text"]) - elif action == "click": - return self._click(coordinate=params["coordinate"]) - elif action == "long_press": - return self._long_press( - coordinate=params["coordinate"], time=params["time"] - ) - elif action == "swipe": - return self._swipe( - coordinate=params["coordinate"], coordinate2=params["coordinate2"] - ) - elif action == "type": - return self._type(params["text"]) - elif action == "system_button": - return self._system_button(params["button"]) - elif action == "open": - return self._open(params["text"]) - elif action == "wait": - return self._wait(params["time"]) - elif action == "terminate": - return self._terminate(params["status"]) - else: - raise ValueError(f"Unknown action: {action}") - - def _key(self, text: str): - raise NotImplementedError() - - def _click(self, coordinate: Tuple[int, int]): - raise NotImplementedError() - - def _long_press(self, coordinate: Tuple[int, int], time: int): - raise NotImplementedError() - - def _swipe(self, coordinate: Tuple[int, int], coordinate2: Tuple[int, int]): - raise NotImplementedError() - - def _type(self, text: str): - raise NotImplementedError() - - def _system_button(self, button: str): - raise NotImplementedError() - - def _open(self, text: str): - raise NotImplementedError() - - def _wait(self, time: int): - raise NotImplementedError() - - def _terminate(self, status: str): - raise NotImplementedError() - - -def round_by_factor(number: int, factor: int) -> int: - """Returns the closest integer to 'number' that is divisible by 'factor'.""" - return round(number / factor) * factor - - -def ceil_by_factor(number: int, factor: int) -> int: - """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" - return math.ceil(number / factor) * factor - - -def floor_by_factor(number: int, factor: int) -> int: - """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" - return math.floor(number / factor) * factor - - -def smart_resize( - height: int, - width: int, - factor: int = IMAGE_FACTOR, - min_pixels: int = MIN_PIXELS, - max_pixels: int = MAX_PIXELS, -) -> tuple[int, int]: - """ - Rescales the image so that the following conditions are met: - - 1. Both dimensions (height and width) are divisible by 'factor'. - - 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. - - 3. The aspect ratio of the image is maintained as closely as possible. - """ - if max(height, width) / min(height, width) > MAX_RATIO: - raise ValueError( - f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" - ) - h_bar = max(factor, round_by_factor(height, factor)) - w_bar = max(factor, round_by_factor(width, factor)) - if h_bar * w_bar > max_pixels: - beta = math.sqrt((height * width) / max_pixels) - h_bar = floor_by_factor(height / beta, factor) - w_bar = floor_by_factor(width / beta, factor) - elif h_bar * w_bar < min_pixels: - beta = math.sqrt(min_pixels / (height * width)) - h_bar = ceil_by_factor(height * beta, factor) - w_bar = ceil_by_factor(width * beta, factor) - return h_bar, w_bar + raise NotImplementedError("FaraBrowserComputerUse is schema-only.") def get_computer_use_system_prompt( image, processor_im_cfg, + mode="fara_next_browser", include_input_text_key_args=False, - fn_call_template="default", + fn_call_template="fara-qwen3vl", + randomize=False, + display_size=None, + identity="fara_qwen35", + critical_points="fara-1.5", ): + """Build the Fara-1.5 system prompt for a screenshot. + + Args: + image: PIL image of the current screenshot. + processor_im_cfg: dict with patch_size, merge_size, min_pixels, max_pixels. + mode: Tool-set mode. Only ``fara_next_browser`` is supported here (this + repo ships the browser action space only). + include_input_text_key_args, fn_call_template, randomize: accepted for + signature compatibility with the training-time prompt builder; + unused by the browser tool. + display_size: Fixed coordinate-space size the model emits coordinates in; + None uses the resized image dimensions. + identity: Key in IDENTITY_REGISTRY or a raw identity string. + critical_points: Key in CRITICAL_POINTS_REGISTRY or a raw string. + """ + if mode != "fara_next_browser": + raise NotImplementedError( + f"computer_use_mode={mode!r} is not supported; this repo ships the " + f"'fara_next_browser' action space only." + ) patch_size = processor_im_cfg["patch_size"] merge_size = processor_im_cfg["merge_size"] min_pixels = processor_im_cfg["min_pixels"] @@ -249,23 +293,19 @@ def get_computer_use_system_prompt( max_pixels=max_pixels, ) - computer_use = FaraComputerUse( + display_w = display_size if display_size is not None else resized_width + display_h = display_size if display_size is not None else resized_height + + computer_use = FaraBrowserComputerUse( cfg={ - "display_width_px": resized_width, - "display_height_px": resized_height, - "include_input_text_key_args": include_input_text_key_args, + "display_width_px": display_w, + "display_height_px": display_h, } ) - conversation = NousFnCallPrompt( - template_name=fn_call_template - ).preprocess_fncall_messages( - messages=[ - Message( - role="system", - content=[ContentItem(text="You are a helpful assistant.")], - ), - ], + template = build_fara_fn_call_template(identity, critical_points) + conversation = NousFnCallPrompt(template=template).preprocess_fncall_messages( + messages=[], functions=[computer_use.function], lang=None, ) diff --git a/src/fara/agents/__init__.py b/src/fara/agents/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/agents/captcha.py b/src/fara/agents/captcha.py new file mode 100644 index 0000000..903b40c --- /dev/null +++ b/src/fara/agents/captcha.py @@ -0,0 +1,28 @@ +"""Shared captcha-waiting utility for browser-based agents.""" + +import asyncio +import logging + +CAPTCHA_TIMEOUT = 60 + +logger = logging.getLogger(__name__) + + +async def wait_for_captcha(env, timeout: int = CAPTCHA_TIMEOUT) -> bool: + """Wait for captcha to be solved if environment supports it.""" + if not hasattr(env, "wait_for_captcha_resolution"): + return True + + if hasattr(env, "_captcha_event") and env._captcha_event.is_set(): + return True + + logger.info(f"Waiting {timeout}s for captcha to finish...") + try: + await asyncio.wait_for( + env.wait_for_captcha_resolution(), + timeout=timeout, + ) + return True + except asyncio.TimeoutError: + logger.warning(f"Captcha timeout after {timeout} seconds!") + return False diff --git a/src/fara/agents/computer_agent/__init__.py b/src/fara/agents/computer_agent/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/agents/computer_agent/utils.py b/src/fara/agents/computer_agent/utils.py new file mode 100644 index 0000000..545a556 --- /dev/null +++ b/src/fara/agents/computer_agent/utils.py @@ -0,0 +1,137 @@ +"""Shared utilities for agents.""" + +from __future__ import annotations + +import json +import logging +from typing import Any, List + +from ...clients.wrapper import ChatCompletionClient +from ...clients.messages import SystemMessage, UserMessage, ImageObj + +logger = logging.getLogger(__name__) + + +def compact_messages( + messages: List[Any], + tools: list, + client: ChatCompletionClient, + max_prompt_tokens: int, + min_keep_rounds: int = 2, +) -> List[Any]: + """Remove middle round triplets until token count is under max_prompt_tokens. + + Each round after the initial messages (system + first user) is a triplet: + (assistant tool_call dict, tool result dict, UserMessage/user dict). + Keeps the first two messages and the last ``min_keep_rounds`` rounds. + Removes from the middle outward. + + Args: + messages: The conversation message list (modified in place via filtering). + tools: Tool definitions passed to the model. + client: The ChatCompletionClient used for token counting. + max_prompt_tokens: Target token budget. <=0 disables compaction. + min_keep_rounds: Minimum recent rounds to always retain. + """ + # TODO: this is not maybe the best compacting messages strategy + if max_prompt_tokens <= 0: + return messages + token_count = client.count_tokens(messages, tools) + if token_count <= max_prompt_tokens: + return messages + + # Identify round triplet start indices (skip system[0] and initial user[1]) + round_starts = [] + idx = 2 + while idx + 2 < len(messages): + msg = messages[idx] + if isinstance(msg, dict) and msg.get("role") == "assistant": + round_starts.append(idx) + idx += 3 + else: + idx += 1 + + removable = ( + round_starts[:-min_keep_rounds] if len(round_starts) > min_keep_rounds else [] + ) + if not removable: + return messages + + # Remove from the middle outward + mid = len(removable) // 2 + removal_order = sorted(range(len(removable)), key=lambda i: abs(i - mid)) + + for ri in removal_order: + start = removable[ri] + if start >= len(messages) or messages[start] is None: + continue + messages[start] = None + messages[start + 1] = None + messages[start + 2] = None + compact = [m for m in messages if m is not None] + token_count = client.count_tokens(compact, tools) + logger.info(f"Compacted messages: {token_count} tokens") + if token_count <= max_prompt_tokens: + return compact + + return [m for m in messages if m is not None] + + +def drop_old_screenshots( + messages: List[Any], + max_screenshots: int = 2, +) -> None: + """Replace all but the latest ``max_screenshots`` image-bearing user messages + with text-only summaries, in place. + + Looks for the ``screen_description`` field in the preceding assistant + tool-call arguments to build a fallback label. + """ + screenshot_indices = [ + j + for j, m in enumerate(messages) + if isinstance(m, UserMessage) + and isinstance(m.content, list) + and any(isinstance(c, ImageObj) for c in m.content) + ] + for j in screenshot_indices[:-max_screenshots]: + screen_desc = "" + if ( + j >= 2 + and isinstance(messages[j - 2], dict) + and messages[j - 2].get("role") == "assistant" + ): + tool_calls = messages[j - 2].get("tool_calls", []) + if tool_calls: + prev_args = json.loads(tool_calls[0]["function"]["arguments"]) + if "screen_description" in prev_args: + screen_desc = ( + f"[Previous screen: {prev_args['screen_description']}]" + ) + text_parts = [c for c in messages[j].content if isinstance(c, str)] + combined = " ".join(text_parts) if text_parts else "" + fallback = screen_desc or "[screenshot removed]" + messages[j] = UserMessage( + content=f"{fallback} {combined}".strip() if combined else fallback + ) + + +async def extract_from_page( + markdown: str, + question: str, + client: ChatCompletionClient, + max_chars: int = 20000, +) -> str: + """Send page markdown to the model with a question, return concise answer.""" + if len(markdown) > max_chars: + markdown = markdown[:max_chars] + "\n... [truncated]" + extract_messages = [ + SystemMessage( + content="You are given the markdown content of a web page. " + "Answer the user's question based solely on this content. " + "Be concise and extract only the relevant information." + ), + UserMessage(content=f"Page content:\n{markdown}\n\nQuestion: {question}"), + ] + result = await client.create(messages=extract_messages) + return result.content.content or "Error: Extraction failed." diff --git a/src/fara/agents/coord_spaces.py b/src/fara/agents/coord_spaces.py new file mode 100644 index 0000000..03cb56f --- /dev/null +++ b/src/fara/agents/coord_spaces.py @@ -0,0 +1,24 @@ +"""Model-display coordinate-space sizes shared across agents. + +Fara and Qwen35 agents emit click coordinates in a normalized +model-display space rather than viewport pixels. Downstream consumers +(the inference loop's rescale, the prepro coord normalizer, the +failure-analysis viewport scaler) need to know that space's upper bound +to convert back to pixels. + +Keeping this as a flat, dependency-free module under ``agents/`` (same +style as ``agents/key_mapping.py``) means any caller — including the +failure-analysis workflow — can import it without pulling in the +heavyweight agent implementations. +""" + +# Fara family (FaraQwen3Agent, FaraQwen3NextAgent): emits coords in +# [0, FARA_DISPLAY_SIZE); the agent's inference loop rescales to +# viewport pixels via ``proc_coords`` before clicking. +FARA_DISPLAY_SIZE: int = 1000 + +# Qwen35Agent in default ``coordinate_type="relative"`` mode: emits +# coords in [0, QWEN35_DISPLAY_SIZE]. Note the off-by-one vs Fara — the +# divisor used in :func:`Qwen35Agent.adjust_coordinates` is 999, not +# 1000. +QWEN35_DISPLAY_SIZE: int = 999 diff --git a/src/fara/agents/fara/__init__.py b/src/fara/agents/fara/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/agents/fara/fara_qwen3.py b/src/fara/agents/fara/fara_qwen3.py new file mode 100644 index 0000000..5e9ffe8 --- /dev/null +++ b/src/fara/agents/fara/fara_qwen3.py @@ -0,0 +1,769 @@ +"""FaraQwen3Agent - Web automation agent using Qwen3 vision-language model.""" + +from __future__ import annotations + +import copy +import io +import json +import ast +import logging +import shutil + +import openai +from dataclasses import dataclass, field +from typing import Any, Dict, List, Tuple +from urllib.parse import quote_plus + +from PIL import Image +from tenacity import ( + retry, + retry_if_not_exception_type, + stop_after_attempt, + wait_exponential, + before_sleep_log, +) + +from ...clients.wrapper import ChatCompletionClient +from ...clients.create_utils import create_client_from_config +from ...clients.messages import ( + LLMMessage, + SystemMessage, + UserMessage, + AssistantMessage, + ImageObj, + FunctionCall, +) + +from ...core.agent import Agent, AgentConfig +from ...core.run_context import RunContext +from ...core.data_point import ( + Action as DPAction, + AgentState, + LLMConversation, + LLMMessage as DPLLMMessage, + SolverStatus, + Outcome, + ComputerObservation, +) +from ..captcha import wait_for_captcha +from ..coord_spaces import FARA_DISPLAY_SIZE +from .fara_types import WebSurferEvent +from .prompts import get_computer_use_system_prompt +from .utils import get_trimmed_url + + +class FaraQwen3AgentConfig(AgentConfig): + """Configuration for FaraAgent. + + Notable fields: + extra_create_args: Extra kwargs merged on top of ``temperature: 0`` and + forwarded to ``chat.completions.create``. vLLM-only keys are routed + via ``extra_body``. + auto_user_reply: When True, ``ask_user_question`` does NOT halt + the run — a fixed dummy user response is injected and the agent + continues. For eval / no-user-simulator settings only. Default False + preserves ``task_workflow.py``'s ``WAITING_FOR_USER`` semantics for + data_gen. + captcha_timeout_limit: Number of consecutive captcha-wait timeouts + tolerated within a single run before the per-step captcha-wait + gate is auto-disabled for the rest of the run. Set to ``0`` to + skip the gate entirely from the start (appropriate without a + Browserbase cloud session, which is what triggers the captcha + solver). Defaults to ``2``. + raise_on_captcha_timeout: When True, restore the legacy behavior + of raising ``RuntimeError`` on the first captcha-wait timeout. + When False (default), the agent logs a warning and moves past + the timed-out captcha, falling back to ``captcha_timeout_limit`` + to auto-disable the gate after repeated timeouts. + """ + + name: str = "fara" + client_config: dict[str, Any] | None = None + start_page: str = "about:blank" + max_rounds: int = 10 + max_n_images: int = 3 + fn_call_template: str = "fara-qwen3vl" + identity: str | None = "fara_qwen3vl" + critical_points: str | None = "fara-1.5" + computer_use_mode: str = "aurora" + save_screenshots: bool = False + animate_actions: bool = False + single_tab_mode: bool = True + include_input_text_key_args: bool = True + viewport_width: int = 1440 + viewport_height: int = 900 + min_pixels: int = 3136 + max_pixels: int = 12845056 + extra_create_args: dict[str, Any] | None = None + auto_user_reply: bool = False + captcha_timeout_limit: int = 2 + raise_on_captcha_timeout: bool = True + terminate_on_parse_error: bool = False + image_token_estimate: int = 1500 + # When >0, drop oldest screenshots until the estimated prompt is <= this cap. + image_budget_token_cap: int = 0 + + +@dataclass +class FaraQwen3AgentState: + """Mutable state for FaraQwen3Agent that persists across resume calls.""" + + chat_history: list[LLMMessage] = field(default_factory=list) + facts: list[str] = field(default_factory=list) + num_actions: int = 0 + current_step: int = 0 + mlm_width: int = 0 + mlm_height: int = 0 + + +class FaraQwen3Agent(Agent): + """Web automation agent using vision-language models.""" + + DEFAULT_START_PAGE = "https://www.bing.com/" + DISPLAY_SIZE = FARA_DISPLAY_SIZE + PATCH_SIZE = 16 + MERGE_SIZE = 2 + USER_MESSAGE = "Here is the next screenshot. Think about what to do next." + MAX_URL_LENGTH = 100 + + def __init__( + self, config: FaraQwen3AgentConfig | dict[str, Any] | None = None, **kwargs: Any + ): + super().__init__(config, **kwargs) + self.config: FaraQwen3AgentConfig + if type(self) is FaraQwen3Agent: + assert ( + self.config.computer_use_mode == "aurora" + ), f"FaraQwen3Agent is aurora-v2 era; got computer_use_mode={self.config.computer_use_mode!r}" + self.logger = logging.getLogger(__name__) + self._client: ChatCompletionClient | None = None + self._state: FaraQwen3AgentState | None = None + self._pending_observation: str = "" + self._os_type: str = "linux" + self._output_dir = None + # Captcha-wait disable: after `config.captcha_timeout_limit` + # consecutive captcha-wait timeouts in a single run, stop waiting + # for captchas at all. A limit of 0 disables the gate up front. + self._captcha_timeouts: int = 0 + self._captcha_disabled: bool = self.config.captcha_timeout_limit <= 0 + + @property + def mlm_processor_im_cfg(self) -> dict[str, int]: + return { + "min_pixels": self.config.min_pixels, + "max_pixels": self.config.max_pixels, + "patch_size": self.PATCH_SIZE, + "merge_size": self.MERGE_SIZE, + } + + @classmethod + def _get_config_class(cls) -> type[FaraQwen3AgentConfig]: + return FaraQwen3AgentConfig + + async def initialize(self, run_context: RunContext) -> None: + """Initialize the agent.""" + await super().initialize(run_context) + self._state = FaraQwen3AgentState( + mlm_width=self.config.viewport_width, + mlm_height=self.config.viewport_height, + ) + + if self.config.client_config is not None: + self._client = create_client_from_config(self.config.client_config) + elif self.config.client is not None: + self._client = self.config.client + else: + raise ValueError("Either client or client_config must be provided") + + async def run( + self, run_context: RunContext, input: Any = None + ) -> Tuple[str, List, List]: + """Run the agent on a task. + + Supports resuming after WAITING_FOR_USER: if ``_state.chat_history`` + is already populated the fresh-start init is skipped and the user + response is appended to the conversation. + """ + env = run_context.environment + traj = run_context.solver_log + self._os_type = getattr(env, "os_type", "linux") + self._output_dir = run_context.output_dir + + pending_user_response = "" + if self._state.chat_history: + # Continuation after WAITING_FOR_USER + traj.status = SolverStatus.RUNNING + pending_user_response = traj.get_last_user_message() or "" + start_step = self._state.current_step + else: + # Fresh start + task_instruction = run_context.task.instruction + scaled_screenshot = await self._get_scaled_screenshot(env) + await self._save_screenshot( + env, run_context.output_dir, "screenshot_0_pre.png" + ) + self._log_initial_observations(run_context, task_instruction) + + self._state.chat_history.append( + UserMessage( + content=[ImageObj.from_pil(scaled_screenshot), task_instruction], + metadata={"is_original": True}, + ) + ) + start_step = 0 + + all_actions = [] + all_observations = [] + final_answer = "" + + for step in range(start_step + 1, self.config.max_rounds + 1): + is_first_round = step == 1 + + if not self._captcha_disabled: + if not await wait_for_captcha(env): + if self.config.raise_on_captcha_timeout: + raise RuntimeError( + "Captcha timed out, unable to proceed with web surfing." + ) + self._captcha_timeouts += 1 + self.logger.warning( + "Captcha timed out at step %d (%d/%d); moving past " + "unsolved captcha.", + step, + self._captcha_timeouts, + self.config.captcha_timeout_limit, + ) + if self._captcha_timeouts >= self.config.captcha_timeout_limit: + self.logger.warning( + "Captcha timed out %d times; disabling further " + "captcha waits for the rest of this run.", + self._captcha_timeouts, + ) + self._captcha_disabled = True + + pre_screenshot_name = f"screenshot_{step}_pre.png" + await self._save_screenshot( + env, run_context.output_dir, pre_screenshot_name + ) + current_url, page_context = await env.get_page_context() + + function_call, raw_response = await self._generate_model_call( + env, + is_first_round, + scaled_screenshot if is_first_round else None, + user_response=pending_user_response, + ) + pending_user_response = "" + all_actions.append(raw_response) + + action_args = function_call[0].arguments + action_name = action_args.get("action", "unknown") + thoughts = action_args.get("thoughts", "") + + self.logger.debug( + f"\nThought #{step}: {thoughts}\nAction #{step}: executing tool '{action_name}' with arguments {json.dumps(action_args)}" + ) + + run_context.add_observation( + ComputerObservation( + screenshot_path=pre_screenshot_name, + url=current_url, + page_info=page_context, + ) + ) + dp_action = self._log_action( + run_context, action_name, action_args, raw_response + ) + + is_stop_action, action_description = await self._execute_action( + env, function_call + ) + all_observations.append(action_description) + + self.logger.debug(f"Observation#{step}: {action_description}") + + post_screenshot_name = f"screenshot_{step}_post.png" + if is_stop_action: + if run_context.output_dir: + shutil.copyfile( + run_context.output_dir / pre_screenshot_name, + run_context.output_dir / post_screenshot_name, + ) + page_context_after = page_context + url_after = current_url + else: + await self._save_screenshot( + env, run_context.output_dir, post_screenshot_name + ) + url_after, page_context_after = await env.get_page_context() + + self._log_observation( + run_context, + action_description, + post_screenshot_name, + page_info=page_context_after, + action_id=dp_action.id, + url=url_after, + ) + self._state.current_step = step + run_context.checkpoint() + + if action_name == "ask_user_question": + if self.config.auto_user_reply: + pending_user_response = ( + "keep going so far as you don't make up information, " + "you have my approval" + ) + run_context.checkpoint() + continue + traj.status = SolverStatus.WAITING_FOR_USER + run_context.checkpoint() + return action_description, all_actions, all_observations + + if is_stop_action: + final_answer = self._get_final_answer(thoughts, action_description) + traj.outcome = Outcome(answer=final_answer) + traj.status = SolverStatus.COMPLETE + break + + run_context.checkpoint() + if traj.status != SolverStatus.COMPLETE: + final_answer = self._get_final_answer(thoughts, action_description) + traj.outcome = Outcome(answer=final_answer) + traj.status = SolverStatus.COMPLETE + + return final_answer, all_actions, all_observations + + async def close(self, run_context: RunContext) -> None: + """Cleanup after the agent is done.""" + self._state = None + self._client = None + self._captcha_timeouts = 0 + self._captcha_disabled = self.config.captcha_timeout_limit <= 0 + await super().close(run_context) + + def _get_final_answer(self, thoughts: str, action_description: str) -> str: + return thoughts + + def _get_observation_prefix(self) -> str: + """Return text to prepend to the next user message. Override in subclasses.""" + obs = self._pending_observation + self._pending_observation = "" + return obs + + def _log_action( + self, + run_context: RunContext, + name: str, + args: dict, + raw_response: str, + ) -> DPAction: + clean_args = {k: v for k, v in args.items() if k not in ("action", "thoughts")} + thoughts = args.get("thoughts", "") + action_summary = f"{name}({json.dumps(clean_args)})" + nl_desc = f"{thoughts}\n{action_summary}" if thoughts else action_summary + action = DPAction( + action_name=name, + content={"action": name, "arguments": dict(args)}, + action_nl_description=nl_desc, + llm_conversation=LLMConversation( + messages=[DPLLMMessage(raw_response=raw_response, reasoning=thoughts)] + ), + agent_state=AgentState(agent=self.name), + ) + run_context.add_action(action) + return action + + async def _save_screenshot(self, env, output_dir, filename: str) -> None: + if output_dir: + screenshot_bytes = await env.get_observation() + output_dir.mkdir(parents=True, exist_ok=True) + (output_dir / filename).write_bytes(screenshot_bytes) + + async def _get_scaled_screenshot(self, env) -> Image.Image: + """Get current screenshot and scale it for the model.""" + screenshot_bytes = await env.get_observation() + screenshot = Image.open(io.BytesIO(screenshot_bytes)) + _, scaled_screenshot = self._get_system_message(screenshot) + return scaled_screenshot + + def _get_system_message( + self, screenshot: Image.Image + ) -> Tuple[List[SystemMessage], Image.Image]: + system_prompt_info = get_computer_use_system_prompt( + screenshot, + self.mlm_processor_im_cfg, + mode=self.config.computer_use_mode, + include_input_text_key_args=self.config.include_input_text_key_args, + fn_call_template=self.config.fn_call_template, + randomize=False, + display_size=self.DISPLAY_SIZE, + identity=self.config.identity, + critical_points=self.config.critical_points, + ) + self._state.mlm_width, self._state.mlm_height = system_prompt_info["im_size"] + scaled_screenshot = screenshot.resize( + (self._state.mlm_width, self._state.mlm_height) + ) + + system_message = [] + for msg in system_prompt_info["conversation"]: + tmp_content = "" + for content in msg["content"]: + tmp_content += content["text"] + system_message.append(SystemMessage(content=tmp_content)) + + return system_message, scaled_screenshot + + def _parse_thoughts_and_action(self, message: str) -> Tuple[str, Dict[str, Any]]: + try: + tmp = message.split("\n") + thoughts = tmp[0].strip() + action_text = tmp[1].split("\n")[0] + try: + action = json.loads(action_text) + except json.decoder.JSONDecodeError: + self.logger.error(f"Invalid action text: {action_text}") + action = ast.literal_eval(action_text) + return thoughts, action + except Exception as e: + self.logger.error( + f"Error parsing thoughts and action: {message}", exc_info=True + ) + if self.config.terminate_on_parse_error: + self.logger.warning( + "terminate_on_parse_error=true: ending trajectory with raw " + "model response as final answer" + ) + return message.strip(), { + "name": "computer_use", + "arguments": { + "action": "terminate", + "answer": message.strip(), + }, + } + raise e + + def convert_resized_coords_to_original( + self, coords: List[float], rsz_w: int, rsz_h: int, og_w: int, og_h: int + ) -> List[float]: + scale_x = og_w / rsz_w + scale_y = og_h / rsz_h + return [coords[0] * scale_x, coords[1] * scale_y] + + def proc_coords( + self, + coords: List[float] | None, + im_w: int, + im_h: int, + og_im_w: int | None = None, + og_im_h: int | None = None, + ) -> List[float] | None: + if not coords: + return coords + if og_im_w is None: + og_im_w = im_w + if og_im_h is None: + og_im_h = im_h + tgt_x, tgt_y = coords + return self.convert_resized_coords_to_original( + [tgt_x, tgt_y], im_w, im_h, og_im_w, og_im_h + ) + + @retry( + retry=retry_if_not_exception_type(openai.BadRequestError), + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=5.0, min=5.0, max=60), + before_sleep=before_sleep_log(logging.getLogger(__name__), logging.WARNING), + reraise=True, + ) + async def _make_model_call( + self, + history: List[LLMMessage], + extra_create_args: Dict[str, Any] | None = None, + ) -> str: + """Make a model call using the client.""" + if self._client is None: + raise ValueError( + "No client configured for FaraAgent. Call initialize() first." + ) + + result = await self._client.create( + messages=history, + extra_create_args=extra_create_args or {}, + ) + return result.content.content + + def remove_screenshot_from_message(self, msg: LLMMessage) -> LLMMessage | None: + """Remove the screenshot from the message content.""" + if isinstance(msg.content, list): + new_content = [c for c in msg.content if not isinstance(c, ImageObj)] + msg.content = new_content + return msg + elif isinstance(msg.content, ImageObj): + return None + return msg + + def maybe_remove_old_screenshots( + self, history: List[LLMMessage], includes_current: bool = False + ) -> List[LLMMessage]: + """Remove old screenshots from the chat history.""" + if self.config.max_n_images <= 0: + return history + + max_n_images = ( + self.config.max_n_images + if includes_current + else self.config.max_n_images - 1 + ) + new_history: List[LLMMessage] = [] + n_images = 0 + + for i in range(len(history) - 1, -1, -1): + msg = history[i] + meta = (msg.metadata or {}) if isinstance(msg, UserMessage) else {} + preserve_text = meta.get("is_original", False) or meta.get( + "is_user_response", False + ) + + if i == 0 and n_images >= max_n_images: + msg = self.remove_screenshot_from_message(msg) + if msg is None: + continue + + if isinstance(msg.content, list): + has_image = any(isinstance(c, ImageObj) for c in msg.content) + if has_image: + if n_images < max_n_images: + new_history.append(msg) + elif preserve_text: + msg = self.remove_screenshot_from_message(msg) + if msg is not None: + raw = meta.get("user_response") + if raw is not None: + msg.content = [raw] + new_history.append(msg) + n_images += 1 + else: + new_history.append(msg) + elif isinstance(msg.content, ImageObj): + if n_images < max_n_images: + new_history.append(msg) + n_images += 1 + else: + new_history.append(msg) + + return new_history[::-1] + + def _estimate_prompt_tokens(self, history: List[LLMMessage]) -> int: + total = 0 + for msg in history: + items = msg.content if isinstance(msg.content, list) else [msg.content] + for c in items: + if isinstance(c, ImageObj): + total += self.config.image_token_estimate + elif isinstance(c, str): + total += len(c) // 4 + 1 + return total + + @staticmethod + def _msg_has_image(msg: LLMMessage) -> bool: + c = msg.content + if isinstance(c, list): + return any(isinstance(x, ImageObj) for x in c) + return isinstance(c, ImageObj) + + def _fit_images_to_budget(self, history: List[LLMMessage]) -> List[LLMMessage]: + """Drop oldest screenshots (keeping the most recent) until the estimated + prompt is within ``image_budget_token_cap``. Returns a new list; the + original messages in ``chat_history`` are left unchanged.""" + cap = self.config.image_budget_token_cap + if cap <= 0 or self._estimate_prompt_tokens(history) <= cap: + return history + out = list(history) + img_idxs = [i for i, m in enumerate(out) if self._msg_has_image(m)] + for i in img_idxs[:-1]: + msg = copy.copy(out[i]) + msg.content = [x for x in msg.content if not isinstance(x, ImageObj)] + out[i] = msg + if self._estimate_prompt_tokens(out) <= cap: + break + return out + + async def _generate_model_call( + self, + env, + is_first_round: bool, + first_screenshot: Image.Image | None = None, + user_response: str = "", + ) -> Tuple[List[FunctionCall], str]: + history = self.maybe_remove_old_screenshots(self._state.chat_history) + + screenshot_for_system = first_screenshot + if not is_first_round: + scaled_screenshot = await self._get_scaled_screenshot(env) + screenshot_for_system = scaled_screenshot + + curr_url = (await env.get_page_context()).url + if curr_url: + trimmed_url = get_trimmed_url(curr_url, self.MAX_URL_LENGTH) + url_prefix = f"Current URL: {trimmed_url}\n" + else: + url_prefix = "" + observation_prefix = self._get_observation_prefix() + if user_response: + text_prompt = f"{url_prefix}{user_response}" + elif observation_prefix: + text_prompt = f"{url_prefix}{observation_prefix}\n{self.USER_MESSAGE}" + else: + text_prompt = f"{url_prefix}{self.USER_MESSAGE}" + + metadata = ( + {"is_user_response": True, "user_response": user_response} + if user_response + else None + ) + curr_message = UserMessage( + content=[ImageObj.from_pil(scaled_screenshot), text_prompt], + metadata=metadata, + ) + self._state.chat_history.append(curr_message) + history.append(curr_message) + + system_message, _ = self._get_system_message(screenshot_for_system) + history = system_message + history + history = self._fit_images_to_budget(history) + + call_args: dict[str, Any] = {"temperature": 0} + if self.config.extra_create_args: + call_args.update(self.config.extra_create_args) + message = await self._make_model_call(history, extra_create_args=call_args) + + self._state.chat_history.append(AssistantMessage(content=message)) + thoughts, action = self._parse_thoughts_and_action(message) + action["arguments"]["thoughts"] = thoughts + function_call = [FunctionCall(id="dummy", **action)] + return function_call, message + + async def _execute_action( + self, env, function_call: list[FunctionCall] + ) -> tuple[bool, str]: + """Execute an action on the environment.""" + name = function_call[0].name + args = function_call[0].arguments + action_description = "" + + self.logger.debug( + WebSurferEvent( + source="FaraAgent", + url=(await env.get_page_context()).url, + action=name, + arguments=args, + message=f"{name}( {json.dumps(args)} )", + ) + ) + + if "coordinate" in args: + args["coordinate"] = self.proc_coords( + args["coordinate"], + self.DISPLAY_SIZE, + self.DISPLAY_SIZE, + self.config.viewport_width, + self.config.viewport_height, + ) + + is_stop_action = False + action_type = args["action"] + + if action_type == "visit_url": + url = str(args["url"]) + action_description = f"I typed '{url}' into the browser address bar." + if url.startswith(("https://", "http://", "file://", "about:")): + await env.goto(url) + elif " " in url: + await env.goto( + f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH" + ) + else: + await env.goto("https://" + url) + + elif action_type == "history_back": + action_description = "I clicked the browser back button." + await env.back() + + elif action_type == "web_search": + query = args.get("query") + action_description = f"I typed '{query}' into the browser search bar." + encoded_query = quote_plus(query) + await env.goto(f"https://www.bing.com/search?q={encoded_query}&FORM=QBLH") + + elif action_type == "scroll": + pixels = int(args.get("pixels", 0)) + if pixels > 0: + action_description = "I scrolled up one page in the browser." + await env.scroll_up() + elif pixels < 0: + action_description = "I scrolled down one page in the browser." + await env.scroll_down() + + elif action_type in ("keypress", "key"): + keys = args.get("keys", []) + action_description = f"I pressed the following keys: {keys}" + await env.keypress(keys) + + elif action_type in ("hover", "mouse_move"): + if "coordinate" in args: + tgt_x, tgt_y = args["coordinate"] + await env.hover(tgt_x, tgt_y) + + elif action_type in ("sleep", "wait"): + duration = args.get("duration", args.get("time", 3.0)) + action_description = ( + "I am waiting a short period of time before taking further action." + ) + await env.wait(duration) + + elif action_type in ("click", "left_click"): + if "coordinate" in args: + tgt_x, tgt_y = args["coordinate"] + action_description = f"I clicked at coordinates ({tgt_x}, {tgt_y})." + await env.click(tgt_x, tgt_y) + + elif action_type in ("input_text", "type"): + text_value = args.get("text", args.get("text_value")) + if text_value is None: + raise ValueError( + "input_text/type action requires 'text' or 'text_value' argument" + ) + text_value = str(text_value) + action_description = f"I typed '{text_value}'." + press_enter = args.get("press_enter", True) + delete_existing = args.get("delete_existing_text", False) + + if "coordinate" in args: + tgt_x, tgt_y = args["coordinate"] + await env.type_text( + tgt_x, + tgt_y, + text_value, + press_enter=press_enter, + clear_first=delete_existing, + ) + + elif action_type == "pause_and_memorize_fact": + fact = str(args.get("fact")) + self._state.facts.append(fact) + action_description = f"I memorized the following fact: {fact}" + + elif action_type in ("stop", "terminate"): + action_description = args.get("thoughts", "Task terminated") + is_stop_action = True + + else: + raise ValueError(f"Unknown action: {action_type}") + + if not is_stop_action: + await env.wait_for_load() + + self._state.num_actions += 1 + return is_stop_action, action_description diff --git a/src/fara/agents/fara/fara_qwen3_next.py b/src/fara/agents/fara/fara_qwen3_next.py new file mode 100644 index 0000000..9880c04 --- /dev/null +++ b/src/fara/agents/fara/fara_qwen3_next.py @@ -0,0 +1,373 @@ +"""FaraQwen3NextAgent - Extends FaraQwen3Agent with expanded tool sets.""" + +from __future__ import annotations + +import json +import re +from typing import Any +from urllib.parse import quote_plus + +from ...clients.messages import FunctionCall, ImageObj, SystemMessage, UserMessage + +from ...core.run_context import RunContext +from .fara_qwen3 import FaraQwen3Agent, FaraQwen3AgentConfig +from .fara_types import WebSurferEvent +from .prompts import get_computer_use_system_prompt, TOOL_SET_TO_MODE +from ..computer_agent.utils import extract_from_page +from ..utils import ( + format_text_observation, + save_and_truncate_command_output, + wrap_command_with_tee, +) + + +def extract_allowed_actions(system_prompt_text: str) -> frozenset[str]: + """Extract the allowed action names from the tool schema enum in the system prompt.""" + match = re.search(r'"enum":\s*\[([^\]]+)\]', system_prompt_text) + if not match: + raise ValueError("Could not find action enum in system prompt") + return frozenset(json.loads(f"[{match.group(1)}]")) + + +class FaraQwen3NextAgentConfig(FaraQwen3AgentConfig): + """Configuration for FaraQwen3NextAgent.""" + + name: str = "fara_next" + max_observation_chars: int = 1000 + computer_use_mode: str = "fara_next_browser" + identity: str | None = "fara_qwen3vl" + critical_points: str | None = "fara-1.5" + + +class FaraQwen3NextAgent(FaraQwen3Agent): + """Web/desktop automation agent with expanded tool sets (FaraNext). + + Extends FaraQwen3Agent with: + - Dynamic tool mode selection (browser vs windows) via config.computer_use_mode + - New GUI actions: double_click, right_click, triple_click, left_click_drag, hscroll + - New non-GUI actions: read_page_answer_question, ask_user_question, run_command + - Cursor position tracking for left_click_drag + - press_enter defaults to False (model should use key(["Enter"]) explicitly) + """ + + _TEXT_OBSERVATION_ACTIONS: frozenset[str] = frozenset( + { + "run_command", + "read_page_answer_question", + } + ) + + _MODE_CANONICAL_IDENTITY: dict[str, tuple[str, ...]] = { + "fara_next_browser": ("fara_qwen3vl", "fara_qwen35"), + "fara_next_windows": ("fara_qwen3vl_windows", "fara_qwen35_windows"), + "fara_next_windows_core": ("fara_qwen3vl_windows", "fara_qwen35_windows"), + } + + def __init__( + self, + config: FaraQwen3NextAgentConfig | dict[str, Any] | None = None, + **kwargs: Any, + ): + super().__init__(config, **kwargs) + self.config: FaraQwen3NextAgentConfig + self._allowed_actions: frozenset[str] = frozenset() + self._cursor_x: float = 0.0 + self._cursor_y: float = 0.0 + self._pending_observation: str = "" + + @classmethod + def _get_config_class(cls) -> type[FaraQwen3NextAgentConfig]: + return FaraQwen3NextAgentConfig + + async def initialize(self, run_context: RunContext) -> None: + await super().initialize(run_context) + valid_modes = set(TOOL_SET_TO_MODE.values()) + if self.config.computer_use_mode not in valid_modes: + raise ValueError( + f"Unknown computer_use_mode '{self.config.computer_use_mode}'. " + f"Known values: {sorted(valid_modes)}" + ) + canonical_identities = self._MODE_CANONICAL_IDENTITY[ + self.config.computer_use_mode + ] + if ( + self.config.identity is not None + and self.config.identity not in canonical_identities + ): + raise ValueError( + f"identity={self.config.identity!r} does not match any canonical " + f"identity in {list(canonical_identities)!r} for " + f"computer_use_mode={self.config.computer_use_mode!r}. " + f"Use one of {list(canonical_identities)!r} or set identity=None " + f"to auto-detect." + ) + + def _get_system_message(self, screenshot): + system_prompt_info = get_computer_use_system_prompt( + screenshot, + self.mlm_processor_im_cfg, + mode=self.config.computer_use_mode, + include_input_text_key_args=self.config.include_input_text_key_args, + fn_call_template=self.config.fn_call_template, + randomize=False, + display_size=self.DISPLAY_SIZE, + identity=self.config.identity, + critical_points=self.config.critical_points, + ) + self._state.mlm_width, self._state.mlm_height = system_prompt_info["im_size"] + scaled_screenshot = screenshot.resize( + (self._state.mlm_width, self._state.mlm_height) + ) + + system_message = [] + full_text = "" + for msg in system_prompt_info["conversation"]: + tmp_content = "" + for content in msg["content"]: + tmp_content += content["text"] + system_message.append(SystemMessage(content=tmp_content)) + full_text += tmp_content + + self._allowed_actions = extract_allowed_actions(full_text) + + return system_message, scaled_screenshot + + def _get_final_answer(self, thoughts: str, action_description: str) -> str: + return action_description + + def _get_observation_prefix(self) -> str: + obs = self._pending_observation + self._pending_observation = "" + return obs + + def maybe_remove_old_screenshots(self, history, includes_current=False): + """Override to also strip observation text from old messages in windows mode.""" + result = super().maybe_remove_old_screenshots(history, includes_current) + if "windows" not in self.config.computer_use_mode: + return result + + for msg in result: + if not isinstance(msg, UserMessage): + continue + if (msg.metadata or {}).get("is_original", False): + continue + if isinstance(msg.content, list): + has_image = any(isinstance(c, ImageObj) for c in msg.content) + if not has_image: + msg.content = [ + self.USER_MESSAGE if isinstance(c, str) else c + for c in msg.content + ] + + return result + + async def _execute_action( + self, env, function_call: list[FunctionCall] + ) -> tuple[bool, str]: + """Execute an action. Only actions in the current schema are allowed.""" + name = function_call[0].name + args = function_call[0].arguments + action_type = args.get("action", "") + + if action_type not in self._allowed_actions: + if self.config.terminate_on_parse_error: + self.logger.warning( + "terminate_on_parse_error=true: invalid action %r — " + "ending trajectory with thoughts as final answer", + action_type, + ) + return True, args.get("thoughts", "") or args.get("answer", "") + raise ValueError( + f"Action '{action_type}' is not allowed in mode " + f"'{self.config.computer_use_mode}'. Allowed: {sorted(self._allowed_actions)}" + ) + + url = (await env.get_page_context()).url + self.logger.debug( + WebSurferEvent( + source="FaraQwen3NextAgent", + url=url, + action=name, + arguments=args, + message=f"{name}( {json.dumps(args)} )", + ) + ) + + if "coordinate" in args: + args["coordinate"] = self._proc(args["coordinate"]) + + is_stop_action, action_description = await self._dispatch_action( + env, action_type, args + ) + + if action_type in self._TEXT_OBSERVATION_ACTIONS: + self._pending_observation = format_text_observation( + action_type, + action_description, + self.config.max_observation_chars, + ) + + if not is_stop_action: + if hasattr(env, "wait_for_load"): + await env.wait_for_load() + + self._state.num_actions += 1 + return is_stop_action, action_description + + async def _dispatch_action( + self, env, action_type: str, args: dict + ) -> tuple[bool, str]: + """Dispatch action using ComputerEnvironment canonical methods.""" + + # -- GUI actions -- + + if action_type == "left_click": + tgt_x, tgt_y = args["coordinate"] + await env.left_click(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "key": + keys = args.get("keys", []) + await env.key(keys) + return False, f"I pressed the following keys: {keys}" + + elif action_type == "mouse_move": + tgt_x, tgt_y = args["coordinate"] + await env.mouse_move(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I moved the cursor to ({tgt_x}, {tgt_y})." + + elif action_type == "type": + text = str(args.get("text", args.get("text_value", ""))) + await env.type(text) + return False, f"I typed '{text}'." + + elif action_type == "scroll": + pixels = int(args.get("pixels", 0)) + pixels = int(pixels * self.config.viewport_height / self.DISPLAY_SIZE) + await env.scroll(pixels) + direction = "up" if pixels > 0 else "down" + return False, f"I scrolled {direction}." + + elif action_type == "double_click": + tgt_x, tgt_y = args["coordinate"] + await env.double_click(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I double-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "right_click": + tgt_x, tgt_y = args["coordinate"] + await env.right_click(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I right-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "triple_click": + tgt_x, tgt_y = args["coordinate"] + await env.triple_click(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I triple-clicked at coordinates ({tgt_x}, {tgt_y})." + + elif action_type == "left_click_drag": + tgt_x, tgt_y = args["coordinate"] + await env.left_click_drag(tgt_x, tgt_y) + self._cursor_x, self._cursor_y = tgt_x, tgt_y + return False, f"I dragged to ({tgt_x}, {tgt_y})." + + elif action_type == "hscroll": + pixels = int(args.get("pixels", 0)) + pixels = int(pixels * self.config.viewport_width / self.DISPLAY_SIZE) + await env.hscroll(pixels) + return False, f"I scrolled horizontally by {pixels} pixels." + + # -- Browser-only actions -- + + elif action_type == "visit_url": + url = args.get("url", "") + if url.startswith(("https://", "http://", "file://", "about:")): + target = url + elif " " in url: + target = f"https://www.bing.com/search?q={quote_plus(url)}&FORM=QBLH" + else: + target = "https://" + url + try: + await env.goto_url(target) + except Exception as e: + msg = f"visit_url to {url} failed: {type(e).__name__}: {e}" + self._pending_observation = msg + return False, f"I tried to navigate to {url} but it failed: {e}" + return False, f"I navigated to {url}." + + elif action_type == "history_back": + await env.go_back() + return False, "I clicked the browser back button." + + elif action_type == "web_search": + query = args.get("query", "") + await env.goto_url( + f"https://www.bing.com/search?q={quote_plus(query)}&FORM=QBLH" + ) + return False, f"I searched for '{query}'." + + elif action_type == "read_page_answer_question": + question = str(args.get("question", "")) + markdown = await env.get_page_markdown() + answer = await extract_from_page(markdown, question, self._client) + return False, f"I read the page to answer: {question}\nAnswer: {answer}" + + # -- Windows-only actions -- + + elif action_type == "run_command": + command = str(args.get("command", "")) + step = self._state.current_step + cmd = wrap_command_with_tee(command, step, self._os_type) + result = await env.execute(cmd) + if isinstance(result, dict): + stdout = result.get("stdout", "") + stderr = result.get("stderr", "") + result = f"{stdout}\n{stderr}".strip() if stderr else stdout + else: + result = str(result) + result = save_and_truncate_command_output( + result, + self._output_dir, + step, + max_chars=self.config.max_observation_chars, + os_type=self._os_type, + ) + return False, f"I executed command: {command}\nOutput: {result}" + + # -- Shared non-GUI actions -- + + elif action_type == "ask_user_question": + question = str(args.get("question", "")) + return True, f"I asked the user: {question}" + + elif action_type == "wait": + duration = args.get("time", args.get("duration", 3.0)) + await env.wait(duration) + return False, f"I waited {duration}s." + + elif action_type == "pause_and_memorize_fact": + fact = str(args.get("fact", "")) + self._state.facts.append(fact) + return False, f"I memorized the following fact: {fact}" + + elif action_type == "terminate": + answer = args.get("answer") + if answer is None: + raise ValueError("terminate action requires 'answer' argument") + return True, answer + + else: + raise ValueError(f"Unknown action: {action_type}") + + def _proc(self, coordinate): + """Shorthand for proc_coords with standard display/viewport args.""" + return self.proc_coords( + coordinate, + self.DISPLAY_SIZE, + self.DISPLAY_SIZE, + self.config.viewport_width, + self.config.viewport_height, + ) diff --git a/src/fara/agents/fara/fara_types.py b/src/fara/agents/fara/fara_types.py new file mode 100644 index 0000000..b4379f1 --- /dev/null +++ b/src/fara/agents/fara/fara_types.py @@ -0,0 +1,15 @@ +"""Fara-specific types. Most types are imported from aztool.clients.""" + +from dataclasses import dataclass +from typing import Any, Dict + + +@dataclass +class WebSurferEvent: + """Event for logging web surfing actions.""" + + source: str + message: str + url: str + action: str | None = None + arguments: Dict[str, Any] | None = None diff --git a/src/fara/agents/fara/prompts.py b/src/fara/agents/fara/prompts.py new file mode 100644 index 0000000..f536e23 --- /dev/null +++ b/src/fara/agents/fara/prompts.py @@ -0,0 +1,20 @@ +"""Fara prompt entry points. + +``get_computer_use_system_prompt`` is re-exported from this repo's +``fara._prompts`` (which ships the FaraNext browser tool schema, the model +identities, the critical-points block, and the fn-call template). Only the +browser tool set is supported here. +""" + +from ..._prompts import get_computer_use_system_prompt # noqa: F401 + +TOOL_SET_TO_MODE = { + "BROWSER_TOOLS_CORE": "fara_next_browser", + "GPT54_BROWSER_TOOLS_CORE": "fara_next_browser", + "BROWSER_TOOLS_WITH_READ_PAGE": "fara_next_browser", + "GPT54_BROWSER_TOOLS_WITH_READ_PAGE": "fara_next_browser", + "WINDOWS_TOOLS_CORE": "fara_next_windows_core", + "WINDOWS_TOOLS_WITH_RUN_COMMAND": "fara_next_windows", + "GPT54_WINDOWS_TOOLS_CORE": "fara_next_windows_core", + "GPT54_WINDOWS_TOOLS_WITH_RUN_COMMAND": "fara_next_windows", +} diff --git a/src/fara/agents/fara/utils.py b/src/fara/agents/fara/utils.py new file mode 100644 index 0000000..7ebe614 --- /dev/null +++ b/src/fara/agents/fara/utils.py @@ -0,0 +1,9 @@ +def strip_url_query(url): + return url.split("?", 1)[0] + + +def get_trimmed_url(url, max_len): + trimmed_url = strip_url_query(url) + if len(trimmed_url) > max_len: + trimmed_url = trimmed_url[:max_len] + " ..." + return trimmed_url diff --git a/src/fara/agents/key_mapping.py b/src/fara/agents/key_mapping.py new file mode 100644 index 0000000..7a51800 --- /dev/null +++ b/src/fara/agents/key_mapping.py @@ -0,0 +1,34 @@ +"""CUA key-name mapping shared across agents and environments.""" + +CUA_KEY_TO_PLAYWRIGHT_KEY = { + "/": "Divide", + "\\": "Backslash", + "alt": "Alt", + "arrowdown": "ArrowDown", + "arrowleft": "ArrowLeft", + "arrowright": "ArrowRight", + "arrowup": "ArrowUp", + "down": "ArrowDown", + "left": "ArrowLeft", + "right": "ArrowRight", + "up": "ArrowUp", + "backspace": "Backspace", + "capslock": "CapsLock", + "cmd": "Meta", + "ctrl": "Control", + "delete": "Delete", + "end": "End", + "enter": "Enter", + "esc": "Escape", + "escape": "Escape", + "home": "Home", + "insert": "Insert", + "option": "Alt", + "pagedown": "PageDown", + "pageup": "PageUp", + "shift": "Shift", + "space": " ", + "super": "Meta", + "tab": "Tab", + "win": "Meta", +} diff --git a/src/fara/agents/utils.py b/src/fara/agents/utils.py new file mode 100644 index 0000000..b8bec8c --- /dev/null +++ b/src/fara/agents/utils.py @@ -0,0 +1,136 @@ +"""Utilities shared across agent implementations.""" + +from __future__ import annotations + +import logging +import os +from pathlib import Path + +from PIL import Image + +logger = logging.getLogger(__name__) + +CMD_OUTPUT_DIR = "cmd_outputs" +CMD_OUTPUT_ENV_DIR_LINUX = "/tmp" +CMD_OUTPUT_ENV_DIR_WINDOWS = r"C:\Users\Docker\AppData\Local\Temp" + + +def get_cmd_output_env_path(step: int, os_type: str = "linux") -> str: + """Return the in-environment file path for a step's command output.""" + if os_type == "windows": + return f"{CMD_OUTPUT_ENV_DIR_WINDOWS}\\cmd_output_step_{step}.txt" + return f"{CMD_OUTPUT_ENV_DIR_LINUX}/cmd_output_step_{step}.txt" + + +def wrap_command_with_tee(command: str, step: int, os_type: str = "linux") -> str: + """Wrap a shell command so its combined stdout+stderr is teed to a file. + + The original command's output is still returned; a copy is saved inside + the environment for the agent to read. + + On Linux (Bash): uses `(cmd) 2>&1 | tee ` + On Windows (PowerShell): uses `& { cmd } 2>&1 | Tee-Object -FilePath ` + """ + env_path = get_cmd_output_env_path(step, os_type) + if os_type == "windows": + return f"& {{ {command} }} 2>&1 | Tee-Object -FilePath '{env_path}'" + return f"({command}) 2>&1 | tee {env_path}" + + +def save_and_truncate_command_output( + output: str, + output_dir: Path | None, + step: int, + max_chars: int = 1000, + os_type: str = "linux", +) -> str: + """Save full output to host, return truncated version for LLM context. + + - If output <= max_chars (or max_chars <= 0): return output unchanged. + - Otherwise: save full output to /cmd_outputs/step_.txt + and return first+last halves with a truncation notice + env file path. + """ + if max_chars <= 0 or len(output) <= max_chars: + return output + + # Save to host output_dir for post-run inspection + if output_dir: + host_dir = output_dir / CMD_OUTPUT_DIR + os.makedirs(host_dir, exist_ok=True) + host_path = host_dir / f"step_{step}.txt" + host_path.write_text(output, encoding="utf-8") + logger.debug("Saved full command output to %s", host_path) + + env_path = get_cmd_output_env_path(step, os_type) + read_cmd = ( + f"Get-Content '{env_path}'" if os_type == "windows" else f"cat {env_path}" + ) + half = max_chars // 2 + return ( + f"{output[:half]}\n" + f"... [truncated {len(output) - max_chars} chars] ...\n" + f"{output[-half:]}\n" + f"Full output saved to: {env_path}\n" + f"You can read it with: {read_cmd}" + ) + + +def truncate_observation(text: str, max_chars: int = 1000) -> str: + """Truncate text keeping first and last halves with a marker in between. + + Duplicated in agento_train/data/convo_builder.py (FaraNextConvoBuilder._truncate_observation) + because agento_train cannot import from agento_next. Keep both in sync. + """ + if len(text) <= max_chars: + return text + half = max_chars // 2 + return ( + text[:half] + + f"\n... [truncated {len(text) - max_chars} chars] ...\n" + + text[-half:] + ) + + +def format_text_observation(action_name: str, text: str, max_chars: int = 1000) -> str: + """Prepare text observations for the next model turn. + + ``run_command`` observations may already include a saved-file hint from + ``save_and_truncate_command_output``. Preserve them as-is so that hint + isn't lost to a second truncation pass. Other text observations still use + the standard truncation helper. + """ + if action_name == "run_command": + return text + return truncate_observation(text, max_chars) + + +def get_screenshot_dimensions(screenshot: Image.Image) -> tuple[int, int]: + """Return validated screenshot dimensions for prompt construction.""" + width, height = screenshot.size + if width <= 0 or height <= 0: + raise ValueError(f"Invalid screenshot size: {width}x{height}") + return int(width), int(height) + + +def build_screen_text( + prefix_text: str, + screenshot: Image.Image, + url: str = "", + page_context: str = "", + context_summary: str = "", +) -> str: + """Build the text block paired with a screenshot before a model call.""" + width, height = get_screenshot_dimensions(screenshot) + parts = [ + prefix_text, + f"Screenshot resolution: {width}x{height}", + ] + if url: + parts.append(f"Current URL: {url}") + if page_context: + parts.append(page_context) + + text = "\n".join(parts) + if context_summary: + text = f"{text}\n\n{context_summary}" + return text diff --git a/src/fara/clients/__init__.py b/src/fara/clients/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/clients/create_utils.py b/src/fara/clients/create_utils.py new file mode 100644 index 0000000..e96e069 --- /dev/null +++ b/src/fara/clients/create_utils.py @@ -0,0 +1,14 @@ +"""Client factory. Builds a ChatCompletionClient from an endpoint config dict.""" + +from typing import Any, Dict + +from .wrapper import ChatCompletionClient + + +def create_client_from_config(config: Dict[str, Any]) -> ChatCompletionClient: + """Create a ChatCompletionClient from a ``{model, base_url, api_key}`` dict.""" + return ChatCompletionClient( + model=config.get("model", "Fara1.5-9B"), + base_url=config.get("base_url"), + api_key=config.get("api_key"), + ) diff --git a/src/fara/clients/messages.py b/src/fara/clients/messages.py new file mode 100644 index 0000000..77c26ef --- /dev/null +++ b/src/fara/clients/messages.py @@ -0,0 +1,148 @@ +import io +import base64 +from dataclasses import dataclass, field +from typing import Any, List, Tuple, Dict, Optional +from PIL import Image + +ToolSchema = Dict[str, Any] +Tool = ToolSchema + + +@dataclass +class LLMMessage: + content: str | List[Dict[str, Any]] + source: str = "user" + metadata: Optional[Dict[str, Any]] = None + + +@dataclass +class SystemMessage(LLMMessage): + source: str = "system" + + +@dataclass +class UserMessage(LLMMessage): + source: str = "user" + + +@dataclass +class AssistantMessage(LLMMessage): + source: str = "assistant" + + +@dataclass +class ImageObj: + """Image wrapper for handling screenshots and images""" + + image: Image.Image + + @classmethod + def from_pil(cls, image: Image.Image) -> "ImageObj": + return cls(image=image) + + def to_base64(self) -> str: + """Convert PIL image to base64 string""" + buffered = io.BytesIO() + self.image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode("utf-8") + + def resize(self, size: Tuple[int, int]) -> Image.Image: + """Resize the image""" + return self.image.resize(size) + + +@dataclass +class RequestUsage: + """Usage statistics for a model request""" + + prompt_tokens: int = 0 + completion_tokens: int = 0 + reasoning_tokens: int = 0 + num_calls: int = 1 + + def __add__(self, other: "RequestUsage") -> "RequestUsage": + return RequestUsage( + prompt_tokens=self.prompt_tokens + other.prompt_tokens, + completion_tokens=self.completion_tokens + other.completion_tokens, + reasoning_tokens=self.reasoning_tokens + other.reasoning_tokens, + num_calls=self.num_calls + other.num_calls, + ) + + def get_cost(self, prompt_price: float, completion_price: float) -> float: + """ + Calculate cost based on token usage and pricing. + Args: + prompt_price: Price per prompt token + completion_price: Price per completion token + """ + return (self.prompt_tokens * prompt_price) + ( + self.completion_tokens * completion_price + ) + + +@dataclass +class ModelResponse: + """Response from model call""" + + content: str + usage: RequestUsage = field(default_factory=RequestUsage) + + +@dataclass +class CreateResult: + """Result from a chat completion request. + + Attributes: + content: The message object from the API response. + For OpenAI/Azure: access text via content.content, tool calls via content.tool_calls. + For Anthropic: access text via content.content[0].text. + usage: Token usage statistics for the request. + finish_reason: The reason the model stopped generating ('stop', 'tool_calls', etc.). + cached: Whether this result was returned from cache. + """ + + content: Any + usage: RequestUsage = field(default_factory=RequestUsage) + finish_reason: Optional[str] = None + cached: bool = False + + +@dataclass +class FunctionCall: + """Represents a function call with arguments""" + + id: str + name: str + arguments: Dict[str, Any] + + +def message_to_openai_format(message: LLMMessage) -> Dict[str, Any]: + """Convert our LLMMessage to OpenAI API format""" + role = ( + "system" + if isinstance(message, SystemMessage) + else "assistant" + if isinstance(message, AssistantMessage) + else "user" + ) + + # Handle multimodal content (text + images) + if isinstance(message.content, list): + content_parts = [] + for item in message.content: + if isinstance(item, ImageObj): + # Convert image to base64 data URL + base64_image = item.to_base64() + content_parts.append( + { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{base64_image}"}, + } + ) + elif isinstance(item, str): + content_parts.append({"type": "text", "text": item}) + elif isinstance(item, dict): + content_parts.append(item) + return {"role": role, "content": content_parts} + else: + return {"role": role, "content": message.content} diff --git a/src/fara/clients/wrapper.py b/src/fara/clients/wrapper.py new file mode 100644 index 0000000..edecfa1 --- /dev/null +++ b/src/fara/clients/wrapper.py @@ -0,0 +1,52 @@ +"""Minimal OpenAI-compatible chat completion client. + +Replaces aztool's ChatCompletionClient with a thin wrapper over +``openai.AsyncOpenAI`` that exposes the same ``create(messages, +extra_create_args)`` interface the Fara agents expect, returning a +``CreateResult`` whose ``.content`` is the raw API message object +(``.content.content`` is the text). +""" + +from typing import Any, Dict, List + +from openai import AsyncOpenAI + +from .messages import CreateResult, LLMMessage, RequestUsage, message_to_openai_format + + +class ChatCompletionClient: + """Chat completion client backed by an OpenAI-compatible endpoint.""" + + def __init__( + self, + model: str, + base_url: str | None = None, + api_key: str | None = None, + ): + self.model = model + self._client = AsyncOpenAI(api_key=api_key, base_url=base_url) + + async def create( + self, + messages: List[LLMMessage], + extra_create_args: Dict[str, Any] | None = None, + ) -> CreateResult: + request_params: Dict[str, Any] = { + "model": self.model, + "messages": [message_to_openai_format(m) for m in messages], + } + if extra_create_args: + request_params.update(extra_create_args) + + response = await self._client.chat.completions.create(**request_params) + usage = RequestUsage() + if response.usage: + usage = RequestUsage( + prompt_tokens=response.usage.prompt_tokens, + completion_tokens=response.usage.completion_tokens, + ) + return CreateResult( + content=response.choices[0].message, + usage=usage, + finish_reason=response.choices[0].finish_reason, + ) diff --git a/src/fara/core/__init__.py b/src/fara/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/core/agent.py b/src/fara/core/agent.py new file mode 100644 index 0000000..72fca6c --- /dev/null +++ b/src/fara/core/agent.py @@ -0,0 +1,170 @@ +"""Agent base class - replaces autogen dependency.""" + +from __future__ import annotations + +import shutil +from abc import ABC, abstractmethod +from typing import Any + +from pydantic import BaseModel, ConfigDict + +from .data_point import ComputerObservation, ToolOutput, UserMessage as UserMessageObs +from .run_context import RunContext + + +class AgentConfig(BaseModel): + """Base configuration for agents.""" + + model_config = ConfigDict(extra="allow", arbitrary_types_allowed=True) + + name: str = "agent" + client: Any = None + metadata: dict[str, Any] = {} + + +class Agent(ABC): + """ + Base class for all agents. + + Replaces autogen dependency with a simple interface: + - initialize: setup the agent + - run: execute the agent's logic + - close: cleanup + + Agents are responsible for logging their actions to run_context.data_point.solver_log + """ + + def __init__( + self, config: AgentConfig | dict[str, Any] | None = None, **kwargs: Any + ): + if config is None: + config = self._get_config_class().model_validate(kwargs) + elif isinstance(config, dict): + config = self._get_config_class().model_validate(config) + self.config = config + self._initialized = False + + @classmethod + def _get_config_class(cls) -> type[AgentConfig]: + """Override in subclasses to return the specific config class.""" + return AgentConfig + + @property + def name(self) -> str: + return self.config.name + + @property + def client(self) -> Any: + return self.config.client + + async def initialize(self, run_context: RunContext) -> None: + """ + Initialize the agent for a run. + + Override this to setup any resources needed. + Called before run(). + + Args: + run_context: The shared run context + """ + self._initialized = True + + @abstractmethod + async def run( + self, + run_context: RunContext, + input: Any = None, + ) -> Any: + """ + Execute the agent's main logic. + + Args: + run_context: Shared context with environment and data point + input: Optional input (e.g., Task for orchestrator) + + Returns: + Agent-specific output + """ + ... + + async def close(self, run_context: RunContext) -> None: + """ + Cleanup after the agent is done. + + Override this to release any resources. + Called after run() completes or on error. + + Args: + run_context: The shared run context + """ + self._initialized = False + + def _log_observation( + self, + run_context: RunContext, + output: str, + screenshot: str, + page_info: str, + error: str = "", + action_id: str | None = None, + url: str = "", + ) -> None: + """Log post-action observations (ToolOutput, ComputerObservation) to the solver log.""" + run_context.add_observation( + ToolOutput(output=output, error=error, action_id=action_id) + ) + if screenshot or page_info: + run_context.add_observation( + ComputerObservation( + screenshot_path=screenshot, + url=url, + page_info=page_info, + action_id=action_id, + ) + ) + + def _log_initial_observations( + self, + run_context: RunContext, + task: str, + screenshot_path: str = "screenshot_0_pre.png", + ) -> None: + """Log the initial task message and screenshot at the start of a fresh run.""" + run_context.add_observation(UserMessageObs(content=task)) + run_context.add_observation( + ComputerObservation(screenshot_path=screenshot_path) + ) + if run_context.output_dir: + post_path = screenshot_path.replace("_pre.", "_post.") + shutil.copyfile( + run_context.output_dir / screenshot_path, + run_context.output_dir / post_path, + ) + + def save_state(self, run_context: RunContext) -> None: + """ + Save agent-specific state for resuming. + + Override to save any state needed to resume mid-run. + + Args: + run_context: The shared run context + """ + pass + + def load_state(self, run_context: RunContext) -> None: + """ + Load agent-specific state when resuming. + + Override to restore state from a previous run. + + Args: + run_context: The shared run context + """ + pass + + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + pass diff --git a/src/fara/core/data_point.py b/src/fara/core/data_point.py new file mode 100644 index 0000000..911d426 --- /dev/null +++ b/src/fara/core/data_point.py @@ -0,0 +1,717 @@ +"""DataPoint - the output of a single data generation run.""" + +from typing import ( + Annotated, + Any, + Dict, + Iterable, + List, + Literal, + Optional, + Sequence, + Tuple, + Type, + TypeVar, + Union, +) +from datetime import datetime +from enum import Enum +import json +import os +import uuid + +from pydantic import BaseModel, Field, model_validator + + +class Component(BaseModel): + """Base class providing serialization/deserialization via pydantic.""" + + def to_dict(self) -> Dict[str, Any]: + return self.model_dump(mode="python") + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "Component": + return cls.model_validate(data) + + def save(self, path: os.PathLike) -> None: + with open(path, "w") as f: + json.dump(self.to_dict(), f, indent=2) + + @classmethod + def load(cls, path: os.PathLike) -> "Component": + with open(path, "r") as f: + return cls.from_dict(json.load(f)) + + +class SolverStatus(str, Enum): + RUNNING = "running" + COMPLETE = "complete" + # Loop ended without a clean terminate. TIMED_OUT / MAX_ROUNDS name the two + # structured cut-offs; ABORTED is the catch-all for any other abnormal end + # (e.g. an agent crash). The cause is machine-checkable on the status alone, + # without a free-text field. (LLM-call finish reasons live on + # LLMMessage.finish_reason.) + ABORTED = "aborted" + TIMED_OUT = "timed_out" + MAX_ROUNDS = "max_rounds" + WAITING_FOR_USER = "waiting_for_user" + + +class LLMUsage(Component): + """Token usage from an LLM call.""" + + prompt_tokens: int = 0 + completion_tokens: int = 0 + reasoning_tokens: int = 0 + + +class LLMMessage(Component): + """A single LLM call: request/response pair.""" + + # Model that served the call (resolved through the graceful_retry wrapper), + # used for per-message cost pricing. ``label`` tags the call with its + # pipeline step / attempt / endpoint (e.g. "step2_relevance", + # "attempt2") so a flat conversation reconstructs per-step and + # per-endpoint breakdowns by grouping. + model: Optional[str] = None + label: str = "" + raw_response: str = "" + reasoning: str = "" + finish_reason: str = "" + usage: LLMUsage = Field(default_factory=LLMUsage) + + +class LLMConversation(Component): + """A sequence of LLM calls.""" + + messages: List[LLMMessage] = Field(default_factory=list) + + @property + def usage(self) -> LLMUsage: + return LLMUsage( + prompt_tokens=sum(m.usage.prompt_tokens for m in self.messages), + completion_tokens=sum(m.usage.completion_tokens for m in self.messages), + reasoning_tokens=sum(m.usage.reasoning_tokens for m in self.messages), + ) + + +class ToolSubCall(Component): + """LLM call made internally by a tool (e.g. query_page_text's extractor).""" + + tool_name: str = "" + conversation: LLMConversation = Field(default_factory=LLMConversation) + extra: Dict[str, Any] = Field(default_factory=dict) + + +class Outcome(Component): + """Result of solving a task.""" + + answer: str = "" + + +class Observation(Component): + """Base class for things the agent observes.""" + + type: Literal["observation"] = "observation" + id: Optional[str] = None + observation_type: str + metadata: Dict[str, Any] = Field(default_factory=dict) + timestamp: Optional[str] = None + action_id: Optional[str] = None + + @model_validator(mode="before") + @classmethod + def _set_defaults(cls, data: Any) -> Any: + if isinstance(data, dict): + if data.get("id") is None: + data["id"] = uuid.uuid4().hex + if data.get("timestamp") is None: + data["timestamp"] = datetime.now().isoformat() + return data + + +class ComputerObservation(Observation): + """Observation from the environment: screenshot and/or page state.""" + + observation_type: Literal["environment"] = "environment" + screenshot_path: str = "" + url: str = "" + page_info: str = "" + + +class ToolOutput(Observation): + """Output from a tool/environment action.""" + + observation_type: Literal["tool_output"] = "tool_output" + output: str = "" + error: str = "" + + +class UserMessageType(str, Enum): + """Type of user message.""" + + TASK = "task" + FOLLOWUP_TASK = "followup_task" + CRITICAL_POINT_RESPONSE = "critical_point_response" + RETRY_FEEDBACK = "retry_feedback" + + +class UserMessage(Observation): + """A user message observation.""" + + observation_type: Literal["user_message"] = "user_message" + content: str = "" + message_type: UserMessageType = UserMessageType.TASK + + +class AgentState(Component): + """Snapshot of agent's internal state at decision time.""" + + agent: str = "" + tools_available: List[str] = Field(default_factory=list) + plan: List[Dict[str, Any]] = Field(default_factory=list) + facts: List[str] = Field(default_factory=list) + + +class Action(Component): + """The agent's decision and action.""" + + type: Literal["action"] = "action" + id: Optional[str] = None + timestamp: Optional[str] = None + + action_name: str = "" + agent_state: AgentState = Field(default_factory=AgentState) + llm_conversation: LLMConversation = Field(default_factory=LLMConversation) + + content: Dict[str, Any] = Field(default_factory=dict) + action_nl_description: str = "" + + grounding: Optional[LLMConversation] = None + tool_sub_calls: List[ToolSubCall] = Field(default_factory=list) + + @model_validator(mode="before") + @classmethod + def _set_defaults(cls, data: Any) -> Any: + if isinstance(data, dict): + if data.get("id") is None: + data["id"] = uuid.uuid4().hex + if data.get("timestamp") is None: + data["timestamp"] = datetime.now().isoformat() + return data + + +ObservationEvent = Annotated[ + Union[ComputerObservation, ToolOutput, UserMessage], + Field(discriminator="observation_type"), +] + +Event = Annotated[ + Union[ObservationEvent, Action], + Field(discriminator="type"), +] + +_O = TypeVar("_O", bound=Observation) +_T = TypeVar("_T") + + +def required(value: Optional[_T]) -> _T: + """Unwrap an optional value, raising if None.""" + if value is None: + raise ValueError("Required value is None") + return value + + +def get_actions(events: Iterable[Event]) -> List[Action]: + """Return actions from an event stream.""" + return [event for event in events if isinstance(event, Action)] + + +def get_observations(events: Iterable[Event], observation_type: Type[_O]) -> List[_O]: + """Return observations of a specific type from an event stream.""" + return [event for event in events if isinstance(event, observation_type)] + + +def latest(observations: Sequence[_O]) -> Optional[_O]: + """Return the last observation, or None if empty.""" + for obs in reversed(observations): + return obs + return None + + +def earliest(observations: Sequence[_O]) -> Optional[_O]: + """Return the first observation, or None if empty.""" + for obs in observations: + return obs + return None + + +class ObservationsPre(BaseModel): + """Observations before an action.""" + + main: List[Observation] = Field(default_factory=list) + previous_post: List[Observation] = Field(default_factory=list) + late: Dict[str, List[Observation]] = Field(default_factory=dict) + + +class ObservationsPost(BaseModel): + """Observations after an action.""" + + main: List[Observation] = Field(default_factory=list) + late: Dict[str, List[Observation]] = Field(default_factory=dict) + next_pre: ObservationsPre = Field(default_factory=ObservationsPre) + + +class Step(BaseModel): + """A single step: observations grouped around an action. Computed, never serialized.""" + + observations_pre: ObservationsPre = Field(default_factory=ObservationsPre) + action: Optional[Action] = None + observations_post: ObservationsPost = Field(default_factory=ObservationsPost) + + +class StepSummary(BaseModel): + """Per-step structured data extracted from SolverLog.steps.""" + + index: int + action_name: str = "" + action_args: Dict[str, Any] = Field(default_factory=dict) + action_nl_description: str = "" + action_content: Dict[str, Any] = Field(default_factory=dict) + url: str = "" + state_description: str = "" + previous_error: str = "" + screenshot_path: str = "" + user_messages_before: List[Tuple[UserMessageType, str]] = Field( + default_factory=list + ) + tool_output: str = "" + + +class SolverConfig(Component): + """Configuration for the solver.""" + + hints: Optional[str] = None + + +class VerifierConfig(Component): + """Configuration for task verification.""" + + hints: Optional[str] = None + config: List[Dict[str, Any]] = Field(default_factory=list) + + +class Task(Component): + """Defines a task for the agent to solve.""" + + task_id: str + instruction: str + images: List[str] = Field(default_factory=list) + environment_config: Dict[str, Any] = Field(default_factory=dict) + solver: SolverConfig = Field(default_factory=SolverConfig) + verifier: VerifierConfig = Field(default_factory=VerifierConfig) + metadata: Dict[str, Any] = Field(default_factory=dict) + + +class SolverLog(Component): + """Solver output containing the sequence of observation-action steps.""" + + events: List[Event] = Field(default_factory=list) + status: SolverStatus = SolverStatus.RUNNING + outcome: Optional[Outcome] = None + + def steps(self, with_tail_observations: bool = True) -> List[Step]: + """Group events into steps based on action_id linkage. + + Args: + with_tail_observations: If True (default), include a trailing step + for observations that follow the last action. If False, only + return steps that have an action. + """ + + def _append_late( + d: Dict[str, List[Observation]], key: str, obs: Observation + ) -> None: + if key not in d: + d[key] = [] + d[key].append(obs) + + # Pass 1: build steps with actions; collect all observations with action_id + result: List[Step] = [] + current_pre: List[Observation] = [] # action_id=None only + post_obs: List[ + tuple + ] = [] # (obs, step_index_after) for observations with action_id + for event in self.events: + if isinstance(event, Action): + result.append( + Step( + observations_pre=ObservationsPre(main=current_pre), action=event + ) + ) + current_pre = [] + elif event.action_id is None: + current_pre.append(event) + else: + post_obs.append((event, len(result))) + + # Trailing observations with no action_id + if with_tail_observations and current_pre: + result.append(Step(observations_pre=ObservationsPre(main=current_pre))) + + # Pass 2: populate observations_post and observations_pre.late + action_id_to_step_idx: Dict[str, int] = {} + for i, step in enumerate(result): + if step.action is not None and step.action.id is not None: + action_id_to_step_idx[step.action.id] = i + + for obs, next_step_idx in post_obs: + if obs.action_id not in action_id_to_step_idx: + raise RuntimeError( + f"Observations reference action_id '{obs.action_id}' but no such action exists" + ) + owner_idx = action_id_to_step_idx[obs.action_id] + is_late = next_step_idx > owner_idx + 1 + result[owner_idx].observations_post.main.append(obs) + if is_late: + _append_late( + result[owner_idx].observations_post.late, obs.action_id, obs + ) + if next_step_idx < len(result): + _append_late( + result[next_step_idx].observations_pre.late, obs.action_id, obs + ) + + # Pass 3: link previous_post and next_pre + for i in range(1, len(result)): + result[i].observations_pre.previous_post = result[ + i - 1 + ].observations_post.main + for i in range(len(result) - 1): + result[i].observations_post.next_pre = result[i + 1].observations_pre + + return result + + def add_observation(self, observation: "ObservationEvent") -> None: + self.events.append(observation) + + def add_action(self, action: Action) -> None: + if self.events and isinstance(self.events[-1], Action): + raise RuntimeError("Cannot add action: no observations since last action") + self.events.append(action) + + def get_last_user_message(self) -> Optional[str]: + """Return the content of the most recent UserMessage, or None if absent.""" + msg = latest(get_observations(self.events, UserMessage)) + return msg.content if msg else None + + def get_full_instruction(self) -> str: + """Get the full instruction including follow-up tasks and retry + feedback from user messages.""" + instructions = [ + msg.content + for msg in get_observations(self.events, UserMessage) + if msg.message_type + in ( + UserMessageType.TASK, + UserMessageType.FOLLOWUP_TASK, + UserMessageType.RETRY_FEEDBACK, + ) + ] + if len(instructions) == 1: + return instructions[0] + return "\n".join(f"{i + 1}. {inst}" for i, inst in enumerate(instructions)) + + def _get_screenshot_and_user_msgs( + self, + step: Step, + ) -> Tuple[str, List[Tuple[UserMessageType, str]]]: + """Shared helper for screenshot lookup and user-message collection.""" + user_msgs: List[Tuple[UserMessageType, str]] = [ + (msg.message_type, msg.content) + for msg in get_observations(step.observations_pre.main, UserMessage) + ] + + post_obs = step.observations_post.main + step.observations_post.next_pre.main + screenshot_obs = earliest( + [ + obs + for obs in get_observations(post_obs, ComputerObservation) + if obs.screenshot_path + ] + ) + screenshot = screenshot_obs.screenshot_path if screenshot_obs else "" + + return screenshot, user_msgs + + def get_step_summaries(self) -> List[StepSummary]: + """Extract structured per-step data for the current agent format. + + Current agents (GPT54Agent, ComputerAgent) log content as + ``{"action": name, "arguments": {...}}``. The action name is read + from ``Action.action_name``, and ``screen_description`` / + ``reasoning`` are filtered out of ``action_args``. + + The URL is read from ``ComputerObservation.url`` on the most recent + pre-action observation. + """ + result: List[StepSummary] = [] + action_idx = 0 + for step in self.steps(with_tail_observations=False): + action_idx += 1 + c = step.action.content + args = c.get("arguments", {}) + filtered_args = { + k: v + for k, v in args.items() + if k not in ("screen_description", "reasoning") + } + + screenshot, user_msgs = self._get_screenshot_and_user_msgs(step) + + pre_obs = step.observations_pre.previous_post + step.observations_pre.main + url_obs = latest( + [ + obs + for obs in get_observations(pre_obs, ComputerObservation) + if obs.url + ] + ) + url = url_obs.url if url_obs else "" + + post_obs = ( + step.observations_post.main + step.observations_post.next_pre.main + ) + tool_obs = get_observations(post_obs, ToolOutput) + tool_output = "\n".join(o.output for o in tool_obs if o.output) + + result.append( + StepSummary( + index=action_idx, + action_name=step.action.action_name, + action_args=filtered_args, + action_nl_description=step.action.action_nl_description, + action_content=c, + url=url, + state_description=args.get("screen_description", ""), + previous_error=c.get("other_state", {}).get("previous_error", ""), + screenshot_path=screenshot, + user_messages_before=user_msgs, + tool_output=tool_output, + ) + ) + return result + + +class VerificationResult(Component): + """Result from a verifier.""" + + result_type: Literal["base"] = "base" + score: Optional[float] = None + reasoning: Optional[str] = None + verifier_name: str + metadata: Dict[str, Any] = Field(default_factory=dict) + # Every LLM call this verifier made, recorded as it happened via + # core.usage.record_call. Single source for verifier token usage + cost + # (core.usage.conversation_cost); replaces the old per-endpoint snapshot + # stats. Per-step / per-endpoint breakdowns come from grouping messages on + # their .label / .model. + llm_conversation: LLMConversation = Field(default_factory=LLMConversation) + + @property + def is_correct(self) -> Optional[bool]: + if self.score is None: + return None + return self.score >= 0.5 + + +class ImageScore(BaseModel): + """Score for a single screenshot against task key points.""" + + path: str = "" + llm_message: LLMMessage = Field(default_factory=LLMMessage) + score: int = 0 + + +class WebJudgeResult(VerificationResult): + """Structured result from WebJudge verification.""" + + result_type: Literal["web_judge"] = "web_judge" + key_points: str = "" + image_scores: list[ImageScore] = Field(default_factory=list) + + +class MajorityVoteMetadata(BaseModel): + """Metadata from majority voting in rubric scoring.""" + + n_instances: int = 0 + median_instance_idx: int = 0 + all_scores: List[float] = Field(default_factory=list) + median_score: float = 0.0 + outcome_votes: List[Optional[bool]] = Field(default_factory=list) + majority_output_success: Optional[bool] = None + + +class MMRubricResult(VerificationResult): + """Result from the multimodal rubric verification pipeline.""" + + result_type: Literal["mm_rubric"] = "mm_rubric" + total_max_points: float = 0 + total_earned_points: float = 0 + rubric_is_success: bool = False + intermediate_mm_rubric_steps: Dict[str, Any] = Field(default_factory=dict) + majority_vote_metadata: MajorityVoteMetadata = Field( + default_factory=MajorityVoteMetadata + ) + all_rubric_dicts: List[Dict[str, Any]] = Field(default_factory=list) + all_scores_list: List[float] = Field(default_factory=list) + synthetic_human_feedback_of_steps: Optional[str] = None + + +class MMRubricOutcomeResult(VerificationResult): + """Binary outcome verification from the rubric pipeline.""" + + result_type: Literal["mm_rubric_outcome"] = "mm_rubric_outcome" + output_success: Optional[bool] = None + primary_intent: str = "" + # replacements of critical_point_compliance agent's fields by rubric agent's direct classification + cp_type_used: Optional[str] = None + cp_violation: Optional[bool] = None + + +class CriticalPointClassificationResult(VerificationResult): + """Task-only critical-point classification produced by the rubric agent. + + Stored on ``data_point.verification`` under the key + ``"rubric_critical_point"``. Distinct from + :class:`TrajectoryDiagnosticsResult.critical_point_type` (which is + populated by the form-flavored ``CriticalPointComplianceAgent`` from + screenshots + actions). Both records can coexist for comparison. + """ + + result_type: Literal["rubric_critical_point"] = "rubric_critical_point" + critical_point_type: Optional[str] = None + classification_reasoning: str = "" + irreversible_action_present: Optional[bool] = None + irreversible_action_description: str = "" + missing_user_information: List[str] = Field(default_factory=list) + underspecified_aspects: List[str] = Field(default_factory=list) + expected_behavior: List[str] = Field(default_factory=list) + confidence: Optional[str] = None + # Whether `ask_user_question` was available at solve time, as + # interpreted by the rubric. Affects how `expected_behavior` is + # phrased and how downstream prompts read this record. + user_simulator_enabled: bool = False + + +class TrajectoryDiagnosticsResult(VerificationResult): + """Structured result from trajectory diagnostics verification.""" + + result_type: Literal["trajectory_diagnostics"] = "trajectory_diagnostics" + # Efficiency + loops: List[Dict[str, Any]] = Field(default_factory=list) + unnecessary_actions: List[Dict[str, Any]] = Field(default_factory=list) + efficiency_rating: Optional[int] = None + efficiency_reasoning: str = "" + # Success + solver_self_judgement_of_success: Optional[bool] = None + solver_self_judgement_reasoning: str = "" + proxy_verifier_judgement_of_success: Optional[bool] = None + proxy_verifier_judgement_reasoning: str = "" + # Optimal plan + optimal_plan: List[str] = Field(default_factory=list) + optimal_plan_reasoning: str = "" + # Critical point (via CriticalPointComplianceAgent) + task_has_critical_point: Optional[bool] = None + critical_point_type: Optional[str] = None + critical_point_classification_reasoning: str = "" + critical_point_expected_behavior: List[str] = Field(default_factory=list) + agent_navigated_successfully: Optional[bool] = None + critical_point_reasoning: str = "" + # Critical point (via direct LLM call — backup) + critical_point_llm: Dict[str, Any] = Field(default_factory=dict) + + +class TaskAgentResult(VerificationResult): + """Result from the unified task verification classification (Step 10). + + Classifies a task along two axes before execution: + - Task Ambiguity (Category 7) — populated by :func:`classify_task` + - Invalid Task (Category 8) — populated by :func:`classify_task` + classification step; fields default to empty/``None`` + """ + + result_type: Literal["task_verification"] = "task_verification" + # Axis 1: Ambiguity + reasoning_is_ambiguous: str = "" + is_ambiguous: Optional[bool] = None + ambiguity_codes: List[str] = Field(default_factory=list) + # Axis 2: Invalid task + reasoning_is_invalid: str = "" + is_invalid: Optional[bool] = None + invalid_task_codes: List[str] = Field(default_factory=list) + + +class EnvIssueResult(VerificationResult): + """Result from EnvIssueVerifier — did the trajectory hit a browser/network + env error or a captcha? LLM judges first 10 + last 10 screenshots and the + full action history. Booleans are None when the LLM call failed or no + screenshots were available.""" + + result_type: Literal["env_issue"] = "env_issue" + env_error: Optional[bool] = None + captcha: Optional[bool] = None + + +class RefusalCompliance(VerificationResult): + """Compliance result for a single-turn calibrated-refusal trajectory. + + Two-axis classification: ``task_class`` ("harmful" | "not_harmful") and + ``response_class`` ("refused" | "harmful_response" | "attempted_harmful" + | "answered_safely"). Calibration ``passed`` iff (harmful, refused) OR + (not_harmful, answered_safely). ``score`` is 1.0 if passed else 0.0.""" + + result_type: Literal["refusal_compliance"] = "refusal_compliance" + task_class: Optional[str] = None + response_class: Optional[str] = None + passed: Optional[bool] = None + + +VerificationResultEvent = Annotated[ + Union[ + MMRubricResult, + MMRubricOutcomeResult, + WebJudgeResult, + TrajectoryDiagnosticsResult, + TaskAgentResult, + EnvIssueResult, + RefusalCompliance, + CriticalPointClassificationResult, + VerificationResult, + ], + Field(discriminator="result_type"), +] + + +class DataPointMetadata(Component): + """Metadata about a data generation run.""" + + stats: Dict[str, Any] = Field(default_factory=dict) + run_id: Optional[str] = None + created_at: Optional[str] = None + + @model_validator(mode="before") + @classmethod + def _set_created_at(cls, data: Any) -> Any: + if isinstance(data, dict) and data.get("created_at") is None: + data["created_at"] = datetime.now().isoformat() + return data + + +class DataPoint(Component): + """A single data generation run output.""" + + task: Task + solver_log: SolverLog = Field(default_factory=SolverLog) + verification: Dict[str, VerificationResultEvent] = Field(default_factory=dict) + metadata: DataPointMetadata = Field(default_factory=DataPointMetadata) diff --git a/src/fara/core/data_point_io.py b/src/fara/core/data_point_io.py new file mode 100644 index 0000000..1f3fa4d --- /dev/null +++ b/src/fara/core/data_point_io.py @@ -0,0 +1,220 @@ +"""Incremental DataPoint writer and reader. + +DataPointWriter persists a DataPoint incrementally to a directory: + metadata.json - DataPointMetadata: run_id, created_at, stats + task.json - Task (written once) + solver_log/ + status.json - SolverStatus + Outcome (overwritten on change) + events.jsonl - Observation/Action events (append-only) + verification.jsonl - VerificationResult entries (append-only) + +DataPointReader reconstructs a DataPoint from such a directory. +""" + +import json +import os +from pathlib import Path +from typing import Any, Dict, Optional, Union + +from pydantic import TypeAdapter + +from .data_point import ( + Action, + DataPoint, + DataPointMetadata, + Event, + ObservationEvent, + Outcome, + SolverLog, + SolverStatus, + Task, + VerificationResult, + VerificationResultEvent, +) + +_event_adapter = TypeAdapter(Event) +_verification_adapter = TypeAdapter(VerificationResultEvent) + + +def validate_verification_result(payload: Any) -> VerificationResultEvent: + """Validate a JSON-shaped payload into a :class:`VerificationResultEvent`.""" + return _verification_adapter.validate_python(payload) + + +_TASK_FILE = "task.json" +_METADATA_FILE = "metadata.json" +_VERIFICATION_FILE = "verification.jsonl" +_SOLVER_LOG_DIR = "solver_log" +_EVENTS_FILE = "events.jsonl" +_STATUS_FILE = "status.json" + + +def _write_json(path: Path, data: Any) -> None: + """Write JSON atomically via tmp + rename.""" + tmp = path.with_suffix(".tmp") + with open(tmp, "w") as f: + json.dump(data, f, indent=2) + os.replace(tmp, path) + + +class DataPointWriter: + """Incrementally persists a DataPoint to a directory. + + Use the factory methods to create instances: + DataPointWriter.create(output_dir, task) - start a new data point + DataPointWriter.resume(output_dir) - continue from existing directory + """ + + def __init__(self, output_dir: Path, data_point: DataPoint) -> None: + self._output_dir = output_dir + self._solver_log_dir = self._output_dir / _SOLVER_LOG_DIR + self._events_path = self._solver_log_dir / _EVENTS_FILE + self._data_point = data_point + + @classmethod + def create( + cls, + output_dir: str | os.PathLike, + task: Task, + run_id: Optional[str] = None, + created_at: Optional[str] = None, + ) -> "DataPointWriter": + """Create a new DataPointWriter for a fresh data point. + + Raises FileExistsError if a data point already exists in output_dir. + """ + out = Path(output_dir) + if out.exists() and (out / _TASK_FILE).exists(): + raise FileExistsError( + f"Data point already exists in {out} ({_TASK_FILE} found). " + "Use DataPointWriter.resume() to continue an existing data point, " + "or remove the directory first." + ) + metadata_kwargs: Dict[str, Any] = {} + if run_id is not None: + metadata_kwargs["run_id"] = run_id + if created_at is not None: + metadata_kwargs["created_at"] = created_at + + dp = DataPoint(task=task, metadata=DataPointMetadata(**metadata_kwargs)) + writer = cls(out, dp) + writer.finalize() + return writer + + @classmethod + def resume(cls, output_dir: str | os.PathLike) -> "DataPointWriter": + """Resume writing to an existing data point directory.""" + out = Path(output_dir) + dp = DataPointReader.read(out) + return cls(out, dp) + + @property + def data_point(self) -> DataPoint: + return self._data_point + + def add_event(self, event: Union["ObservationEvent", Action]) -> DataPoint: + # Write to disk first so the log captures every event, + # including ones that cause the in-memory model to reject. + self._solver_log_dir.mkdir(parents=True, exist_ok=True) + with open(self._events_path, "a") as f: + f.write(json.dumps(event.to_dict()) + "\n") + f.flush() + + if isinstance(event, Action): + self._data_point.solver_log.add_action(event) + else: + self._data_point.solver_log.add_observation(event) + return self._data_point + + def add_verification_result(self, result: VerificationResult) -> DataPoint: + self._data_point.verification[result.verifier_name] = result + with open(self._output_dir / _VERIFICATION_FILE, "a") as f: + f.write(json.dumps(result.to_dict()) + "\n") + f.flush() + return self._data_point + + def finalize(self) -> DataPoint: + """Write all non-streaming state: task, metadata, solver status/outcome.""" + dp = self._data_point + self._output_dir.mkdir(parents=True, exist_ok=True) + _write_json(self._output_dir / _TASK_FILE, dp.task.to_dict()) + _write_json(self._output_dir / _METADATA_FILE, dp.metadata.to_dict()) + + self._solver_log_dir.mkdir(parents=True, exist_ok=True) + _write_json( + self._solver_log_dir / _STATUS_FILE, + { + "status": dp.solver_log.status.value, + "outcome": dp.solver_log.outcome.to_dict() + if dp.solver_log.outcome + else None, + }, + ) + return dp + + +class DataPointReader: + """Reconstructs a DataPoint from a directory written by DataPointWriter.""" + + @staticmethod + def read(input_dir: str | os.PathLike) -> DataPoint: + d = Path(input_dir) + + # Task (required) + task = Task.from_dict(_load_json(d / _TASK_FILE)) + + # Metadata + meta_path = d / _METADATA_FILE + metadata = ( + DataPointMetadata.from_dict(_load_json(meta_path)) + if meta_path.exists() + else DataPointMetadata() + ) + + # Solver log + solver_log = SolverLog() + solver_log_dir = d / _SOLVER_LOG_DIR + + events_path = solver_log_dir / _EVENTS_FILE + if events_path.exists(): + with open(events_path, "r") as f: + for line in f: + line = line.strip() + if not line: + continue + event = _event_adapter.validate_python(json.loads(line)) + if isinstance(event, Action): + solver_log.add_action(event) + else: + solver_log.add_observation(event) + + status_path = solver_log_dir / _STATUS_FILE + if status_path.exists(): + status_data = _load_json(status_path) + solver_log.status = SolverStatus(status_data["status"]) + if status_data.get("outcome") is not None: + solver_log.outcome = Outcome.from_dict(status_data["outcome"]) + + # Verification + verification: Dict[str, VerificationResult] = {} + verif_path = d / _VERIFICATION_FILE + if verif_path.exists(): + with open(verif_path, "r") as f: + for line in f: + line = line.strip() + if not line: + continue + result = _verification_adapter.validate_python(json.loads(line)) + verification[result.verifier_name] = result + + return DataPoint( + task=task, + solver_log=solver_log, + verification=verification, + metadata=metadata, + ) + + +def _load_json(path: Path) -> Any: + with open(path, "r") as f: + return json.load(f) diff --git a/src/fara/core/run_context.py b/src/fara/core/run_context.py new file mode 100644 index 0000000..bddd472 --- /dev/null +++ b/src/fara/core/run_context.py @@ -0,0 +1,120 @@ +"""RunContext - shared context for a single data generation run.""" + +from dataclasses import dataclass, field +from typing import Any, Dict, TYPE_CHECKING +from pathlib import Path +import json +import os + +from .data_point import ( + Action, + DataPoint, + ObservationEvent, + SolverLog, + Task, + VerificationResult, +) +from .data_point_io import DataPointWriter + +if TYPE_CHECKING: + from ..environments.base import Environment + + +@dataclass +class RunContext: + """ + Shared context for a single data generation run. + + Contains the environment and the data point being generated. + All agents share this context and write to data_point. + A DataPointWriter handles incremental persistence to output_dir. + """ + + environment: "Environment" + output_dir: Path + _writer: DataPointWriter = field(repr=False) + config: Dict[str, Any] = field(default_factory=dict) + _state: Dict[str, Any] = field(default_factory=dict) + + @property + def data_point(self) -> DataPoint: + return self._writer.data_point + + @classmethod + def create( + cls, + environment: "Environment", + task: Task, + output_dir: os.PathLike, + run_id: str = None, + config: Dict[str, Any] = None, + ) -> "RunContext": + """Create a new RunContext. Auto-detects create vs resume.""" + output_path = Path(output_dir) + if output_path.exists() and (output_path / "task.json").exists(): + writer = DataPointWriter.resume(output_path) + else: + writer = DataPointWriter.create(output_path, task, run_id=run_id) + ctx = cls( + environment=environment, + output_dir=output_path, + config=config or {}, + _writer=writer, + ) + state_path = output_path / "run_state.json" + if state_path.exists(): + with open(state_path, "r") as f: + ctx._state = json.load(f) + return ctx + + def add_action(self, action: Action) -> None: + """Append an action event, writing to disk immediately.""" + self._writer.add_event(action) + + def add_observation(self, observation: ObservationEvent) -> None: + """Append an observation event, writing to disk immediately.""" + self._writer.add_event(observation) + + def add_verification_result(self, result: VerificationResult) -> None: + """Append a verification result, writing to disk immediately.""" + key = result.verifier_name + if key in self.data_point.verification: + count = sum( + 1 + for k in self.data_point.verification + if k == key or k.startswith(f"{key}_") + ) + key = f"{key}_{count}" + result.verifier_name = key + self._writer.add_verification_result(result) + + def save(self) -> None: + """Save the current state to output_dir.""" + self._writer.finalize() + self.data_point.save(self.output_dir / "data_point.json") + with open(self.output_dir / "run_state.json", "w") as f: + json.dump(self._state, f, indent=2) + + def checkpoint(self) -> None: + """Save a checkpoint of current progress.""" + self.save() + + @property + def task(self) -> Task: + return self.data_point.task + + @property + def solver_log(self) -> SolverLog: + return self.data_point.solver_log + + def set_state(self, key: str, value: Any) -> None: + """Set a state value (for resuming runs).""" + self._state[key] = value + + def get_state(self, key: str, default: Any = None) -> Any: + """Get a state value.""" + return self._state.get(key, default) + + def is_resuming(self) -> bool: + """Check if this is a resumed run with existing progress.""" + return len(self.solver_log.events) > 0 diff --git a/src/fara/environments/__init__.py b/src/fara/environments/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fara/environments/base.py b/src/fara/environments/base.py new file mode 100644 index 0000000..d052ddf --- /dev/null +++ b/src/fara/environments/base.py @@ -0,0 +1,77 @@ +"""Environment base class.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any, Dict + +from pydantic import BaseModel, ConfigDict + + +class EnvironmentConfig(BaseModel): + """Base configuration for environments.""" + + model_config = ConfigDict(extra="allow") + + metadata: dict[str, Any] = {} + + +class Environment(ABC): + """Base class for all environments.""" + + def __init__( + self, config: EnvironmentConfig | dict[str, Any] | None = None, **kwargs: Any + ): + if config is None: + config = self._get_config_class().model_validate(kwargs) + elif isinstance(config, dict): + config = self._get_config_class().model_validate(config) + self.config = config + self._initialized = False + + @classmethod + def _get_config_class(cls) -> type[EnvironmentConfig]: + """Override in subclasses to return the specific config class.""" + return EnvironmentConfig + + @abstractmethod + async def initialize(self, **kwargs) -> None: + """Initialize the environment.""" + ... + + @abstractmethod + async def close(self) -> None: + """Close the environment and cleanup resources.""" + ... + + @abstractmethod + async def get_observation(self) -> Any: + """Get current observation from the environment.""" + ... + + async def is_alive(self) -> bool: + """Check if the environment is still responsive.""" + raise NotImplementedError + + async def reinitialize(self) -> None: + """Reset the environment by closing and re-initializing.""" + raise NotImplementedError + + async def save_state(self) -> Dict[str, Any]: + """Save environment state for resuming.""" + return {} + + async def load_state(self, state: Dict[str, Any]) -> None: + """Load environment state when resuming.""" + pass + + async def __aenter__(self): + await self.initialize() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + await self.close() + + @property + def is_initialized(self) -> bool: + return self._initialized diff --git a/src/fara/environments/computer.py b/src/fara/environments/computer.py new file mode 100644 index 0000000..64fcc40 --- /dev/null +++ b/src/fara/environments/computer.py @@ -0,0 +1,184 @@ +"""Base classes for pixel-level GUI environments. + +Defines the canonical action interface from action_space.md: +- ComputerEnvironment: desktop-level GUI actions (click, type, scroll, etc.) +- BrowserEnvironment: adds browser navigation (goto_url, go_back, refresh) +""" + +from __future__ import annotations + +from abc import abstractmethod +from enum import StrEnum +from typing import NamedTuple + +from pydantic import model_validator + +from .base import Environment, EnvironmentConfig +from .screen_resolutions import sample_random_screen_resolution + + +class OSType(StrEnum): + """Operating system type for computer environments.""" + + LINUX = "linux" + WINDOWS = "windows" + MACOS = "macos" + + +class PageContext(NamedTuple): + """URL and viewport info returned by get_page_context.""" + + url: str + page_info: str + + +class ComputerEnvironmentConfig(EnvironmentConfig): + """Configuration for computer (GUI) environments.""" + + viewport_width: int = 1920 + viewport_height: int = 1080 + randomize_screen_res: bool = False + + @model_validator(mode="after") + def _apply_randomize_screen_res(self) -> "ComputerEnvironmentConfig": + if self.randomize_screen_res: + w, h = sample_random_screen_resolution() + self.viewport_width = w + self.viewport_height = h + return self + + +class ComputerEnvironment(Environment): + """Base class for pixel-level GUI environments (desktop or browser). + + Subclasses must implement all core GUI actions. Extended actions + raise NotImplementedError by default and can be overridden. + """ + + @property + @abstractmethod + def os_type(self) -> OSType: + """The OS this environment runs on. Concrete subclasses must set this.""" + ... + + async def save_state(self) -> dict: + """Persist the resolved viewport so prepro gets the actual (possibly + randomized) dims rather than the raw seed values.""" + cfg: ComputerEnvironmentConfig = self.config # type: ignore[assignment] + return { + "viewport_width": cfg.viewport_width, + "viewport_height": cfg.viewport_height, + } + + # --- Core GUI actions (required) --- + + @abstractmethod + async def left_click(self, x: int, y: int) -> None: ... + + @abstractmethod + async def right_click(self, x: int, y: int) -> None: ... + + @abstractmethod + async def double_click(self, x: int, y: int) -> None: ... + + @abstractmethod + async def mouse_move(self, x: int, y: int) -> None: ... + + @abstractmethod + async def left_click_drag(self, end_x: int, end_y: int) -> None: + """Drag from current cursor position to (end_x, end_y).""" + ... + + async def drag_from_to( + self, start_x: int, start_y: int, end_x: int, end_y: int + ) -> None: + """Move to (start_x, start_y), then drag to (end_x, end_y).""" + await self.mouse_move(start_x, start_y) + await self.left_click_drag(end_x, end_y) + + @abstractmethod + async def key(self, keys: list[str]) -> None: ... + + @abstractmethod + async def type(self, text: str) -> None: ... + + @abstractmethod + async def scroll(self, pixels: int) -> None: + """Scroll vertically. Positive=up, negative=down.""" + ... + + @abstractmethod + async def wait(self, duration: float) -> None: ... + + @abstractmethod + async def get_screenshot(self) -> bytes: ... + + async def get_screenshot_with_retry( + self, retries: int = 3, delay: float = 5 + ) -> bytes: + """Get screenshot with retries for transient environment errors.""" + import asyncio + + for attempt in range(retries): + try: + return await self.get_screenshot() + except Exception as e: + if attempt == retries - 1: + raise RuntimeError( + f"RETRYABLE_ERROR: screenshot failed after {retries} attempts: {e}" + ) from e + await asyncio.sleep(delay) + + # --- Extended actions (optional) --- + + async def middle_click(self, x: int, y: int) -> None: + raise NotImplementedError + + async def triple_click(self, x: int, y: int) -> None: + raise NotImplementedError + + async def left_mouse_down(self, x: int, y: int) -> None: + raise NotImplementedError + + async def left_mouse_up(self, x: int, y: int) -> None: + raise NotImplementedError + + async def hold_key(self, key: str, duration: float) -> None: + raise NotImplementedError + + async def cursor_position(self) -> tuple[int, int]: + raise NotImplementedError + + async def hscroll(self, pixels: int) -> None: + """Horizontal scroll. Positive=right, negative=left.""" + raise NotImplementedError + + async def screenshot_region(self, x0: int, y0: int, x1: int, y1: int) -> bytes: + raise NotImplementedError + + async def get_page_context(self) -> PageContext: + """Return page URL and text summary of page state. + + Returns empty PageContext by default. Browser environments override this. + """ + return PageContext(url="", page_info="") + + +class BrowserEnvironmentConfig(ComputerEnvironmentConfig): + """Configuration for browser-based environments.""" + + start_page: str = "about:blank" + extra_sleep_time: float = 3.0 + + +class BrowserEnvironment(ComputerEnvironment): + """ComputerEnvironment extended with browser navigation actions.""" + + @abstractmethod + async def goto_url(self, url: str) -> None: ... + + @abstractmethod + async def go_back(self) -> None: ... + + async def refresh(self) -> None: + raise NotImplementedError("refresh not supported by this environment") diff --git a/src/fara/environments/playwright/__init__.py b/src/fara/environments/playwright/__init__.py new file mode 100644 index 0000000..7f57d1a --- /dev/null +++ b/src/fara/environments/playwright/__init__.py @@ -0,0 +1,3 @@ +from .environment import PlaywrightEnvironment, PlaywrightEnvironmentConfig + +__all__ = ["PlaywrightEnvironment", "PlaywrightEnvironmentConfig"] diff --git a/src/fara/environments/playwright/_types.py b/src/fara/environments/playwright/_types.py new file mode 100644 index 0000000..c8dbbcb --- /dev/null +++ b/src/fara/environments/playwright/_types.py @@ -0,0 +1,97 @@ +"""Types for playwright controller.""" + +from typing import Any, Dict, List, TypedDict, Union + + +class DOMRectangle(TypedDict): + x: Union[int, float] + y: Union[int, float] + width: Union[int, float] + height: Union[int, float] + top: Union[int, float] + right: Union[int, float] + bottom: Union[int, float] + left: Union[int, float] + + +class VisualViewport(TypedDict): + height: Union[int, float] + width: Union[int, float] + offsetLeft: Union[int, float] + offsetTop: Union[int, float] + pageLeft: Union[int, float] + pageTop: Union[int, float] + scale: Union[int, float] + clientWidth: Union[int, float] + clientHeight: Union[int, float] + scrollWidth: Union[int, float] + scrollHeight: Union[int, float] + + +class InteractiveRegion(TypedDict): + tag_name: str + role: str + aria_name: str + v_scrollable: bool + rects: List[DOMRectangle] + + +def _get_str(d: Any, k: str) -> str: + val = d[k] + assert isinstance(val, str) + return val + + +def _get_number(d: Any, k: str) -> Union[int, float]: + val = d[k] + assert isinstance(val, int) or isinstance(val, float) + return val + + +def _get_bool(d: Any, k: str) -> bool: + val = d[k] + assert isinstance(val, bool) + return val + + +def domrectangle_from_dict(rect: Dict[str, Any]) -> DOMRectangle: + return DOMRectangle( + x=_get_number(rect, "x"), + y=_get_number(rect, "y"), + width=_get_number(rect, "width"), + height=_get_number(rect, "height"), + top=_get_number(rect, "top"), + right=_get_number(rect, "right"), + bottom=_get_number(rect, "bottom"), + left=_get_number(rect, "left"), + ) + + +def interactiveregion_from_dict(region: Dict[str, Any]) -> InteractiveRegion: + typed_rects: List[DOMRectangle] = [] + for rect in region["rects"]: + typed_rects.append(domrectangle_from_dict(rect)) + + return InteractiveRegion( + tag_name=_get_str(region, "tag_name"), + role=_get_str(region, "role"), + aria_name=_get_str(region, "aria-name"), + v_scrollable=_get_bool(region, "v-scrollable"), + rects=typed_rects, + ) + + +def visualviewport_from_dict(viewport: Dict[str, Any]) -> VisualViewport: + return VisualViewport( + height=_get_number(viewport, "height"), + width=_get_number(viewport, "width"), + offsetLeft=_get_number(viewport, "offsetLeft"), + offsetTop=_get_number(viewport, "offsetTop"), + pageLeft=_get_number(viewport, "pageLeft"), + pageTop=_get_number(viewport, "pageTop"), + scale=_get_number(viewport, "scale"), + clientWidth=_get_number(viewport, "clientWidth"), + clientHeight=_get_number(viewport, "clientHeight"), + scrollWidth=_get_number(viewport, "scrollWidth"), + scrollHeight=_get_number(viewport, "scrollHeight"), + ) diff --git a/src/fara/environments/playwright/environment.py b/src/fara/environments/playwright/environment.py new file mode 100644 index 0000000..b73dcb3 --- /dev/null +++ b/src/fara/environments/playwright/environment.py @@ -0,0 +1,849 @@ +"""Playwright-based browser environment. + +Implements BrowserEnvironment with canonical action names. Old method names +(click, hover, goto, back, keypress, scroll_down, scroll_up) are kept as +backward-compatible aliases. Playwright-specific methods (id-based, CUA) +are also preserved. +""" + +from __future__ import annotations + +import asyncio +import atexit +import logging +import os +import platform +import signal +import subprocess +import time +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional + +from playwright.async_api import ( + BrowserContext, + Download, + Page, + Playwright, + async_playwright, +) + +from ...agents.key_mapping import CUA_KEY_TO_PLAYWRIGHT_KEY +from ..computer import BrowserEnvironment, BrowserEnvironmentConfig, OSType, PageContext +from .playwright_controller import PlaywrightController + + +class PlaywrightEnvironmentConfig(BrowserEnvironmentConfig): + """Configuration for PlaywrightEnvironment.""" + + viewport_width: int = 1440 + viewport_height: int = 900 + headless: bool = True + browser_channel: str = "chromium" + start_page: str = "about:blank" + downloads_folder: str | None = None + browser_data_dir: str | None = None + animate_actions: bool = False + single_tab_mode: bool = True + default_timeout: int = 60000 + page_script_path: str | None = None + use_browserbase: bool = False + browserbase_project_id: str | None = None + browserbase_api_key: str | None = None + enable_find_overlay: bool = True + + +class PlaywrightEnvironment(BrowserEnvironment): + """Browser environment using Playwright. + + Supports regular Chromium/Firefox/WebKit, persistent browser contexts, + and BrowserBase cloud sessions. + """ + + os_type = OSType.LINUX + + # Browser-chrome shortcuts that Playwright can't trigger via page.keyboard + # (they affect browser UI, not the page). Dispatched to env methods so the + # agent can "press the shortcut like on a real computer" and it just works. + _BROWSER_CHROME_DISPATCH: Dict[frozenset, str] = { + frozenset({"Control", "r"}): "refresh", + frozenset({"Control", "Shift", "r"}): "refresh", + frozenset({"F5"}): "refresh", + frozenset({"Alt", "ArrowLeft"}): "go_back", + frozenset({"Alt", "ArrowRight"}): "forward", + } + _FIND_CHORD = frozenset({"Control", "f"}) + _FIND_OVERLAY_JS = (Path(__file__).parent / "find_overlay.js").read_text() + + @staticmethod + def _normalize_chord(keys: list[str]) -> frozenset: + """Map CUA/raw keys to Playwright names and lowercase single letters.""" + mapped = [CUA_KEY_TO_PLAYWRIGHT_KEY.get(k.lower(), k) for k in keys] + return frozenset(k if len(k) > 1 else k.lower() for k in mapped) + + def __init__( + self, + config: PlaywrightEnvironmentConfig | dict[str, Any] | None = None, + **kwargs: Any, + ): + super().__init__(config, **kwargs) + self.config: PlaywrightEnvironmentConfig + + self._playwright: Playwright | None = None + self._context: BrowserContext | None = None + self._page: Page | None = None + self._browser = None + self._xvfb_process = None + self._download_handler: Callable[[Download], None] | None = None + self._last_download: Download | None = None + + self.logger = logging.getLogger(__name__) + self._is_linux = platform.system() == "Linux" + + self._controller: PlaywrightController | None = None + + # BrowserBase support + self._bb = None + self._session = None + self._captcha_event = asyncio.Event() + self._captcha_event.set() # Initially set (no captcha) + + # Optional task identifier — prepended to BB log lines for + # session<->task linkage in eval logs. Set via initialize(task_id=...). + self._task_id: str | None = None + + def _tag(self) -> str: + """Per-task log prefix; empty when no task_id was supplied.""" + return f"[task={self._task_id}] " if self._task_id else "" + + @classmethod + def _get_config_class(cls) -> type[PlaywrightEnvironmentConfig]: + return PlaywrightEnvironmentConfig + + def _default_download_handler(self, download: Download) -> None: + self._last_download = download + + async def initialize(self, **kwargs) -> None: + """Initialize the browser environment.""" + self._task_id = kwargs.pop("task_id", None) + self._playwright = await async_playwright().start() + self._download_handler = self._default_download_handler + + self._controller = PlaywrightController( + animate_actions=self.config.animate_actions, + downloads_folder=self.config.downloads_folder, + viewport_width=self.config.viewport_width, + viewport_height=self.config.viewport_height, + _download_handler=self._download_handler, + to_resize_viewport=True, + single_tab_mode=self.config.single_tab_mode, + logger=self.logger, + ) + + if self.config.use_browserbase: + # _init_browserbase runs _setup_browser inside its retry loop so + # a start_page navigation failure or stuck captcha triggers a + # fresh session instead of bubbling up. + await self._init_browserbase() + elif self.config.browser_data_dir: + await self._init_persistent_browser() + await self._setup_browser() + else: + await self._init_regular_browser() + await self._setup_browser() + + self._initialized = True + + async def _init_regular_browser(self) -> None: + """Initialize regular browser.""" + if not self.config.headless and self._is_linux: + self._start_xvfb() + + launch_args = {"headless": self.config.headless} + + if self.config.browser_channel == "chromium": + self._browser = await self._playwright.chromium.launch(**launch_args) + elif self.config.browser_channel == "firefox": + self._browser = await self._playwright.firefox.launch(**launch_args) + elif self.config.browser_channel == "webkit": + self._browser = await self._playwright.webkit.launch(**launch_args) + else: + raise ValueError( + f"Unsupported browser channel: {self.config.browser_channel}" + ) + + self._context = await self._browser.new_context( + user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36 Edg/122.0.0.0" + ) + self._page = await self._context.new_page() + + _BROWSERBASE_MAX_ATTEMPTS = 5 + _BROWSERBASE_RATE_LIMIT_BACKOFF_S = 10 + _BROWSERBASE_CAPTCHA_WAIT_S = 90 + + async def _init_browserbase(self) -> None: + """Initialize BrowserBase connection. + + Retries up to ``_BROWSERBASE_MAX_ATTEMPTS`` times. Each attempt + creates a fresh session, connects via CDP, runs the standard + browser setup (which navigates to ``start_page``), and waits + briefly for any captcha. On failure the session is released and + the next attempt starts clean. + """ + import browserbase + from browserbase import Browserbase + + self.logger.info(f"{self._tag()}Initializing BrowserBase session...") + + api_key = self.config.browserbase_api_key or os.environ.get( + "BROWSERBASE_API_KEY" + ) + project_id = self.config.browserbase_project_id or os.environ.get( + "BROWSERBASE_PROJECT_ID" + ) + if not api_key or not project_id: + raise ValueError("BrowserBase API key and project ID are required") + + self._bb = Browserbase(api_key=api_key) + + for attempt in range(1, self._BROWSERBASE_MAX_ATTEMPTS + 1): + try: + await self._connect_browserbase_once(project_id) + if attempt > 1: + self.logger.info( + f"{self._tag()}BrowserBase init succeeded on attempt {attempt}" + ) + return + except Exception as e: + await self._teardown_browserbase_session(project_id) + if attempt == self._BROWSERBASE_MAX_ATTEMPTS: + raise RuntimeError( + f"BrowserBase init failed after " + f"{self._BROWSERBASE_MAX_ATTEMPTS} attempts: " + f"{type(e).__name__}: {e}" + ) from e + delay = self._browserbase_retry_delay(e, attempt, browserbase) + self.logger.warning( + f"{self._tag()}BrowserBase init attempt {attempt}/" + f"{self._BROWSERBASE_MAX_ATTEMPTS} failed " + f"({type(e).__name__}: {e}); retrying in {delay}s" + ) + await asyncio.sleep(delay) + + async def _connect_browserbase_once(self, project_id: str) -> None: + """Run one BrowserBase bring-up: session, CDP, setup, captcha wait.""" + # Clean slate: a prior attempt may have left _captcha_event cleared + # if it saw "solving-started" but the session died before + # "solving-finished" — the dead session can never set it. + self._captcha_event.set() + + self._session = self._bb.sessions.create( + project_id=project_id, + proxies=True, + browser_settings={"advanced_stealth": True, "enablePdfViewer": True}, + keep_alive=True, + timeout=7200, + api_timeout=3600, + ) + assert self._session.id is not None + assert ( + self._session.status == "RUNNING" + ), f"Session status is {self._session.status}" + + self._browser = await self._playwright.chromium.connect_over_cdp( + self._session.connect_url + ) + self.logger.info( + f"{self._tag()}Connected to BrowserBase: " + f"https://browserbase.com/sessions/{self._session.id}" + ) + + self._context = self._browser.contexts[0] + assert len(self._context.pages) == 1 + self._page = self._context.pages[0] + + # Context-level "console" already bubbles up page console messages + # for every current and future page in the context, so we don't + # also register on _page. + self._context.on("console", self._handle_browserbase_console) + + # Navigate to start_page + wire handlers. Failures here trigger + # a retry with a fresh session. + await self._setup_browser() + + # Best-effort wait for a captcha triggered on start_page. + # _captcha_event is already set when no captcha is active, so + # this returns immediately in the common case. + try: + await asyncio.wait_for( + self._captcha_event.wait(), + timeout=self._BROWSERBASE_CAPTCHA_WAIT_S, + ) + except asyncio.TimeoutError: + self.logger.warning( + f"{self._tag()}Captcha not resolved within " + f"{self._BROWSERBASE_CAPTCHA_WAIT_S}s on start_page; proceeding" + ) + + def _handle_browserbase_console(self, msg) -> None: + """Track BrowserBase captcha-solving lifecycle via console messages.""" + if msg.text == "browserbase-solving-started": + self.logger.info(f"{self._tag()}Captcha Solving In Progress!") + self._captcha_event.clear() + elif msg.text == "browserbase-solving-finished": + self.logger.info(f"{self._tag()}Captcha Solving Completed!") + asyncio.create_task(self._resume_after_captcha()) + + async def _resume_after_captcha(self) -> None: + """Release the captcha event after a best-effort post-captcha settle.""" + try: + await asyncio.sleep(3) + try: + # Prefer "domcontentloaded" over "networkidle" — the latter + # can hang on sites with persistent network activity + # (analytics, websockets). If captcha triggered a + # navigation, this waits for the new DOM; otherwise it + # returns immediately. + await self._page.wait_for_load_state("domcontentloaded", timeout=10000) + except Exception as e: + self.logger.warning( + f"Post-captcha wait_for_load_state failed " + f"(continuing anyway): {e}" + ) + finally: + self._captcha_event.set() + + def _browserbase_retry_delay(self, exc: Exception, attempt: int, bb_module) -> int: + """Pick a backoff delay for a failed BrowserBase init attempt.""" + if isinstance(exc, bb_module.RateLimitError): + return self._BROWSERBASE_RATE_LIMIT_BACKOFF_S + return 2 ** (attempt - 1) + + async def _teardown_browserbase_session(self, project_id: str) -> None: + """Release a partially-built BrowserBase session and reset state.""" + browser_connected = self._browser is not None and self._browser.is_connected() + if self._browser is not None: + try: + await self._browser.close() + except Exception: + pass + self._browser = None + self._context = None + self._page = None + if self._session is not None and self._bb is not None: + session_id = self._session.id + try: + self._bb.sessions.update( + self._session.id, + status="REQUEST_RELEASE", + project_id=project_id, + ) + except Exception: + pass + # Single canonical terminal line per session — grep with [BB-END] + # to reconstruct session<->task<->status without parsing tricks. + self.logger.info( + f"[BB-END] task={self._task_id} session={session_id} " + f"browser_connected_at_teardown={browser_connected}" + ) + self._session = None + + async def _init_persistent_browser(self) -> None: + """Initialize persistent browser with data directory.""" + if not self.config.headless and self._is_linux: + self._start_xvfb() + + launch_args = {"headless": self.config.headless} + self._context = await self._playwright.chromium.launch_persistent_context( + self.config.browser_data_dir, **launch_args + ) + self._page = await self._context.new_page() + + # Hacky, brittle, undocumented: this cookie makes Bing serve search + # results as same-tab links instead of target="_blank", which skips + # the per-click popup-capture overhead on bing.com. Reverse-engineered + # empirically; Microsoft can change it anytime. See PR #1114. + _BING_SAME_TAB_COOKIE = { + "name": "SRCHHPGUSR", + "value": "EXLKNT=0", + "domain": ".bing.com", + "path": "/", + "secure": True, + } + + async def _setup_browser(self) -> None: + """Set up common browser features.""" + self._context.set_default_timeout(self.config.default_timeout) + + await self._controller.on_new_page(self._page) + + if self._download_handler: + self._page.on("download", self._download_handler) + + await self._page.set_viewport_size( + {"width": self.config.viewport_width, "height": self.config.viewport_height} + ) + + if self.config.page_script_path and os.path.exists( + self.config.page_script_path + ): + await self._page.add_init_script(path=self.config.page_script_path) + + # Inject Bing same-tab cookie before navigation so it applies to + # the very first bing.com load. See _BING_SAME_TAB_COOKIE above. + try: + await self._context.add_cookies([self._BING_SAME_TAB_COOKIE]) + except Exception as e: + self.logger.warning(f"Failed to inject Bing same-tab cookie: {e}") + + await self._page.goto(self.config.start_page, wait_until="commit") + + async def _swap_to_new_page(self, new_page: Page) -> None: + """Promote a popup the agent's action just opened to the active page. + + Browsers foreground a newly opened tab on `target="_blank"` and + `window.open()`, so the agent observing the new page matches what + a real user would see. In ``single_tab_mode`` we also close the + previous page to preserve the one-page worldview; otherwise we + leave it on the context for a future multi-tab action space to + switch back to. + """ + old_page = self._page + if new_page is old_page: + return + self._page = new_page + if self.config.single_tab_mode and not old_page.is_closed(): + try: + await old_page.close() + except Exception as e: + self.logger.warning(f"Error closing previous page after swap: {e}") + + def _start_xvfb(self) -> None: + """Start Xvfb virtual display server.""" + display_num = 99 + self._xvfb_process = subprocess.Popen( + ["Xvfb", f":{display_num}", "-screen", "0", "1280x1024x24", "-ac"], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + os.environ["DISPLAY"] = f":{display_num}" + time.sleep(1) + atexit.register(self._stop_xvfb) + + def _stop_xvfb(self) -> None: + """Stop the Xvfb process.""" + if self._xvfb_process: + self._xvfb_process.send_signal(signal.SIGTERM) + self._xvfb_process.wait() + self._xvfb_process = None + + async def wait_for_captcha_resolution(self) -> None: + """Wait for captcha to be resolved if one is being solved.""" + await self._captcha_event.wait() + + async def close(self) -> None: + """Close the browser and cleanup.""" + self.logger.info("Closing browser...") + + if self._page: + await self._page.close() + self._page = None + + if self._context: + await self._context.close() + self._context = None + + if self._browser: + if self.config.use_browserbase and self._session and self._bb: + project_id = self.config.browserbase_project_id or os.environ.get( + "BROWSERBASE_PROJECT_ID" + ) + session_id = self._session.id + browser_connected = self._browser.is_connected() + self._bb.sessions.update( + self._session.id, + status="REQUEST_RELEASE", + project_id=project_id, + ) + self.logger.info( + f"[BB-END] task={self._task_id} session={session_id} " + f"browser_connected_at_teardown={browser_connected}" + ) + self._session = None + await self._browser.close() + self._browser = None + + if self._playwright: + await self._playwright.stop() + self._playwright = None + + if not self.config.headless: + self._stop_xvfb() + + self._initialized = False + + async def get_observation(self) -> bytes: + """Get screenshot of current page.""" + return await self.get_screenshot() + + # --- Core GUI actions (ComputerEnvironment interface) --- + + async def left_click(self, x: int, y: int) -> None: + new_page = await self._controller.click_coords(self._page, x, y) + if new_page is not None: + await self._swap_to_new_page(new_page) + + async def right_click(self, x: int, y: int) -> None: + new_page = await self._controller.cua_click(self._page, x, y, "right") + if new_page is not None: + await self._swap_to_new_page(new_page) + + async def double_click(self, x: int, y: int) -> None: + new_page = await self._controller.cua_double_click(self._page, x, y) + if new_page is not None: + await self._swap_to_new_page(new_page) + + async def mouse_move(self, x: int, y: int) -> None: + await self._controller.cua_move(self._page, x, y) + + async def left_click_drag(self, end_x: int, end_y: int) -> None: + await self._page.mouse.down() + await self._page.mouse.move(end_x, end_y) + await self._page.mouse.up() + + async def key(self, keys: list[str]) -> None: + chord = self._normalize_chord(keys) + if self.config.enable_find_overlay and chord == self._FIND_CHORD: + await self._page.evaluate(self._FIND_OVERLAY_JS) + return + handler = self._BROWSER_CHROME_DISPATCH.get(chord) + if handler is not None: + await getattr(self, handler)() + return + await self._controller.keypress(self._page, keys) + + async def type(self, text: str) -> None: + await self._controller.cua_type(self._page, text) + + async def scroll(self, pixels: int) -> None: + """Scroll vertically. Positive=up, negative=down.""" + await self._page.mouse.wheel(0, -pixels) + + async def wait(self, duration: float = 1.0) -> None: + await self._controller.sleep(self._page, duration) + + async def get_screenshot(self, path: str | None = None) -> bytes: + """Capture a screenshot of the current page.""" + return await self._controller.get_screenshot(self._page, path=path) + + # --- Browser navigation (BrowserEnvironment interface) --- + + async def goto_url(self, url: str) -> None: + await self._controller.visit_page(self._page, url) + + async def go_back(self) -> None: + await self._controller.back(self._page) + + async def refresh(self) -> None: + await self._page.reload(wait_until="commit") + + # --- Extended actions --- + + async def middle_click(self, x: int, y: int) -> None: + await self._page.mouse.click(x, y, button="middle") + + async def triple_click(self, x: int, y: int) -> None: + await self._page.mouse.click(x, y, click_count=3) + + async def left_mouse_down(self, x: int, y: int) -> None: + await self._page.mouse.move(x, y) + await self._page.mouse.down() + + async def left_mouse_up(self, x: int, y: int) -> None: + await self._page.mouse.move(x, y) + await self._page.mouse.up() + + async def cursor_position(self) -> tuple[int, int]: + pos = self._controller.last_cursor_position + return (int(pos[0]), int(pos[1])) + + async def hscroll(self, pixels: int) -> None: + """Horizontal scroll. Positive=right, negative=left.""" + await self._page.mouse.wheel(pixels, 0) + + # --- Backward-compat aliases --- + + async def click(self, x: float, y: float) -> Dict[str, Any]: + """Click at coordinates.""" + await self.left_click(int(x), int(y)) + return {"success": True} + + async def hover(self, x: float, y: float) -> Dict[str, Any]: + """Hover at coordinates.""" + await self._controller.hover_coords(self._page, x, y) + return {"success": True} + + async def goto(self, url: str) -> Dict[str, Any]: + """Navigate to a URL.""" + await self.goto_url(url) + return {"success": True} + + async def back(self) -> Dict[str, Any]: + """Go back in browser history.""" + await self.go_back() + return {"success": True} + + async def forward(self) -> Dict[str, Any]: + """Go forward in browser history.""" + await self._controller.cua_forward(self._page) + return {"success": True} + + async def keypress(self, keys: List[str]) -> Dict[str, Any]: + """Press a sequence of keys.""" + await self.key(keys) + return {"success": True} + + async def scroll_down(self, amount: int = 400) -> Dict[str, Any]: + """Scroll down the page.""" + await self._controller.page_down(self._page, amount=amount) + return {"success": True} + + async def scroll_up(self, amount: int = 400) -> Dict[str, Any]: + """Scroll up the page.""" + await self._controller.page_up(self._page, amount=amount) + return {"success": True} + + # --- Playwright-specific methods (id-based, CUA, etc.) --- + + async def click_id(self, identifier: str) -> Dict[str, Any]: + """Click on an element by its identifier.""" + new_page = await self._controller.click_id(self._page, identifier) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def type_text( + self, + x: float, + y: float, + text: str, + press_enter: bool = True, + clear_first: bool = False, + ) -> Dict[str, Any]: + """Type text at coordinates.""" + new_page = await self._controller.fill_coords( + self._page, + x, + y, + text, + press_enter=press_enter, + delete_existing_text=clear_first, + ) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def type_id( + self, + identifier: str, + text: str, + press_enter: bool = True, + clear_first: bool = False, + ) -> Dict[str, Any]: + """Type text into an element by its identifier.""" + await self._controller.fill_id( + self._page, + identifier, + text, + press_enter=press_enter, + delete_existing_text=clear_first, + ) + return {"success": True} + + async def hover_id(self, identifier: str) -> Dict[str, Any]: + """Hover over an element by its identifier.""" + await self._controller.hover_id(self._page, identifier) + return {"success": True} + + async def scroll_id(self, identifier: str, direction: str) -> Dict[str, Any]: + """Scroll within an element by its identifier.""" + await self._controller.scroll_id(self._page, identifier, direction) + return {"success": True} + + async def wait_for_load( + self, state: str = "domcontentloaded", timeout: Optional[int] = None + ) -> Dict[str, Any]: + """Wait for the page to reach a load state.""" + await self._controller.wait_for_load_state( + self._page, state=state, timeout=timeout + ) + if self.config.extra_sleep_time > 0: + await asyncio.sleep(self.config.extra_sleep_time) + return {"success": True} + + async def get_url(self) -> str: + """Get the current page URL.""" + return await self._controller.get_page_url(self._page) + + async def get_title(self) -> str: + """Get the current page title.""" + return await self._controller.get_page_title(self._page) + + async def get_text(self, n_lines: int = 100) -> str: + """Get the visible text on the page.""" + return await self._controller.get_webpage_text(self._page, n_lines=n_lines) + + async def get_interactive_rects(self) -> Dict[str, Any]: + """Get interactive element rectangles.""" + return await self._controller.get_interactive_rects(self._page) + + async def get_visual_viewport(self) -> Dict[str, Any]: + """Get the visual viewport information.""" + return await self._controller.get_visual_viewport(self._page) + + async def get_page_metadata(self) -> Dict[str, Any]: + """Get page metadata.""" + return await self._controller.get_page_metadata(self._page) + + async def get_focused_rect_id(self) -> str: + """Get the ID of the currently focused element.""" + return await self._controller.get_focused_rect_id(self._page) + + async def get_page_markdown(self) -> str: + """Get page content as markdown.""" + return await self._controller.get_page_markdown(self._page) + + async def get_page_context(self) -> PageContext: + """Return current URL and viewport scroll position.""" + url = await self._controller.get_page_url(self._page) + parts = [f"Current URL: {url}"] + vp = await self._controller.get_visual_viewport(self._page) + page_top = vp.get("pageTop", 0) + viewport_h = vp.get("height", 0) + scroll_h = vp.get("scrollHeight", 0) + if scroll_h > 0: + bottom = page_top + viewport_h + pct = min(int(bottom / scroll_h * 100), 100) + parts.append( + f"Viewport: scrolled to {pct}% of page " + f"({int(page_top)}-{int(bottom)}px of {int(scroll_h)}px)" + ) + return PageContext(url=url, page_info="\n".join(parts)) + + async def fill_coords( + self, + x: float, + y: float, + text: str, + press_enter: bool = True, + delete_existing_text: bool = False, + ) -> Dict[str, Any]: + """Fill text at coordinates.""" + new_page = await self._controller.fill_coords( + self._page, + x, + y, + text, + press_enter=press_enter, + delete_existing_text=delete_existing_text, + ) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def fill_id( + self, + identifier: str, + text: str, + press_enter: bool = True, + delete_existing_text: bool = False, + ) -> Dict[str, Any]: + """Fill text into an element by its identifier.""" + await self._controller.fill_id( + self._page, + identifier, + text, + press_enter=press_enter, + delete_existing_text=delete_existing_text, + ) + return {"success": True} + + async def select_option(self, identifier: str) -> Dict[str, Any]: + """Select an option element by its identifier.""" + new_page = await self._controller.select_option(self._page, identifier) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def cua_click(self, x: int, y: int, button: str = "left") -> Dict[str, Any]: + """CUA click action.""" + new_page = await self._controller.cua_click(self._page, x, y, button) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def cua_double_click(self, x: int, y: int) -> Dict[str, Any]: + """CUA double click action.""" + new_page = await self._controller.cua_double_click(self._page, x, y) + if new_page is not None: + await self._swap_to_new_page(new_page) + return {"success": True} + + async def cua_scroll( + self, x: int, y: int, scroll_x: int, scroll_y: int + ) -> Dict[str, Any]: + """CUA scroll action.""" + await self._controller.cua_scroll(self._page, x, y, scroll_x, scroll_y) + return {"success": True} + + async def cua_type(self, text: str) -> Dict[str, Any]: + """CUA type action.""" + await self._controller.cua_type(self._page, text) + return {"success": True} + + async def cua_wait(self, ms: int = 1000) -> Dict[str, Any]: + """CUA wait action.""" + await self._controller.cua_wait(self._page, ms) + return {"success": True} + + async def cua_move(self, x: int, y: int) -> Dict[str, Any]: + """CUA move mouse action.""" + await self._controller.cua_move(self._page, x, y) + return {"success": True} + + async def cua_drag(self, path: List[Dict[str, int]]) -> Dict[str, Any]: + """CUA drag action along a path.""" + await self._controller.cua_drag(self._page, path) + return {"success": True} + + async def evaluate(self, script: str) -> Any: + """Evaluate JavaScript on the page.""" + return await self._page.evaluate(script) + + async def save_state(self) -> Dict[str, Any]: + """Save environment state.""" + return { + "url": self._page.url if self._page else None, + "viewport_width": self.config.viewport_width, + "viewport_height": self.config.viewport_height, + } + + async def load_state(self, state: Dict[str, Any]) -> None: + """Load environment state.""" + url = state.get("url") + if url and self._page: + await self._controller.visit_page(self._page, url) + + @property + def page(self) -> Page | None: + """Get the current page.""" + return self._page + + @property + def context(self) -> BrowserContext | None: + """Get the browser context.""" + return self._context + + @property + def controller(self) -> PlaywrightController | None: + """Get the playwright controller.""" + return self._controller diff --git a/src/fara/environments/playwright/find_overlay.js b/src/fara/environments/playwright/find_overlay.js new file mode 100644 index 0000000..22191c8 --- /dev/null +++ b/src/fara/environments/playwright/find_overlay.js @@ -0,0 +1,204 @@ +// In-page find overlay injected by PlaywrightEnvironment on Ctrl+F. +// The input is focused so the agent's follow-up type() + keypress(["Enter"]) +// flow into it naturally. Enter walks text nodes, highlights all matches, +// scrolls to the current one, and shows N/M. Repeated Enter advances and +// wraps. Esc removes the overlay and clears highlights. +(() => { + const ID = '__agento_find__'; + const HL_CLASS = '__agento_find_hl__'; + const HL_ACTIVE = '__agento_find_hl_active__'; + + const EXISTING = document.getElementById(ID); + if (EXISTING) { EXISTING.querySelector('input').focus(); return; } + + // Inject highlight styles once. + if (!document.getElementById(ID + '_style')) { + const style = document.createElement('style'); + style.id = ID + '_style'; + style.textContent = + '.' + HL_CLASS + '{background:#ffe066;color:#000;border-radius:2px;' + + 'box-shadow:0 0 0 1px #f59f00}' + + '.' + HL_ACTIVE + '{background:#ff922b;color:#000;' + + 'box-shadow:0 0 0 2px #d9480f}' + + '#' + ID + ' input::placeholder{color:#e8eaed;opacity:1}' + + '@keyframes __agento_caret_blink{50%{opacity:0}}' + + '#' + ID + ' input::placeholder{' + + 'animation:__agento_caret_blink 1s step-end infinite}'; + document.head.appendChild(style); + } + + const wrap = document.createElement('div'); + wrap.id = ID; + wrap.style.cssText = [ + 'position:fixed', 'top:4px', 'right:8px', 'z-index:2147483647', + 'background:#3c4043', 'color:#e8eaed', + 'border-radius:4px', 'padding:4px 8px', + 'font:13px system-ui,-apple-system,Segoe UI,sans-serif', + 'box-shadow:0 2px 6px rgba(0,0,0,.25)', + 'display:flex', 'align-items:center', 'gap:8px', + ].join(';'); + + const input = document.createElement('input'); + input.type = 'text'; + input.placeholder = '|'; + input.style.cssText = [ + 'width:180px', 'border:none', 'outline:none', + 'background:transparent', 'color:#e8eaed', + 'font:inherit', 'padding:2px 0', + ].join(';'); + wrap.appendChild(input); + + const status = document.createElement('span'); + status.style.cssText = 'color:#9aa0a6;font-size:12px;min-width:32px;text-align:right'; + wrap.appendChild(status); + + const btnStyle = [ + 'background:rgba(255,255,255,0.08)', 'border:1px solid rgba(255,255,255,0.15)', + 'color:#e8eaed', 'font:14px system-ui', 'cursor:pointer', + 'padding:6px 10px', 'border-radius:4px', 'line-height:1', + 'min-width:30px', 'min-height:28px', 'display:inline-flex', + 'align-items:center', 'justify-content:center', + ].join(';'); + const prevBtn = document.createElement('button'); + prevBtn.type = 'button'; + prevBtn.textContent = '\u25B2'; // up triangle + prevBtn.title = 'Previous match (Shift+Enter)'; + prevBtn.style.cssText = btnStyle; + wrap.appendChild(prevBtn); + const nextBtn = document.createElement('button'); + nextBtn.type = 'button'; + nextBtn.textContent = '\u25BC'; // down triangle + nextBtn.title = 'Next match (Enter)'; + nextBtn.style.cssText = btnStyle; + wrap.appendChild(nextBtn); + const closeBtn = document.createElement('button'); + closeBtn.type = 'button'; + closeBtn.textContent = '\u2715'; // x + closeBtn.title = 'Close (Escape)'; + closeBtn.style.cssText = btnStyle; + wrap.appendChild(closeBtn); + + document.body.appendChild(wrap); + input.focus(); + + let lastQuery = null; + let ranges = []; // Range objects for all matches + let spans = []; // highlight elements matching ranges + let cursor = -1; + + function clearHighlights() { + for (const span of spans) { + const parent = span.parentNode; + if (!parent) continue; + while (span.firstChild) parent.insertBefore(span.firstChild, span); + parent.removeChild(span); + parent.normalize(); + } + spans = []; + ranges = []; + } + + function collectMatches(q) { + const needle = q.toLowerCase(); + const hits = []; + const walker = document.createTreeWalker(document.body, NodeFilter.SHOW_TEXT, { + acceptNode: (n) => { + if (!n.nodeValue) return NodeFilter.FILTER_REJECT; + if (n.parentElement && n.parentElement.closest('#' + ID)) + return NodeFilter.FILTER_REJECT; + if (n.parentElement && n.parentElement.closest('.' + HL_CLASS)) + return NodeFilter.FILTER_REJECT; + return NodeFilter.FILTER_ACCEPT; + } + }); + let node; + while ((node = walker.nextNode())) { + const hay = node.nodeValue.toLowerCase(); + let i = 0; + while ((i = hay.indexOf(needle, i)) !== -1) { + hits.push({ node, offset: i }); + i += needle.length; + } + } + return hits; + } + + function highlightAll(hits, q) { + // Process bottom-up per text node so earlier offsets remain valid. + const byNode = new Map(); + for (const h of hits) { + if (!byNode.has(h.node)) byNode.set(h.node, []); + byNode.get(h.node).push(h.offset); + } + const newSpans = []; + for (const [node, offsets] of byNode) { + offsets.sort((a, b) => b - a); + for (const off of offsets) { + const range = document.createRange(); + range.setStart(node, off); + range.setEnd(node, off + q.length); + const span = document.createElement('span'); + span.className = HL_CLASS; + range.surroundContents(span); + newSpans.push(span); + } + } + // Reorder in document order so cursor-advance matches visual reading. + newSpans.sort((a, b) => { + if (a === b) return 0; + const pos = a.compareDocumentPosition(b); + return pos & Node.DOCUMENT_POSITION_FOLLOWING ? -1 : 1; + }); + return newSpans; + } + + function setActive(idx) { + for (const s of spans) s.classList.remove(HL_ACTIVE); + if (idx < 0 || idx >= spans.length) return; + const span = spans[idx]; + span.classList.add(HL_ACTIVE); + span.scrollIntoView({ block: 'center' }); + } + + function advance(direction) { + const q = input.value; + if (!q) { + status.textContent = ''; + clearHighlights(); + return; + } + if (q !== lastQuery) { + clearHighlights(); + lastQuery = q; + const hits = collectMatches(q); + if (hits.length === 0) { + status.textContent = '0/0'; + return; + } + spans = highlightAll(hits, q); + cursor = -1; + } + if (spans.length === 0) return; + cursor = (cursor + direction + spans.length) % spans.length; + setActive(cursor); + status.textContent = (cursor + 1) + '/' + spans.length; + } + + function close() { + clearHighlights(); + wrap.remove(); + } + + input.addEventListener('keydown', (e) => { + if (e.key === 'Enter') { + e.preventDefault(); + advance(e.shiftKey ? -1 : 1); + } else if (e.key === 'Escape') { + e.preventDefault(); + close(); + } + }); + prevBtn.addEventListener('click', () => { advance(-1); input.focus(); }); + nextBtn.addEventListener('click', () => { advance(1); input.focus(); }); + closeBtn.addEventListener('click', close); +})(); diff --git a/src/fara/environments/playwright/page_script.js b/src/fara/environments/playwright/page_script.js new file mode 100644 index 0000000..36cf56a --- /dev/null +++ b/src/fara/environments/playwright/page_script.js @@ -0,0 +1,610 @@ +var MultimodalWebSurfer = MultimodalWebSurfer || (function() { + let nextLabel = 10; + + let roleMapping = { + "a": "link", + "area": "link", + "button": "button", + "input, type=button": "button", + "input, type=checkbox": "checkbox", + "input, type=email": "textbox", + "input, type=number": "spinbutton", + "input, type=radio": "radio", + "input, type=range": "slider", + "input, type=reset": "button", + "input, type=search": "searchbox", + "input, type=submit": "button", + "input, type=tel": "textbox", + "input, type=text": "textbox", + "input, type=url": "textbox", + "search": "search", + "select": "combobox", + "option": "option", + "textarea": "textbox" + }; + + let getCursor = function (elm) { + return window.getComputedStyle(elm)["cursor"]; + }; + + let isVisible = function (element) { + return !!(element.offsetWidth || element.offsetHeight || element.getClientRects().length); + }; + + /** + * Finds interactive elements in the regular DOM (excluding Shadow DOM) + * Looks for elements that are: + * 1. Standard interactive elements (inputs, buttons, links) + * 2. Elements with ARIA roles indicating interactivity + * 3. Elements with cursor styles suggesting interactivity + * + * @returns {Array} Array of DOM elements that are deemed interactive + */ + let getInteractiveElementsNoShaddow = function () { + let results = [] + let roles = ["scrollbar", "searchbox", "slider", "spinbutton", "switch", "tab", "treeitem", "button", "checkbox", "gridcell", "link", "menuitem", "menuitemcheckbox", "menuitemradio", "option", "progressbar", "radio", "textbox", "combobox", "menu", "tree", "treegrid", "grid", "listbox", "radiogroup", "widget"]; + let inertCursors = ["auto", "default", "none", "text", "vertical-text", "not-allowed", "no-drop"]; + + // Get the main interactive elements + let nodeList = document.querySelectorAll("input, select, textarea, button, [href], [onclick], [contenteditable], [tabindex]:not([tabindex='-1'])"); + for (let i = 0; i < nodeList.length; i++) { // Copy to something mutable + // make sure not disabled and visible + if (nodeList[i].disabled || !isVisible(nodeList[i])) { + continue; + } + results.push(nodeList[i]); + } + + // Anything not already included that has a suitable role + nodeList = document.querySelectorAll("[role]"); + for (let i = 0; i < nodeList.length; i++) { // Copy to something mutable + // make sure not disabled and visible + if (nodeList[i].disabled || !isVisible(nodeList[i])) { + continue; + } + if (results.indexOf(nodeList[i]) == -1) { + let role = nodeList[i].getAttribute("role"); + if (roles.indexOf(role) > -1) { + results.push(nodeList[i]); + } + } + } + + // Any element that changes the cursor to something implying interactivity + nodeList = document.querySelectorAll("*"); + for (let i = 0; i < nodeList.length; i++) { + let node = nodeList[i]; + if (node.disabled || !isVisible(node)) { + continue; + } + + // Cursor is default, or does not suggest interactivity + let cursor = getCursor(node); + if (inertCursors.indexOf(cursor) >= 0) { + continue; + } + + // Move up to the first instance of this cursor change + let parent = node.parentNode; + while (parent && getCursor(parent) == cursor) { + node = parent; + parent = node.parentNode; + } + + // Add the node if it is new + if (results.indexOf(node) == -1) { + results.push(node); + } + } + + return results; + }; + + /** + * Recursively gathers elements matching specified roles from both regular DOM and Shadow DOM + * @param {Array} roles - Array of role selectors to match + * @param {Document|ShadowRoot} root - Root element to start search from + * @returns {Array} Array of matching elements + */ + function gatherAllElements(roles, root = document) { + const elements = []; + const stack = [root]; + const selector = roles.join(","); + + while (stack.length > 0) { + const currentRoot = stack.pop(); + + // Add elements at current level + elements.push(...Array.from(currentRoot.querySelectorAll(selector))); + + // Add shadow roots to stack + currentRoot.querySelectorAll("*").forEach(el => { + if (el.shadowRoot && el.shadowRoot.mode === "open") { + stack.push(el.shadowRoot); + } + }); + } + + return elements; + } + + /** + * Gets all interactive elements from both regular DOM and Shadow DOM + * Filters elements to ensure they are visible and accessible + * @returns {Array} Array of interactive elements + */ + let getInteractiveElements = function () { + // Get all elements that are interactive without the shadow DOM + const interactive_roles = ["input", "option", "select", "textarea", "button", "href", "onclick", "contenteditable", "tabindex:not([tabindex='-1'])"]; + + let results = []; + + let elements_no_shaddow = getInteractiveElementsNoShaddow(); + for (let i = 0; i < elements_no_shaddow.length; i++) { + if (results.indexOf(elements_no_shaddow[i]) == -1) { + // check if it has a rect + let rects = elements_no_shaddow[i].getClientRects(); + for (const rect of rects) { + let x = rect.left + rect.width / 2; + let y = rect.top + rect.height / 2; + if (isTopmost(elements_no_shaddow[i], x, y)) { + results.push(elements_no_shaddow[i]); + break; + } + } + } + } + + // From the shadow DOM get all interactive elements and options that are not in the no shadow list + let elements_all = gatherAllElements(interactive_roles); + + // Filter and process interactive elements + elements_all.forEach(element => { + // if file, auto add + if (element.tagName.toLowerCase() === "input" && element.getAttribute("type") == "file") { + results.push(element); + return; + } + // if option auto add + if (element.tagName.toLowerCase() === "option") { + results.push(element); + return; + } + if (element.disabled || !isVisible(element)) { + return; + } + + if (interactive_roles.includes(element.tagName.toLowerCase())) { + results.push(element); + } + }); + + return results; + }; + + /** + * Assigns unique identifiers to interactive elements + * @param {Array} elements - Array of elements to label + * @returns {Array} Updated array of interactive elements + */ + let labelElements = function (elements) { + for (let i = 0; i < elements.length; i++) { + if (!elements[i].hasAttribute("__elementId")) { + elements[i].setAttribute("__elementId", "" + (nextLabel++)); + } + } + return getInteractiveElements(); + }; + + /** + * Checks if an element is the topmost element at given coordinates + * @param {Element} element - Element to check + * @param {number} x - X coordinate + * @param {number} y - Y coordinate + * @returns {boolean} True if element is topmost at coordinates + */ + let isTopmost = function (element, x, y) { + let hit = document.elementFromPoint(x, y); + + // Hack to handle elements outside the viewport + if (hit === null) { + return true; + } + + while (hit) { + if (hit == element) return true; + hit = hit.parentNode; + } + return false; + }; + + let getFocusedElementId = function () { + let elm = document.activeElement; + while (elm) { + if (elm.hasAttribute && elm.hasAttribute("__elementId")) { + return elm.getAttribute("__elementId"); + } + elm = elm.parentNode; + } + return null; + }; + + let trimmedInnerText = function (element) { + if (!element) { + return ""; + } + let text = element.innerText; + if (!text) { + return ""; + } + return text.trim(); + }; + + let getApproximateAriaName = function (element) { + if (element.hasAttribute("aria-label")) { + return element.getAttribute("aria-label"); + } + + // check if element has span that is called label and grab the inner text + if (element.querySelector("span.label")) { + return element.querySelector("span.label").innerText; + } + + // Check for aria labels + if (element.hasAttribute("aria-labelledby")) { + let buffer = ""; + let ids = element.getAttribute("aria-labelledby").split(" "); + for (let i = 0; i < ids.length; i++) { + let label = document.getElementById(ids[i]); + if (label) { + buffer = buffer + " " + trimmedInnerText(label); + } + } + return buffer.trim(); + } + + if (element.hasAttribute("aria-label")) { + return element.getAttribute("aria-label"); + } + + // Check for labels + if (element.hasAttribute("id")) { + let label_id = element.getAttribute("id"); + let label = ""; + try { + // Escape special characters in the ID + let escaped_id = CSS.escape(label_id); + let labels = document.querySelectorAll(`label[for="${escaped_id}"]`); + for (let j = 0; j < labels.length; j++) { + label += labels[j].innerText + " "; + } + label = label.trim(); + if (label != "") { + return label; + } + } catch (e) { + console.warn("Error finding label for element:", e); + } + } + + if (element.hasAttribute("name")) { + return element.getAttribute("name"); + } + + if (element.parentElement && element.parentElement.tagName == "LABEL") { + return element.parentElement.innerText; + } + + // Check for alt text or titles + if (element.hasAttribute("alt")) { + return element.getAttribute("alt") + } + + if (element.hasAttribute("title")) { + return element.getAttribute("title") + } + + return trimmedInnerText(element); + }; + + let getApproximateAriaRole = function (element) { + let tag = element.tagName.toLowerCase(); + if (tag == "input" && element.hasAttribute("type")) { + tag = tag + ", type=" + element.getAttribute("type"); + } + + if (element.hasAttribute("role")) { + return [element.getAttribute("role"), tag]; + } + else if (tag in roleMapping) { + return [roleMapping[tag], tag]; + } + else { + return ["", tag]; + } + }; + + /** + * Gets information about all interactive elements including their: + * - Position and dimensions + * - ARIA roles and names + * - Tag names + * - Scrollability + * + * @returns {Object} Map of element IDs to their properties + */ + let getInteractiveRects = function () { + let elements = labelElements(getInteractiveElements()); + let results = {}; + for (let i = 0; i < elements.length; i++) { + let key = elements[i].getAttribute("__elementId"); + let rects = elements[i].getBoundingClientRect(); + + // Skip options unless their select is focused + if (elements[i].tagName.toLowerCase() === "option") { + + let select_focused = false; + let select = elements[i].closest("select"); + if (select && select.hasAttribute("__elementId") && + getFocusedElementId() === select.getAttribute("__elementId")) { + select_focused = true; + } + // check if option is visible without select being focused + let option_visible = false; + if (isVisible(elements[i])) { + option_visible = true; + } + // check if select is expanded even if not focused + let select_expanded = false; + if (select && select.hasAttribute("open")) { + select_expanded = true; + } + if (!(select_focused || option_visible || select_expanded)) { + continue; + } + } + + let ariaRole = getApproximateAriaRole(elements[i]); + let ariaName = getApproximateAriaName(elements[i]); + let vScrollable = elements[i].scrollHeight - elements[i].clientHeight >= 1; + + let record = { + "tag_name": ariaRole[1], + "role": ariaRole[0], + "aria-name": ariaName, + "v-scrollable": vScrollable, + "rects": [] + }; + + if (rects.length > 0) { + for (const rect of rects) { + let x = rect.left + rect.width / 2; + let y = rect.top + rect.height / 2; + if (isTopmost(elements[i], x, y)) { + record["rects"].push(JSON.parse(JSON.stringify(rect))); + } + } + } + else { + record["rects"].push(JSON.parse(JSON.stringify(rects))); + } + + results[key] = record; + } + return results; + }; + + /** + * Gets current viewport information including dimensions and scroll positions + * @returns {Object} Viewport properties + */ + let getVisualViewport = function () { + let vv = window.visualViewport; + let de = document.documentElement; + return { + "height": vv ? vv.height : 0, + "width": vv ? vv.width : 0, + "offsetLeft": vv ? vv.offsetLeft : 0, + "offsetTop": vv ? vv.offsetTop : 0, + "pageLeft": vv ? vv.pageLeft : 0, + "pageTop": vv ? vv.pageTop : 0, + "scale": vv ? vv.scale : 0, + "clientWidth": de ? de.clientWidth : 0, + "clientHeight": de ? de.clientHeight : 0, + "scrollWidth": de ? de.scrollWidth : 0, + "scrollHeight": de ? de.scrollHeight : 0 + }; + }; + + let _getMetaTags = function () { + let meta = document.querySelectorAll("meta"); + let results = {}; + for (let i = 0; i < meta.length; i++) { + let key = null; + if (meta[i].hasAttribute("name")) { + key = meta[i].getAttribute("name"); + } + else if (meta[i].hasAttribute("property")) { + key = meta[i].getAttribute("property"); + } + else { + continue; + } + if (meta[i].hasAttribute("content")) { + results[key] = meta[i].getAttribute("content"); + } + } + return results; + }; + + let _getJsonLd = function () { + let jsonld = []; + let scripts = document.querySelectorAll('script[type="application/ld+json"]'); + for (let i = 0; i < scripts.length; i++) { + jsonld.push(scripts[i].innerHTML.trim()); + } + return jsonld; + }; + + // From: https://www.stevefenton.co.uk/blog/2022/12/parse-microdata-with-javascript/ + let _getMicrodata = function () { + function sanitize(input) { + return input.replace(/\s/gi, ' ').trim(); + } + + function addValue(information, name, value) { + if (information[name]) { + if (typeof information[name] === 'array') { + information[name].push(value); + } else { + const arr = []; + arr.push(information[name]); + arr.push(value); + information[name] = arr; + } + } else { + information[name] = value; + } + } + + function traverseItem(item, information) { + const children = item.children; + + for (let i = 0; i < children.length; i++) { + const child = children[i]; + + if (child.hasAttribute('itemscope')) { + if (child.hasAttribute('itemprop')) { + const itemProp = child.getAttribute('itemprop'); + const itemType = child.getAttribute('itemtype'); + + const childInfo = { + itemType: itemType + }; + + traverseItem(child, childInfo); + + itemProp.split(' ').forEach(propName => { + addValue(information, propName, childInfo); + }); + } + + } else if (child.hasAttribute('itemprop')) { + const itemProp = child.getAttribute('itemprop'); + itemProp.split(' ').forEach(propName => { + if (propName === 'url') { + addValue(information, propName, child.href); + } else { + addValue(information, propName, sanitize(child.getAttribute("content") || child.content || child.textContent || child.src || "")); + } + }); + traverseItem(child, information); + } else { + traverseItem(child, information); + } + } + } + + const microdata = []; + + document.querySelectorAll("[itemscope]").forEach(function (elem, i) { + const itemType = elem.getAttribute('itemtype'); + const information = { + itemType: itemType + }; + traverseItem(elem, information); + microdata.push(information); + }); + + return microdata; + }; + + let getPageMetadata = function () { + let jsonld = _getJsonLd(); + let metaTags = _getMetaTags(); + let microdata = _getMicrodata(); + let results = {} + if (jsonld.length > 0) { + try { + results["jsonld"] = JSON.parse(jsonld); + } + catch (e) { + results["jsonld"] = jsonld; + } + } + if (microdata.length > 0) { + results["microdata"] = microdata; + } + for (let key in metaTags) { + if (metaTags.hasOwnProperty(key)) { + results["meta_tags"] = metaTags; + break; + } + } + return results; + }; + + /** + * Extracts all visible text content from the viewport + * Preserves basic formatting with newlines for block elements + * @returns {string} Visible text content + */ + let getVisibleText = function () { + // Get the window's current viewport boundaries + const viewportHeight = window.innerHeight || document.documentElement.clientHeight; + const viewportWidth = window.innerWidth || document.documentElement.clientWidth; + + let textInView = ""; + const walker = document.createTreeWalker( + document.body, + NodeFilter.SHOW_TEXT, + null, + false + ); + + while (walker.nextNode()) { + const textNode = walker.currentNode; + // Create a range to retrieve bounding rectangles of the current text node + const range = document.createRange(); + range.selectNodeContents(textNode); + + const rects = range.getClientRects(); + + // Check if any rect is inside (or partially inside) the viewport + for (const rect of rects) { + const isVisible = + rect.width > 0 && + rect.height > 0 && + rect.bottom >= 0 && + rect.right >= 0 && + rect.top <= viewportHeight && + rect.left <= viewportWidth; + + if (isVisible) { + textInView += textNode.nodeValue.replace(/\s+/g, " "); + // Is the parent a block element? + if (textNode.parentNode) { + const parent = textNode.parentNode; + const style = window.getComputedStyle(parent); + if (["inline", "hidden", "none"].indexOf(style.display) === -1) { + textInView += "\n"; + } + } + break; // No need to check other rects once found visible + } + } + } + + // Remove blank lines from textInView + textInView = textInView.replace(/^\s*\n/gm, "").trim().replace(/\n+/g, "\n"); + return textInView; + }; + + // Public API + return { + getInteractiveRects: getInteractiveRects, + getVisualViewport: getVisualViewport, + getFocusedElementId: getFocusedElementId, + getPageMetadata: getPageMetadata, + }; +})(); \ No newline at end of file diff --git a/src/fara/environments/playwright/playwright_controller.py b/src/fara/environments/playwright/playwright_controller.py new file mode 100644 index 0000000..6b002fb --- /dev/null +++ b/src/fara/environments/playwright/playwright_controller.py @@ -0,0 +1,1187 @@ +import asyncio +import base64 +import os +import logging +import functools +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Tuple, + Union, + TypeVar, + Awaitable, + cast, +) + +from playwright._impl._errors import Error as PlaywrightError +from playwright._impl._errors import TimeoutError, TargetClosedError +from playwright.async_api import Download, Page +from playwright.async_api import TimeoutError as PlaywrightTimeoutError +from ._types import ( + InteractiveRegion, + VisualViewport, + interactiveregion_from_dict, + visualviewport_from_dict, +) +from .webpage_text_utils import WebpageTextUtilsPlaywright + +# Some of the Code for clicking coordinates and keypresses adapted from https://github.com/openai/openai-cua-sample-app/blob/main/computers/base_playwright.py +# Copyright 2025 OpenAI - MIT License +from ...agents.key_mapping import CUA_KEY_TO_PLAYWRIGHT_KEY + +F = TypeVar("F", bound=Callable[..., Awaitable[Any]]) + + +def handle_target_closed(max_retries: int = 2, timeout_secs: int = 30): + """ + Decorator to handle TargetClosedError and tunnel connection errors by attempting to recover the page. + + Args: + max_retries: Maximum number of retry attempts + timeout_secs: Timeout for page operations during recovery + """ + + def decorator(func: F) -> F: + @functools.wraps(func) + async def wrapper(*args, **kwargs): + # Extract the page object - assume it's the first argument after self + logger = args[0].logger + page = None + if len(args) >= 2 and hasattr( + args[1], "url" + ): # Check if second arg looks like a Page + page = args[1] + + retries = 0 + last_error = None + + while retries <= max_retries: + try: + return await func(*args, **kwargs) + except (TargetClosedError, PlaywrightError) as e: + # Check if this is a tunnel connection error + is_tunnel_error = "net::ERR_TUNNEL_CONNECTION_FAILED" in str(e) + is_target_closed = isinstance( + e, TargetClosedError + ) or "Target page, context or browser has been closed" in str(e) + + if not (is_tunnel_error or is_target_closed): + # Not an error we handle, re-raise + raise e + + last_error = e + retries += 1 + + if retries > max_retries: + raise e + + if page is None: + # Can't recover without page reference + raise e + + error_type = ( + "tunnel connection" if is_tunnel_error else "target closed" + ) + logger.warning( + f"{error_type} error in {func.__name__}, attempting recovery (retry {retries}/{max_retries})" + ) + + try: + # Attempt to recover the page + await _recover_page(page, timeout_secs, logger) + # Small delay before retry + await asyncio.sleep(0.5) + except Exception as recovery_error: + logger.error(f"Page recovery failed: {recovery_error}") + # If recovery fails, raise the original error + raise e from recovery_error + + # This shouldn't be reached, but just in case + raise last_error + + return wrapper + + return decorator + + +async def _recover_page(page: Page, timeout_secs: int = 30, logger=None) -> None: + """ + Attempt to recover a closed page by reloading it. + + Args: + page: The Playwright page object to recover + timeout_secs: Timeout for recovery operations + """ + logger = logger or logging.getLogger("playwright_controller") + try: + # First, try to check if the page is still responsive + await page.evaluate("1", timeout=1000) + # If we get here, the page is actually fine + return + except Exception: + # Page is indeed problematic, attempt recovery + pass + + # If the underlying browser is gone (BB session died, CDP dropped), + # reload/goto on this page will both fail with the same TargetClosed — + # ~60s wasted per recovery. Bail out early. + browser = page.context.browser if page.context else None + if browser is not None and not browser.is_connected(): + raise Exception( + "playwright_controller._recover_page(): browser disconnected, recovery impossible" + ) + + try: + # Stop any ongoing navigation + await page.evaluate("window.stop()", timeout=2000) + except Exception: + # Ignore errors from window.stop() + pass + + try: + # Try to reload the page + await page.reload(timeout=timeout_secs * 1000, wait_until="commit") + logger.info("playwright_controller._recover_page(): Page recovery successful") + except Exception as e: + logger.error(f"playwright_controller._recover_page(): Page reload failed: {e}") + + # Try alternative recovery: navigate to current URL + try: + current_url = page.url + if current_url and current_url != "about:blank": + await page.goto( + current_url, + timeout=timeout_secs * 1000, + wait_until="commit", + ) + logger.info( + "playwright_controller._recover_page(): Page recovery via goto successful" + ) + else: + raise Exception( + "playwright_controller._recover_page(): No valid URL to navigate to" + ) + except Exception as goto_error: + raise Exception( + f"playwright_controller._recover_page(): All recovery methods failed. Reload error: {e}, Goto error: {goto_error}" + ) + + +# Enhanced version that can handle browser context recreation +def handle_target_closed_with_context(max_retries: int = 2, timeout_secs: int = 30): + """ + Enhanced decorator that can also handle browser context recreation. + Use this for critical operations where you have access to the browser context. + """ + + def decorator(func: F) -> F: + @functools.wraps(func) + async def wrapper(*args, **kwargs): + logger = args[0].logger + page = None + if len(args) >= 2 and hasattr(args[1], "url"): + page = args[1] + + retries = 0 + last_error = None + + while retries <= max_retries: + try: + return await func(*args, **kwargs) + except (TargetClosedError, PlaywrightError) as e: + # Check if this is a tunnel connection error + is_tunnel_error = "net::ERR_TUNNEL_CONNECTION_FAILED" in str(e) + is_target_closed = isinstance( + e, TargetClosedError + ) or "Target page, context or browser has been closed" in str(e) + + if not (is_tunnel_error or is_target_closed): + # Not an error we handle, re-raise + raise e + + last_error = e + retries += 1 + + if retries > max_retries: + raise e + + if page is None: + raise e + + error_type = ( + "tunnel connection" if is_tunnel_error else "target closed" + ) + logger.warning( + f"playwright_controller.handle_target_closed_with_context(): {error_type} error in {func.__name__}, attempting enhanced recovery (retry {retries}/{max_retries})" + ) + + try: + # Check if the browser context is still alive + context = page.context + browser = context.browser + + if browser and not browser.is_connected(): + # Browser connection is lost - this is a more serious issue + logger.error( + "playwright_controller.handle_target_closed_with_context(): Browser connection lost - cannot recover automatically" + ) + raise e + + # Try basic recovery first + await _recover_page(page, timeout_secs) + await asyncio.sleep(0.5) + + except Exception as recovery_error: + logger.error( + f"playwright_controller.handle_target_closed_with_context(): Enhanced page recovery failed: {recovery_error}" + ) + raise e from recovery_error + + raise last_error + + return wrapper + + return decorator + + +class PlaywrightController: + def __init__( + self, + animate_actions: bool = False, + downloads_folder: Optional[str] = None, + viewport_width: int = 1440, + viewport_height: int = 900, + _download_handler: Optional[Callable[[Download], None]] = None, + to_resize_viewport: bool = True, + single_tab_mode: bool = False, + sleep_after_action: int = 10, + timeout_load: int = 1, + logger=None, + ) -> None: + """ + A controller for Playwright to interact with web pages. + animate_actions: If True, actions will be animated. + downloads_folder: The folder to save downloads to. + viewport_width: The width of the viewport. + viewport_height: The height of the viewport. + _download_handler: A handler for downloads. + to_resize_viewport: If True, the viewport will be resized. + single_tab_mode (bool): If True, forces navigation to happen in the same tab rather than opening new tabs/windows. + + """ + self.animate_actions = animate_actions + self.downloads_folder = downloads_folder + self.viewport_width = viewport_width + self.viewport_height = viewport_height + self._download_handler = _download_handler + self.to_resize_viewport = to_resize_viewport + self.single_tab_mode = single_tab_mode + self._sleep_after_action = sleep_after_action + self._timeout_load = timeout_load + self.logger = logger or logging.getLogger("playwright_controller") + + # Set up the download handler + + self._page_script: str = "" + self.last_cursor_position: Tuple[float, float] = (0.0, 0.0) + self._text_utils = WebpageTextUtilsPlaywright() + + # Read page_script + with open( + os.path.join(os.path.abspath(os.path.dirname(__file__)), "page_script.js"), + "rt", + ) as fh: + self._page_script = fh.read() + + async def sleep(self, page: Page, duration: Union[int, float]) -> None: + await asyncio.sleep(duration) + + @handle_target_closed() + async def get_interactive_rects(self, page: Page) -> Dict[str, InteractiveRegion]: + await self._ensure_page_ready(page) + # Read the regions from the DOM + try: + await page.evaluate(self._page_script) + except Exception: + pass + result = cast( + Dict[str, Dict[str, Any]], + await page.evaluate("MultimodalWebSurfer.getInteractiveRects();"), + ) + + # Convert the results into appropriate types + assert isinstance(result, dict) + typed_results: Dict[str, InteractiveRegion] = {} + for k in result: + assert isinstance(k, str) + typed_results[k] = interactiveregion_from_dict(result[k]) + + return typed_results + + @handle_target_closed() + async def get_visual_viewport(self, page: Page) -> VisualViewport: + await self._ensure_page_ready(page) + try: + await page.evaluate(self._page_script) + except Exception: + self.logger.warning("Failed to inject page script for visual viewport") + return visualviewport_from_dict({}) + return visualviewport_from_dict( + await page.evaluate("MultimodalWebSurfer.getVisualViewport();") + ) + + @handle_target_closed() + async def get_focused_rect_id(self, page: Page) -> str: + await self._ensure_page_ready(page) + try: + await page.evaluate(self._page_script) + except Exception: + pass + result = await page.evaluate("MultimodalWebSurfer.getFocusedElementId();") + return str(result) + + @handle_target_closed() + async def get_page_metadata(self, page: Page) -> Dict[str, Any]: + assert page is not None + attempts = 3 + while attempts > 0: + try: + try: + await self._ensure_page_ready(page) + await page.evaluate(self._page_script) + except Exception: + pass + result = await page.evaluate("MultimodalWebSurfer.getPageMetadata();") + assert isinstance(result, dict) + return cast(Dict[str, Any], result) + except Exception as e: + self.logger.error( + f"Error: playwrite_controller.get_page_metadata: {str(e)},\nattempting again..." + ) + attempts -= 1 + await asyncio.sleep(0.5) + + @handle_target_closed() + async def on_new_page(self, page: Page) -> None: + assert page is not None + # bring page to front just in case + await page.bring_to_front() + page.on("download", self._download_handler) # type: ignore + if self.to_resize_viewport and self.viewport_width and self.viewport_height: + await page.set_viewport_size( + {"width": self.viewport_width, "height": self.viewport_height} + ) + + await self.sleep(page, 0.2) + await page.add_init_script( + path=os.path.join( + os.path.abspath(os.path.dirname(__file__)), "page_script.js" + ) + ) + try: + await page.wait_for_load_state("domcontentloaded", timeout=60000) + except PlaywrightTimeoutError: + self.logger.error("WARNING: Page load timeout, page might not be loaded") + # stop page loading + await page.evaluate("window.stop()") + + @handle_target_closed() + async def _ensure_page_ready(self, page: Page) -> None: + assert page is not None + await self.on_new_page(page) + + @handle_target_closed() + async def get_screenshot(self, page: Page, path: str | None = None) -> bytes: + """ + Capture a screenshot of the current page. + + Args: + page (Page): The Playwright page object. + path (str, optional): The file path to save the screenshot. If None, the screenshot will be returned as bytes. Default: None + """ + await self._ensure_page_ready(page) + try: + screenshot = await page.screenshot(path=path, timeout=15000) + return screenshot + except Exception: + await page.evaluate("window.stop()") + # try again + screenshot = await page.screenshot(path=path, timeout=15000) + return screenshot + + @handle_target_closed() + async def back(self, page: Page) -> None: + await self._ensure_page_ready(page) + await page.go_back() + + @handle_target_closed() + async def visit_page(self, page: Page, url: str) -> Tuple[bool, bool]: + await self._ensure_page_ready(page) + reset_prior_metadata_hash = False + reset_last_download = False + try: + # Regular webpage + await page.goto(url, wait_until="commit") + reset_prior_metadata_hash = True + except Exception as e_outer: + # Downloaded file + if self.downloads_folder and "net::ERR_ABORTED" in str(e_outer): + async with page.expect_download() as download_info: + try: + await page.goto(url, wait_until="commit") + except Exception as e_inner: + if "net::ERR_ABORTED" in str(e_inner): + pass + else: + raise e_inner + download = await download_info.value + fname = os.path.join( + self.downloads_folder, download.suggested_filename + ) + await download.save_as(fname) + message = f"

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