Lab Tracker

Lab Tracker turns the notes, photos, voice memos, and figures you already produce into a connected account of what changed, why it mattered, and what evidence supports it—so next week's meeting and next year's report start from a scientific thread already assembled. Capture evidence while you work, let AI propose where it belongs, and decide what enters the durable record yourself.

A file named 2025_12_10_Rig2_session001.nwb says when and where it was collected. It rarely says why it exists, what you expected, or what you learned. That reasoning lives on whiteboards, on paper towels, and in people's heads—and walks out the door when they do. Lab Tracker gives it a queryable home alongside projects, questions, sessions, datasets, analyses, claims, and visualizations.

Read-only demo · Deploy to Render · Documentation · Run locally

Status: Lab Tracker is at 0.1.0 and under active development. It is intended for evaluation and early research use; interfaces and deployment defaults may still change. The supported v1 surface is authoritative.

The payoff: start from an assembled record

Author and time-window filters let an authorized assistant assemble what a person committed within a period: sessions, datasets, analyses, figures, claims, and notes. That can become the evidence packet for a progress report, grant renewal, trainee meeting, lab meeting, or committee update. You supply the window—Lab Tracker does not own the meeting calendar—and the assistant starts from your curated record instead of a blank page.

The result is only as complete as what the lab captured and committed. Lab Tracker does not invent missing experiments or evidence, and assistant-written prose remains a draft to verify against the underlying graph.

From capture to durable context

The core loop is deliberately small:

1. Capture. Send a text note, photo, voice note, or photo-and-voice bundle from the browser or a paired phone. In Python, lab_tracker_client.savefig, capture_figures(), and run_context() stage figures with content hashes and git context; MATLAB has labtracker.savefig. The lt watch, lt repo, and lt hpc adapters keep durable local outboxes for folder, repository, and Slurm evidence, then sync compact records when the API is reachable. Large source artifacts can remain in their original data store.
2. AI proposes. For an individual image note, the configured vision-capable model receives the image itself and can use visible text, handwriting, plots, and diagrams as context—there is no separate OCR-to-transcript step first. Scheduled batch drafts use the available text, voice transcripts, capture hints, links, and metadata. Both paths propose typed graph changes with rationale, confidence, and source references; neither creates canonical graph records.
3. A person decides. The review assignee can edit, accept, or reject individual operations, or ask the model to revise the draft. A project owner commits the accepted operations. No AI-proposed graph change is committed until a person accepts it, and non-interactive service or automation principals cannot accept, bulk-accept, or commit. Acceptance provenance distinguishes individually selected operations from bulk acceptance.
4. Retrieve and export. Search the record, inspect the project graph, ask an MCP-capable assistant for bounded decision context, or export PROV-O / JSON-LD provenance and plaintext sidecars that can travel with the data.

Direct human work is intentionally different from AI output. A person with project contributor access can create and commit datasets, analyses, claims, and visualizations without a mandatory second-person approval step. The human gate protects the record from machine-authored changes; it is not a universal peer-review workflow.

The daily review is part of the science

The daily review is the human half of the system: time set aside to return to the work, notice what changed, test the model's interpretation, connect results to the questions that made them worth collecting, and decide what remains uncertain. That reflection is scientific work in its own right—often the part bench work leaves too little room for—so Lab Tracker is built to support attention rather than rush it.

Review proposals one at a time when they deserve close attention, or use bulk acceptance for routine items. The record preserves which review mode you used, so bulk acceptance is never represented later as individual review. Proposal preparation can run on a schedule; judgment cannot. Some reviews take minutes, and some science deserves longer.

Captured notes stay staged and visible until they are incorporated or archived with a reason. If you skip a review, the captures wait for you and the coverage gap remains inspectable instead of silently making the record look complete.

Questions are the spine, not a claim that everything is complete

Lab Tracker starts with the questions a project is trying to answer. Broad questions can branch into atomic experimental, method, control, and analysis questions, and a question may have more than one parent.

The schema enforces a few important links:

• A dataset must name one primary question; secondary question links are optional.
• An analysis must name its source dataset or datasets.
• A visualization must name its analysis.
• A claim marked supported must cite a dataset or analysis as evidence.

Other useful links remain optional. Claims do not universally have to answer a question, visualizations do not universally have to name a related claim, and notes can target the most relevant retained entity. That distinction matters: Lab Tracker makes provenance explicit where it exists without pretending that every historical record arrived fully connected.

What ships today

• Research context: projects and lab groups, role-based access, question graphs, notes, sessions, datasets, analyses, claims, visualizations, goals, and exploration records for decisions, dead ends, and pivots.
• Evidence capture: browser and paired-device capture, raw files and editable voice transcripts, Python and MATLAB figure capture, and offline-first watch-folder, repository, and HPC adapters.
• Human-gated drafting: note-scoped and batch graph drafts, scheduled or run-now drafting, accept/edit/reject review, and durable curation provenance.
• Retrieval and portability: substring search, project graph views, permission-bounded assistant context, publication-readiness checks, provenance export, and hash-verified external artifact references.

Lab Tracker owns the question-and-provenance spine. It integrates with data stores, analysis repositories, experiment trackers, and ELNs rather than trying to replace them.

Run locally

The shortest source install on macOS or Linux uses uv:

git clone https://github.com/SamuelBrudner/lab-tracker.git
cd lab-tracker
uv venv
source .venv/bin/activate
uv pip install -e .
lab-tracker serve

lab-tracker serve applies migrations, snapshots a file-backed SQLite database before migrating it, opens http://127.0.0.1:8000/app, and starts the server. Windows instructions and development dependencies are in the setup guide. Double-click macOS and Windows launchers live in deploy/launchers/.

SQLite is the convenient single-client local default. Use Postgres, enable authentication, and establish backups for a shared or hosted lab instance; see deployment options and self-hosted operations.

For a no-terminal hosted start:

Configure AI only if you want drafting

The graph and direct human workflows work without an AI provider. Graph drafting supports three server-side providers:

Provider	Draft setting	Voice transcription
OpenAI	openai	Yes
Anthropic (Claude)	anthropic or claude	No
Google (Gemini)	google or gemini	Yes

Set LAB_TRACKER_GRAPH_DRAFT_PROVIDER and the matching server-held API key. With a vision-capable model, each provider can draft directly from an individual image note or figure as well as text. Scheduled batch drafts currently use text, voice transcripts, capture hints, links, and metadata rather than image bytes. Voice transcription is a separate, explicit note action—not an automatic upload step—and currently requires OpenAI or Google; Anthropic can draft from a transcript created another way.

Any MCP-capable coding agent can read the same permission-bounded context. lab_tracker init scaffolds common client configurations, and the policy stays the same across vendors: agents may suggest and stage when asked, but they do not operate the human commit gate. Follow the AI and agent setup guide for credentials, scheduling, MCP, and verification.

Documentation

• Start and deploy: local setup · deployment options · configuration
• Capture and integrate: phone capture · watch folders · MATLAB integration · provenance export
• Understand the model: vision · review and commit · supported v1 surface
• Operate AI drafting: agent setup · scheduled review

Caveats

• This repository does not currently declare an open-source license. Public source availability should not be read as permission to redistribute it.
• Standalone OCR-to-transcript workflows, semantic/vector search, automatic transcription on every upload, and a standing system-selected extraction inbox are outside the supported v1 surface. Individual image-note drafting remains multimodal.
• Full research artifacts often remain outside Lab Tracker. External resolution is opt-in, read-only, bounded, and hash-checked; operators must configure the allowed roots or remotes.
• A shared deployment is an operated service: the lab remains responsible for provider approval, credentials, access policy, database backups, and durable file storage.