diff --git a/docs/products/franklin.md b/docs/products/franklin.md index a397ed6..3c13a2a 100644 --- a/docs/products/franklin.md +++ b/docs/products/franklin.md @@ -59,7 +59,7 @@ Franklin is the autonomous agent on top of the BlockRun stack — it uses the sa ::::cards :::card{title="ClawRouter" href="routing/clawrouter.md" icon="Route"} -The router Franklin uses — 15-dimension scoring plus portfolio ranking picks the cheapest capable model. +The router Franklin uses — 15-dimension scoring plus portfolio ranking picks the cheapest capable model. ::: :::card{title="BlockRun MCP" href="../mcp/blockrun-mcp.md" icon="Terminal"} diff --git a/docs/products/routing/clawrouter.md b/docs/products/routing/clawrouter.md index b868d44..6075465 100644 --- a/docs/products/routing/clawrouter.md +++ b/docs/products/routing/clawrouter.md @@ -71,9 +71,9 @@ No external API calls. No LLM inference in the classification step. Pure keyword Steps 4 and 7 are the V3 portfolio layer: the tier says how much capability the request needs, the task type says what *kind* of work it is, and the portfolio ranks the eligible models against calibrated per-task evidence. The tier primary is a starting point, not the answer — a code-agent turn and a multiple-choice question in the same tier get different models. -### 15-Dimension Scoring +### 15-Dimension Scoring -The classifier reads the prompt and scores it across 15 weighted dimensions: +The classifier reads the prompt and scores it across 15 weighted dimensions: | Dimension | Weight | Detects | |---|---|---| @@ -163,7 +163,7 @@ Default `auto` profile primaries (cost-balanced; switch to `free` profile for $0 | **COMPLEX** | google/gemini-3.1-pro | Advanced reasoning, research, long documents | | **REASONING** | xai/grok-4-1-fast-reasoning | Math, logic, proofs | -The primary is where the tier starts, not where the request necessarily lands: the portfolio ranks every capability-eligible candidate for the detected task, so a tool-calling turn and a proof in the same tier resolve to different models. Switch to the `free` profile for $0 routing across the 5 free models. +The primary is where the tier starts, not where the request necessarily lands: the portfolio ranks every capability-eligible candidate for the detected task, so a tool-calling turn and a proof in the same tier resolve to different models. Switch to the `free` profile for $0 routing across the 5 free models. ## Smart Routing Examples @@ -179,7 +179,7 @@ The primary is where the tier starts, not where the request necessarily lands: t ### 100% Local Routing -- 15-dimension weighted scoring runs on your machine in <1ms +- 15-dimension weighted scoring runs on your machine in <1ms - No external API calls for routing decisions - Full privacy - your prompts never leave your machine for routing @@ -195,7 +195,7 @@ Access all major providers through one wallet: - **Z.AI**: GLM-5.2 (flagship, 1M context), GLM-5.1, GLM-5, GLM-5 Turbo - **Moonshot**: Kimi K3 (flagship, 1M context, image + text), Kimi K2.7 (256K, image + video) - **MiniMax**: MiniMax M3 -- **Free tier (all FREE)**: 5 NVIDIA-hosted chat, reasoning and vision models with no per-token charge +- **Free tier (all FREE)**: 5 NVIDIA-hosted chat, reasoning and vision models with no per-token charge [View all models →](../intelligence/pricing.md) diff --git a/docs/sdks/python.md b/docs/sdks/python.md index 3f83b1a..0d46c56 100644 --- a/docs/sdks/python.md +++ b/docs/sdks/python.md @@ -117,13 +117,13 @@ print(f"Paying from: {address}") ## Smart Routing (Router Core) -**Save 88% on LLM costs automatically.** +**Save 88% on LLM costs automatically.** Routing runs on [Router Core](https://github.com/BlockRunAI/router-core) — the same engine the TypeScript SDK and the BlockRun gateway use, so an identical request routes identically everywhere. Decisions are local (<1ms, no extra model call): your prompts never leave your machine to be routed. Three stages: -1. **Classify** — 15 weighted dimensions map the request onto a capability tier, and a task classifier labels the shape of the work (`chat`, `code_edit`, `code_agent`, `tool_agent`, `reasoning_math`, `long_context`, `extraction`, `vision`, …). +1. **Classify** — 15 weighted dimensions map the request onto a capability tier, and a task classifier labels the shape of the work (`chat`, `code_edit`, `code_agent`, `tool_agent`, `reasoning_math`, `long_context`, `extraction`, `vision`, …). 2. **Filter** — capability constraints are hard filters. A model that cannot hold the conversation, emit the requested `max_tokens`, call tools, or read images is dropped *before* scoring, so the router never picks a model the request would fail on. 3. **Rank** — survivors are scored on task affinity, cost, speed and reliability. The winner serves the request; the rest become the fallback chain, walked automatically on a timeout, a saturated upstream (429) or a 5xx. @@ -186,7 +186,7 @@ response = client.chat_completion("blockrun/auto", messages) | Profile | Behavior | Best For | |---------|----------|----------| -| `"free"` | Only the 5 $0 NVIDIA models — no wallet needed | Development, testing | +| `"free"` | Only the 5 $0 NVIDIA models — no wallet needed | Development, testing | | `"eco"` | Cheapest capable model per tier | Bulk processing | | `"auto"` | Balances quality and cost (default) | Production workloads | | `"premium"` | Top-tier models | Critical tasks | @@ -207,7 +207,7 @@ result = client.smart_chat("Review this contract for legal issues...", routing_p ### Capability tiers -The classifier places every request in one of 4 tiers. Under `auto`, the tier primary is the starting point — the portfolio then ranks the eligible candidates and may promote a better-suited model for the task. +The classifier places every request in one of 4 tiers. Under `auto`, the tier primary is the starting point — the portfolio then ranks the eligible candidates and may promote a better-suited model for the task. | Tier | Auto primary | Use Case | |------|--------------|----------| diff --git a/docs/sdks/typescript.md b/docs/sdks/typescript.md index 81e2372..b1a5f3e 100644 --- a/docs/sdks/typescript.md +++ b/docs/sdks/typescript.md @@ -130,7 +130,7 @@ console.log(`Paying from: ${address}`); **Save 88% on LLM costs automatically.** -The `smartChat()` method routes each request on [Router Core](https://github.com/BlockRunAI/router-core) — 15 weighted dimensions classify the request, capability constraints are applied as hard filters, and the surviving candidates are ranked on task affinity, cost, speed and reliability. Decisions run locally in <1ms — your prompts never leave your machine for routing, and no extra model call is made to decide. +The `smartChat()` method routes each request on [Router Core](https://github.com/BlockRunAI/router-core) — 15 weighted dimensions classify the request, capability constraints are applied as hard filters, and the surviving candidates are ranked on task affinity, cost, speed and reliability. Decisions run locally in <1ms — your prompts never leave your machine for routing, and no extra model call is made to decide. ### Basic Usage diff --git a/docs/sdks/xrpl.md b/docs/sdks/xrpl.md index c9fae05..420469f 100644 --- a/docs/sdks/xrpl.md +++ b/docs/sdks/xrpl.md @@ -141,7 +141,7 @@ print(f"Paying from: {address}") **Save up to 94% on LLM costs automatically.** -The `smart_chat()` method routes each request on [Router Core](https://github.com/BlockRunAI/router-core) — 15 weighted dimensions classify the request, capability constraints are applied as hard filters, and the surviving candidates are ranked on task affinity, cost, speed and reliability. Decisions run locally in <1ms — your prompts never leave your machine for routing, and no extra model call is made to decide. +The `smart_chat()` method routes each request on [Router Core](https://github.com/BlockRunAI/router-core) — 15 weighted dimensions classify the request, capability constraints are applied as hard filters, and the surviving candidates are ranked on task affinity, cost, speed and reliability. Decisions run locally in <1ms — your prompts never leave your machine for routing, and no extra model call is made to decide. ### Basic Usage