reasonblocks 0.1.0

Drop-in agent observability + steering for LangChain, LangGraph, OpenAI Agents SDK, Claude Agent SDK, and the Anthropic Messages API.

ReasonBlocks SDK

Python SDK that integrates ReasonBlocks into LangChain 1.0 agents as middleware. Scores each agent step, injects steering signals and E-traces, and optionally switches models based on difficulty.

Installation

pip install reasonblocks

Quick Start

from reasonblocks import ReasonBlocks

rb = ReasonBlocks(api_key="rb_live_...")

agent = create_agent(
    model="anthropic:claude-sonnet-4-20250514",
    tools=[...],
    system_prompt="...",
    middleware=[rb.middleware()],
)

One import. One init. One middleware addition.

Tagging runs for the dashboard

rb.middleware() accepts identifying metadata that lands on the dashboard runs row:

agent = create_agent(
    ...,
    middleware=[rb.middleware(
        org_id="6d3f...",         # uuid; "default" if omitted
        project_id="a91b...",     # uuid; "default" if omitted
        run_id="my-run-1",        # auto-generated if omitted
        agent_name="bugfixer",    # free-form filter key
        task="fix the TypeError",
        model="claude-sonnet-4-20250514",
        framework="langchain",
        codebase_id="myrepo@sha:abc123",
    )],
)

The dashboard's Quickstart page (/platform/dashboard/quickstart) shows the user's actual org_id / project_id next to a copy-pasteable snippet — the easiest way to get the values without hardcoding.

Configuration

rb = ReasonBlocks(
    api_key="rb-...",
    token_budget=100_000,
    monitor_names=["loop", "confidence", "evidence", "budget", "strategy_exhaustion"],
    fsm_thresholds={
        "fast_threshold": 0.2,
        "slow_threshold": 0.6,
        "skip_threshold": 0.85,
    },
    model_routing={
        "FAST": "anthropic:claude-haiku-4-5-20251001",
        "SLOW": "anthropic:claude-sonnet-4-20250514",
    },
    e_traces_enabled=True,
)

Architecture

The middleware hooks into two points in the LangChain agent loop:

• before_model -- scores the agent's last reasoning step, updates the FSM state, runs all monitors, retrieves E-traces, and injects steering signals as a system message.
• wrap_model_call -- overrides the model based on FSM state (if routing is configured) and tracks token usage.

Development

pip install -e ".[dev]"
pytest