Use ReasonBlocks with the Anthropic Messages API

run_messages_agent_loop is a batteries-included driver for Anthropic’s Messages tool-use loop. Pair it with a SteeringSession from rb.claude_messages_session() to run the full ReasonBlocks pipeline at every turn — FSM step scoring, server-side monitor steering, E1 / E2 / E3 injection, model routing, and live telemetry — all without LangChain in the dependency graph. This is the parity path with the LangChain middleware against an Anthropic-native client.

Walkthrough

Install

pip install reasonblocks anthropic

Initialize the client and an Anthropic SDK client

import os
from anthropic import Anthropic
from reasonblocks import ReasonBlocks

rb = ReasonBlocks(
    api_key=os.environ["REASONBLOCKS_API_KEY"],
    model_routing={
        "FAST": "anthropic:claude-haiku-4-5-20251001",
        "SLOW": "anthropic:claude-sonnet-4-6",
    },
)
client = Anthropic()

Model identifiers in model_routing may use the LangChain-style "anthropic:..." prefix for parity; the loop strips it before forwarding to the Messages API. Bare slugs like "claude-haiku-4-5-20251001" work too.

Build the codebase memory tools

make_claude_tools returns Anthropic-shaped tool specs plus a dispatch callable that runs the tools when the agent invokes them.

from reasonblocks import CodebaseMemory
from reasonblocks.integrations.claude_tools import make_claude_tools

memory = CodebaseMemory(
    codebase_id="my-org/my-repo",
    api_key=os.environ["REASONBLOCKS_API_KEY"],
)

tool_specs, dispatch = make_claude_tools(memory)

See Claude tools reference for the full factory signature.

Create a steering session and run the loop

rb.claude_messages_session(...) builds a SteeringSession wired with the same FSM, monitors, and injections that rb.middleware() would produce. Pass it through run_messages_agent_loop(..., session=...).

from reasonblocks.integrations.claude_tools import run_messages_agent_loop

with rb.claude_messages_session(
    agent_name="reviewer",
    task="review PR #42",
    model="claude-haiku-4-5-20251001",
    codebase_id="my-org/my-repo",
) as session:
    outcome = run_messages_agent_loop(
        client,
        model="claude-haiku-4-5-20251001",
        messages=[{"role": "user", "content": "Review the changes in PR #42."}],
        tool_specs=tool_specs,
        dispatch=dispatch,
        system="You are a senior code reviewer.",
        session=session,
    )

print(outcome["final_text"])

On every turn the loop:

Pulls the last assistant text out of the message history (the “thought”).
Calls session.begin_step(...) to score it, advance the FSM, and run server-side monitor evaluation + E-trace retrieval.
Appends a [REASONBLOCKS] block to system if anything fired.
Strips any provider: prefix from the routed model id.
Calls client.messages.create(...).
Calls session.end_step(...) with the response’s token count, tool-call names, and per-call latency.

Inspect the step log

After the run, read session.step_log for per-step difficulty, FSM state, monitors fired, injection text, model id used, tokens, and latency.

for entry in session.step_log:
    print(entry.as_dict())

What `run_messages_agent_loop` returns

{
    "final_text": "<the agent's final text response>",
    "messages":   [...],   # full message history including tool_use / tool_result turns
    "stop_reason": "end_turn" | "max_steps" | "...",
    "tool_calls": [(name, input, result), ...],
}

When session= is passed, the same data is mirrored into session.step_log per step.

Without steering

session=None (the default) runs the loop as a plain Messages API driver — no scoring, no monitors, no injection. This was the original behavior and remains supported for callers who only want the tool-use convenience.

Streaming

Streaming responses are not supported in the first cut. client.messages.create is called synchronously per turn. If you need streaming, drop down to the LangChain middleware (which supports streaming responses through LangChain’s runtime) or hand-roll the loop and call session.begin_step / session.end_step yourself.

Claude tools reference

make_claude_tools and run_messages_agent_loop API surface.

SteeringSession reference

The shared core driving every framework integration.

Getting Started

Concepts

Using ReasonBlocks

Connectors and sync

Use ReasonBlocks with the Anthropic Messages API

Walkthrough

What `run_messages_agent_loop` returns

Without steering

Streaming

Claude tools reference

SteeringSession reference

​Walkthrough

​What run_messages_agent_loop returns

​Without steering

​Streaming

​Related

Claude tools reference

SteeringSession reference

Walkthrough

What `run_messages_agent_loop` returns

Without steering

Streaming

Related