agensflow-langgraph

agensflow-langgraph

Decorator-based routing for LangGraph agents. Learn which model in your declared pool each node of your graph should use — automatically, per node, from feedback.

Status: v0.1.3 (alpha).

The free-tier bundle

Everything needed to run an adaptive MAS end-to-end, no hosted service, no external dependencies:

Try it in Colab

Two notebooks, one warm-start path each. Both run the same 6-node security-domain MAS on the same paper task; only Section 4 (the starter import) differs.

Both boot the policy server in-process (no separate uvicorn), issue an API key, import the chosen starter, build a real 6-node MAS with verifier gate + revision loop, and run against OpenRouter. Runtime ~5-8 min. Cost ~$1-2.

Install

pip install agensflow-langgraph
# For the notebook / examples:
pip install agensflow-mcp langchain-openai jupyter

60-second example

from langgraph.graph import StateGraph
from langchain_openai import ChatOpenAI
from agensflow_langgraph import agensflow, record_reward

# OpenRouter unifies every provider under one API key — one env var, many models
def or_model(model_id: str):
    return ChatOpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key=os.environ["OPENROUTER_API_KEY"],
        model=model_id,
    )

# Declare a per-node pool. The substrate learns which arm wins.
@agensflow(pool={
    "cheap":    or_model("openai/gpt-4o-mini"),
    "balanced": or_model("openai/gpt-4o"),
    "deep":     or_model("anthropic/claude-sonnet-4"),
})
async def classify_intent(state, model, config=None):
    return {"messages": [await model.ainvoke(state["messages"])]}

graph = StateGraph(State)
graph.add_node("classify_intent", classify_intent)
# ... rest of your graph unchanged

# Attach an explicit reward at the graph terminal
def terminal(state):
    record_reward(thread_id=state.get("thread_id"), quality=0.85)
    return {...}

Every model call inside a decorated node routes through the policy server; UCB1 over per-node signatures picks the pool arm. Cost + tokens are captured via LangChain's AIMessage.usage_metadata callback path.

Two topology examples

• examples/evidence_heavy_mas/ — linear planner → memory → solver → verifier → evaluator with a verifier-gate revision loop. Exercises conditional edges + revision cycles.
• examples/parallel_critic_mas/ — solver fans out to critic + verifier in parallel, evaluator merges both signals. Exercises parallel node execution + fan-in state reducers.

Both use real ChatOpenAI(base_url=openrouter) calls, structured Pydantic handoffs via .with_structured_output(method="function_calling"), and InMemorySaver checkpointing.

Configuration

export AGENSFLOW_SERVER_URL="https://mcp.agensflow.ai"   # or your self-hosted URL
export AGENSFLOW_API_KEY="agf_..."                        # bearer token
export OPENROUTER_API_KEY="sk-or-..."                     # for the examples

Or pass them per-decorator:

@agensflow(pool={...}, server_url="http://localhost:8000", tenant_key="agf_...")

Documentation

• Integration guide — fail-open behavior, three reward paths (explicit / verifier / OpenRouter judge), signature derivation, idempotency, warm-start, streaming, checkpointer + thread_id, real-model cost capture
• Starter policies — how to import a converged policy for warm-start
• Individual example READMEs in examples/*/README.md

License

Apache-2.0