LangGraph

Connect LangGraph to DashClaw and get your first governed action into /decisions in under 20 minutes.

Instance URL detected: https://your-dashclaw-instance.example.com

Deploy DashClaw

Get a running instance. Click the Vercel deploy button or run locally.

Already have an instance? Skip to Step 2.

Install the DashClaw Python SDK and LangGraph

Create a virtual environment and install the required packages.

Terminal

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install dashclaw==2.6.0 langgraph==1.1.3 langchain-core==1.2.21 python-dotenv

Set environment variables

Create a .env file with your DashClaw connection details. No LLM API key required for the example.

.env

DASHCLAW_BASE_URL=https://your-dashclaw-instance.example.com
DASHCLAW_API_KEY=<your-workspace-api-key>

Add a governance node to your LangGraph graph

The governance node calls DashClaw guard before the research node runs. If the guard blocks, the research node skips execution.

main.py

from dashclaw import DashClaw
from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    topic: str
    research_result: str
    governance_decision: str
    action_id: str

claw = DashClaw(
    base_url=os.environ["DASHCLAW_BASE_URL"],
    api_key=os.environ["DASHCLAW_API_KEY"],
    agent_id="langgraph-research-agent",
)

def governance_node(state: AgentState) -> AgentState:
    """Check DashClaw guard before proceeding."""
    result = claw.guard({
        "action_type": "research",
        "declared_goal": f"Research topic: {state['topic']}",
        "risk_score": 30,
    })
    decision = result.get("decision", "allow")
    if decision == "block":
        return {**state, "governance_decision": "blocked"}
    action = claw.create_action(
        "research",
        f"Research topic: {state['topic']}",
        risk_score=30,
    )
    return {**state, "governance_decision": decision, "action_id": action["action_id"]}

# Wire the graph
graph = StateGraph(AgentState)
graph.add_node("governance", governance_node)
graph.add_node("research", research_node)
graph.set_entry_point("governance")
graph.add_edge("governance", "research")
graph.add_edge("research", END)
app = graph.compile()

The governance node runs before your tool node. If the guard decision is 'block', the research node returns early. If 'allow', it proceeds and calls update_outcome when done.

Run the governed LangGraph agent

Execute the example and watch the governance flow.

Terminal

python main.py

No OPENAI_API_KEY needed — the example simulates LLM output. Only the DashClaw SDK calls are real.

See the result in DashClaw

Open your DashClaw dashboard to confirm the action was recorded.

Go to /decisions — you should see your action in the ledger with action_type 'research', agent_id 'langgraph-research-agent', and status 'completed'.

Clone the full example

The complete runnable example is in the DashClaw repo.

Terminal

git clone https://github.com/ucsandman/DashClaw.git
cd DashClaw/examples/langgraph-governed
pip install -r requirements.txt
python main.py

For production LangChain integrations, the Python SDK also includes a DashClawCallbackHandler (sdk-python/dashclaw/integrations/langchain.py) that automatically governs all LLM calls.

What success looks like

Go to /decisions — you should see your action in the ledger with action_type 'research', agent_id 'langgraph-research-agent', and status 'completed'.

Navigate to /decisions in your DashClaw instance. Your action should appear in the ledger within seconds of the agent run.

Governance as Code

Drop a guardrails.yml in your project root to enforce policies without code changes. DashClaw evaluates these rules at the guard step before any action executes.

guardrails.yml

version: 1
project: my-langgraph-agent
description: >
  Governance policy for a LangGraph research agent.
  High-risk external writes require approval.
  Low-risk reads are auto-allowed.

policies:
  - id: approve_external_writes
    description: Writing to external systems requires human approval
    applies_to:
      tools:
        - api.post
        - file.write
        - database.insert
    rule:
      require: approval

  - id: allow_research
    description: Read-only research is low risk
    applies_to:
      tools:
        - web.search
        - document.read
    rule:
      allow: true