LangGraph

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

Instance URL detected: https://studio.osuite.ai

Governance context

Runtime class

Code-First Single-Agent Runtime

A self-built agent where the customer owns the orchestration code and can add guard calls directly.

Recommended surfaces

Embedded Runtime SDK / Package

Instrument customer-controlled runtimes, tools, and orchestrators from inside the code.

Control Plane

Run policy, approvals, replay, and evidence management as the operator system of record.

Typical governance range

Advisory Governance -> Approval-Orchestrated Governance -> Runtime-Enforced Governance

Self-host and future SaaS are deployment model choices for the OSuite control plane. They do not determine governance level by themselves.

Deploy OSuite

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

Already have an instance? Skip to Step 2.

Install the OSuite 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 osuite==2.6.0 langgraph==1.1.3 langchain-core==1.2.21 python-dotenv

Set environment variables

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

.env

OSUITE_BASE_URL=https://studio.osuite.ai
OSUITE_API_KEY=<your-workspace-api-key>

Add a governance node to your LangGraph graph

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

main.py

from osuite import Osuite
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 = OSuite(
    base_url=os.environ["OSUITE_BASE_URL"],
    api_key=os.environ["OSUITE_API_KEY"],
    agent_id="langgraph-research-agent",
)

def governance_node(state: AgentState) -> AgentState:
    """Check OSuite 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 OSuite SDK calls are real.

See the result in OSuite

Open your OSuite 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 OSuite repo.

Terminal

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

For production LangChain integrations, the Python SDK also includes a OSuiteCallbackHandler (sdk-python/osuite/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 OSuite 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. OSuite 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