Framework examples

Agent Assembly governs third-party agent frameworks without those frameworks needing to be aware of it. You call init_assembly() once; the SDK detects which supported frameworks are importable in your process and installs governance hooks for each, in priority order (see Architecture).

The universal pattern

The integration is the same for every framework — there is no per-framework setup call:

from agent_assembly import init_assembly

with init_assembly(
    gateway_url="http://localhost:7391",
    api_key="dev-key",
    agent_id="my-agent",
    mode="sdk-only",
):
    # Build and run your agent exactly as you normally would.
    # Every tool call now passes through the policy gate and is audited.
    ...

Inside the with block, the relevant adapter has monkey-patched the framework's tool-call entry points. On exit, all hooks are removed in reverse order.

Supported frameworks

Each framework below ships an adapter under agent_assembly/adapters/. The Runnable example column reflects whether a complete, validated example exists in this repository today.

Framework	Adapter	Runnable example
LangChain	agent_assembly.adapters.langchain	✅ Validated — see Quick start below (runs offline against a mock LLM).
LangGraph	agent_assembly.adapters.langgraph	⏳ Planned — adapter ships; wraps StateGraph.compile().
CrewAI	agent_assembly.adapters.crewai	⏳ Planned — adapter ships.
OpenAI Agents	agent_assembly.adapters.openai_agents	⏳ Planned — adapter ships.
Pydantic AI	agent_assembly.adapters.pydantic_ai	⏳ Planned — adapter ships.
Google ADK	agent_assembly.adapters.google_adk	⏳ Planned — adapter ships.
MCP servers	agent_assembly.adapters.mcp	⏳ Planned — adapter ships.

Adapter present vs. example present

A ⏳ row means the adapter is implemented and registered — init_assembly() will detect and hook the framework if it's installed — but a curated, end-to-end runnable example is not yet vendored in examples/. Contributions adding one are welcome; see CONTRIBUTING.md.

LangChain quick start

A governed LangChain ReAct agent that runs offline against a mock LLM — no real API keys and no network calls. Install the SDK plus LangChain:

pip install agent-assembly langchain langchain-community

from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain_community.llms import FakeListLLM
from langchain_core.prompts import PromptTemplate

from agent_assembly import init_assembly

with init_assembly(
    gateway_url="http://localhost:7391",
    api_key="dev-key",
    agent_id="quickstart-agent",
    mode="sdk-only",
):
    llm = FakeListLLM(responses=[
        "Thought: I should look up the user.\nAction: whoami\nAction Input: alice\n",
        "Thought: I have the answer.\nFinal Answer: alice is in engineering\n",
    ])
    tools = [Tool(name="whoami", func=lambda name: f"{name} is in engineering", description="who")]
    prompt = PromptTemplate.from_template(
        "Use the tools.\n{tools}\nTool names: {tool_names}\nQ: {input}\n{agent_scratchpad}"
    )
    executor = AgentExecutor(agent=create_react_agent(llm, tools, prompt), tools=tools, max_iterations=2)
    print(executor.invoke({"input": "Which team is alice on?"})["output"])

The same snippet lives in the repository README and is the canonical, validated example. See the examples/ directory for additional runnable scripts and their status.