LangChain

Integrate AgentSuite with LangChain via the Virtue Gateway for AgentGuard runtime enforcement, access control, MCP server scanning (where enabled), and session observability in the dashboard.

Installation

pip install agentsuite-sdk[langchain]

The [langchain] extra does not include an LLM provider — install the one you need separately (e.g. langchain-openai, langchain-anthropic, langchain-google-genai). The model string follows LangChain's init_chat_model format (e.g. openai:gpt-4o).

How It Works

• adapter.get_tools() — returns gateway tools to pass into create_agent(tools=[...]).
• adapter.create_callback() — pass to config={"callbacks": [...]} for session tracking.
• Use the adapter as an async context manager (async with adapter) to manage the MCP connection lifecycle.

Quickstart

from langchain.agents import create_agent
from langchain_core.messages import HumanMessage
from agentsuite import GatewayClient

client = GatewayClient(url="...", api_key="sk-vai-...")
adapter = client.langchain()

async with adapter:
    tools = await adapter.get_tools()
    graph = create_agent(
        model="openai:gpt-4o",
        tools=tools,
        system_prompt="You are a helpful assistant.",
    )
    result = await graph.ainvoke(
        {"messages": [HumanMessage(content="What are my open tickets?")]},
        config={"callbacks": [adapter.create_callback()]},
    )

Full runnable example: demo_langchain.py

Example Output

The agent responds to the query and prints the session ID:

View the full session trace in the VirtueAgent dashboard (Observability → Sessions):

Environment Variables

Variable	Description
VIRTUE_GATEWAY_URL	Gateway MCP endpoint URL
VIRTUE_API_KEY	VirtueAI API key
OPENAI_API_KEY	OpenAI API key (only when using an openai: model)
AGENT_MODEL	Optional; init_chat_model-style string (demo default: openai:gpt-4o)