Getting Started with LangChain

OpenBox integrates with LangChain by adding middleware to your agent. Your model, tools, prompts, and invocation pattern stay in place while OpenBox adds governance, approvals, guardrails, DID signing, and operational telemetry.

One Middleware Change

LangChain
OpenBox

agent.py
from langchain.agents import create_agent

agent = create_agent(
    model="openai:gpt-4o",
    tools=[search_web, lookup_customer],
)

result = agent.invoke({"messages": [("user", "Check this customer issue")]})

agent.py
import os

from langchain.agents import create_agent
from openbox_langchain import create_openbox_langchain_middleware

middleware = create_openbox_langchain_middleware(
    api_url=os.environ["OPENBOX_URL"],
    api_key=os.environ["OPENBOX_API_KEY"],
    agent_did=os.environ["OPENBOX_AGENT_DID"],
    agent_private_key=os.environ["OPENBOX_AGENT_PRIVATE_KEY"],
    agent_name="SupportAgent",
)

agent = create_agent(
    model="openai:gpt-4o",
    tools=[search_web, lookup_customer],
    middleware=[middleware],
)

result = agent.invoke({"messages": [("user", "Check this customer issue")]})

Newly created OpenBox agents require DID signing by default. If Require signing is disabled for the agent, omit agent_did and agent_private_key. See Agent DID Identity for the required configuration.

Choose Your Path

LangChain 101

Get the LangChain concepts that matter for OpenBox before you wire governance into a real agent.

Wrap an Existing Agent

Add OpenBox to an existing LangChain codebase without rewriting your model, tools, or prompts.

What OpenBox Captures

From a single middleware integration point, OpenBox captures:

Agent lifecycle events for each run
Model call lifecycle events, including token metadata when the provider returns it
Tool call lifecycle events for governed tool execution
User prompt signals for auditability
HTTP, database, file, and traced-function telemetry
Governance decisions, approvals, and guardrail outcomes

What To Expect In The UI

After integration, OpenBox gives you:

A run timeline for agent, model, and tool events
Policy and guardrail decisions on governed boundaries
Session replay with runtime context
Model and token usage when provider metadata is available
Tool health metrics for agents that actually execute tools

Next Steps

Read LangChain 101 if you want the conceptual model first.
Use Wrap an Existing Agent if you already have LangChain in production.
Read LangChain SDK (Python) for the full SDK reference.

One Middleware Change​

Choose Your Path​

LangChain 101​

Wrap an Existing Agent​

What OpenBox Captures​

What To Expect In The UI​

Next Steps​