Setup Install Deep Agents with Braintrust and the LangChain model package you use. ```bash uv theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} uv add braintrust deepagents langchain-openai ``` ```bash pip theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} pip install braintrust deepagents langchain-openai ``` ```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} export BRAINTRUST_API_KEY="YOUR_BRAINTRUST_API_KEY" export OPENAI_API_KEY="YOUR_OPENAI_API_KEY" ``` Auto-instrumentation To trace Deep Agents without wiring callbacks into each agent, call `braintrust.auto_instrument()` before importing Deep Agents or LangChain packages. Braintrust uses the LangChain integration because Deep Agents is built on LangChain and LangGraph. ```python Python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} import braintrust braintrust.auto_instrument() braintrust.init_logger(project="My Project") from deepagents import create_deep_agent from langchain_openai import ChatOpenAI def get_weather(city: str) -> str: """Return a short weather report for a city.""" return f"The weather in {city} is sunny." model = ChatOpenAI(model="gpt-5-mini") agent = create_deep_agent( model=model, tools=[get_weather], system_prompt="You are a concise weather assistant.", name="weather-agent", ) result = agent.invoke( {"messages": [{"role": "user", "content": "What is the weather in San Francisco?"}]} ) print(result["messages"][-1].content) ``` Manual instrumentation To control which Deep Agents runs are traced, attach `BraintrustCallbackHandler` through the LangChain callback configuration. ```python Python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}} from braintrust import init_logger from braintrust.integrations.langchain import BraintrustCallbackHandler from deepagents import create_deep_agent from langchain_openai import ChatOpenAI def get_weather(city: str) -> str: """Return a short weather report for a city.""" return f"The weather in {city} is sunny." logger = init_logger(project="My Project") handler = BraintrustCallbackHandler(logger=logger) model = ChatOpenAI(model="gpt-5-mini") agent = create_deep_agent( model=model, tools=[get_weather], system_prompt="You are a concise weather assistant.", name="weather-agent", ).with_config({"callbacks": [handler]}) result = agent.invoke( {"messages": [{"role": "user", "content": "What is the weather in San Francisco?"}]} ) print(result["messages"][-1].content) ``` If you already use LangChain callbacks, add `handler` to the same `callbacks` list as your existing handlers. What Braintrust traces Braintrust captures: * Deep Agents run spans (``), with input messages, output messages, LangChain tags, and Deep Agents metadata such as `ls_integration`, `lc_agent_name`, and `lc_versions` * Chat model spans (``), with input messages, invocation parameters, output, model name, and token usage when the model provider returns LangChain usage metadata * Tool spans (``), with parsed tool inputs, outputs, metadata, and errors * Streaming timing on model spans, with `time_to_first_token` when LangChain emits new-token callbacks * Streaming timing on model spans, with `time_to_first_token` when LangChain emits new-token callbacks. Tracing resources * [Deep Agents documentation](https://docs.langchain.com/oss/python/deepagents/overview) * [Deep Agents API reference](https://reference.langchain.com/python/deepagents/) * [LangChain integration](/integrations/sdk-integrations/langchain) * [LangGraph integration](/integrations/agent-frameworks/langgraph)