LangChain DeepAgents

Automatically capture the full multi-agent hierarchy of DeepAgents pipelines. DeepAgents builds on LangChain, so TraceRoot instruments it via the LangChain callback manager.

Install

Python
TypeScript

pip install traceroot deepagents langchain-core langchain-anthropic

npm install @traceroot-ai/traceroot deepagents @langchain/core @langchain/anthropic

Set up

Python
TypeScript

import traceroot
from traceroot import Integration

traceroot.initialize(integrations=[Integration.LANGCHAIN])

import * as lcCallbackManager from '@langchain/core/callbacks/manager';
import { TraceRoot } from '@traceroot-ai/traceroot';

TraceRoot.initialize({
  instrumentModules: { langchain: lcCallbackManager },
});

Usage

Python
TypeScript

from traceroot import observe, using_attributes
from deepagents import create_deep_agent

research_subagent = {
    "name": "research-agent",
    "description": "Searches the web to gather information on a topic.",
    "system_prompt": "You are a thorough research agent. Cite sources and organise findings clearly.",
    "tools": [web_search],
}

supervisor = create_deep_agent(
    model="claude-sonnet-4-6",
    system_prompt="You are a research supervisor. Delegate tasks to sub-agents and synthesise their outputs.",
    subagents=[research_subagent],
)

@observe(name="research_session", type="agent")
def run_research(query: str) -> str:
    result = supervisor.invoke({"messages": [{"role": "user", "content": query}]})
    messages = result.get("messages", [])
    return messages[-1].content if messages else ""

with using_attributes(user_id="demo-user", session_id="research-session"):
    report = run_research("What are the latest AI agent frameworks in 2025?")

import { observe, usingAttributes } from '@traceroot-ai/traceroot';
import { createDeepAgent, type SubAgent } from 'deepagents';
import { ChatAnthropic } from '@langchain/anthropic';
import { HumanMessage } from '@langchain/core/messages';

const llm = new ChatAnthropic({ model: 'claude-sonnet-4-20250514', temperature: 0 });

const researchSubAgent: SubAgent = {
  name: 'research-agent',
  description: 'Researches a specific topic in depth.',
  systemPrompt: 'You are a thorough research agent. Gather comprehensive, current information and cite sources.',
  tools: [internetSearch],
};

const supervisor = createDeepAgent({
  model: llm,
  tools: [internetSearch],
  systemPrompt: 'You are a research supervisor. Delegate tasks to sub-agents and synthesise their outputs.',
  subagents: [researchSubAgent],
});

await usingAttributes(
  { userId: 'demo-user', sessionId: 'research-session' },
  () =>
    observe({ name: 'research_session', type: 'agent' }, async () => {
      const result = await supervisor.invoke(
        { messages: [new HumanMessage('What are the latest AI agent frameworks in 2025?')] },
        { recursionLimit: 100 },
      );
      const messages = result.messages ?? [];
      console.log(messages[messages.length - 1]?.content);
    }),
);

Verify

Run your code and confirm the trace arrives:

In the TraceRoot UI: Navigate to Tracing > Traces in TraceRoot Cloud. You should see a trace with root span research_session, the name passed to observe.
Via the CLI: Run traceroot traces list to inspect your recent traces from the terminal.

What gets captured

Attribute	Description
Supervisor steps	Each supervisor reasoning and delegation step
Sub-agent calls	Each sub-agent invocation with its own span
Tool calls	Tool inputs and outputs within sub-agents
LLM calls	All LLM calls across the full agent hierarchy
Token usage	Aggregated across all agents and LLM calls
Cost	Aggregated when the underlying model IDs match TraceRoot’s pricing table

Limitations

DeepAgents is built on LangChain. For TypeScript projects, instrumentModules must be configured with @langchain/core/callbacks/manager.

Run the example

Clone the repo and run a complete agent end-to-end.

Python

Run the Python example

TypeScript

Run the TypeScript example

Next steps

Python SDK Reference

Explore the full Python SDK reference and instrumentation options.

TypeScript SDK Reference

Explore the full TypeScript SDK reference and configuration options.

Overview

Native

Frameworks

Model Providers

Internal Model Providers

LangChain DeepAgents

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Python

TypeScript

Next steps

Python SDK Reference

TypeScript SDK Reference

​Install

​Set up

​Usage

​Verify

​What gets captured

​Limitations

​Run the example

Python

TypeScript

​Next steps

Python SDK Reference

TypeScript SDK Reference

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Next steps