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.

Setup

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);
    }),
);

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	Total cost for the full multi-agent run

Run the example

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

Python

Run the Python example

TypeScript

Run the TypeScript example

​Setup

​Usage

​What Gets Captured

​Run the example