langchain-deepagents

LangChain Deep Agents Skill

Expert assistance for building LangChain Deep Agents in Python: stateful agents with virtual filesystems, parallel subagents, tool permissions, human-in-the-loop, and deployment via LangSmith.

Reference corpus: 1473 pages of official docs in references/llms-txt.md (5.4 MB) and references/llms-full.md (10 MB). Use view references/llms-full.md when detailed implementation is needed.

When to Use This Skill

Activate when:

• Building a Deep Agent — creating a stateful agent with virtual filesystem, backends, or subagents
• Configuring subagents — setting up parallel or async subagents with permission inheritance
• Implementing human-in-the-loop — adding approval gates for sensitive tool calls
• Deploying to LangSmith — setting up langgraph.json, Agent Server, or deployment pipelines
• Tracing and evaluating — instrumenting agents with @traceable, running client.evaluate()
• Debugging LangGraph state — working with StateGraph, checkpointers, or thread state
• Using Agent Server API — managing threads, runs, assistants, crons, or streaming
• Integrating retrievers or chains — connecting vector stores, RAG pipelines, or tool middleware

Quick Reference

Create a basic Deep Agent with state and checkpointer

from langgraph.graph import StateGraph, MessagesState
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents import create_agent

# Checkpointer persists agent state across runs
checkpointer = InMemorySaver()

agent = create_agent(
    skills=[...],          # Skill middleware layers
    checkpointer=checkpointer,
)

Trace agent functions with LangSmith

from langsmith import traceable
from langsmith.schemas import Attachment
from pathlib import Path

@traceable
def my_agent_step(inputs: dict) -> dict:
    # Automatically traced in LangSmith
    return {"output": process(inputs)}

# Attach files to traces
@traceable
def analyze_file(path: Path) -> dict:
    attachment = Attachment(mime_type="text/plain", data=path.read_bytes())
    return {"result": process(attachment)}

Evaluate agent with LangSmith client

from langsmith import Client

client = Client()

def target(inputs):
    return {"output": my_agent.invoke(inputs)}

def accuracy_evaluator(run, example):
    score = evaluate_output(run.outputs, example.outputs)
    return {"key": "accuracy", "score": score}

# Non-blocking: stream results as they arrive
results = client.evaluate(
    target,
    data="my_test_dataset",
    evaluators=[accuracy_evaluator],
    blocking=False,
)
for result in results:
    print(result)

Distributed tracing across services (LangGraph)

import langsmith as ls
from langgraph.graph import StateGraph, MessagesState

# Accept trace context propagated from upstream callers
# Headers passed via config["configurable"]["langsmith-trace"]
def my_node(state: MessagesState, config: dict):
    trace_headers = config.get("configurable", {})
    with ls.trace(headers=trace_headers):
        return process(state)

Subagent permission scoping

# Subagents inherit parent permissions by default.
# Setting permissions REPLACES (does not extend) parent rules.
subagent_spec = {
    "name": "restricted-subagent",
    "permissions": [
        {"path": "/workspace", "access": "read-write"},
        # Parent's other permissions are NOT inherited
    ]
}

LangSmith custom authentication handler

from langgraph_sdk.auth import Auth

auth = Auth()

@auth.authenticate
async def handler(request):
    user = await validate_token(request.headers.get("Authorization"))
    return {
        "identity": user.id,
        "role": user.role,
        # Accessible in graph via config["configurable"]["langgraph_auth_user"]
    }

Reference Files

File	Size	Contents
references/llms-txt.md	5.4 MB	Full doc corpus — summaries of 1473 pages
references/llms-full.md	10 MB	Complete page content with all code examples
references/llms.md	104 KB	Site index — all doc URLs with descriptions
references/index.md	1 KB	Category index

To find specific content: Search references/llms.md for topic URLs, then look up full content in references/llms-full.md.

Key Deep Agents Topics (Python)

From references/llms.md — Python-specific deep agents docs at /oss/python/deepagents/:

• Overview & quickstart: /oss/python/deepagents/overview
• Subagents: /oss/python/deepagents/subagents — parallel execution, permission scoping
• Human-in-the-loop: /oss/python/deepagents/human-in-the-loop — approval gates for tool calls
• Backends & filesystem: /oss/python/deepagents/backends — CompositeBackend, virtual FS, route prefixes
• Context engineering: /oss/python/deepagents/context-engineering — managing long-running context
• Frontend (todo list pattern): /oss/python/deepagents/frontend/todo-list — useStream + custom state keys
• Data analysis example: /oss/python/deepagents/data-analysis
• Deep research example: /oss/python/deepagents/deep-research

Agent Server API Quick Reference

Operation	Method + Path
Create thread	POST /threads
Stream run	POST /threads/{id}/runs/stream
Create assistant	POST /assistants
Schedule cron	POST /crons
Store/retrieve state	PUT/GET /store/{namespace}/{key}
Health check	GET /ok
Server info	GET /info

Security Best Practices for Sandboxed Agents

• Enable human-in-the-loop for all tool calls when using sensitive credentials
• Block or restrict sandbox network access to limit data exfiltration paths
• Use narrowest possible credential scope with shortest possible lifetime
• CompositeBackend requires explicit route prefixes — path restrictions alone cannot prevent sandbox filesystem access via shell commands
• Treat all sandbox outputs as untrusted input before acting on them
• Apply middleware to filter/redact sensitive patterns in tool outputs

Updating

To re-scrape with tighter scope (deep agents section only):

skill-seekers scrape --url "https://docs.langchain.com/oss/python/deepagents/overview" --name langchain-deepagents