deepagents-langchain

deepagents-langchain

USE FOR:

• "production-ready agent with LangGraph"
• "batteries-included coding/research agent"
• "sub-agents with isolated context windows"
• "LangChain agent framework"
• "agent with planning + filesystem + shell"
• "MCP tools in LangGraph agent" tags: [LangGraph, LangChain, agent, production, sub-agents, MCP, planning, filesystem, shell, open-source] kind: framework category: ai-agent-builder

---

What Is DeepAgents?

Production-ready, batteries-included LangGraph agent by LangChain. No manual setup — create_deep_agent() returns a fully functional agent.

• Repo: https://github.com/langchain-ai/deepagents
• Install: pip install deepagents
• Framework: LangGraph (compiled graph, streaming, persistence, checkpointing)
• LangGraph Studio compatible

---

Quick Start

pip install deepagents

from deepagents import create_deep_agent

agent = create_deep_agent()
result = agent.invoke({
    "messages": [{"role": "user", "content": "Research the latest AI agent frameworks and summarize"}]
})
print(result["messages"][-1].content)

---

Built-in Capabilities

Capability	Tools Included
Planning	write_todos — task decomposition + progress tracking
Filesystem	read, write, edit, search files
Shell	execute commands (with sandboxing)
Sub-agents	delegate tasks with isolated context windows
Context	auto-summarization, large output → file handling

---

Architecture (LangGraph)

# Returns a compiled LangGraph graph
agent = create_deep_agent()

# Supports all LangGraph features:
# - Streaming
for chunk in agent.stream({"messages": [("user", "task")]}):
    print(chunk)

# - Persistence / checkpointing
from langgraph.checkpoint.memory import MemorySaver
agent = create_deep_agent(checkpointer=MemorySaver())

# - LangGraph Studio compatibility (visual debug)

---

Customization

from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic

# Custom model
agent = create_deep_agent(
    model=ChatAnthropic(model="claude-opus-4-6")
)

# Add custom tools
from langchain_core.tools import tool

@tool
def my_tool(query: str) -> str:
    """Custom tool description"""
    return do_something(query)

agent = create_deep_agent(tools=[my_tool])

# Custom system prompt
agent = create_deep_agent(
    system_prompt="You are an expert financial analyst..."
)