deepagents-langchain

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name: deepagents-langchain description: DeepAgents — production-ready LangGraph agent framework by LangChain. Batteries-included: planning (write_todos), filesystem ops, shell execution, sub-agents with isolated context, auto context summarization. pip install deepagents → create_deep_agen kind: agent category: ai/agents status: active tags: [ai-agents, deepagents, langchain] related_skills: [agent-development, ai-agent-builder]

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

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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

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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)

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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

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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)

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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..."
)

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MCP Integration

from langchain_mcp_adapters import MCPToolkit

# Connect any MCP server
toolkit = MCPToolkit(server_command=["npx", "gitnexus", "mcp"])
mcp_tools = toolkit.get_tools()

agent = create_deep_agent(tools=mcp_tools)

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Sub-Agents Pattern

# Main agent delegates to sub-agents with isolated contexts
# Sub-agents don't share main agent's conversation history
# Useful for: parallel research, isolated code execution

agent = create_deep_agent(
    enable_subagents=True,
    subagent_model=ChatAnthropic(model="claude-haiku-4-5-20251001")  # cheaper for subtasks
)

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Use Cases

• Research pipeline: fetch + summarize + synthesize across sources
• Code automation: read codebase → plan changes → edit files → run tests
• Data processing: ingest files → transform → write outputs
• Multi-step workflows: plan → delegate subtasks → aggregate results

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vs. Other Agent Frameworks

Feature	DeepAgents	OpenAlice	AutoHedge
Framework	LangGraph	Custom TS	Swarms
Built-in tools	✓ (full)	✓ (trading)	✓ (trading)
Sub-agents	✓	✗	✗
MCP support	✓	✓ (planned)	✗
Domain	General	Trading	Trading
Studio UI	✓ LangGraph	✓ Web	✗

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