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..."
)

---

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)

---

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
)

---

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

---

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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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mahmoud20138
• Source: mahmoud20138/Tradecraft
• License: MIT

Install and usage instructions live in the source repository linked above.