deepagents-quickstart

name	DeepAgents Quickstart
description	This skill should be used when the user asks to "start a deepagent project", "create a new agent", "quickstart agent", "simple agent example", "get started with deepagents", or needs a quick introduction to building agents with LangChain's DeepAgents framework. Provides minimal setup and basic patterns for rapid prototyping.

DeepAgents Quickstart

Build production-ready deep agents with planning, context management, and subagent delegation in minutes.

What is DeepAgents?

DeepAgents (langchain-ai/deepagents) is a high-level framework for building agentic applications with planning, filesystem backends, subagent orchestration, and auto-summarization built in. The core function is create_deep_agent, which provides:

• Planning & summarization — Built-in skills for structured reasoning and context management
• Subagent delegation — Define subagents as dicts, compiled into CompiledSubAgent instances
• Filesystem backends — FilesystemBackend, StateBackend, StoreBackend, CompositeBackend
• AGENTS.md memory — Declarative agent memory pattern for capability awareness and context persistence
• Built-in tools — write_todos, read_file, write_file, edit_file, ls, glob, grep, execute, task
• Human-in-the-loop — Rich interrupt_on configuration for tool approval

Installation

pip install deepagents

Quick Start

Minimal Agent

from deepagents import create_deep_agent

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    system_prompt="You are a helpful research assistant.",
    tools=[],
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Research AI trends"}]
})
print(result["messages"][-1].content)

Agent with Custom Tools

from deepagents import create_deep_agent
from langchain.tools import tool

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    tools=[search_web],
    system_prompt="You are a research assistant.",
)

Agent with Subagents

Define subagents as dicts — the framework compiles them into CompiledSubAgent instances:

from deepagents import create_deep_agent

# Subagents defined as dicts (native pattern)
agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    system_prompt="You coordinate research projects. Delegate research to the researcher and writing to the writer.",
    tools=[],
    subagents=[
        {
            "name": "researcher",
            "model": "openai:gpt-4o",
            "tools": [search_web],
            "system_prompt": "You are an expert researcher. Summarize findings concisely.",
        },
        {
            "name": "writer",
            "tools": [write_document],
            "system_prompt": "You write clear, structured documents.",
        },
    ],
)

Agent with Backend and Memory

Use FilesystemBackend for file-first agents and AGENTS.md for declarative memory:

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    system_prompt="You are a project assistant.",
    tools=[],
    backend=FilesystemBackend(root_dir="./workspace"),
    memory=["./AGENTS.md"],
    skills=["./skills/"],              # Load SKILL.md files from directory
    checkpointer=MemorySaver(),
)

# Invoke with thread persistence
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Set up the project structure"}]},
    config={"configurable": {"thread_id": "project-1"}},
)

Built-in Tools

Every create_deep_agent automatically includes these tools via default middleware:

Tool	Middleware	Description
write_todos	Planning	Create structured task lists
read_todos	Planning	View current tasks
ls	Filesystem	List directory contents
read_file	Filesystem	Read file content with pagination
write_file	Filesystem	Create or overwrite files
edit_file	Filesystem	Exact string replacements
glob	Filesystem	Find files matching patterns
grep	Filesystem	Search text in files
execute	Filesystem	Run commands in sandbox (if backend supports it)
task	SubAgent	Delegate to subagents with isolated contexts

Security Tip: Use interrupt_on on dangerous tools to require human confirmation before execution. Use ToolRuntime from langchain.tools to inject user identity and permissions as secure context, rather than embedding user IDs in tool parameters.

When to Use DeepAgents

Use DeepAgents When:

• Tasks require 5+ tool calls
• Need to break complex tasks into subtasks
• Managing large context (research, analysis)
• Delegating to specialized subagents
• Building production agent systems

Don't Use DeepAgents When:

• Simple linear tasks (< 5 tool calls)
• MVP/prototyping phase
• Deterministic workflows (use scripts)
• Single-purpose automation

Common Patterns

Research Agent

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    system_prompt="""You conduct comprehensive research.
    1. Plan research steps
    2. Search for information
    3. Synthesize into final report""",
    tools=[search_tool],
    backend=FilesystemBackend(root_dir="./research"),
    skills=["./skills/"],
)

Customer Support Agent

from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    system_prompt="You coordinate customer support. Route inquiries to the appropriate specialist.",
    tools=[],
    subagents=[
        {
            "name": "inquiry-handler",
            "tools": [knowledge_base_tool],
            "system_prompt": "You answer customer questions accurately.",
        },
        {
            "name": "issue-resolver",
            "tools": [ticketing_tool],
            "system_prompt": "You resolve customer problems.",
        },
        {
            "name": "order-specialist",
            "tools": [order_tool],
            "system_prompt": "You manage customer orders.",
        },
    ],
    checkpointer=MemorySaver(),
    interrupt_on={"order_tool": {"allowed_decisions": ["approve", "reject"]}},  # keyed by tool name
)

Model Configuration

from langchain.chat_models import init_chat_model

# Claude (recommended)
model = init_chat_model("anthropic:claude-sonnet-4-5-20250929")

# OpenAI
model = init_chat_model("openai:gpt-4o")

# Google
model = init_chat_model("google_genai:gemini-2.0-flash")

agent = create_deep_agent(model=model, system_prompt="...", tools=[...])

Interactive Chat Console

Test your agent interactively with tool call logging:

# chat.py
import uuid
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

def create_my_agent():
    return create_deep_agent(
        model="anthropic:claude-sonnet-4-5-20250929",
        tools=[...],
        system_prompt="Your system prompt here.",
        checkpointer=MemorySaver(),
    )

def main():
    agent = create_my_agent()
    thread_id = str(uuid.uuid4())
    config = {"configurable": {"thread_id": thread_id}}

    print("Chat with your agent (type 'exit' to quit, 'new' for new thread)")
    while True:
        user_input = input("\nYou: ").strip()
        if not user_input:
            continue
        if user_input.lower() in ("exit", "quit", "salir"):
            break
        if user_input.lower() in ("new", "nuevo"):
            thread_id = str(uuid.uuid4())
            config = {"configurable": {"thread_id": thread_id}}
            print(f"  New thread: {thread_id[:8]}...")
            continue

        result = agent.invoke(
            {"messages": [{"role": "user", "content": user_input}]},
            config=config,
        )

        # Log tool calls
        for msg in result["messages"]:
            if hasattr(msg, "tool_calls") and msg.tool_calls:
                for tc in msg.tool_calls:
                    args = ", ".join(f"{k}={v!r}" for k, v in tc["args"].items())
                    print(f"  Tool: {tc['name']}({args})")

        print(f"\nAgent: {result['messages'][-1].content}")

if __name__ == "__main__":
    main()

Use /add-interactive-chat to generate a chat console tailored to your specific agent.

Next Steps

After basic setup, explore:

• Architecture: Design agent topologies and bounded contexts
• Patterns: System prompts, tool design, anti-patterns
• Tool Design: AI-friendly tool design principles. Run /design-tools to create your tool catalog.
• Evals: Evals-Driven Development — design scenarios, build datasets, iterate. Run /design-evals to get started.
• Evolution: Maturity model and refactoring. Run /assess to check your agent's maturity level.
• API Cheatsheet: Quick reference for create_deep_agent parameters

Commands

Build:

• /new-sdk-app — Scaffold a new DeepAgents project
• /design-agent — Design a simple single agent (role, tools, prompt)
• /design-topology — Design optimal agent topology
• /design-tools — Design AI-friendly tool catalog
• /add-interactive-chat — Generate interactive chat console

Test (EDD):

• /design-evals — Scaffold eval suite from JTBD
• /eval — Run evals (snapshot | --smoke | --full | --report | --diagnose)
• /add-scenario — Add eval scenario interactively or from trace
• /eval-status — Eval dataset health dashboard
• /eval-update — Review changed snapshots

Validate & Evolve:

• /validate-agent — Anti-pattern and security check
• /tool-status — Tool quality dashboard
• /add-tool — Add a single tool to existing catalog
• /assess — Architecture maturity assessment (80-point)
• /evolve — Guided refactoring to next maturity level