deep-agent-subagents

Deep Agent Subagents

Create specialized subagents that the main agent delegates tasks to via the built-in task tool. Subagents run in isolation with their own context window.

When to Use

• When the main agent needs to delegate specialized work
• When tasks would bloat the main agent's context
• When different tasks need different models or tools
• When building multi-agent orchestration

Available Operations

1. Define subagent configurations
2. Register subagents with the main agent
3. Use different models per subagent
4. Give subagents their own tools and skills

Multi-Step Workflow

Step 1: Define Subagent Configs

# agent/subagents.py

research_agent = {
    "name": "research-agent",
    "description": "Research topics in-depth using web search and documentation",
    "system_prompt": "You are a thorough researcher. Always cite sources.",
    "tools": [internet_search, read_documentation],
    "model": "anthropic:claude-sonnet-4-6",
}

code_agent = {
    "name": "code-agent",
    "description": "Write, refactor, and fix code following clean architecture",
    "system_prompt": "You are an expert programmer. Follow SOLID principles.",
    "tools": [run_tests, lint_code],
    "model": "anthropic:claude-sonnet-4-6",
}

review_agent = {
    "name": "review-agent",
    "description": "Review code for quality, security, and best practices",
    "system_prompt": "You are a senior code reviewer. Be thorough but constructive.",
    "tools": [lint_code, search_codebase],
    "model": "openai:gpt-4o",  # Can use different model
}

Step 2: Register with Main Agent

from deepagents import create_deep_agent

orchestrator = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[custom_tool],
    subagents=[research_agent, code_agent, review_agent],
    system_prompt="""You are an orchestrator. Delegate tasks to subagents:
    - research-agent: for research and documentation
    - code-agent: for writing and fixing code
    - review-agent: for code reviews"""
)