deep-agents-core

Foundation framework for building multi-step agents with built-in planning, memory, and skill delegation. The agent harness provides these capabilities automatically - you configure, not implement. @tool def get_weather(city: str) -> str: """Get the weather for a given city.""" return f"It is always sunny in {city}" agent = create_deep_agent( model="claude-sonnet-4-5-20250929", tools=[get_weather], system_prompt="You are a helpful assistant" ) config = {"configurable": {"thread_id": "user-123"}} result = agent.invoke({ "messages": [{"role": "user", "content": "What's the weather in Tokyo?"}] }, config=config) agent = create_deep_agent( name="my-assistant", model="claude-sonnet-4-5-20250929", tools=[custom_tool1, custom_tool2], system_prompt="Custom instructions", subagents=[research_agent, code_agent], backend=FilesystemBackend(root_dir=".", virtual_mode=True), interrupt_on={"write_file": True}, skills=["./skills/"], checkpointer=MemorySaver(), store=InMemoryStore() ) agent = create_deep_agent( backend=FilesystemBackend(root_dir=".", virtual_mode=True), skills=["./skills/"], checkpointer=MemorySaver() ) result = agent.invoke({ "messages": [{"role": "user", "content": "Use the python-testing skill"}] }, config={"configurable": {"thread_id": "session-1"}}) store = InMemoryStore() ...""" store.put( namespace=("filesystem",), key="/skills/python-testing/SKILL.md",...