deepagents-implementation

# Deep Agents Implementation

## Core Concepts

Deep Agents provides a batteries-included agent harness built on LangGraph:

- **`create_deep_agent`**: Factory function that creates a configured agent
- **Middleware**: Injected capabilities (filesystem, todos, subagents, summarization)
- **Backends**: Pluggable file storage (state, filesystem, store, composite)
- **Subagents**: Isolated task execution via the `task` tool

The agent returned is a compiled LangGraph `StateGraph`, compatible with streaming, checkpointing, and LangGraph Studio.

## Essential Imports

```python
# Core
from deepagents import create_deep_agent

# Subagents
from deepagents import CompiledSubAgent

# Backends
from deepagents.backends import (
    StateBackend,       # Ephemeral (default)
    FilesystemBackend,  # Real disk
    StoreBackend,       # Persistent cross-thread
    CompositeBackend,   # Route paths to backends
)

# LangGraph (for checkpointing, store, streaming)
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.store.memory import InMemoryStore

# LangChain (for custom models, tools)
from langchain.chat_models import init_chat_model
from langchain_core.tools import tool
```

## Basic Usage

### Minimal Agent

```python
from deepagents import create_deep_agent

# Uses Claude Sonnet 4 by default
agent = create_deep_agent()

result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})
```

### With Custom Tools

```python
from langchain_core.tools import tool
from deepagents import create_deep_agent

@tool
def web_search(query: str) -> str:
    """Search the web for information."""
    return tavily_client.search(query)

agent = create_deep_agent(
    tools=[web_search],
    system_prompt="You are a research assistant. Search the web to answer questions.",
)

result = agent.invoke({"messages": [{"role": "user", "content": "What is LangGraph?"}]})
```

### With Custom Model

```python
from langc