deepagents-architecture

# Deep Agents Architecture Decisions

## When to Use Deep Agents

### Use Deep Agents When You Need:

- **Long-horizon tasks** - Complex workflows spanning dozens of tool calls
- **Planning capabilities** - Task decomposition before execution
- **Filesystem operations** - Reading, writing, and editing files
- **Subagent delegation** - Isolated task execution with separate context windows
- **Persistent memory** - Long-term storage across conversations
- **Human-in-the-loop** - Approval gates for sensitive operations
- **Context management** - Auto-summarization for long conversations

### Consider Alternatives When:

| Scenario | Alternative | Why |
|----------|-------------|-----|
| Single LLM call | Direct API call | Deep Agents overhead not justified |
| Simple RAG pipeline | LangChain LCEL | Simpler abstraction |
| Custom graph control flow | LangGraph directly | More flexibility |
| No file operations needed | `create_react_agent` | Lighter weight |
| Stateless tool use | Function calling | No middleware needed |

## Backend Selection

### Backend Comparison

| Backend | Persistence | Use Case | Requires |
|---------|-------------|----------|----------|
| `StateBackend` | Ephemeral (per-thread) | Working files, temp data | Nothing (default) |
| `FilesystemBackend` | Disk | Local development, real files | `root_dir` path |
| `StoreBackend` | Cross-thread | User preferences, knowledge bases | LangGraph `store` |
| `CompositeBackend` | Mixed | Hybrid memory patterns | Multiple backends |

### Backend Decision Tree

```
Need real disk access?
├─ Yes → FilesystemBackend(root_dir="/path")
└─ No
   └─ Need persistence across conversations?
      ├─ Yes → Need mixed ephemeral + persistent?
      │  ├─ Yes → CompositeBackend
      │  └─ No → StoreBackend
      └─ No → StateBackend (default)
```

### CompositeBackend Routing

Route different paths to different storage backends:

```python
from deepagents import create_deep_agent
from deepagents.backends import CompositeB