# Deepagents Architecture

> Guides architectural decisions for Deep Agents applications.

Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.

- Page: https://ss33.ai/skills/existential-birds/deepagents-architecture-28888383902
- Publisher: existential-birds
- Category: [AI & Agents](https://ss33.ai/skills/category/ai-and-agents-y6jz7i) › [Orchestration & multi-agent](https://ss33.ai/skills/category/ai-and-agents-y6jz7i/orchestration-and-multi-agent-tlbx40)
- License: Apache-2.0
- Source: https://github.com/existential-birds/beagle/blob/d1a74899fbfec74974d1818e4cac7c3d54d44b65/plugins/beagle-ai/skills/deepagents-architecture/SKILL.md

## Skill

SKILL.md: https://ss33.ai/api/v1/ss33/download/existential-birds/deepagents-architecture-28888383902

# 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 CompositeBackend, StateBackend, StoreBackend

agent = create_deep_agent(
    backend=CompositeBackend(
        default=StateBackend(),  # Working files (ephemeral)
        routes={
            "/memories/": StoreBackend(store=store),    # Persistent
            "/preferences/": StoreBackend(store=store), # Persistent
        },
    ),
)
```

## Subagent Architecture

### When to Use Subagents

**Use subagents when:**
- Task is complex, multi-step, and can run independently
- Task requires heavy context that would bloat the main thread
- Multiple independent tasks can run in parallel
- You need isolated execution (sandboxing)
- You only care about the final result, not intermediate steps

**Don't use subagents when:**
- Task is trivial (few tool calls)
- You need to see intermediate reasoning
- Splitting adds latency without benefit
- Task depends on main thread state mid-execution

### Subagent Patterns

#### Pattern 1: Parallel Research
```
         ┌─────────────┐
         │  Orchestrator│
         └──────┬──────┘
    ┌──────────┼──────────┐
    ▼          ▼          ▼
┌──────┐  ┌──────┐  ┌──────┐
│Task A│  │Task B│  │Task C│
└──┬───┘  └──┬───┘  └──┬───┘
   └──────────┼──────────┘
              ▼
      ┌─────────────┐
      │  Synthesize │
      └─────────────┘
```

Best for: Research on multiple topics, parallel analysis, batch processing.

#### Pattern 2: Specialized Agents
```python
research_agent = {
    "name": "researcher",
    "description": "Deep research on complex topics",
    "system_prompt": "You are an expert researcher...",
    "tools": [web_search, document_reader],
}

coder_agent = {
    "name": "coder",
    "description": "Write and review code",
    "system_prompt": "You are an expert programmer...",
    "tools": [code_executor, linter],
}

agent = create_deep_agent(subagents=[research_agent, coder_agent])
```

Best for: Domain-specific expertise, different tool sets per task type.

#### Pattern 3: Pre-compiled Subagents
```python
from deepagents import CompiledSubAgent, create_deep_agent

# Use existing LangGraph graph as subagent
custom_graph = create_react_agent(model=..., tools=...)

agent = create_deep_agent(
    subagents=[CompiledSubAgent(
        name="custom-workflow",
        description="Runs specialized workflow",
        runnable=custom_graph
    )]
)
```

Best for: Reusing existing LangGraph graphs, complex custom workflows.

## Middleware Architecture

### Built-in Middleware Stack

Deep Agents applies middleware in this order:

1. **TodoListMiddleware** - Task planning with `write_todos`/`read_todos`
2. **FilesystemMiddleware** - File ops: `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`, `execute`
3. **SubAgentMiddleware** - Delegation via `task` tool
4. **SummarizationMiddleware** - Auto-summarizes at ~85% context or 170k tokens
5. **AnthropicPromptCachingMiddleware** - Caches system prompts (Anthropic only)
6. **PatchToolCallsMiddleware** - Fixes dangling tool calls from interruptions
7. **HumanInTheLoopMiddleware** - Pauses for approval (if `interrupt_on` configured)

### Custom Middleware Placement

```python
from langchain.agents.middleware import AgentMiddleware

class MyMiddleware(AgentMiddleware):
    tools = [my_custom_tool]

    def transform_request(self, request):
        # Modify system prompt, inject context
        return request

    def transform_response(self, response):
        # Post-process, log, filter
        return response

# Custom middleware added AFTER built-in stack
agent = create_deep_agent(middleware=[MyMiddleware()])
```

### Middleware vs Tools Decision

| Need | Use Middleware | Use Tools |
|------|----------------|-----------|
| Inject system prompt content | ✅ | ❌ |
| Add tools dynamically | ✅ | ❌ |
| Transform requests/responses | ✅ | ❌ |
| Standalone capability | ❌ | ✅ |
| User-invokable action | ❌ | ✅ |

### Subagent Middleware Inheritance

Subagents receive their own middleware stack by default:
- TodoListMiddleware
- FilesystemMiddleware (shared backend)
- SummarizationMiddleware
- AnthropicPromptCachingMiddleware
- PatchToolCallsMiddleware

Override with `default_middleware=[]` in SubAgentMiddleware or per-subagent `middleware` key.

## Gates: architecture decisions before implementation

Complete **in order**. A step **passes** only when the stated artifact exists in the design note, ADR stub, or ticket; internal intent alone does not count.

1. **Fit** - Confirm Deep Agents vs alternatives (see tables above).
   - **Pass:** Short written rationale that either names one matching "Use Deep Agents When You Need" bullet **or** one "Consider Alternatives" row plus the chosen alternative.

2. **Backend** - Match the Backend Decision Tree to a concrete choice.
   - **Pass:** Backend name(s) from the Backend Comparison table; if `FilesystemBackend` or `CompositeBackend`, `root_dir` and any route prefixes are written down (path placeholders OK).

3. **Subagents** - Decide delegation boundaries.
   - **Pass:** Either "no subagents" plus one sentence why **or** a named list where each subagent maps to at least one "When to Use Subagents" reason; parallel plans state what merges outputs.

4. **Human-in-the-loop** - Approval surface.
   - **Pass:** Explicit list of tools/operations that use `interrupt_on`, **or** "no HITL" plus one-line risk acceptance.

5. **Middleware** - Custom vs built-in only.
   - **Pass:** Either "custom middleware: none" **or** each custom piece named, placed after the built-in stack, and tied to prompt injection, tools, or request/response transforms.

6. **Context** - Long threads and large inputs.
   - **Pass:** Stated plan for default summarization behavior (~85% context / ~170k tokens) or an alternative cap; large files handled via references/chunking or equivalent, named in text.

7. **Checkpointing** - Resume and durability.
   - **Pass:** Checkpoint/checkpointer approach named for the graph **or** "none" with one-line rationale (e.g. ephemeral demo only).