/deepagents-architecture

name: deepagents-architecture
description: 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.

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:

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

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

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

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 | ✅ | ❌