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	✅	❌
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.

Architecture Decision Checklist

Before implementing:

1. Is Deep Agents the right tool? (vs LangGraph directly, vs simpler agent)
2. Backend strategy chosen?
   • Ephemeral only → StateBackend (default)
   • Need disk access → FilesystemBackend
   • Need cross-thread persistence → StoreBackend or CompositeBackend
3. Subagent strategy defined?
   • Which tasks benefit from isolation?
   • Custom subagents with specialized tools/prompts?
   • Parallel execution opportunities identified?
4. Human-in-the-loop points defined?
   • Which tools need approval?
   • Approval flow (approve/edit/reject)?
5. Custom middleware needed?
   • System prompt injection?
   • Request/response transformation?
6. Context management considered?
   • Long conversations → summarization triggers
   • Large file handling → use references
7. Checkpointing strategy? (for persistence/resume)