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:

TodoListMiddleware - Task planning with write_todos/read_todos FilesystemMiddleware - File ops: ls, read_file, write_file, edit_file, glob, grep, execute SubAgentMiddleware - Delegation via task tool SummarizationMiddleware - Auto-summarizes at ~85% context or 170k tokens AnthropicPromptCachingMiddleware - Caches system prompts (Anthropic only) PatchToolCallsMiddleware - Fixes dangling tool calls from interruptions 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:

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