Deepagents Architecture

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

text

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

text

         ┌─────────────┐
         │  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).