{"slug":"existential-birds-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.","long_description":"---\nname: deepagents-architecture\ndescription: 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.\n---\n\n# Deep Agents Architecture Decisions\n\n## When to Use Deep Agents\n\n### Use Deep Agents When You Need:\n\n- **Long-horizon tasks** - Complex workflows spanning dozens of tool calls\n- **Planning capabilities** - Task decomposition before execution\n- **Filesystem operations** - Reading, writing, and editing files\n- **Subagent delegation** - Isolated task execution with separate context windows\n- **Persistent memory** - Long-term storage across conversations\n- **Human-in-the-loop** - Approval gates for sensitive operations\n- **Context management** - Auto-summarization for long conversations\n\n### Consider Alternatives When:\n\n| Scenario | Alternative | Why |\n|----------|-------------|-----|\n| Single LLM call | Direct API call | Deep Agents overhead not justified |\n| Simple RAG pipeline | LangChain LCEL | Simpler abstraction |\n| Custom graph control flow | LangGraph directly | More flexibility |\n| No file operations needed | `create_react_agent` | Lighter weight |\n| Stateless tool use | Function calling | No middleware needed |\n\n## Backend Selection\n\n### Backend Comparison\n\n| Backend | Persistence | Use Case | Requires |\n|---------|-------------|----------|----------|\n| `StateBackend` | Ephemeral (per-thread) | Working files, temp data | Nothing (default) |\n| `FilesystemBackend` | Disk | Local development, real files | `root_dir` path |\n| `StoreBackend` | Cross-thread | User preferences, knowledge bases | LangGraph `store` |\n| `CompositeBackend` | Mixed | Hybrid memory patterns | Multiple backends |\n\n### Backend Decision Tree\n\n```\nNeed real disk access?\n├─ Yes → FilesystemBackend(root_dir=\"/path\")\n└─ No\n   └─ Need persistence across conversations?\n      ├─ Yes → Need mixed ephemeral + persistent?\n      │  ├─ Yes → CompositeBackend\n      │  └─ No → StoreBackend\n      └─ No → StateBackend (default)\n```\n\n### CompositeBackend Routing\n\nRoute different paths to different storage backends:\n\n```python\nfrom deepagents import create_deep_agent\nfrom deepagents.backends import CompositeBackend, StateBackend, StoreBackend\n\nagent = create_deep_agent(\n    backend=CompositeBackend(\n        default=StateBackend(),  # Working files (ephemeral)\n        routes={\n            \"/memories/\": StoreBackend(store=store),    # Persistent\n            \"/preferences/\": StoreBackend(store=store), # Persistent\n        },\n    ),\n)\n```\n\n## Subagent Architecture\n\n### When to Use Subagents\n\n**Use subagents when:**\n- Task is complex, multi-step, and can run independently\n- Task requires heavy context that would bloat the main thread\n- Multiple independent tasks can run in parallel\n- You need isolated execution (sandboxing)\n- You only care about the final result, not intermediate steps\n\n**Don't use subagents when:**\n- Task is trivial (few tool calls)\n- You need to see intermediate reasoning\n- Splitting adds latency without benefit\n- Task depends on main thread state mid-execution\n\n### Subagent Patterns\n\n#### Pattern 1: Parallel Research\n```\n         ┌─────────────┐\n         │  Orchestrator│\n         └──────┬──────┘\n    ┌──────────┼──────────┐\n    ▼          ▼          ▼\n┌──────┐  ┌──────┐  ┌──────┐\n│Task A│  │Task B│  │Task C│\n└──┬───┘  └──┬───┘  └──┬───┘\n   └──────────┼──────────┘\n              ▼\n      ┌─────────────┐\n      │  Synthesize │\n      └─────────────┘\n```\n\nBest for: Research on multiple topics, parallel analysis, batch processing.\n\n#### Pattern 2: Specialized Agents\n```python\nresearch_agent = {\n    \"name\": \"researcher\",\n    \"description\": \"Deep research on complex topics\",\n    \"system_prompt\": \"You are an expert researcher...\",\n    \"tools\": [web_search, document_reader],\n}\n\ncoder_agent = {\n    \"name\": \"coder\",\n    \"description\": \"Write and review code\",\n    \"system_prompt\": \"You are an expert programmer...\",\n    \"tools\": [code_executor, linter],\n}\n\nagent = create_deep_agent(subagents=[research_agent, coder_agent])\n```\n\nBest for: Domain-specific expertise, different tool sets per task type.\n\n#### Pattern 3: Pre-compiled Subagents\n```python\nfrom deepagents import CompiledSubAgent, create_deep_agent\n\n# Use existing LangGraph graph as subagent\ncustom_graph = create_react_agent(model=..., tools=...)\n\nagent = create_deep_agent(\n    subagents=[CompiledSubAgent(\n        name=\"custom-workflow\",\n        description=\"Runs specialized workflow\",\n        runnable=custom_graph\n    )]\n)\n```\n\nBest for: Reusing existing LangGraph graphs, complex custom workflows.\n\n## Middleware Architecture\n\n### Built-in Middleware Stack\n\nDeep Agents applies middleware in this order:\n\n1. **TodoListMiddleware** - Task planning with `write_todos`/`read_todos`\n2. **FilesystemMiddleware** - File ops: `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`, `execute`\n3. **SubAgentMiddleware** - Delegation via `task` tool\n4. **SummarizationMiddleware** - Auto-summarizes at ~85% context or 170k tokens\n5. **AnthropicPromptCachingMiddleware** - Caches system prompts (Anthropic only)\n6. **PatchToolCallsMiddleware** - Fixes dangling tool calls from interruptions\n7. **HumanInTheLoopMiddleware** - Pauses for approval (if `interrupt_on` configured)\n\n### Custom Middleware Placement\n\n```python\nfrom langchain.agents.middleware import AgentMiddleware\n\nclass MyMiddleware(AgentMiddleware):\n    tools = [my_custom_tool]\n\n    def transform_request(self, request):\n        # Modify system prompt, inject context\n        return request\n\n    def transform_response(self, response):\n        # Post-process, log, filter\n        return response\n\n# Custom middleware added AFTER built-in stack\nagent = create_deep_agent(middleware=[MyMiddleware()])\n```\n\n### Middleware vs Tools Decision\n\n| Need | Use Middleware | Use Tools |\n|------|----------------|-----------|\n| Inject system prompt content | ✅ | ❌ |\n| Add tools dynamically | ✅ | ❌ |\n| Transform requests/responses | ✅ | ❌ |\n| Standalone capability | ❌ | ✅ |\n| User-invokable action | ❌ | ✅ |\n\n### Subagent Middleware Inheritance\n\nSubagents receive their own middleware stack by default:\n- TodoListMiddleware\n- FilesystemMiddleware (shared backend)\n- SummarizationMiddleware\n- AnthropicPromptCachingMiddleware\n- PatchToolCallsMiddleware\n\nOverride with `default_middleware=[]` in SubAgentMiddleware or per-subagent `middleware` key.\n\n## Gates: architecture decisions before implementation\n\nComplete **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.\n\n1. **Fit** - Confirm Deep Agents vs alternatives (see tables above).\n   - **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.\n\n2. **Backend** - Match the Backend Decision Tree to a concrete choice.\n   - **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).\n\n3. **Subagents** - Decide delegation boundaries.\n   - **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.\n\n4. **Human-in-the-loop** - Approval surface.\n   - **Pass:** Explicit list of tools/operations that use `interrupt_on`, **or** \"no HITL\" plus one-line risk acceptance.\n\n5. **Middleware** - Custom vs built-in only.\n   - **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.\n\n6. **Context** - Long threads and large inputs.\n   - **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.\n\n7. **Checkpointing** - Resume and durability.\n   - **Pass:** Checkpoint/checkpointer approach named for the graph **or** \"none\" with one-line rationale (e.g. ephemeral demo only).\n","tagline":"Guides architectural decisions for Deep Agents applications. 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