deep-agents-memory

作者: langchain-ai

當你的 Deep Agent 需要記憶、持久化或檔案系統存取時,請呼叫此技能。涵蓋 StateBackend(暫存)、StoreBackend(持久化)…

npx skills add https://github.com/langchain-ai/skills-benchmarks --skill deep-agents-memory
Deep Agents use pluggable backends for file operations and memory:

Short-term (StateBackend): Persists within a single thread, lost when thread ends Long-term (StoreBackend): Persists across threads and sessions Hybrid (CompositeBackend): Route different paths to different backends

FilesystemMiddleware provides tools: ls, read_file, write_file, edit_file, glob, grep

Use CaseBackendWhy
Temporary working filesStateBackendDefault, no setup
Local development CLIFilesystemBackendDirect disk access
Cross-session memoryStoreBackendPersists across threads
Hybrid storageCompositeBackendMix ephemeral + persistent
Default StateBackend stores files ephemerally within a thread. ```python from deepagents import create_deep_agent

agent = create_deep_agent() # Default: StateBackend result = agent.invoke({ "messages": [{"role": "user", "content": "Write notes to /draft.txt"}] }, config={"configurable": {"thread_id": "thread-1"}})

/draft.txt is lost when thread ends

</python>
<typescript>
Default StateBackend stores files ephemerally within a thread.
```typescript
import { createDeepAgent } from "deepagents";

const agent = await createDeepAgent();  // Default: StateBackend
const result = await agent.invoke({
  messages: [{ role: "user", content: "Write notes to /draft.txt" }]
}, { configurable: { thread_id: "thread-1" } });
// /draft.txt is lost when thread ends
Configure CompositeBackend to route paths to different storage backends. ```python from deepagents import create_deep_agent from deepagents.backends import CompositeBackend, StateBackend, StoreBackend from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

composite_backend = lambda rt: CompositeBackend( default=StateBackend(rt), routes={"/memories/": StoreBackend(rt)} )

agent = create_deep_agent(backend=composite_backend, store=store)

/draft.txt -> ephemeral (StateBackend)

/memories/user-prefs.txt -> persistent (StoreBackend)

</python>
<typescript>
Configure CompositeBackend to route paths to different storage backends.
```typescript
import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();

const agent = await createDeepAgent({
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
  store
});

// /draft.txt -> ephemeral (StateBackend)
// /memories/user-prefs.txt -> persistent (StoreBackend)
Files in /memories/ persist across threads via StoreBackend routing. ```python # Using CompositeBackend from previous example config1 = {"configurable": {"thread_id": "thread-1"}} agent.invoke({"messages": [{"role": "user", "content": "Save to /memories/style.txt"}]}, config=config1)

config2 = {"configurable": {"thread_id": "thread-2"}} agent.invoke({"messages": [{"role": "user", "content": "Read /memories/style.txt"}]}, config=config2)

Thread 2 can read file saved by Thread 1

</python>
<typescript>
Files in /memories/ persist across threads via StoreBackend routing.
```typescript
// Using CompositeBackend from previous example
const config1 = { configurable: { thread_id: "thread-1" } };
await agent.invoke({ messages: [{ role: "user", content: "Save to /memories/style.txt" }] }, config1);

const config2 = { configurable: { thread_id: "thread-2" } };
await agent.invoke({ messages: [{ role: "user", content: "Read /memories/style.txt" }] }, config2);
// Thread 2 can read file saved by Thread 1
Use FilesystemBackend for local development with real disk access and human-in-the-loop. ```python from deepagents import create_deep_agent from deepagents.backends import FilesystemBackend from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent( backend=FilesystemBackend(root_dir=".", virtual_mode=True), # Restrict access interrupt_on={"write_file": True, "edit_file": True}, checkpointer=MemorySaver() )

Agent can read/write actual files on disk

</python>
<typescript>
Use FilesystemBackend for local development with real disk access and human-in-the-loop.
```typescript
import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";

const agent = await createDeepAgent({
  backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
  interruptOn: { write_file: true, edit_file: true },
  checkpointer: new MemorySaver()
});

Security: Never use FilesystemBackend in web servers - use StateBackend or sandbox instead.

Access the store directly in custom tools for long-term memory operations. ```python from langchain.tools import tool, ToolRuntime from langchain.agents import create_agent from langgraph.store.memory import InMemoryStore

@tool def get_user_preference(key: str, runtime: ToolRuntime) -> str: """Get a user preference from long-term storage.""" store = runtime.store result = store.get(("user_prefs",), key) return str(result.value) if result else "Not found"

@tool def save_user_preference(key: str, value: str, runtime: ToolRuntime) -> str: """Save a user preference to long-term storage.""" store = runtime.store store.put(("user_prefs",), key, {"value": value}) return f"Saved {key}={value}"

store = InMemoryStore()

agent = create_agent( model="gpt-4.1", tools=[get_user_preference, save_user_preference], store=store )

</python>
</ex-store-in-custom-tools>

<boundaries>
### What Agents CAN Configure

- Backend type and configuration
- Routing rules for CompositeBackend
- Root directory for FilesystemBackend
- Human-in-the-loop for file operations

### What Agents CANNOT Configure

- Tool names (ls, read_file, write_file, edit_file, glob, grep)
- Access files outside virtual_mode restrictions
- Cross-thread file access without proper backend setup
</boundaries>

<fix-storebackend-requires-store>
<python>
StoreBackend requires a store instance.
```python
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))

# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())
StoreBackend requires a store instance. ```typescript // WRONG const agent = await createDeepAgent({ backend: (c) => new StoreBackend(c) });

// CORRECT const agent = await createDeepAgent({ backend: (c) => new StoreBackend(c), store: new InMemoryStore() });

</typescript>
</fix-storebackend-requires-store>

<fix-statebackend-files-dont-persist>
<python>
StateBackend files are thread-scoped - use same thread_id or StoreBackend for cross-thread access.
```python
# WRONG: thread-2 can't read file from thread-1
agent.invoke({"messages": [...]}, config={"configurable": {"thread_id": "thread-1"}})  # Write
agent.invoke({"messages": [...]}, config={"configurable": {"thread_id": "thread-2"}})  # File not found!
StateBackend files are thread-scoped - use same thread_id or StoreBackend for cross-thread access. ```typescript // WRONG: thread-2 can't read file from thread-1 await agent.invoke({ messages: [...] }, { configurable: { thread_id: "thread-1" } }); // Write await agent.invoke({ messages: [...] }, { configurable: { thread_id: "thread-2" } }); // File not found! ``` Path must match CompositeBackend route prefix for persistence. ```python # With routes={"/memories/": StoreBackend(rt)}: agent.invoke(...) # /prefs.txt -> ephemeral (no match) agent.invoke(...) # /memories/prefs.txt -> persistent (matches route) ``` Path must match CompositeBackend route prefix for persistence. ```typescript // With routes: { "/memories/": StoreBackend }: await agent.invoke(...); // /prefs.txt -> ephemeral (no match) await agent.invoke(...); // /memories/prefs.txt -> persistent (matches route) ``` Use PostgresStore for production (InMemoryStore lost on restart). ```python # WRONG # CORRECT store = InMemoryStore() store = PostgresStore(connection_string="postgresql://...") ``` Use PostgresStore for production (InMemoryStore lost on restart). ```typescript // WRONG // CORRECT const store = new InMemoryStore(); const store = new PostgresStore({ connectionString: "..." }); ``` Enable virtual_mode=True to restrict path access (prevents ../ and ~/ escapes). ```python backend = FilesystemBackend(root_dir="/project", virtual_mode=True) # Secure ``` CompositeBackend matches longest prefix first. ```python routes = {"/mem/": StoreBackend(rt), "/mem/temp/": StateBackend(rt)} # /mem/file.txt -> StoreBackend, /mem/temp/file.txt -> StateBackend (longer match) ```

來自 langchain-ai 的更多技能

deepagents-thread-inspector
langchain-ai
檢查並解釋本地 Deep Agents Code SQLite 工作階段儲存庫中的對話。當 LangSmith 追蹤工具不可用時作為備用方案,用於…
deepagents-python-quickstart
langchain-ai
按照官方快速入門,在 Python 中搭建一個最小的本地 Deep Agent,使用提供者原生的網路搜尋而非 Tavily。當使用者想要……時使用。
deepagents-typescript-quickstart
langchain-ai
按照官方快速入門指南,以 TypeScript 搭建一個最小的本地 Deep Agent,使用供應商原生的網路搜尋而非 Tavily。當使用者……時使用
eval-engineering
langchain-ai
反覆檢查 agent 儲存庫與使用者提供的可選追蹤資料,訪談使用者,並逐一建立、執行及稽核 Harbor evals。用於……
LangChain RAG Pipeline
langchain-ai
在構建任何檢索增強生成(RAG)系統時,請調用此技能。涵蓋文檔加載器、遞迴字符文本分割器、嵌入(OpenAI)等。
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — 一個可安裝的 AI 代理技能,由 langchain-ai/langchain-skills 發布。
LangSmith Datasets
langchain-ai
當從追蹤建立評估資料集,或將資料集上傳至 LangSmith,或查詢資料集時,請調用此技能。涵蓋資料集類型(final_response、…)
langsmith-evaluator
langchain-ai
在為 LangSmith 建立評估管道時,請調用此技能。涵蓋三個核心組件:(1) 建立評估器 - LLM 作為評審、自訂程式碼;(2)…