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INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

Use this Skill: https://skilld.dev/gh/langchain-ai/langchain-skills/deep-agents-memory

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SKILL.md

≈56 tokens always: the name and description. ≈2.4k when used: this file.

<overview> 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 </overview>

<backend-selection>
Use Case Backend Why
Temporary working files StateBackend Default, no setup
Local development CLI FilesystemBackend Direct disk access
Cross-session memory StoreBackend Persists across threads
Hybrid storage CompositeBackend Mix ephemeral + persistent
</backend-selection><ex-default-state-backend> <python> Default StateBackend stores files ephemerally within a thread.
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.
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
</typescript> </ex-default-state-backend><ex-composite-backend-for-hybrid> <python> Configure CompositeBackend to route paths to different storage backends.
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.
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)
</typescript> </ex-composite-backend-for-hybrid><ex-cross-session-memory> <python> Files in /memories/ persist across threads via StoreBackend routing.
# 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.
// 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
</typescript> </ex-cross-session-memory><ex-filesystem-backend-local-dev> <python> Use FilesystemBackend for local development with real disk access and human-in-the-loop.
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.
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()
});
</typescript>

Security: Never use FilesystemBackend in web servers - use StateBackend or sandbox instead. </ex-filesystem-backend-local-dev>

<ex-store-in-custom-tools> <python> Access the store directly in custom tools for long-term memory operations.
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.
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))

# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())
</python> <typescript> StoreBackend requires a store instance.
// 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.
# 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!
</python> <typescript> StateBackend files are thread-scoped - use same thread_id or StoreBackend for cross-thread access.
// 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!
</typescript> </fix-statebackend-files-dont-persist><fix-path-prefix-for-persistence> <python> Path must match CompositeBackend route prefix for persistence.
# With routes={"/memories/": StoreBackend(rt)}:
agent.invoke(...)  # /prefs.txt -> ephemeral (no match)
agent.invoke(...)  # /memories/prefs.txt -> persistent (matches route)
</python> <typescript> Path must match CompositeBackend route prefix for persistence.
// With routes: { "/memories/": StoreBackend }:
await agent.invoke(...);  // /prefs.txt -> ephemeral (no match)
await agent.invoke(...);  // /memories/prefs.txt -> persistent (matches route)
</typescript> </fix-path-prefix-for-persistence><fix-production-store> <python> Use PostgresStore for production (InMemoryStore lost on restart).
# WRONG                              # CORRECT
store = InMemoryStore()              store = PostgresStore(connection_string="postgresql://...")
</python> <typescript> Use PostgresStore for production (InMemoryStore lost on restart).
// WRONG                                    // CORRECT
const store = new InMemoryStore();          const store = new PostgresStore({ connectionString: "..." });
</typescript> </fix-production-store><fix-filesystem-backend-needs-virtual-mode> <python> Enable virtual_mode=True to restrict path access (prevents ../ and ~/ escapes).
backend = FilesystemBackend(root_dir="/project", virtual_mode=True)  # Secure
</python> </fix-filesystem-backend-needs-virtual-mode><fix-longest-prefix-match> <python> CompositeBackend matches longest prefix first.
routes = {"/mem/": StoreBackend(rt), "/mem/temp/": StateBackend(rt)}
# /mem/file.txt -> StoreBackend, /mem/temp/file.txt -> StateBackend (longer match)
</python> </fix-longest-prefix-match>

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    This skill provides memory and filesystem management capabilities for agents. It includes security considerations regarding filesystem access and potential indirect prompt injection through file-reading tools. These are addressed by the skill's recommended use of virtual modes and human-in-the-loop checkpoints for write operations. See detailed analysis for context.

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    Score: 93/100 · 2 sections analyzed

Signed by skilld at d00eed0. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 5 months ago
  • Python
  • TypeScript
  • deep-agents
  • langchain
  • memory
  • persistence
  • statebackend
  • storebackend
  • filesystem

README badge

README badge for langchain-ai/langchain-skills/deep-agents-memory

Configures memory and file persistence for Deep Agents using pluggable backends: StateBackend for thread-scoped ephemeral storage, StoreBackend for cross-session persistence, and CompositeBackend to route different paths to different backends. Provides filesystem tools (ls, read_file, write_file, edit_file, glob, grep) and integrates with LangGraph's store layer for long-term memory across agent invocations.

Generated from the current SKILL.md.

What's the difference between StateBackend and StoreBackend?
StateBackend stores files ephemerally within a single thread and loses them when the thread ends. StoreBackend persists files across threads and sessions, making it suitable for cross-session memory.
How do I make files persist across different agent invocations?
Use CompositeBackend to route paths to StoreBackend (e.g., `/memories/` prefix), or use StoreBackend directly. You must also pass a store instance (InMemoryStore or PostgresStore) to the agent.
Can I use FilesystemBackend in production?
No. FilesystemBackend is for local development only. Never use it in web servers due to security risks; use StateBackend or a sandboxed environment instead.
What file tools does FilesystemMiddleware provide?
The middleware provides ls, read_file, write_file, edit_file, glob, and grep tools for the agent to interact with files.
What store should I use in production?
Use PostgresStore with a connection string instead of InMemoryStore, which loses all data on restart.

Generated from the current SKILL.md. These answers refresh after source changes.