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Deep Agents Memory

LangChainOfficialPublished by LangChain

  • Skill
  • Tool integration
  • Agent tooling
  • MIT
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From the author

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

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Deep Agents memory provides a flexible system for managing agent state and file access through pluggable backends. It supports ephemeral storage for single-thread tasks, persistent storage for cross-session memory, and direct filesystem access for local development. By using a CompositeBackend, developers can route specific file paths to different storage types, enabling hybrid memory architectures within a single agent.

What it does

  • Provides StateBackend for thread-scoped ephemeral file storage.
  • Enables StoreBackend for persistent, cross-session memory.
  • Supports FilesystemBackend for direct disk access during local development.
  • Allows routing of different file paths to specific backends via CompositeBackend.
  • Includes standard file operation tools like read, write, edit, and grep.
  • Supports secure local development with virtualized path restrictions and human-in-the-loop interrupts.

When to use it

  • When an agent needs to maintain state across multiple conversation threads.
  • When you need to separate temporary working files from long-term user preferences.
  • When building local CLI tools that require reading or writing to the host filesystem.
  • When you need to implement custom memory logic using a persistent store.

Advantages

  • Highly configurable storage architecture.
  • Prevents accidental data loss by choosing the appropriate persistence layer.
  • Enhances security for local development through virtualized path modes.
  • Seamless integration with existing agent workflows and toolsets.

Usage examples

“Configure my agent to save files in /memories/ to a persistent store while keeping other files ephemeral.”
The agent is initialized with a CompositeBackend that routes /memories/ paths to a StoreBackend, ensuring persistence across threads.
“How do I safely allow my agent to edit local files during development?”
The agent is configured with a FilesystemBackend in virtual_mode=True, with interrupts enabled for write and edit operations to ensure human oversight.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/langchain-ai/deep-agents-memory/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip deep-agents-memory.zip -d .agents/skills

Documentation by LangChain

<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 CaseBackendWhy
Temporary working filesStateBackendDefault, no setup
Local development CLIFilesystemBackendDirect disk access
Cross-session memoryStoreBackendPersists across threads
Hybrid storageCompositeBackendMix 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>

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
LangChain
Publisher
LangChain (Official)
Source
langchain-ai/langchain-skills · config/skills/deep-agents-memory · commit 126c384
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download deep-agents-memory.zip