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Skills/langchain-ai/langchain-skills/deep-agents-memory
deep-agents-memory logo

deep-agents-memory

langchain-ai/langchain-skills
10K installs795 stars
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Installation

npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-memory

Summary

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

SKILL.md

<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>

Score

0–100
74/ 100

Grade

B

Popularity26/30

10,274 installs — strong, widely used.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Deep Agents Memory skill score badge previewScore badge

Markdown

[![Deep Agents Memory skill](https://www.remoteopenclaw.com/skills/langchain-ai/langchain-skills/deep-agents-memory/badges/score.svg)](https://www.remoteopenclaw.com/skills/langchain-ai/langchain-skills/deep-agents-memory)

HTML

<a href="https://www.remoteopenclaw.com/skills/langchain-ai/langchain-skills/deep-agents-memory"><img src="https://www.remoteopenclaw.com/skills/langchain-ai/langchain-skills/deep-agents-memory/badges/score.svg" alt="Deep Agents Memory skill"/></a>

Deep Agents Memory FAQ

How do I install the Deep Agents Memory skill?

Run “npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-memory” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Deep Agents Memory skill do?

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Deep Agents Memory skill free?

Yes. Deep Agents Memory is a free, open-source skill published from langchain-ai/langchain-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Deep Agents Memory work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Deep Agents Memory works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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