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Deep agents come with a local filesystem to offload memory. By default, this filesystem is stored in agent state and is transient to a single thread—files are lost when the conversation ends. You can extend deep agents with long-term memory by using a CompositeBackend that routes specific paths to persistent storage. This enables hybrid storage where some files persist across threads while others remain ephemeral.

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Setup

Configure long-term memory by using a CompositeBackend that routes the /memories/ path to a StoreBackend: 
import { createDeepAgent } from "deepagents";
import { CompositeBackend, StateBackend, StoreBackend } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph-checkpoint";

const agent = createDeepAgent({
  store: new InMemoryStore(),  // Required for StoreBackend
  backend: (config) => new CompositeBackend(
    new StateBackend(config),  // Ephemeral storage
    { "/memories/": new StoreBackend(config) }  // Persistent storage
  ),
});

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How it works

When using CompositeBackend, deep agents maintain two separate filesystems:

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1. Short-term (transient) filesystem

  • Stored in the agent’s state (via StateBackend)
  • Persists only within a single thread
  • Files are lost when the thread ends
  • Accessed through standard paths: /notes.txt, /workspace/draft.md

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2. Long-term (persistent) filesystem

  • Stored in a LangGraph Store (via StoreBackend)
  • Persists across all threads and conversations
  • Survives agent restarts
  • Accessed through paths prefixed with /memories/: /memories/preferences.txt

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Path routing

The CompositeBackend routes file operations based on path prefixes:
  • Files with paths starting with /memories/ are stored in the Store (persistent)
  • Files without this prefix remain in transient state
  • All filesystem tools (ls, read_file, write_file, edit_file) work with both
// Transient file (lost after thread ends)
await agent.invoke({
  messages: [{ role: "user", content: "Write draft to /draft.txt" }],
});

// Persistent file (survives across threads)
await agent.invoke({
  messages: [{ role: "user", content: "Save final report to /memories/report.txt" }],
});

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Cross-thread persistence

Files in /memories/ can be accessed from any thread: 
import { v4 as uuidv4 } from "uuid";

// Thread 1: Write to long-term memory
const config1 = { configurable: { thread_id: uuidv4() } };
await agent.invoke({
  messages: [{ role: "user", content: "Save my preferences to /memories/preferences.txt" }],
}, config1);

// Thread 2: Read from long-term memory (different conversation!)
const config2 = { configurable: { thread_id: uuidv4() } };
await agent.invoke({
  messages: [{ role: "user", content: "What are my preferences?" }],
}, config2);
// Agent can read /memories/preferences.txt from the first thread

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Use cases

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User preferences

Store user preferences that persist across sessions: 
const agent = createDeepAgent({
  store: new InMemoryStore(),
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
  systemPrompt: `When users tell you their preferences, save them to /memories/user_preferences.txt so you remember them in future conversations.`,
});

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Self-improving instructions

An agent can update its own instructions based on feedback: 
const agent = createDeepAgent({
  store: new InMemoryStore(),
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
  systemPrompt: `You have a file at /memories/instructions.txt with additional instructions and preferences.

  Read this file at the start of conversations to understand user preferences.

  When users provide feedback like "please always do X" or "I prefer Y", update /memories/instructions.txt using the edit_file tool.`,
});
Over time, the instructions file accumulates user preferences, helping the agent improve.

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Knowledge base

Build up knowledge over multiple conversations: 
// Conversation 1: Learn about a project
await agent.invoke({
  messages: [{ role: "user", content: "We're building a web app with React. Save project notes." }],
});

// Conversation 2: Use that knowledge
await agent.invoke({
  messages: [{ role: "user", content: "What framework are we using?" }],
});
// Agent reads /memories/project_notes.txt from previous conversation

​
Research projects

Maintain research state across sessions: 
const researchAgent = createDeepAgent({
  store: new InMemoryStore(),
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
  systemPrompt: `You are a research assistant.

  Save your research progress to /memories/research/:
  - /memories/research/sources.txt - List of sources found
  - /memories/research/notes.txt - Key findings and notes
  - /memories/research/report.md - Final report draft

  This allows research to continue across multiple sessions.`,
});

​
Store implementations

Any LangGraph BaseStore implementation works:

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InMemoryStore (development)

Good for testing and development, but data is lost on restart: 
import { InMemoryStore } from "@langchain/langgraph-checkpoint";
import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend } from "deepagents";

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

​
PostgresStore (production)

For production, use a persistent store: 
import { PostgresStore } from "@langchain/langgraph-checkpoint-postgres";
import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend } from "deepagents";

const store = new PostgresStore({
  connectionString: process.env.DATABASE_URL,
});
const agent = createDeepAgent({
  store,
  backend: (config) => new CompositeBackend(
    new StateBackend(config),
    { "/memories/": new StoreBackend(config) }
  ),
});

​
Best practices

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Use descriptive paths

Organize persistent files with clear paths: 
/memories/user_preferences.txt
/memories/research/topic_a/sources.txt
/memories/research/topic_a/notes.txt
/memories/project/requirements.md

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Document the memory structure

Tell the agent what’s stored where in your system prompt: 
Your persistent memory structure:
- /memories/preferences.txt: User preferences and settings
- /memories/context/: Long-term context about the user
- /memories/knowledge/: Facts and information learned over time

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Prune old data

Implement periodic cleanup of outdated persistent files to keep storage manageable.

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Choose the right storage

  • Development: Use InMemoryStore for quick iteration
  • Production: Use PostgresStore or other persistent stores
  • Multi-tenant: Consider using assistant_id-based namespacing in your store
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