Memory

import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend, createFileData } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();  // Use platform store when deploying to LangSmith

// Seed the memory file
await store.put(
  ["my-agent"],
  "/memories/AGENTS.md",
  createFileData(`## Response style
- Keep responses concise
- Use code examples where possible
`),
);

// Seed a skill
await store.put(
  ["my-agent"],
  "/skills/langgraph-docs/SKILL.md",
  createFileData(`---
name: langgraph-docs
description: Fetch relevant LangGraph documentation to provide accurate guidance.
---

# langgraph-docs

Use the fetch_url tool to read https://docs.langchain.com/llms.txt, then fetch relevant pages.
`),
);

const agent = createDeepAgent({
  memory: ["/memories/AGENTS.md"],
  skills: ["/skills/"],
  backend: (rt) => new CompositeBackend(
    new StateBackend(rt),
    {
      "/memories/": new StoreBackend(rt, {
        namespace: (rt) => ["my-agent"],
      }),
      "/skills/": new StoreBackend(rt, {
        namespace: (rt) => ["my-agent"],
      }),
    },
  ),
  store,
});

// Thread 1: the agent learns a new preference and saves it to memory
const config1 = { configurable: { thread_id: crypto.randomUUID() } };
await agent.invoke({
  messages: [{ role: "user", content: "I prefer detailed explanations. Remember that." }],
}, config1);

// Thread 2: the agent reads memory and applies the preference
const config2 = { configurable: { thread_id: crypto.randomUUID() } };
await agent.invoke({
  messages: [{ role: "user", content: "Explain how transformers work." }],
}, config2);

import { createDeepAgent, CompositeBackend, StateBackend, StoreBackend, createFileData } from "deepagents";
import { InMemoryStore } from "@langchain/langgraph";

const store = new InMemoryStore();  // Use platform store when deploying to LangSmith

// Seed preferences for two users
await store.put(
  ["user-alice"],
  "/memories/preferences.md",
  createFileData(`## Preferences
- Likes concise bullet points
- Prefers Python examples
`),
);
await store.put(
  ["user-bob"],
  "/memories/preferences.md",
  createFileData(`## Preferences
- Likes detailed explanations
- Prefers TypeScript examples
`),
);

// Seed a skill for Alice
await store.put(
  ["user-alice"],
  "/skills/langgraph-docs/SKILL.md",
  createFileData(`---
name: langgraph-docs
description: Fetch relevant LangGraph documentation to provide accurate guidance.
---

# langgraph-docs

Use the fetch_url tool to read https://docs.langchain.com/llms.txt, then fetch relevant pages.
`),
);

const agent = createDeepAgent({
  memory: ["/memories/preferences.md"],
  skills: ["/skills/"],
  backend: (rt) => new CompositeBackend(
    new StateBackend(rt),
    {
      "/memories/": new StoreBackend(rt, {
        namespace: (rt) => [rt.serverInfo.user.identity],
      }),
      "/skills/": new StoreBackend(rt, {
        namespace: (rt) => [rt.serverInfo.user.identity],
      }),
    },
  ),
  store,
});

// When deployed, each authenticated request resolves
// `rt.serverInfo.user.identity` to the calling user, so Alice and Bob
// automatically see only their own preferences.
await agent.invoke(
  { messages: [{ role: "user", content: "How do I read a CSV file?" }] },
  { configurable: { thread_id: crypto.randomUUID() } },
);