Letta TypeScript SDK

Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.

Quicklinks:

• Developer Documentation: Learn how to create agents using Python or TypeScript
• TypeScript API Reference: Complete TypeScript SDK documentation
• Agent Development Environment (ADE): A no-code UI for building stateful agents
• Letta Cloud: The fastest way to try Letta

Get started

Install the Letta TypeScript SDK:

npm install @letta-ai/letta-client

Simple Hello World example

In the example below, we'll create a stateful agent with two memory blocks. We'll initialize the human memory block with incorrect information, and correct the agent in our first message - which will trigger the agent to update its own memory with a tool call.

To run the examples, you'll need to get a LETTA_API_KEY from Letta Cloud, or run your own self-hosted server (see our guide)

import { Letta } from "@letta-ai/letta-client";

const client = new Letta({ apiKey: process.env.LETTA_API_KEY });
// const client = new Letta({ baseUrl: "http://localhost:8283" });  // if self-hosting

const agentState = await client.agents.create({
    model: "openai/gpt-4.1",
    embedding: "openai/text-embedding-3-small",
    memory_blocks: [
        {
          label: "human",
          value: "The human's name is Chad. They like vibe coding."
        },
        {
          label: "persona",
          value: "My name is Sam, a helpful assistant."
        }
    ],
    tools: ["web_search", "run_code"]
});

console.log("Agent created with ID:", agentState.id);

const response = await client.agents.messages.create(agentState.id, {
    input: "Hey, nice to meet you, my name is Brad."
});

// the agent will think, then edit its memory using a tool
for (const message of response.messages) {
    console.log(message);
}

// The content of this memory block will be something like
// "The human's name is Brad. They like vibe coding."
// Fetch this block's content with:
const human_block = await client.agents.blocks.retrieve("human", { agent_id: agentState.id });
console.log(human_block.value);

Core concepts in Letta:

Letta is built on the MemGPT research paper, which introduced the concept of the "LLM Operating System" for memory management:

1. Memory Hierarchy: Agents have self-editing memory split between in-context and out-of-context memory
2. Memory Blocks: In-context memory is composed of persistent editable blocks
3. Agentic Context Engineering: Agents control their context window using tools to edit, delete, or search memory
4. Perpetual Self-Improving Agents: Every agent has a perpetual (infinite) message history

Local Development

Connect to a local Letta server instead of the cloud:

const client = new Letta({
  baseUrl: "http://localhost:8283"
});

Run Letta locally with Docker:

docker run \
  -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
  -p 8283:8283 \
  -e OPENAI_API_KEY="your_key" \
  letta/letta:latest

See the self-hosting guide for more options.

Key Features

Memory Management (full guide)

Memory blocks are persistent, editable sections of an agent's context window:

// Create agent with memory blocks
const agent = await client.agents.create({
  memory_blocks: [
    { label: "persona", value: "I'm a helpful assistant." },
    { label: "human", value: "User preferences and info." }
  ]
});

// Update blocks manually
await client.agents.blocks.update("human", {
  agent_id: agent.id,
  value: "Updated user information"
});

// Retrieve a block
const block = await client.agents.blocks.retrieve("human", { agent_id: agent.id });

Multi-agent Shared Memory (full guide)

Memory blocks can be attached to multiple agents. All agents will have an up-to-date view on the contents of the memory block -- if one agent modifies it, the other will see it immediately.

Here is how to attach a single memory block to multiple agents:

// Create shared block
const sharedBlock = await client.blocks.create({
  label: "organization",
  value: "Shared team context"
});

// Attach to multiple agents
const agent1 = await client.agents.create({
  memory_blocks: [{ label: "persona", value: "I am a supervisor" }],
  block_ids: [sharedBlock.id]
});

const agent2 = await client.agents.create({
  memory_blocks: [{ label: "persona", value: "I am a worker" }],
  block_ids: [sharedBlock.id]
});

Sleep-time Agents (full guide)

Background agents that share memory with your primary agent:

const agent = await client.agents.create({
  model: "openai/gpt-4.1",
  enable_sleeptime: true  // creates a sleep-time agent
});

Agent File Import/Export (full guide)

Save and share agents with the .af file format:

import { readFileSync } from 'fs';

// Import agent
const file = new Blob([readFileSync('/path/to/agent.af')]);
const agent = await client.agents.importFile(file);

// Export agent
const schema = await client.agents.exportFile(agent.id);

MCP Tools (full guide)

Connect to Model Context Protocol servers:

// First, create an MCP server (example: weather server)
const weatherServer = await client.mcpServers.create({
  server_name: "weather-server",
  config: {
    mcp_server_type: "streamable_http",
    server_url: "https://weather-mcp.example.com/mcp",
  },
});

// List tools available from the MCP server
const tools = await client.mcpServers.tools.list(weatherServer.id);

// Create agent with MCP tool
const agent = await client.agents.create({
  model: "openai/gpt-4.1",
  tool_ids: [tool.id]
});

Filesystem (full guide)

Give agents access to files:

import { createReadStream } from 'fs';

// Create folder and upload file
const folder = await client.folders.create({
  name: "my_folder",
});

await client.folders.files.upload(createReadStream("file.txt"), folder.id);

// Attach to agent
await client.agents.folders.attach(agent.id, folder.id);

Long-running Agents (full guide)

Background execution with resumable streaming:

const stream = await client.agents.messages.create(agent.id, {
  messages: [
    { role: "user", content: "Analyze this dataset" }
  ],
  background: true
});

let run_id, last_seq_id;
for await (const chunk of stream) {
  run_id = chunk.run_id;
  last_seq_id = chunk.seq_id;
}

// Resume if disconnected
for await (const chunk of client.runs.stream(run_id, { starting_after: last_seq_id })) {
  console.log(chunk);
}

Streaming (full guide)

Stream responses in real-time:

const stream = await client.agents.messages.stream(agent.id, {
  messages: [
    { role: "user", content: "Hello!" }
  ]
});

for await (const chunk of stream) {
  console.log(chunk);
}

Message Types (full guide)

Agent responses contain different message types. Handle them with the message_type discriminator:

const messagesPage = await client.agents.messages.list(agent.id);

for await (const message of messagesPage) {
  switch (message.message_type) {
    case "user_message":
      console.log("User:", message.content);
      break;
    case "assistant_message":
      console.log("Agent:", message.content);
      break;
    case "reasoning_message":
      console.log("Reasoning:", message.reasoning);
      break;
    case "tool_call_message":
      console.log("Tool:", message.tool_call.name);
      break;
    case "tool_return_message":
      console.log("Result:", message.tool_return);
      break;
  }
}

TypeScript Support

Full TypeScript support with exported types:

import { Letta } from "@letta-ai/letta-client";

const request: Letta.CreateAgentRequest = {
  model: "openai/gpt-4.1",
  memory_blocks: [...]
};

Error Handling

import { LettaError } from "@letta-ai/letta-client";

try {
  await client.agents.messages.create(agentId, {...});
} catch (err) {
  if (err instanceof LettaError) {
    console.log(err.statusCode);
    console.log(err.message);
    console.log(err.body);
  }
}

Advanced Configuration

Retries

const response = await client.agents.create({...}, {
  maxRetries: 3 // Default: 2
});

Timeouts

const response = await client.agents.create({...}, {
  timeoutInSeconds: 30 // Default: 60
});

Custom Headers

const response = await client.agents.create({...}, {
  headers: {
    'X-Custom-Header': 'value'
  }
});

Contributing

Letta is an open source project built by over a hundred contributors. There are many ways to get involved in the Letta OSS project!

• Join the Discord: Chat with the Letta devs and other AI developers.
• Chat on our forum: If you're not into Discord, check out our developer forum.
• Follow our socials: Twitter/X, LinkedIn, YouTube

This SDK is generated programmatically. For SDK changes, please open an issue.

README contributions are always welcome!

Resources

• Documentation
• TypeScript API Reference
• Example Applications

License

MIT

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Legal notices: By using Letta and related Letta services (such as the Letta endpoint or hosted service), you are agreeing to our privacy policy and terms of service.