Building Stateful Agents with Letta

Letta agents can automatically manage long-term memory, load data from external sources, and call custom tools. Unlike in other frameworks, Letta agents are stateful, so they keep track of historical interactions and reserve part of their context to read and write memories which evolve over time.

Letta’s key features:

Letta manages a reasoning loop for agents. At each agent step (i.e. iteration of the loop), the state of the agent is checkpointed and persisted to the database.

You can interact with agents from a REST API, the ADE, and TypeScript / Python SDKs. As long as they are connected to the same service, all of these interfaces can be used to interact with the same agents.

If you’re interested in learning more about stateful agents, read our blog post.

Agents vs Threads

In Letta, you can think of an agent as a single entity that has a single message history which is treated as infinite. The sequence of interactions the agent has experienced through its existence make up the agent’s state (or memory).

One distinction between Letta and other agent frameworks is that Letta does not have the notion of message threads (or sessions). Instead, there are only stateful agents, which have a single perpetual thread (sequence of messages).

The reason we use the term agent rather than thread is because Letta is based on the principle that all agents interactions should be part of the persistent memory, as opposed to building agent applications around ephemeral, short-lived interactions (like a thread or session).

If you would like to create common starting points for new conversation “threads”, we recommending using agent templates to create new agents for each conversation, or directly copying agent state from an existing agent.

For multi-users applications, we recommend creating an agent per-user, though you can also have multiple users message a single agent (but it will be a single shared message history).

Create an agent

To start creating agents, you can run a Letta Server locally using Letta Desktop, deploy a server locally + remotely with Docker, or use Letta Cloud. See our quickstart guide for more information.

Assuming we’re running a Letta Server locally at http://localhost:8283, we can create a new agent via the REST API, Python SDK, or TypeScript SDK:

1curl -X POST http://localhost:8283/v1/agents/ \
2     -H "Content-Type: application/json" \
3     -d '{
4  "memory_blocks": [
5    {
6      "value": "The human'\''s name is Bob the Builder.",
7      "label": "human"
8    },
9    {
10      "value": "My name is Sam, the all-knowing sentient AI.",
11      "label": "persona"
12    }
13  ],
14  "model": "openai/gpt-4o-mini",
15  "context_window_limit": 16000,
16  "embedding": "openai/text-embedding-3-small"
17}'

You can also create an agent without any code using the Agent Development Environment (ADE). All Letta agents are stored in a database on the Letta Server, so you can access the same agents from the ADE, the REST API, the Python SDK, and the TypeScript SDK.

The response will include information about the agent, including its id:

1{
2  "id": "agent-43f8e098-1021-4545-9395-446f788d7389",
3  "name": "GracefulFirefly",
4  ...
5}

Once an agent is created, you can message it:

1curl --request POST \
2  --url http://localhost:8283/v1/agents/$AGENT_ID/messages \
3  --header 'Content-Type: application/json' \
4  --data '{
5  "messages": [
6    {
7      "role": "user",
8      "content": "hows it going????"
9    }
10  ]
11}'

Message Types

The response object contains the following attributes:

  • usage: The usage of the agent after the message was sent (the prompt tokens, completition tokens, and total tokens)
  • message: A list of LettaMessage objects, generated by the agent

LettaMessage

The LettaMessage object is a simplified version of the Message object stored in the database backend. Since a Message can include multiple events like a chain-of-thought and function calls, LettaMessage simplifies messages to have the following types:

  • reasoning_message: The inner monologue (chain-of-thought) of the agent
  • tool_call_message: An agent’s tool (function) call
  • tool_call_return: The result of executing an agent’s tool (function) call
  • assistant_message: An agent calling the send_message tool to communicate with the user
  • system_message: A system message (for example, an alert about the user logging in)
  • user_message: A user message

The assistant_message message type is a convenience wrapper around the tool_call_message when the tool call is the predefined send_message tool that makes it easier to parse agent messages. If you prefer to see the raw tool call even in the send_message case, you can set use_assistant_message to false in the request config (see the endpoint documentation).

Common agent operations

For more in-depth guide on the full set of Letta agent operations, check out our API reference, our extended Python SDK and TypeScript SDK examples, as well as our other cookbooks.

If you’re using a self-hosted Letta server, you should set the base URL (base_url in Python, baseUrl in TypeScript) to the Letta server’s URL (e.g. http://localhost:8283) when you create your client. See an example here.

If you’re using a self-hosted server, you can omit the token if you’re not using password protection. If you are using password protection, set your token to the password. If you’re using Letta Cloud, you should set the token to your Letta Cloud API key.

Retrieving an agent’s state

The agent’s state is always persisted, so you can retrieve an agent’s state by its ID.

The result of the call is an AgentState object:

List agents

Replace agent_id with your actual agent ID.

The result of the call is a list of AgentState objects:

Delete an agent

To delete an agent, you can use the DELETE endpoint with your agent_id:

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