---
title: "Your First Letta Agent"
subtitle: Create an agent, send messages, and understand basic memory
slug: examples/hello-world
---

This example walks you through creating your first Letta agent from scratch. Unlike traditional chatbots that forget everything between conversations, Letta agents are **stateful** - they maintain persistent memory and can learn about you over time.

By the end of this guide, you'll understand how to create an agent, send it messages, and see how it automatically updates its memory based on your interactions.

<Note>
**This example uses Letta Cloud.** Generate an API key at [app.letta.com/api-keys](https://app.letta.com/api-keys) and set it as `LETTA_API_KEY` in your environment. Self-hosted servers only need an API key if authentication is enabled. You can learn more about self-hosting [here](/guides/selfhosting).
</Note>

## What You'll Learn

- Initializing the Letta client
- Creating an agent with [memory blocks](/guides/agents/memory-blocks)
- Sending messages and receiving responses
- How agents update their own memory
- Inspecting memory tool calls and block contents

## Prerequisites

You will need to install `letta-client` to interface with a Letta server:

<CodeGroup>
```bash TypeScript
npm install @letta-ai/letta-client
```
```bash Python
pip install letta-client
```
</CodeGroup>

## Steps

### Step 1: Initialize Client

A __client__ is a connection to a Letta server. It's used to create and interact with agents, as well as any of Letta's other features.

<CodeGroup>
```typescript TypeScript
import { LettaClient } from '@letta-ai/letta-client';

// Initialize the Letta client using LETTA_API_KEY environment variable
const client = new LettaClient({ token: process.env.LETTA_API_KEY });

// If self-hosting, specify the base URL:
// const client = new LettaClient({ baseUrl: "http://localhost:8283" });
```
```python Python
from letta_client import Letta
import os

# Initialize the Letta client using LETTA_API_KEY environment variable
client = Letta(token=os.getenv("LETTA_API_KEY"))

# If self-hosting, specify the base URL:
# client = Letta(base_url="http://localhost:8283")
```
</CodeGroup>

### Step 2: Create Agent

Now that we have a client, let's create an agent with memory blocks that define what the agent knows about itself and you. Memory blocks can be used for any purpose, but we're building a simple chatbot that stores information about its personality (`persona`) and you (`human`).

<CodeGroup>
```typescript TypeScript
// Create your first agent
// API Reference: https://docs.letta.com/api-reference/agents/create
const agent = await client.agents.create({
    name: "hello_world_assistant",

    // Memory blocks define what the agent knows about itself and you.
    // Agents can modify these blocks during conversations using memory
    // tools like memory_replace, memory_insert, memory_rethink, and memory.
    memoryBlocks: [
        {
            label: "persona",
            value: "I am a friendly AI assistant here to help you learn about Letta."
        },
        {
            label: "human",
            value: "Name: User\nFirst interaction: Learning about Letta"
        }
    ],

    // Model configuration
    model: "openai/gpt-4o-mini",
    // embedding: "openai/text-embedding-3-small", // Only set this if self-hosting
});

console.log(`Created agent: ${agent.id}`);
```
```python Python
# Create your first agent
# API Reference: https://docs.letta.com/api-reference/agents/create
agent = client.agents.create(
    name="hello_world_assistant",

    # Memory blocks define what the agent knows about itself and you
    memory_blocks=[
        {
            "label": "persona",
            "value": "I am a friendly AI assistant here to help you learn about Letta."
        },
        {
            "label": "human",
            "value": "Name: User\nFirst interaction: Learning about Letta"
        }
    ],

    # Model configuration
    model="openai/gpt-4o-mini",
    # embedding="openai/text-embedding-3-small", # Only set this if self-hosting
)

print(f"Created agent: {agent.id}")
```
</CodeGroup>

<Accordion title="Expected Output">
```
Created agent: agent-a1b2c3d4-e5f6-7890-abcd-ef1234567890
```
</Accordion>

<Note>
**Memory blocks** are the foundation of Letta agents. The `persona` block defines the agent's identity and behavior, while the `human` block stores information about the user. Learn more in the [Memory Blocks guide](/guides/agents/memory-blocks).
</Note>

### Step 3: Send Your First Message

Now let's send a message to the agent to see what it can do.

<CodeGroup>
```typescript TypeScript
// Send a message to your agent
// API Reference: https://docs.letta.com/api-reference/agents/messages/create
const response = await client.agents.messages.create(agent.id, {
    messages: [{
        role: "user",
        content: "Hello! What's your purpose?"
    }]
});

// Extract and print the assistant's response
for (const message of response.messages) {
    if (message.messageType === "assistant_message") {
        console.log(`Assistant: ${message.content}`);
    }
}
```
```python Python
# Send a message to your agent
# API Reference: https://docs.letta.com/api-reference/agents/messages/create
response = client.agents.messages.create(
    agent_id=agent.id,
    messages=[{
        "role": "user",
        "content": "Hello! What's your purpose?"
    }]
)

# Extract and print the assistant's response
for message in response.messages:
    if message.message_type == "assistant_message":
        print(f"Assistant: {message.content}")
```
</CodeGroup>

<Accordion title="Expected Output">
```
Assistant: Hello! I'm here to help you learn about Letta and answer any questions
you might have. Letta is a framework for building stateful AI agents with long-term
memory. I can explain concepts, provide examples, and guide you through using the
platform. What would you like to know?
```
</Accordion>

### Step 4: Provide Information for the Agent to Remember

Now let's give the agent some information about yourself. If prompted correctly, the agent can add this information to a relevant memory block using one of its default memory tools. Unless tools are modified during creation, new agents usually have `memory_insert` and `memory_replace` tools.

<CodeGroup>
```typescript TypeScript
// Send information about yourself
const response2 = await client.agents.messages.create(agent.id, {
    messages: [{
        role: "user",
        content: "My name is Cameron. Please store this information in your memory."
    }]
});

// Print out tool calls and the assistant's response
for (const msg of response2.messages) {
    if (msg.messageType === "assistant_message") {
        console.log(`Assistant: ${msg.content}\n`);
    }
    if (msg.messageType === "tool_call_message") {
        console.log(`Tool call: ${msg.toolCall.name}(${JSON.stringify(msg.toolCall.arguments)})`);
    }
}
```
```python Python
# Send information about yourself
response = client.agents.messages.create(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "My name is Cameron. Please store this information in your memory."}]
)

# Print out tool calls and the assistant's response
for msg in response.messages:
    if msg.message_type == "assistant_message":
        print(f"Assistant: {msg.content}\n")
    if msg.message_type == "tool_call_message":
        print(f"Tool call: {msg.tool_call.name}({msg.tool_call.arguments})")
```
</CodeGroup>

<Accordion title="Expected Output">
```
Tool call: memory_replace({"block_label": "human", "old_content": "Name: User", "new_content": "Name: Cameron"})
Assistant: Got it! I've updated my memory with your name, Cameron. How can I assist you today?
```
</Accordion>

<Note>
Notice the `tool_call_message` showing the agent using the `memory_replace` tool to update the `human` block. This is how Letta agents manage their own memory.
</Note>

### Step 5: Inspect Agent Memory

Let's see what the agent remembers. We'll print out both the summary and the full content of each memory block:

<CodeGroup>
```typescript TypeScript
// Retrieve the agent's current memory blocks
// API Reference: https://docs.letta.com/api-reference/agents/blocks/list
const blocks = await client.agents.blocks.list(agent.id);

console.log("Current Memory:");
for (const block of blocks) {
    console.log(`  ${block.label}: ${block.value.length}/${block.limit} chars`);
    console.log(`  ${block.value}\n`);
}
```
```python Python
# Retrieve the agent's current memory blocks
# API Reference: https://docs.letta.com/api-reference/agents/blocks/list
blocks = client.agents.blocks.list(agent_id=agent.id)

print("Current Memory:")
for block in blocks:
    print(f"  {block.label}: {len(block.value)}/{block.limit} chars")
    print(f"  {block.value}\n")
```
</CodeGroup>

The `persona` block should have:

> I am a friendly AI assistant here to help you learn about Letta.

The `human` block should have something like:

> Name: Cameron


<Note>
Notice how the `human` block now contains "Name: Cameron" instead of "Name: User". The agent used the `memory_replace` tool to update its memory based on the information you provided.
</Note>

## Complete Example

Here's the full code in one place that you can run:

<CodeGroup>
```typescript TypeScript
import { LettaClient } from '@letta-ai/letta-client';

// Initialize client using LETTA_API_KEY environment variable
const client = new LettaClient({ token: process.env.LETTA_API_KEY });

// If self-hosting, specify the base URL:
// const client = new LettaClient({ baseUrl: "http://localhost:8283" });

// Create agent
const agent = await client.agents.create({
    name: "hello_world_assistant",
    memoryBlocks: [
        {
            label: "persona",
            value: "I am a friendly AI assistant here to help you learn about Letta."
        },
        {
            label: "human",
            value: "Name: User\nFirst interaction: Learning about Letta"
        }
    ],
    model: "openai/gpt-4o-mini",
    // embedding: "openai/text-embedding-3-small", // Only set this if self-hosting
});

console.log(`Created agent: ${agent.id}\n`);

// Send first message
let response = await client.agents.messages.create(agent.id, {
    messages: [{ role: "user", content: "Hello! What's your purpose?" }]
});

for (const msg of response.messages) {
    if (msg.messageType === "assistant_message") {
        console.log(`Assistant: ${msg.content}\n`);
    }
}

// Send information about yourself
response = await client.agents.messages.create(agent.id, {
    messages: [{ role: "user", content: "My name is Cameron. Please store this information in your memory." }]
});

// Print out tool calls and the assistant's response
for (const msg of response.messages) {
    if (msg.messageType === "assistant_message") {
        console.log(`Assistant: ${msg.content}\n`);
    }
    if (msg.messageType === "tool_call_message") {
        console.log(`Tool call: ${msg.toolCall.name}(${JSON.stringify(msg.toolCall.arguments)})`);
    }
}

// Inspect memory
const blocks = await client.agents.blocks.list(agent.id);
console.log("Current Memory:");
for (const block of blocks) {
    console.log(`  ${block.label}: ${block.value.length}/${block.limit} chars`);
    console.log(`  ${block.value}\n`);
}
```
```python Python
from letta_client import Letta
import os

# Initialize client using LETTA_API_KEY environment variable
client = Letta(token=os.getenv("LETTA_API_KEY"))

# If self-hosting, specify the base URL:
# client = Letta(base_url="http://localhost:8283")

# Create agent
agent = client.agents.create(
    name="hello_world_assistant",
    memory_blocks=[
        {
            "label": "persona",
            "value": "I am a friendly AI assistant here to help you learn about Letta."
        },
        {
            "label": "human",
            "value": "Name: User\nFirst interaction: Learning about Letta"
        }
    ],
    model="openai/gpt-4o-mini",
    # embedding="openai/text-embedding-3-small", # Only set this if self-hosting
)

print(f"Created agent: {agent.id}\n")

# Send first message
response = client.agents.messages.create(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "Hello! What's your purpose?"}]
)

for msg in response.messages:
    if msg.message_type == "assistant_message":
        print(f"Assistant: {msg.content}\n")

# Send information about yourself
response = client.agents.messages.create(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "My name is Cameron. Please store this information in your memory."}]
)

# Print out tool calls and the assistant's response
for msg in response.messages:
    if msg.message_type == "assistant_message":
        print(f"Assistant: {msg.content}\n")
    if msg.message_type == "tool_call_message":
        print(f"Tool call: {msg.tool_call.name}({msg.tool_call.arguments})")

# Inspect memory
blocks = client.agents.blocks.list(agent_id=agent.id)
print("Current Memory:")
for block in blocks:
    print(f"  {block.label}: {len(block.value)}/{block.limit} chars")
    print(f"  {block.value}\n")
```
</CodeGroup>

## Key Concepts

<CardGroup cols={2}>
<Card title="Stateful Agents" icon="brain">
Letta agents maintain memory across conversations, unlike stateless chat APIs
</Card>

<Card title="Memory Blocks" icon="cube">
Modular memory components that agents can read and update during conversations
</Card>

<Card title="Persistent Context" icon="clock">
Agents remember user preferences, conversation history, and learned information
</Card>

<Card title="Automatic Updates" icon="rotate">
Agents intelligently update their memory as they learn more about you
</Card>
</CardGroup>

## Next Steps

<Card title="Memory Blocks Guide" icon="database" href="/guides/agents/memory-blocks">
Learn how to work with memory blocks, update them, and control agent knowledge
</Card>
