---
title: 'Quickstart'
description: 'Start building with Honcho in under 5 minutes.'
icon: 'bolt'
---

For production-level use, Honcho offers two powerful ways to leverage ambient personalization: our managed platform and our open source solution. Read further if you want to explore the quickstart demo.

<CardGroup cols={2}>
  <Card title="Honcho Platform" icon="cloud" href="https://app.honcho.dev">
    Fully managed, hassle-free solution with one-click deployment
  </Card>
  <Card title="Honcho Open Source" icon="github" href="https://github.com/plastic-labs/honcho">
    Self-hosted, fully customizable, and open source
  </Card>
</CardGroup>

# Getting Started

Have your project use Honcho's ambient personalization capabilities in just a few steps. No signup required!

<Info>
By default, the SDK uses the demo server hosted at demo.honcho.dev. The demo server is meant for quick experimentation and the data is cleared on a regular basis. Do not use for production applications.

For production use:
1. Get your API key at [app.honcho.dev/api-keys](https://app.honcho.dev/api-keys)
2. Set `environment="production"` and provide your `api_key`
</Info>

## 1. Install the SDK

<CodeGroup>
```bash Python (uv)
uv add honcho-ai
```

```bash Python (pip)
pip install honcho-ai
```

```bash TypeScript (npm)
npm install @honcho-ai/sdk
```

```bash TypeScript (yarn)
yarn add @honcho-ai/sdk
```

```bash TypeScript (pnpm)
pnpm add @honcho-ai/sdk
```
</CodeGroup>

## 2. Initialize the Client

The Honcho client is the main entry point for interacting with Honcho's API. By default, it uses the demo environment and a default workspace.

### Demo Environment (Default)

<CodeGroup>
```python Python
from honcho import Honcho

# Initialize client (uses demo environment and default workspace)
client = Honcho()

```

```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';

// Initialize client (uses demo environment and default workspace)
const client = new Honcho({});

```
</CodeGroup>

### Production Environment

<CodeGroup>
```python Python
import os
from honcho import Honcho

# Production environment with API key
client = Honcho(
    api_key=os.environ["HONCHO_API_KEY"],
    environment="production",
    # Create a workspace, otherwise set to "default"
    # workspaceId="your-workspace-id"
)
```

```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';

// Production environment with API key
const client = new Honcho({
    apiKey: process.env.HONCHO_API_KEY!,
    environment: "production",
    // Create a workspace, otherwise set to "default"
    // workspace: "your-workspace-id"
});
```
</CodeGroup>

## 3. Create Peers

Peers represent individual users, AI agents, or any conversational entity in your system:

<CodeGroup>
```python Python
alice = client.peer("alice")
bob = client.peer("bob")
```

```typescript TypeScript
const alice = await client.peer("alice")
const bob = await client.peer("bob")
```
</CodeGroup>

## 4. Create a Session

Sessions are independent conversations that can include multiple peers:

<CodeGroup>
```python Python
session = client.session("session_1")
session.add_peers([alice, bob])
```

```typescript TypeScript
const session = await client.session("session_1")
await session.addPeers([alice, bob])
```
</CodeGroup>

## 5. Add Messages

Add some conversation messages. Honcho automatically learns from these interactions:

<CodeGroup>
```python Python
session.add_messages([
    alice.message("Hi Bob, how are you?"),
    bob.message("I'm good, thank you!"),
    alice.message("What are you doing today after work?"),
    bob.message("I'm going to the gym! I've been trying to get back in shape."),
    alice.message("That's great! I should probably start exercising too."),
    bob.message("You should! I find that evening workouts help me relax."),
])
```

```typescript TypeScript
await session.addMessages([
    alice.message("Hi Bob, how are you?"),
    bob.message("I'm good, thank you!"),
    alice.message("What are you doing today after work?"),
    bob.message("I'm going to the gym! I've been trying to get back in shape."),
    alice.message("That's great! I should probably start exercising too."),
    bob.message("You should! I find that evening workouts help me relax."),
])
```
</CodeGroup>

## 6. Query for Insights

Now ask Honcho what it's learned - this is where the magic happens:

<CodeGroup>
```python Python
# Ask what Bob is like
response = alice.chat("Tell me about Bob's interests and habits")
print(response)

# Returns rich context like:
# "Bob is health-conscious and has been working on getting back in shape.
# He regularly goes to the gym, particularly in the evenings, and finds
# exercise helps him relax. He's encouraging about fitness and willing
# to share advice about workout routines."
```

```typescript TypeScript
(async () => {
    // Ask what Bob is like
    const response = await alice.chat("Tell me about Bob's interests and habits");
    console.log(response);

    // Returns rich context like:
    // "Bob is health-conscious and has been working on getting back in shape.
    // He regularly goes to the gym, particularly in the evenings, and finds
    // exercise helps him relax. He's encouraging about fitness and willing
    // to share advice about workout routines."
})();
```
</CodeGroup>

## What Just Happened?

Honcho automatically built rich psychological profiles from just a few messages:

- **Theory of Mind Processing**: Understanding personality, preferences, and patterns
- **Ambient Learning**: No surveys or explicit training - just natural conversation
- **Rich Context**: Far more detailed than simple conversation history

The response isn't just retrieving stored text - it's synthesizing insights about Bob's personality, habits, and communication style.

## Next Steps

This covers the core concepts: **peers**, **sessions**, **messages**, and **dialectic queries**.

- For production use, [sign up for the managed platform](https://app.honcho.dev) or get an [overview here](../reference/platform).
- For detailed API reference, check out our [SDK documentation](../reference/sdk).
- For more examples, explore our [guides](../guides/overview).

---