179 lines
4.9 KiB
Plaintext
179 lines
4.9 KiB
Plaintext
---
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title: 'Quickstart'
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description: 'Start building with Honcho in under 5 minutes.'
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icon: 'bolt'
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---
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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.
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<CardGroup cols={2}>
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<Card title="Honcho Platform" icon="cloud" href="https://app.honcho.dev">
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Fully managed, hassle-free solution with one-click deployment
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</Card>
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<Card title="Honcho Open Source" icon="github" href="https://github.com/plastic-labs/honcho">
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Self-hosted, fully customizable, and open source
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</Card>
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</CardGroup>
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# Getting Started
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Have your project use Honcho's ambient personalization capabilities in just a few steps. No signup required!
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<Note>
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This quickstart uses the demo server at https://demo.honcho.dev
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</Note>
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## 1. Install the SDK
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<CodeGroup>
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```bash Python (uv)
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uv add honcho-ai
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```
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```bash Python (pip)
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pip install honcho-ai
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```
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```bash TypeScript (npm)
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npm install @honcho-ai/sdk
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```
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```bash TypeScript (yarn)
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yarn add @honcho-ai/sdk
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```
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```bash TypeScript (pnpm)
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pnpm add @honcho-ai/sdk
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```
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</CodeGroup>
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## 2. Initialize the Client
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<CodeGroup>
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```python Python
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from honcho import Honcho
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# Initialize client (using the demo environment)
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client = Honcho()
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```
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```typescript TypeScript
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import { Honcho } from '@honcho-ai/sdk';
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// Initialize client (using the demo environment)
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const client = new Honcho();
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```
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</CodeGroup>
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## 3. Create Peers
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Peers represent individual users, AI agents, or any conversational entity in your system:
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<CodeGroup>
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```python Python
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alice = client.peer("alice")
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bob = client.peer("bob")
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```
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```typescript TypeScript
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const alice = client.peer("alice")
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const bob = client.peer("bob")
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```
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</CodeGroup>
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## 4. Create a Session
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Sessions are independent conversations that can include multiple peers:
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<CodeGroup>
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```python Python
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session = client.session("session_1")
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session.add_peers([alice, bob])
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```
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```typescript TypeScript
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const session = client.session("session_1")
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session.addPeers([alice, bob])
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```
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</CodeGroup>
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## 5. Add Messages
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Add some conversation messages. Honcho automatically learns from these interactions:
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<CodeGroup>
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```python Python
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session.add_messages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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])
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```
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```typescript TypeScript
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session.addMessages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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])
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```
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</CodeGroup>
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## 6. Query for Insights
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Now ask Honcho what it's learned - this is where the magic happens:
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<CodeGroup>
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```python Python
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# Ask what Bob is like
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response = alice.chat("Tell me about Bob's interests and habits")
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print(response)
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# Returns rich context like:
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# "Bob is health-conscious and has been working on getting back in shape.
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# He regularly goes to the gym, particularly in the evenings, and finds
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# exercise helps him relax. He's encouraging about fitness and willing
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# to share advice about workout routines."
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```
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```typescript TypeScript
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(async () => {
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// Ask what Bob is like
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const response = await alice.chat("Tell me about Bob's interests and habits");
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console.log(response);
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// Returns rich context like:
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// "Bob is health-conscious and has been working on getting back in shape.
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// He regularly goes to the gym, particularly in the evenings, and finds
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// exercise helps him relax. He's encouraging about fitness and willing
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// to share advice about workout routines."
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})();
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```
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</CodeGroup>
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## What Just Happened?
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Honcho automatically built rich psychological profiles from just a few messages:
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- **Theory of Mind Processing**: Understanding personality, preferences, and patterns
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- **Ambient Learning**: No surveys or explicit training - just natural conversation
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- **Rich Context**: Far more detailed than simple conversation history
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The response isn't just retrieving stored text - it's synthesizing insights about Bob's personality, habits, and communication style.
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## Next Steps
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This covers the core concepts: **peers**, **sessions**, **messages**, and **dialectic queries**.
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- For production use, [sign up for the managed platform](https://app.honcho.dev) or get an [overview here](/v2/documentation/platform/overview).
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- For detailed API reference, check out our [SDK documentation](/v2/documentation/platform/sdk).
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- For more examples, explore our [guides](/v2/guides).
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---
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