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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Getting Started

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

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1. Install the SDK

uv add honcho-ai

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2. Initialize the Client

from honcho import Honcho

# Initialize client (using the demo environment)
client = Honcho()

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3. Create Peers

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

alice = client.peer("alice")
bob = client.peer("bob")

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4. Create a Session

Sessions are independent conversations that can include multiple peers:

session = client.session("session_1")
session.add_peers([alice, bob])

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5. Add Messages

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

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."),
])

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6. Query for Insights

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

# 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."

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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.

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Next Steps

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

• For production use, sign up for the managed platform or get an overview here.
• For detailed API reference, check out our SDK documentation.
• For more examples, explore our guides.