Getting Started with sxth-mind

This guide will walk you through building your first AI application with sxth-mind.

What You’ll Build

A simple sales assistant that:

Remembers user context across conversations
Detects patterns in user behavior
Adapts its responses based on journey stage

Prerequisites

Python 3.10+
An OpenAI API key (or any LLM provider)

Installation

pip install sxth-mind[openai]

Step 1: Create Your First Mind

import asyncio
from sxth_mind import Mind
from examples.sales import SalesAdapter

async def main():
    # Create a Mind with the Sales adapter
    mind = Mind(adapter=SalesAdapter())

    # Have a conversation
    response = await mind.chat(
        user_id="user_1",
        message="I'm working on a deal with Acme Corp"
    )
    print(f"Assistant: {response}")

asyncio.run(main())

That’s it! The Mind will:

Create a UserMind for user_1 (first interaction)
Create a ProjectMind for the default project
Generate a context-aware response
Update state after the interaction

Step 2: See How State Accumulates

async def conversation_demo():
    mind = Mind(adapter=SalesAdapter())

    # First message
    response1 = await mind.chat("user_1", "Working on a deal with Acme Corp")
    print(f"1: {response1}\n")

    # Second message
    response2 = await mind.chat("user_1", "Sent them a proposal yesterday")
    print(f"2: {response2}\n")

    # Third message
    response3 = await mind.chat("user_1", "No response yet, should I follow up?")
    print(f"3: {response3}\n")

    # Check what the Mind knows
    state = await mind.get_state("user_1")
    print(f"Interactions: {state['user_mind']['total_interactions']}")
    print(f"Journey stage: {state['project_mind']['journey_stage']}")

asyncio.run(conversation_demo())

By the third message, the Mind knows:

User has been interacting about this deal
They’re in the “qualifying” or “proposing” stage
They’re asking about follow-up timing

Step 3: Add Persistence

By default, state is stored in memory and lost when the program exits. Add SQLite for persistence:

pip install sxth-mind[sqlite]

from sxth_mind import Mind
from sxth_mind.storage import SQLiteStorage
from examples.sales import SalesAdapter

# State persists to minds.db
mind = Mind(
    adapter=SalesAdapter(),
    storage=SQLiteStorage("minds.db"),
)

Now user state survives restarts.

Step 4: Configure the LLM Provider

By default, sxth-mind uses OpenAI with gpt-4o-mini. To customize:

from sxth_mind import Mind
from sxth_mind.providers.openai import OpenAIProvider
from examples.sales import SalesAdapter

mind = Mind(
    adapter=SalesAdapter(),
    provider=OpenAIProvider(
        api_key="sk-...",           # Or set OPENAI_API_KEY env var
        default_model="gpt-4o",     # Use GPT-4o instead
    ),
)

Step 5: Run the HTTP API

For production use, run sxth-mind as an API server:

pip install sxth-mind[api]
sxth-mind serve --adapter sales --port 8000

Then make requests:

# Send a message
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"user_id": "user_1", "message": "Working on a new deal"}'

# Get user state
curl http://localhost:8000/state/user_1

# Get explanation
curl http://localhost:8000/explain/user_1

Step 6: Try Different Adapters

sxth-mind comes with three example adapters:

Habits

from examples.habits import HabitCoachAdapter

mind = Mind(adapter=HabitCoachAdapter())

await mind.chat("user_1", "I want to start exercising daily")
await mind.chat("user_1", "Did my workout today!")
await mind.chat("user_1", "Missed yesterday, feeling bad about it")

Learning

from examples.learning import LearningAdapter

mind = Mind(adapter=LearningAdapter())

await mind.chat("user_1", "I want to learn Python")
await mind.chat("user_1", "What are list comprehensions?")
await mind.chat("user_1", "I don't understand, can you show an example?")

Step 7: Build Your Own Adapter

Create an adapter for your domain:

from sxth_mind import BaseAdapter, Mind
from sxth_mind.schemas import UserMind, ProjectMind

class CustomerSuccessAdapter(BaseAdapter):
    @property
    def name(self) -> str:
        return "customer_success"

    @property
    def display_name(self) -> str:
        return "Customer Success"

    def get_identity_types(self):
        return [
            {"key": "champion", "traits": ["enthusiastic", "internal advocate"]},
            {"key": "skeptic", "traits": ["needs proof", "risk-averse"]},
            {"key": "busy", "traits": ["time-constrained", "wants quick wins"]},
            {"key": "technical", "traits": ["detail-oriented", "wants to understand"]},
        ]

    def get_journey_stages(self):
        return [
            {
                "key": "onboarding",
                "tone": "helpful",
                "guidance": "Guide them through setup. Be patient and thorough.",
            },
            {
                "key": "adopting",
                "tone": "encouraging",
                "guidance": "Help them see value. Celebrate small wins.",
            },
            {
                "key": "expanding",
                "tone": "strategic",
                "guidance": "Suggest advanced features. Connect to business goals.",
            },
            {
                "key": "at_risk",
                "tone": "supportive",
                "guidance": "Understand blockers. Offer extra help.",
            },
        ]

    def detect_journey_stage(self, project_mind: ProjectMind) -> str:
        if project_mind.interaction_count < 5:
            return "onboarding"
        if project_mind.days_since_activity > 14:
            return "at_risk"
        if project_mind.momentum_score > 0.7:
            return "expanding"
        return "adopting"

    def get_nudge_templates(self):
        return {
            "check_in": {
                "title": "How's it going?",
                "template": "Haven't heard from you in {days} days. Need any help?",
                "priority": 5,
            },
            "feature_tip": {
                "title": "Did you know?",
                "template": "Based on your usage, you might like {feature}.",
                "priority": 3,
            },
        }

    def get_system_prompt(self, user_mind: UserMind, project_mind: ProjectMind) -> str:
        stage = project_mind.journey_stage or self.detect_journey_stage(project_mind)
        stage_info = next(
            (s for s in self.get_journey_stages() if s["key"] == stage),
            self.get_journey_stages()[0]
        )

        return f"""You are a helpful Customer Success Manager.

Current stage: {stage_info['key']}
Tone: {stage_info['tone']}
Guidance: {stage_info['guidance']}

User interactions: {user_mind.total_interactions}
"""

# Use it
mind = Mind(adapter=CustomerSuccessAdapter())
response = await mind.chat("user_1", "Hi, just signed up!")

Next Steps

Read the full API docs - See all available methods and options
Explore the example adapters - Study how Sales, Habits, and Learning work
Check out the nudge engine - Add proactive outreach
Join the community - Share your adapters and get help

Common Patterns

Multi-Project Support

Track multiple contexts per user:

# User working on multiple deals
await mind.chat("user_1", "Update on Acme Corp", project_id="deal_acme")
await mind.chat("user_1", "Starting work on BigCo", project_id="deal_bigco")

Explain State for Debugging

explanation = await mind.explain_state("user_1")
print(explanation)
# "User has had 12 interactions. Currently in 'proposing' stage.
#  Pattern: tends to send proposals early. Last active 2 days ago."

Generate Nudges

from sxth_mind.engine import BaselineNudgeEngine

engine = BaselineNudgeEngine(mind.adapter, mind.storage)
nudges = await engine.check_and_generate("user_1")

for nudge in nudges:
    print(f"[{nudge.priority}] {nudge.title}: {nudge.message}")

Troubleshooting

”OpenAI package not installed"

pip install sxth-mind[openai]

"No module named ‘aiosqlite’"

pip install sxth-mind[sqlite]

"No response from LLM”

Check your OPENAI_API_KEY environment variable
Or pass the key explicitly: OpenAIProvider(api_key="sk-...")

State not persisting

Make sure you’re using SQLiteStorage:

from sxth_mind.storage import SQLiteStorage
mind = Mind(adapter=..., storage=SQLiteStorage("minds.db"))

Questions? Issues? Open a GitHub issue.

Getting Started

Core Concepts

Guides

Examples

API Reference

Community

Getting Started

Getting Started with sxth-mind

What You’ll Build

Prerequisites

Installation

Step 1: Create Your First Mind

Step 2: See How State Accumulates

Step 3: Add Persistence

Step 4: Configure the LLM Provider

Step 5: Run the HTTP API

Step 6: Try Different Adapters

Habits

Learning

Step 7: Build Your Own Adapter

Next Steps

Common Patterns

Multi-Project Support

Explain State for Debugging

Generate Nudges

Troubleshooting

”OpenAI package not installed"

"No module named ‘aiosqlite’"

"No response from LLM”

State not persisting

Getting Started

Core Concepts

Guides

Examples

API Reference

Community

​Getting Started with sxth-mind

​What You’ll Build

​Prerequisites

​Installation

​Step 1: Create Your First Mind

​Step 2: See How State Accumulates

​Step 3: Add Persistence

​Step 4: Configure the LLM Provider

​Step 5: Run the HTTP API

​Step 6: Try Different Adapters

​Habits

​Learning

​Step 7: Build Your Own Adapter

​Next Steps

​Common Patterns

​Multi-Project Support

​Explain State for Debugging

​Generate Nudges

​Troubleshooting

​”OpenAI package not installed"

​"No module named ‘aiosqlite’"

​"No response from LLM”

​State not persisting

Getting Started with sxth-mind

What You’ll Build

Prerequisites

Installation

Step 1: Create Your First Mind

Step 2: See How State Accumulates

Step 3: Add Persistence

Step 4: Configure the LLM Provider

Step 5: Run the HTTP API

Step 6: Try Different Adapters

Habits

Learning

Step 7: Build Your Own Adapter

Next Steps

Common Patterns

Multi-Project Support

Explain State for Debugging

Generate Nudges

Troubleshooting

”OpenAI package not installed"

"No module named ‘aiosqlite’"

"No response from LLM”

State not persisting