parlant 3.0.4

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Finally, LLM agents that actually follow instructions

🌐 Website • ⚡ Quick Start • 💬 Discord • 📖 Examples

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🎯 The Problem Every AI Developer Faces

You build an AI agent. It works great in testing. Then real users start talking to it and...

• ❌ It ignores your carefully crafted system prompts
• ❌ It hallucinates responses in critical moments
• ❌ It can't handle edge cases consistently
• ❌ Each conversation feels like a roll of the dice

Sound familiar? You're not alone. This is the #1 pain point for developers building production AI agents.

⚡ The Solution: Stop Fighting Prompts, Teach Principles

Parlant flips the script on AI agent development. Instead of hoping your LLM will follow instructions, Parlant ensures it.

# Traditional approach: Cross your fingers 🤞
system_prompt = "You are a helpful assistant. Please follow these 47 rules..."

# Parlant approach: Ensured compliance ✅
await agent.create_guideline(
    condition="Customer asks about refunds",
    action="Check order status first to see if eligible",
    tools=[check_order_status],
)

Parlant gives you all the structure you need to build customer-facing agents that behave exactly as your business requires:

• Journeys: Define clear customer journeys and how your agent should respond at each step.

• Behavioral Guidelines: Easily craft agent behavior; Parlant will match the relevant elements contextually.

• Tool Use: Attach external APIs, data fetchers, or backend services to specific interaction events.

• Domain Adaptation: Teach your agent domain-specific terminology and craft personalized responses.

• Canned Responses: Use response templates to eliminate hallucinations and guarantee style consistency.

• Explainability: Understand why and when each guideline was matched and followed.

🚀 Get Your Agent Running in 60 Seconds

pip install parlant

import parlant.sdk as p

@p.tool
async def get_weather(context: p.ToolContext, city: str) -> p.ToolResult:
    # Your weather API logic here
    return p.ToolResult(f"Sunny, 72°F in {city}")

@p.tool
async def get_datetime(context: p.ToolContext) -> p.ToolResult:
    from datetime import datetime
    return p.ToolResult(datetime.now())

async def main():
    async with p.Server() as server:
        agent = await server.create_agent(
            name="WeatherBot",
            description="Helpful weather assistant"
        )

        # Have the agent's context be updated on every response (though
        # update interval is customizable) using a context variable.
        await agent.create_variable(name="current-datetime", tool=get_datetime)

        # Control and guide agent behavior with natural language
        await agent.create_guideline(
            condition="User asks about weather",
            action="Get current weather and provide a friendly response with suggestions",
            tools=[get_weather]
        )

        # Add other (reliably enforced) behavioral modeling elements
        # ...

        # 🎉 Test playground ready at http://localhost:8800
        # Integrate the official React widget into your app,
        # or follow the tutorial to build your own frontend!

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

That's it! Your agent is running with ensured rule-following behavior.

🎬 See It In Action

🔥 Why Developers Are Switching to Parlant

🏗️ Traditional AI Frameworks	⚡ Parlant
Write complex system prompts Hope the LLM follows them Debug unpredictable behaviors Scale by prompt engineering Cross fingers for reliability	Define rules in natural language Ensured rule compliance Predictable, consistent behavior Scale by adding guidelines Production-ready from day one

🎯 Perfect For Your Use Case

Financial Services	Healthcare	E-commerce	Legal Tech
Compliance-first design	HIPAA-ready agents	Customer service at scale	Precise legal guidance
Built-in risk management	Patient data protection	Order processing automation	Document review assistance

🛠️ Enterprise-Grade Features

• 🧭 Conversational Journeys - Lead the customer step-by-step to a goal
• 🎯 Dynamic Guideline Matching - Context-aware rule application
• 🔧 Reliable Tool Integration - APIs, databases, external services
• 📊 Conversation Analytics - Deep insights into agent behavior
• 🔄 Iterative Refinement - Continuously improve agent responses
• 🛡️ Built-in Guardrails - Prevent hallucination and off-topic responses
• 📱 React Widget - Drop-in chat UI for any web app
• 🔍 Full Explainability - Understand every decision your agent makes

📈 Join 8,000+ Developers Building Better AI

Companies using Parlant:

Financial institutions • Healthcare providers • Legal firms • E-commerce platforms

🌟 What Developers Are Saying

"By far the most elegant conversational AI framework that I've come across! Developing with Parlant is pure joy." — Vishal Ahuja, Senior Lead, Customer-Facing Conversational AI @ JPMorgan Chase

🏃‍♂️ Quick Start Paths

🎯 I want to test it myself	→ 5-minute quickstart
🛠️ I want to see an example	→ Healthcare agent example
🚀 I want to get involved	→ Join our Discord community

🤝 Community & Support

• 💬 Discord Community - Get help from the team and community
• 📖 Documentation - Comprehensive guides and examples
• 🐛 GitHub Issues - Bug reports and feature requests
• 📧 Direct Support - Direct line to our engineering team

📄 License

Apache 2.0 - Use it anywhere, including commercial projects.

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Ready to build AI agents that actually work?

⭐ Star this repo • 🚀 Try Parlant now • 💬 Join Discord

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