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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.
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 ✅awaitagent.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.
pip install parlantimportparlant.sdkasp@p.toolasyncdefget_weather(context: p.ToolContext, city: str) ->p.ToolResult:
# Your weather API logic herereturnp.ToolResult(f"Sunny, 72°F in {city}")
@p.toolasyncdefget_datetime(context: p.ToolContext) ->p.ToolResult:
fromdatetimeimportdatetimereturnp.ToolResult(datetime.now())
asyncdefmain():
asyncwithp.Server() asserver:
agent=awaitserver.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.awaitagent.create_variable(name="current-datetime", tool=get_datetime)
# Control and guide agent behavior with natural languageawaitagent.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__":
importasyncioasyncio.run(main())That's it! Your agent is running with ensured rule-following behavior.
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|
| 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 |
- 🧭 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
Companies using Parlant:
Financial institutions • Healthcare providers • Legal firms • E-commerce platforms
"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
| 🎯 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 |
- 💬 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
Apache 2.0 - Use it anywhere, including commercial projects.
Ready to build AI agents that actually work?
⭐ Star this repo • 🚀 Try Parlant now • 💬 Join Discord
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