
parlant
LLM agents built for control. Designed for real-world use. Deployed in minutes.
Stars: 12640

Parlant is a structured approach to building and guiding customer-facing AI agents. It allows developers to create and manage robust AI agents, providing specific feedback on agent behavior and helping understand user intentions better. With features like guidelines, glossary, coherence checks, dynamic context, and guided tool use, Parlant offers control over agent responses and behavior. Developer-friendly aspects include instant changes, Git integration, clean architecture, and type safety. It enables confident deployment with scalability, effective debugging, and validation before deployment. Parlant works with major LLM providers and offers client SDKs for Python and TypeScript. The tool facilitates natural customer interactions through asynchronous communication and provides a chat UI for testing new behaviors before deployment.
README:

π Website β’ β‘ Quick Start β’ π¬ Discord β’ π Examples
Deutsch | EspaΓ±ol | franΓ§ais | ζ₯ζ¬θͺ | νκ΅μ΄ | PortuguΓͺs | Π ΡΡΡΠΊΠΈΠΉ | δΈζ
You build an AI agent. It works great in testing. Then real users start talking to it and...
- β It ignores your carefully crafted system prompts
- β It hallucinates responses in critical moments
- β It can't handle edge cases consistently
- β 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 π€
system_prompt = "You are a helpful assistant. Please follow these 47 rules..."
# Parlant approach: Ensured compliance β
await agent.create_guideline(
condition="Customer asks about refunds",
action="Check order status first to see if eligible",
tools=[check_order_status],
)
β Blog: How Parlant Ensures Agent Compliance
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 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, 72Β°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
# ...
# π 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.
|
|
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 |
- π§ Conversational Journeys - Lead the customer step-by-step to a goal
- π― Dynamic Guideline Matching - Context-aware rule application
- π§ Reliable Tool Integration - APIs, databases, external services
- π Conversation Analytics - Deep insights into agent behavior
- π Iterative Refinement - Continuously improve agent responses
- π‘οΈ Built-in Guardrails - Prevent hallucination and off-topic responses
- π± React Widget - Drop-in chat UI for any web app
- π Full Explainability - Understand every decision your agent makes
Companies using Parlant:
Financial institutions β’ Healthcare providers β’ Legal firms β’ E-commerce platforms
"By far the most elegant conversational AI framework that I've come across! Developing with Parlant is pure joy." β Vishal Ahuja, Senior Lead, Customer-Facing Conversational AI @ JPMorgan Chase
π― I want to test it myself | β 5-minute quickstart |
π οΈ I want to see an example | β Healthcare agent example |
π I want to get involved | β Join our Discord community |
- π¬ Discord Community - Get help from the team and community
- π Documentation - Comprehensive guides and examples
- π GitHub Issues - Bug reports and feature requests
- π§ 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?
β Star this repo β’ π Try Parlant now β’ π¬ Join Discord
Built with β€οΈ by the team at Emcie
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