emcie-co / parlant
LLM agents built for control. Designed for real-world use. Deployed in minutes.
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Repository Summary (README)
Previewπ― The Problem Every AI Developer Faces
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.
β‘ 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 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
- π Blog: Parlant vs LangGraph
- π Blog: Parlant vs DSPy
- βοΈ Blog: Inside Parlant's Guideline Matching Engine
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.
How It Works
When your agent receives a message, Parlant's engine prepares a fully-aligned response before generating it:
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#e8f5e9', 'primaryTextColor': '#1b5e20', 'primaryBorderColor': '#81c784', 'lineColor': '#66bb6a', 'secondaryColor': '#fff9e1', 'tertiaryColor': '#F3F5F6'}}}%%
flowchart LR
A(User):::outputNode
subgraph Engine["Parlant Engine"]
direction LR
B["Match Guidelines and Resolve Journey States"]:::matchNode
C["Call Contextually-Associated Tools"]:::toolNode
D["Generated Message"]:::composeNode
E["Canned Message"]:::cannedNode
end
A a@-->|π¬ User Input| B
B b@--> C
C c@-->|Fluid Output Mode?| D
C d@-->|Strict Output Mode?| E
D e@-->|π¬ Fluid Output| A
E f@-->|π¬ Canned Output| A
a@{animate: true}
b@{animate: true}
c@{animate: true}
d@{animate: true}
e@{animate: true}
f@{animate: true}
linkStyle 2 stroke-width:2px
linkStyle 4 stroke-width:2px
linkStyle 3 stroke-width:2px,stroke:#3949AB
linkStyle 5 stroke-width:2px,stroke:#3949AB
classDef composeNode fill:#F9E9CB,stroke:#AB8139,stroke-width:2px,color:#7E5E1A,stroke-width:0
classDef cannedNode fill:#DFE3F9,stroke:#3949AB,stroke-width:2px,color:#1a237e,stroke-width:0
The guidelines and tools relevant to the current conversational state are carefully matched and enforced, keeping your agent focused and aligned, even with complex behavioral configurations.
<div align="center">π Get Your Agent Running in 60 Seconds
</div>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 tips and 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.
π¬ See It In Action
<img alt="Parlant Demo" src="https://github.com/emcie-co/parlant/blob/develop/docs/demo.gif?raw=true" width="100%" />π§ͺ Test Your Agent
Validate agent behavior with the integrated testing & evaluation framework.
from parlant.testing import Suite, InteractionBuilder
from parlant.testing.steps import AgentMessage, CustomerMessage
suite = Suite(server_url="http://localhost:8800", agent_id="your_agent")
@suite.scenario
async def test_booking_flow():
async with suite.session() as session:
# Build conversation history
history = (
InteractionBuilder()
.step(CustomerMessage("Man it's cold today"))
.step(AgentMessage("Tell me about it, I'm freezing my nuts and bolts off."))
.step(CustomerMessage("Where are you from? I'm from Boston"))
.step(AgentMessage("What a dream! I'm stuck in a data center in San Fran..."))
.build()
)
# Preload session with event history
await session.add_events(history)
# Send customer message
response = await session.send("What's the temperature there today?")
# Assert on agent response using LLM-as-a-Judge
await response.should("provide weather details for San Francisco")
Run with: parlant-test your_tests.py
π₯ Why Developers Are Switching to Parlant
<table width="100%"> <tr> <td width="50%">ποΈ Traditional AI Frameworks
</td> <td width="50%">β‘ Parlant
</td> </tr> <tr> <td width="50%">- 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
π― Perfect For Your Use Case
<div align="center">| 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 οΈ Enterprise-Grade Features
- 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
π Join 10,000+ Developers Building Better AI
<div align="center">Companies using Parlant:
Financial institutions β’ Healthcare providers β’ Legal firms β’ E-commerce platforms
π What Developers Are Saying
"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
πββοΈ Quick Start Paths
<table border="0"> <tr> <td><strong>π― I want to test it myself</strong></td> <td><a href="https://www.parlant.io/docs/quickstart/installation">β 5-minute quickstart</a></td> </tr> <tr> <td><strong>π οΈ I want to see an example</strong></td> <td><a href="https://www.parlant.io/docs/quickstart/examples">β Healthcare agent example</a></td> </tr> <tr> <td><strong>π I want to get involved</strong></td> <td><a href="https://discord.gg/duxWqxKk6J">β Join our Discord community</a></td> </tr> </table>π€ Community & Support
- 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
π License
Apache 2.0 - Use it anywhere, including commercial projects.
<div align="center">
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
</div>