---
title: "parlant"
type: "tool"
slug: "emcie-co-parlant"
canonical_url: "https://www.graphcanon.com/tools/emcie-co-parlant"
github_url: "https://github.com/emcie-co/parlant"
homepage_url: "https://www.parlant.io"
stars: 18166
forks: 1534
primary_language: "Python"
license: "Apache-2.0"
categories: ["ai-agents"]
tags: ["customer-service", "ai-alignment", "genai", "llm", "gemini", "customer-success", "llama3", "ai-agents"]
updated_at: "2026-07-07T18:27:54.720451+00:00"
---

# parlant

> Interaction control harness for customer-facing AI agents

Parlant optimizes Large Language Model (LLM) interactions to ensure reliability, consistency, and predictability in customer service and support scenarios.

## Facts

- Repository: https://github.com/emcie-co/parlant
- Homepage: https://www.parlant.io
- Stars: 18,166 · Forks: 1,534 · Open issues: 41 · Watchers: 102
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-06-30T17:57:05+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)

## Tags

customer-service, ai-alignment, genai, llm, gemini, customer-success, llama3, ai-agents

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## README (excerpt)

```text
<div align="center">

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://github.com/emcie-co/parlant/blob/develop/docs/LogoTransparentLight.png?raw=true">
  <img alt="Parlant" src="https://github.com/emcie-co/parlant/blob/develop/docs/LogoTransparentDark.png?raw=true" width=400 />
</picture>

### The interaction control harness for customer-facing AI agents

<p>
  <a href="https://pypi.org/project/parlant/"><img alt="PyPI" src="https://img.shields.io/pypi/v/parlant?color=blue"></a>
  <img alt="Python 3.10+" src="https://img.shields.io/badge/python-3.10+-blue">
  <a href="https://opensource.org/licenses/Apache-2.0"><img alt="License" src="https://img.shields.io/badge/license-Apache%202.0-green"></a>
  <a href="https://discord.gg/duxWqxKk6J"><img alt="Discord" src="https://img.shields.io/discord/1312378700993663007?color=7289da&logo=discord&logoColor=white"></a>
  <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/emcie-co/parlant?style=social">
</p>

<p>
  <a href="https://www.parlant.io/" target="_blank">Website</a> &bull;
  <a href="https://www.parlant.io/docs/quickstart/installation" target="_blank">Quick Start</a> &bull;
  <a href="https://www.parlant.io/docs/quickstart/examples" target="_blank">Examples</a> &bull;
  <a href="https://discord.gg/duxWqxKk6J" target="_blank">Discord</a>
</p>

<p>
  <a href="https://zdoc.app/de/emcie-co/parlant">Deutsch</a> |
  <a href="https://zdoc.app/es/emcie-co/parlant">Español</a> |
  <a href="https://zdoc.app/fr/emcie-co/parlant">français</a> |
  <a href="https://zdoc.app/ja/emcie-co/parlant">日本語</a> |
  <a href="https://zdoc.app/ko/emcie-co/parlant">한국어</a> |
  <a href="https://zdoc.app/pt/emcie-co/parlant">Português</a> |
  <a href="https://zdoc.app/ru/emcie-co/parlant">Русский</a> |
  <a href="https://zdoc.app/zh/emcie-co/parlant">中文</a>
</p>

<a href="https://trendshift.io/repositories/12768" target="_blank">
  <img src="https://trendshift.io/api/badge/repositories/12768" alt="Trending" style="width: 250px; height: 55px;" width="250" height="55"/>
</a>

</div>

&nbsp;

> **Looking for an open-source alternative to Ada, Decagon, or Sierra?**

**Parlant is production-ready. It streamlines the development and maintenance of enterprise-grade B2C (business-to-consumer) and sensitive B2B interactions that need to be consistent, compliant, on-brand, and comprehensively traceable.**

## Why Parlant?

Conversational context engineering is hard because real-world interactions are diverse, nuanced, and non-linear.

### ❌ The Problem: What you've probably tried and couldn't get to work at scale
**System prompts** work until production complexity kicks in. The more instructions you add to a prompt, the faster your agent stops paying attention to any of them.

**Routed graphs** solve the prompt-overload problem, but the more routing you add, the more fragile it becomes when faced with the chaos of natural interactions.

### 🔑 The Solution: Context engineering, optimized for conversational control
Parlant is an agentic harness offering optimized [context engineering](https://www.gartner.com/en/articles/context-engineering) for conversational use cases: getting the right context, no more and no less, into the prompt at the right time. You define rules, knowledge, and tools once, while the engine narrows the context down in real-time to what's immediately relevant to each turn of the conversation.

<img alt="Parlant Demo" src="https://github.com/emcie-co/parlant/blob/develop/docs/demo.gif?raw=true" width="100%" />

### How is Parlant different from LangGraph or DSPy?
Parlant focuses on conversational governance and behavioral control and consistency, while LangGraph is ideal for workflow automation, and DSPy is ideal for low-level prompt optimization.

## Design goals

Parlant is built around three goals that shape every decision in the framework:

### 1. Maximum control over the conversation experience

Parlant was designed around a simple idea: developers sh
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/emcie-co-parlant`](/api/graphcanon/tools/emcie-co-parlant)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_