parlant

A client/server framework for guided customer-facing agents based on LLMs

Stars: 148

Visit

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:

Parlant

A structured approach to building and guiding customer-facing AI agents

Website | Introduction | Installation | Tutorial | About

What is Parlant?

Parlant is a structured way to create and manage guided customer-facing AI agents. It lets you build robust agents from scratch; or, in more advanced cases, a robust interaction layer for your prebuilt agents.

Structure is what lets Parlant get better accuracy than most other agentic frameworks. It allows Parlant to run pinpointed, real-time conformance checks that ensure your agents adhere to your instruction. It also gives you specific feedback on where agents may have misinterpreted your instructions, so you can quickly troubleshoot and improve.

Just as importantly, Parlant's way of breaking behavioral guidance down to different elements helps it understand your intentions better. Using this understanding, Parlant actively gives you feedback on your configuration to help you maintain a behavioral configuration for your agents that's coherent and free of confusing contradictions.

Why use Parlant?

Building conversational AI agents is relatively simple for most developers—at least, it's relatively simple to build an initial prototype.

But these prototypes are rarely ready to meet customers. Once the prototype is functional, it has to be continually tuned so that its behavior actually provides customers with a good experience that business stakeholders approve.

With DIY prompt-engineering, reliably incorporating feedback from stakeholders is challenging, as simple implementations tend to be fragile and inconsistent.

Parlant bridges this gap by making it easy and fast for developers to adjust the behavior of AI agents reliably, allowing you to iterate quickly with feedback from customers and business stakeholders.

Real-world impact

Revenued, a business capital provider, uses Parlant to infuse human-like conversations for their Sales Copilot. They leverage Parlant's structured CLI to modify the agent's behavior quickly and easily based on feedback from company stakeholders.

Key benefits

Control that actually works

Guidelines: Control responses by writing contextual rules - like "offer limited time coupons if it's a holiday" or "make it very clear we don't offer loans if a customer asks about it". By using condition/action definitions, you define exactly when and how your agent should respond
Glossary: Teach your agent your business-specific terminology so that both you and your customers can speak to it naturally in your language
Coherence checks: Catch conflicts by having Parlant evaluate new guidelines against existing ones before they're applied
Dynamic context: Make your agent context-aware by setting user-specific variables like customer account or subscription tier. These shape how your agent responds to each user
Guided tool use: Control API access by linking tools to specific guidelines. This way, your agent only calls APIs when appropriate and with clear intent

Developer friendly

See changes instantly: Modify behavior on the fly by updating guidelines directly, no retraining or redeployment needed
Track changes in Git: Manage agent behavior like code by storing configuration as JSON in your repo. Review, rollback, branch, and merge just like any other code
Clean architecture: Separate concerns by keeping business logic in tools and conversation patterns in guidelines. Each piece does what it does best
Type safety: Build rapidly using native TypeScript/JavaScript SDKs with proper type definitions

Deploy with confidence

Reliable at scale: Parlant filters and selects guidelines per context, allowing you to scale your agent's complexity and use-cases while maintaining consistent, focused behavior
Debug with ease: Troubleshoot effectively by tracing which guidelines were applied and why for any given response
Test before deploy: Validate changes using the built-in chat UI to test new behaviors before they reach customers

Works with all major LLM providers

OpenAI (also via Azure)
Gemini
Meta Llama 3 (via Together AI or Cerebras)
Anthropic (also via AWS Bedrock)

Getting started

$ pip install parlant
$ parlant-server
$ # Open http://localhost:8000 and play

Install client SDKs:

$ # For Python clients:
$ pip install parlant-client
$ # For TypeScript clients:
$ npm install parlant-client

To start building with Parlant, visit our documentation portal.

Need help? Send us a message on Discord. We're happy to answer questions and help you get up and running!

Usage Example

Adding a guideline for an agent—for example, to ask a counter-question to get more info when a customer asks a question:

parlant guideline create \
    --agent-id CUSTOMER_SUCCESS_AGENT_ID \
    --condition "a free-tier customer is asking how to use our product" \
    --action "first seek to understsand what they're trying to achieve"

In Parlant, Customer-Agent interaction happens asynchronously, to enable more natural customer interactions, rather than forcing a strict and unnatural request-reply mode.

Here's a basic example of a simple client (using the TypeScript client SDK):

import { ParlantClient } from 'parlant-client';

const client = ParlantClient({ environment: SERVER_ADDRESS });

session_id = "...";

// Post customer message
const customerEvent = await client.sessions.createEvent(session_id, {
   kind: "message",
   source: "customer",
   message: "Hey, I'd like to book a room please",
});

// Wait for and get the agent's reply
const [agentEvent] = (await client.sessions.listEvents(session_id, {
   kinds: "message",
   source: "ai_agent",
   minOffset: customerEvent.offset,
   waitForData: 60 // Wait up to 60 seconds for an answer
}));

// Print the agent's reply
const { agentMessage } = agentEvent.data as { message: string };
console.log(agentMessage);

// Inspect the details of the message generation process
const { trace } = await client.sessions.inspectEvent(
   session_id,
   agentEvent.id
);

Contributing

We're currently finalizing our Contributor License Agreement (CLA). Full contribution guidelines will be available soon!

Want to get involved? Join us on Discord and let's discuss how you can help shape Parlant. We're excited to work with contributors directly while we set up our formal processes!

For Tasks:

Click tags to check more tools for each tasks

customize responses debug agent behavior validate changes manage agent behavior scale agent complexity

For Jobs:

ai engineer conversational designer machine learning engineer chatbot developer customer experience specialist

Alternative AI tools for parlant

Similar Open Source Tools

parlant

github

: 148

pathway

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

github

: 8.4k

autogen

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

github

: 36.2k

reductstore

ReductStore is a high-performance time series database designed for storing and managing large amounts of unstructured blob data. It offers features such as real-time querying, batching data, and HTTP(S) API for edge computing, computer vision, and IoT applications. The database ensures data integrity, implements retention policies, and provides efficient data access, making it a cost-effective solution for applications requiring unstructured data storage and access at specific time intervals.

github

: 170

kitops

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

github

: 586

SalesGPT

SalesGPT is an open-source AI agent designed for sales, utilizing context-awareness and LLMs to work across various communication channels like voice, email, and texting. It aims to enhance sales conversations by understanding the stage of the conversation and providing tools like product knowledge base to reduce errors. The agent can autonomously generate payment links, handle objections, and close sales. It also offers features like automated email communication, meeting scheduling, and integration with various LLMs for customization. SalesGPT is optimized for low latency in voice channels and ensures human supervision where necessary. The tool provides enterprise-grade security and supports LangSmith tracing for monitoring and evaluation of intelligent agents built on LLM frameworks.

github

: 1.9k

OmAgent

OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

github

: 1.4k

copilot

OpenCopilot is a tool that allows users to create their own AI copilot for their products. It integrates with APIs to execute calls as needed, using LLMs to determine the appropriate endpoint and payload. Users can define API actions, validate schemas, and integrate a user-friendly chat bubble into their SaaS app. The tool is capable of calling APIs, transforming responses, and populating request fields based on context. It is not suitable for handling large APIs without JSON transformers. Users can teach the copilot via flows and embed it in their app with minimal code.

github

: 5.1k

AgentPilot

Agent Pilot is an open source desktop app for creating, managing, and chatting with AI agents. It features multi-agent, branching chats with various providers through LiteLLM. Users can combine models from different providers, configure interactions, and run code using the built-in Open Interpreter. The tool allows users to create agents, manage chats, work with multi-agent workflows, branching workflows, context blocks, tools, and plugins. It also supports a code interpreter, scheduler, voice integration, and integration with various AI providers. Contributions to the project are welcome, and users can report known issues for improvement.

github

: 295

design-studio

Tiledesk Design Studio is an open-source, no-code development platform for creating chatbots and conversational apps. It offers a user-friendly, drag-and-drop interface with pre-ready actions and integrations. The platform combines the power of LLM/GPT AI with a flexible 'graph' approach for creating conversations and automations with ease. Users can automate customer conversations, prototype conversations, integrate ChatGPT, enhance user experience with multimedia, provide personalized product recommendations, set conditions, use random replies, connect to other tools like HubSpot CRM, integrate with WhatsApp, send emails, and seamlessly enhance existing setups.

github

: 169

crewAI

crewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It provides a flexible and structured approach to AI collaboration, enabling users to define agents with specific roles, goals, and tools, and assign them tasks within a customizable process. crewAI supports integration with various LLMs, including OpenAI, and offers features such as autonomous task delegation, flexible task management, and output parsing. It is open-source and welcomes contributions, with a focus on improving the library based on usage data collected through anonymous telemetry.

github

: 17.2k

doc2plan

doc2plan is a browser-based application that helps users create personalized learning plans by extracting content from documents. It features a Creator for manual or AI-assisted plan construction and a Viewer for interactive plan navigation. Users can extract chapters, key topics, generate quizzes, and track progress. The application includes AI-driven content extraction, quiz generation, progress tracking, plan import/export, assistant management, customizable settings, viewer chat with text-to-speech and speech-to-text support, and integration with various Retrieval-Augmented Generation (RAG) models. It aims to simplify the creation of comprehensive learning modules tailored to individual needs.

github

: 138

EdgeChains

EdgeChains is an open-source chain-of-thought engineering framework tailored for Large Language Models (LLMs)- like OpenAI GPT, LLama2, Falcon, etc. - With a focus on enterprise-grade deployability and scalability. EdgeChains is specifically designed to **orchestrate** such applications. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure.

github

: 376

promptflow

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

github

: 9.2k

admyral

Admyral is an open-source Cybersecurity Automation & Investigation Assistant that provides a unified console for investigations and incident handling, workflow automation creation, automatic alert investigation, and next step suggestions for analysts. It aims to tackle alert fatigue and automate security workflows effectively by offering features like workflow actions, AI actions, case management, alert handling, and more. Admyral combines security automation and case management to streamline incident response processes and improve overall security posture. The tool is open-source, transparent, and community-driven, allowing users to self-host, contribute, and collaborate on integrations and features.

github

: 293

cody

Cody is a free, open-source AI coding assistant that can write and fix code, provide AI-generated autocomplete, and answer your coding questions. Cody fetches relevant code context from across your entire codebase to write better code that uses more of your codebase's APIs, impls, and idioms, with less hallucination.

github

: 2.9k

For similar tasks

wechat-bot

WeChat Bot is a simple and easy-to-use WeChat robot based on chatgpt and wechaty. It can help you automatically reply to WeChat messages or manage WeChat groups/friends. The tool requires configuration of AI services such as Xunfei, Kimi, or ChatGPT. Users can customize the tool to automatically reply to group or private chat messages based on predefined conditions. The tool supports running in Docker for easy deployment and provides a convenient way to interact with various AI services for WeChat automation.

github

: 5.0k

webwhiz

WebWhiz is an open-source tool that allows users to train ChatGPT on website data to build AI chatbots for customer queries. It offers easy integration, data-specific responses, regular data updates, no-code builder, chatbot customization, fine-tuning, and offline messaging. Users can create and train chatbots in a few simple steps by entering their website URL, automatically fetching and preparing training data, training ChatGPT, and embedding the chatbot on their website. WebWhiz can crawl websites monthly, collect text data and metadata, and process text data using tokens. Users can train custom data, but bringing custom open AI keys is not yet supported. The tool has no limitations on context size but may limit the number of pages based on the chosen plan. WebWhiz SDK is available on NPM, CDNs, and GitHub, and users can self-host it using Docker or manual setup involving MongoDB, Redis, Node, Python, and environment variables setup. For any issues, users can contact [email protected].

github

: 886

parlant

github

: 148

douyin-chatgpt-bot

Douyin ChatGPT Bot is an AI-driven system for automatic replies on Douyin, including comment and private message replies. It offers features such as comment filtering, customizable robot responses, and automated account management. The system aims to enhance user engagement and brand image on the Douyin platform, providing a seamless experience for managing interactions with followers and potential customers.

github

: 142

council

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

github

: 815

TEN-Agent

TEN Agent is an open-source multimodal agent powered by the world’s first real-time multimodal framework, TEN Framework. It offers high-performance real-time multimodal interactions, multi-language and multi-platform support, edge-cloud integration, flexibility beyond model limitations, and real-time agent state management. Users can easily build complex AI applications through drag-and-drop programming, integrating audio-visual tools, databases, RAG, and more.

github

: 3.7k

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 572

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 897

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 951

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.0k