Langroid is an open-source Python framework designed to streamline the development of applications powered by Large Language Models (LLMs). It introduces a multi-agent programming paradigm, treating agents as first-class citizens to manage multiple LLM conversations, each responsible for different aspects of a task. This approach facilitates efficient collaboration through message exchange, enabling developers to build complex, intelligent applications with ease. Langroid supports integration with various LLMs, vector databases, and function-calling tools, offering a modular and extensible architecture for AI application development.

langroid

An open-source Python framework that simplifies LLM application development using a multi-agent programming paradigm.

Langroid

ChatArena is an open-source Python framework designed to facilitate the development of multi-agent language game environments for large language models (LLMs). It provides a flexible structure for defining multiple players, environments, and their interactions, enabling researchers and developers to explore and enhance AI communication and collaboration skills. With user-friendly interfaces, including both web UI and command-line options, ChatArena supports the creation of diverse language game scenarios, such as conversational simulations, educational settings, and social deduction games.

chatarena

An open-source framework for creating multi-agent language game environments to enhance AI communication and collaboration capabilities.

ChatArena

FloAI is an open-source Python framework designed to streamline the development of composable AI agents and workflows. It enables developers to build, launch, and scale autonomous AI applications efficiently by providing a flexible, modular approach to constructing agent-based systems. With features like YAML-based configuration, support for various agent types, and integration with tools from the LangChain community, FloAI facilitates the creation of sophisticated AI solutions tailored to specific needs.

floai

An open-source Python framework that simplifies the creation of composable AI agents and workflows for complex tasks.

FloAI

Restack is an open-source framework designed to assist engineers in creating, deploying, and scaling autonomous AI applications. It offers a comprehensive suite of tools, including the Restack AI Framework for coding multistep workflows with features like concurrency, scheduling, retries, and rate-limiting. Additionally, the Restack Desktop provides a developer environment with a user interface to visualize and debug workflows, enhancing the development process. By leveraging Restack, developers can build resilient AI products capable of running long-lived processes with persistent state, facilitating the creation of autonomous agents that self-improve over time based on feedback loops and long-term memory.

restack

An open-source framework empowering developers to build, launch, and scale autonomous AI products efficiently.

Restack

Guardrails AI is an open-source Python framework that helps developers build reliable AI applications by adding guardrails to large language models (LLMs). It performs two key functions: running input/output guards to detect and mitigate risks, and generating structured data from LLMs. Guardrails can be integrated with any LLM, providing features such as real-time hallucination detection, validation of generated text for toxicity, truthfulness, and PII compliance, and serving as a standalone service via a REST API. The framework supports streaming validation, structured data generation, and monitoring, enhancing the safety and reliability of AI applications.

guardrails-ai

An open-source Python framework designed to add guardrails to large language models, ensuring reliable and safe AI application development.

Guardrails AI

Ax is an open-source TypeScript framework designed to facilitate the development of agents and workflows powered by large language models (LLMs). Inspired by Stanford's DSPy paper, Ax provides tools for creating chat interfaces, retrieval-augmented generation (RAG) systems, streaming applications, and multi-modal interactions. It supports various LLMs and vector databases, offers automatic prompt generation from simple signatures, and includes features like output validation during streaming, multi-agent orchestration, and OpenTelemetry tracing for observability. Ax aims to streamline the creation of efficient, production-ready AI applications.

An open-source TypeScript framework for building LLM-powered agents and agentic workflows.

LiteLLM is an open-source toolkit that streamlines interactions with over 100 large language models (LLMs) by providing a unified API in the OpenAI format. It offers both a Python SDK and a proxy server (LLM Gateway) to manage authentication, load balancing, and spend tracking across various LLM providers, including OpenAI, Azure OpenAI, Vertex AI, and Amazon Bedrock. LiteLLM supports features such as retry and fallback logic, rate limiting, and logging integrations with tools like Langfuse and OpenTelemetry. It is designed to simplify the integration of multiple LLMs into applications, ensuring consistent output formats and efficient resource management.

litellm

An open-source Python SDK and proxy server for managing authentication, load balancing, and spend tracking across 100+ LLMs using a unified OpenAI format.

LiteLLM

LlamaGym is an open-source Python framework designed to simplify the fine-tuning of large language model (LLM) agents through online reinforcement learning. By providing a standardized environment similar to OpenAI's Gym, LlamaGym allows developers to efficiently train LLM-based agents by managing conversation context, episode batching, reward assignment, and proximal policy optimization (PPO) setup. This framework enables rapid experimentation with agent prompting and hyperparameters across various Gym environments, facilitating the development of more capable and responsive AI agents.

llamagym

An open-source Python framework for fine-tuning large language model (LLM) agents using online reinforcement learning.

LlamaGym

LangGraph is an open-source library designed to facilitate the development of stateful, multi-actor applications utilizing large language models (LLMs). It enables the creation of complex agent and multi-agent workflows, supporting features such as cycles, controllability, and persistence. LangGraph allows developers to define flows involving cycles, essential for most agentic architectures, and provides fine-grained control over both the flow and state of applications. Additionally, it includes built-in persistence, enabling advanced human-in-the-loop and memory features, making it suitable for creating reliable, fault-tolerant agent-based systems.

langgraph

An open-source AI framework for building stateful, multi-actor applications with large language models (LLMs).

LangGraph

LlamaIndex is an open-source data framework designed to facilitate the integration of large language models (LLMs) with custom data sources, enabling the development of AI-powered applications. It provides a flexible structure for building agentic generative AI applications, allowing LLMs to work with data in various formats. LlamaIndex offers state-of-the-art retrieval-augmented generation (RAG) algorithms, reliable integrations across data loading, indexing, and retrieval, and supports the creation of LLM-powered agents capable of performing complex workflows over data and services. With a focus on production readiness, LlamaIndex serves as a comprehensive solution for developers aiming to build knowledge assistants and other AI applications over enterprise data.

llamaindex

An open-source data framework that enables large language models to interact with custom data sources for building AI applications.

LlamaIndex

The Multi-Agent Orchestrator is an open-source framework developed by AWS Labs, designed to manage multiple AI agents and handle complex conversations efficiently. It intelligently routes user queries to the most suitable agent based on context and content, maintaining coherent dialogues across multiple domains. With dual language support in Python and TypeScript, it offers pre-built components for quick deployment and allows easy integration of custom agents and conversation message storage solutions. Key features include intelligent intent classification, flexible agent responses supporting both streaming and non-streaming interactions, and seamless coordination of multiple AI agents within a single system.

multi-agent-orchestrator

A flexible and powerful open-source framework for managing multiple AI agents and handling complex conversations.

Multi-Agent Orchestrator

DSPy, which stands for Declarative Self-improving Python, is an open-source framework designed to facilitate the programming of language models (LMs) rather than relying on traditional prompt engineering. It allows developers to rapidly build modular AI systems, offering algorithms for optimizing prompts and weights. Whether constructing simple classifiers, sophisticated retrieval-augmented generation (RAG) pipelines, or agent loops, DSPy provides a compositional Pythonic approach to enhance the quality and reliability of AI outputs.

dspy

An open-source Python framework for programming language models, enabling rapid development of modular AI systems with optimization capabilities.

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