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L2MAC (Large Language Model Automatic Computer) is an AI framework designed to generate extensive outputs, such as entire codebases or lengthy texts, from a single prompt. It employs a multi-agent system where each agent executes a portion of the task, effectively bypassing the fixed context window constraints of traditional LLMs. This approach allows L2MAC to produce coherent and large-scale outputs, significantly enhancing the capabilities of AI in software development and content creation.

L2MAC

l2mac

An AI framework enabling large-scale code generation by overcoming LLM context limitations.

TensorFlow is an end-to-end open-source platform for machine learning developed by the Google Brain Team. It offers a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning-powered applications efficiently. TensorFlow supports various programming languages, including Python, C++, and JavaScript, and is compatible with multiple platforms such as Linux, macOS, Windows, Android, and JavaScript. Since its initial release in 2015, TensorFlow has become one of the most widely adopted deep learning frameworks, facilitating tasks ranging from research prototyping to production deployment.

TensorFlow

tensorflow

An open-source machine learning platform developed by Google for building and deploying ML models.

Gwenflow, developed by Gwenlake, is an open-source framework designed to streamline the creation of customized, production-ready applications centered around autonomous AI agents and Large Language Models (LLMs). It provides developers with tools to integrate LLMs and agents seamlessly, enabling efficient and scalable solutions tailored to specific business or user needs. Gwenflow supports modular agent architecture, intuitive workflow representation, and an extensible system for tools and plugins, facilitating intelligent process automation, automated customer support systems, and AI-driven data processing pipelines.

Gwenflow

gwenflow

A framework for orchestrating applications powered by autonomous AI agents and LLMs.

The Temporal AI Agent is an open-source framework that leverages Temporal workflows to facilitate durable, multi-turn conversations with AI agents. Designed to collect information towards specific goals, the agent can execute various tools and handle complex tasks autonomously. It supports integration with multiple Large Language Models (LLMs) such as OpenAI's GPT-4, Google's Gemini, and Anthropic's Claude. The framework allows for dynamic goal setting and tool definitions, enabling the agent to work towards any objective as long as the appropriate tools and goals are provided. Temporal's robust workflow management ensures reliability and fault tolerance in the agent's operations.

Temporal AI Agent

temporal-ai-agent

An open-source AI agent framework utilizing Temporal workflows for durable, multi-turn conversations.

Inworld AI offers a comprehensive framework for creating real-time AI agents, enabling developers to build agentic experiences with client-side logic and local model inference. The platform is optimized for real-time data ingestion, low latency, and scalability, making it suitable for applications in gaming, entertainment, and other interactive media. Inworld AI integrates various AI components, tools, and infrastructure to assist developers in crafting dynamic and immersive experiences.

Inworld AI

inworld-ai

A framework for building real-time AI agents optimized for low latency and massive scale.

Stagehand is an open-source AI web browsing framework that enhances browser automation by integrating AI capabilities with Playwright. It offers three simple AI APIs—act, extract, and observe—that allow developers to build robust and resilient web automations using natural language instructions. Stagehand aims to simplify the creation of browser-based AI agents, making them more adaptable to dynamic web environments.

Stagehand

stagehand

An AI web browsing framework focused on simplicity and extensibility.

ElizaOS is an open-source protocol designed to facilitate the creation and operation of autonomous AI agents, known as 'Elizas.' It serves as the foundational framework for ai16z, the pioneering AI-led DAO that integrates artificial intelligence with blockchain technology to drive decentralized decision-making and innovation. ElizaOS provides developers with the tools to build AI agents capable of autonomous interactions, decision-making, and contributions within decentralized ecosystems, promoting transparency, security, and efficiency.

ElizaOS

elizaos

An open-source protocol for autonomous AI agents, powering ai16z—the first AI-led Decentralized Autonomous Organization (DAO).

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

restack

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

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

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.

LiveKit Agents is an open-source framework designed to facilitate the development of AI-driven server programs capable of real-time interaction through voice, video, and data channels. It enables the creation of programmable, multimodal AI agents that can process and generate audio, video, and text, integrating seamlessly with large language models (LLMs) and other AI models. The framework supports flexible integrations, including plugins for popular LLMs, speech-to-text (STT), text-to-speech (TTS), and voice activity detection (VAD) services. LiveKit Agents also offers built-in task scheduling, load balancing, and real-time media transport over WebRTC, making it suitable for applications such as AI voice assistants, call centers, transcription services, and real-time translation.

LiveKit Agents

livekit-agents

An open-source framework for building real-time, multimodal AI applications that can see, hear, and speak.

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

llamagym

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

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