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Showing 10 open source projects for "set"
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OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows
The OpenAI Agents Python SDK is a powerful yet lightweight framework for developing multi-agent workflows. This framework enables developers to create and manage agents that can coordinate tasks autonomously, using a set of instructions, tools, guardrails, and handoffs. The SDK allows users to configure workflows in which agents can pass control to other agents as necessary, ensuring dynamic task management. It also includes a built-in tracing system for tracking, debugging, and optimizing agent activities. -
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Self-Operating Computer
A framework to enable multimodal models to operate a computer
...Notably, it was the first known project to implement a multimodal model capable of viewing and controlling a computer screen. The framework supports features like Optical Character Recognition (OCR) and Set-of-Mark (SoM) prompting to enhance visual grounding capabilities. It is designed to be compatible with macOS, Windows, and Linux (with X server installed), and is released under the MIT license. -
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Atomic Agents
Building AI agents, atomically
The Atomic Agents framework is designed around the concept of atomicity to be an extremely lightweight and modular framework for building Agentic AI pipelines and applications without sacrificing developer experience and maintainability. The framework provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization. All logic and control flows are written in Python, enabling developers to apply familiar best practices and workflows from traditional software development without compromising flexibility or clarity. -
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AutoGPT
Powerful tool that lets you create and run intelligent agents
AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, AutoGPT pushes the boundaries of what is possible with AI. -
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kagent
Kubernetes native framework for building AI agents
...It models core agent concepts declaratively using Kubernetes custom resources, so teams can manage agents similarly to other platform components via YAML, controllers, and standard cluster workflows. In kagent’s design, an “Agent” represents a system prompt plus a set of tools and other agents, along with an LLM configuration, making the agent definition portable and repeatable across environments. It supports multiple model providers through a dedicated configuration resource, allowing teams to switch providers or run mixed environments while keeping the agent spec stable. A major focus is tool integration via MCP: agents can connect to MCP servers for tool access, and kagent includes an MCP server with tools for common Kubernetes and platform engineering systems. -
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GitAgent
A framework-agnostic, git-native standard for defining AI agents
GitAgent is an open standard and toolkit for defining portable AI agents using Git repositories as their foundational structure. The core idea behind the project is that an AI agent can be fully described by a set of files stored in a repository, allowing developers to clone the repository and instantly obtain a runnable agent. Unlike many frameworks that tightly couple agents to specific ecosystems, GitAgent is designed to be framework-agnostic so that the same agent definition can operate across multiple platforms and AI tooling environments. ... -
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Agents-Flex
Agents-Flex is an elegant LLM Application Framework like LangChain
Agents-Flex includes a variety of network protocols for connecting LLMs, such as HTTP, SSE and WS. Its simple and flexible design allows developers to easily connect to various LLMs, including OpenAI, LLama, and other AI. Agents-Flex provides a rich set of development templates and Prompt Frameworks, including FEW-SHOT, CRISPE, BROKE, and ICIO. Developers can also customize their own unique prompt templates. Agents-Flex has a very flexible Function Calling component. It supports local method definitions, parsing, callbacks through LLMs, and executing local methods to obtain results. ... -
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Habitat-Lab
A modular high-level library to train embodied AI agents
...Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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Agent Payments Protocol (AP2)
Building a Secure and Interoperable Future for AI-Driven Payments
AP2 is a project released by Google’s “Agentic Commerce” initiative, focusing on a protocol and reference implementation for agent-driven or AI-mediated payments. In effect, AP2 aims to define a secure, interoperable protocol that allows software agents to act on behalf of users—making payments or shopping decisions autonomously—while preserving necessary security, auditability, and trust. The repository contains sample scenarios (in Python, Android, etc.) that illustrate how agents,... -
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AgentVerse
Designed to facilitate the deployment of multiple LLM-based agents
AgentVerse is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation.
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