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*[ADK](https://github.com/google/adk-python) - ADK is Google's Agent Development Kit for Python, a framework for building production-ready AI agents.
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*[Agent Lightning](https://github.com/microsoft/agent-lightning) - Agent Lightning is a framework for building production-ready AI agents with Lightning AI.
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*[AgentAPI](https://github.com/coder/agentapi) - Control Claude Code, AmazonQ, Opencode, Goose, Aider, Gemini, GitHub Copilot, Sourcegraph Amp, Codex, Auggie, and Cursor CLI with an HTTP API.
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*[AgentLab](https://github.com/ServiceNow/AgentLab) - AgentLab is an open-source framework for developing, testing, and benchmarking web agents on diverse tasks, designed for scalability and reproducibility.
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*[AgentKit](https://github.com/inngest/agent-kit) - AgentKit help agent developers build multi-agent networks with deterministic routing and rich tooling via MCP.
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*[AgentOps](https://github.com/AgentOps-AI/agentops) - AgentOps helps developers build, evaluate, and monitor AI agents from prototype to production.
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*[Agents](https://github.com/livekit/agents) - Agents allows users to build AI-driven server programs that can see, hear, and speak in realtime.
*[Hephaestus](https://github.com/Ido-Levi/Hephaestus) - Hephaestus is an open-source, semi-structured agentic framework where AI agents dynamically build workflows and tasks as they discover needs, instead of adhering to predefined plans.
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*[IntellAgent](https://github.com/plurai-ai/intellagent) - IntellAgent is an advanced multi-agent framework that transforms the evaluation and optimization of conversational agents.
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*[kagent](https://github.com/kagent-dev/kagent) - kagent is a Kubernetes native framework for building AI agents.
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*[KAOS](https://github.com/axsaucedo/kaos) - KAOS is a Kubernetes-native framework for deploying and orchestrating AI agents with tool access, multi-agent coordination, and seamless LLM integration.
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*[LangGraph](https://github.com/langchain-ai/langgraph) - LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.
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*[Modelscope-Agent](https://github.com/modelscope/modelscope-agent) - Modelscope-Agent is a customizable and scalable agent framework.
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*[n8n](https://github.com/n8n-io/n8n) - n8n is a workflow automation platform that gives technical teams the flexibility of code with the speed of no-code.
*[GenAI Processors](https://github.com/google-gemini/genai-processors) - GenAI Processors is a lightweight Python library that enables efficient, parallel content processing.
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*[LitServe](https://github.com/Lightning-AI/LitServe) - LitServe is a minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.
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*[SGLang](https://github.com/sgl-project/sglang) - SGLang is a fast serving framework for large language models and vision language models.
*[DeepTeam](https://github.com/msoedov/agentic_security) - Agentic Security is a vulnerability scanner for agentic workflows, protecting AI systems from jailbreaks, fuzzing, and multimodal attacks.
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*[DeepTeam](https://github.com/confident-ai/deepteam) - DeepTeam is a simple-to-use, open-source LLM red teaming framework, for penetration testing and safe guarding large-language model systems.
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*[promptfoo](https://github.com/promptfoo/promptfoo) - promptfoo is an LLM red teaming and evaluation framework for testing jailbreaks, prompt injection, and vulnerabilities with adversarial attacks and CI/CD integration.
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*[ps-fuzz](https://github.com/prompt-security/ps-fuzz) - ps-fuzz is a tool to test and harden GenAI system prompts against security vulnerabilities and adversarial attacks.
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*[Purple Llama](https://github.com/meta-llama/PurpleLlama) - Purple Llama is a set of tools to assess and improve LLM security for building responsible GenAI models.
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*[Rogue](https://github.com/qualifire-dev/rogue) - Rogue is an AI agent evaluator and red team platform for testing agents against business policies and security vulnerabilities.
*[ell](https://github.com/MadcowD/ell) - ell is a language model programming library that treats prompts as programs. Features automatic versioning, serialization, and rich tooling for prompt engineering with Ell Studio for visualization.
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*[Latitude](https://github.com/latitude-dev/latitude-llm) - Latitude is the open-source prompt engineering platform to build, evaluate, and refine prompts with AI. Features prompt management, playground testing, AI gateway, and evaluations.
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*[PromptIDE](https://x.ai/blog/prompt-ide) - PromptIDE by xAI is an integrated development environment for prompt engineering and interpretability research, providing transparent access to Grok-1 with rich analytics and Python SDK support.
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*[PromptLayer](https://github.com/MagnivOrg/prompt-layer-library) - PromptLayer is a platform that allows you to track, manage, and share your GPT prompt engineering by acting as middleware to log all OpenAI API requests.
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*[PromptSource](https://github.com/bigscience-workshop/promptsource) - PromptSource is a toolkit for creating, sharing and using natural language prompts. Contains a growing collection of prompts (P3: Public Pool of Prompts) written in Jinja templating language.
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*[Prompty](https://github.com/microsoft/prompty) - Prompty makes it easy to create, manage, debug, and evaluate LLM prompts for AI applications. An asset class and format for LLM prompts designed to enhance observability, understandability, and portability.
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