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|[🧮 Computation Optimisation](#computation-optimisation)|[🏷️ Data Annotation & Synthesis](#data-annotation-and-synthesis)|[🧵 Data Pipeline](#data-pipeline)|
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|[📓 Data Science Notebook](#data-science-notebook)|[💾 Data Storage Optimisation](#data-storage-optimisation)|[💸 Data Stream Processing](#data-stream-processing)|
|[🏷️ Data Annotation & Synthesis](#data-annotation-and-synthesis)|[🧵 Data Pipeline](#data-pipeline)|[📓 Data Science Notebook](#data-science-notebook)|
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|[💾 Data Storage Optimisation](#data-storage-optimisation)|[💸 Data Stream Processing](#data-stream-processing)|[💪 Deployment & Serving](#deployment-and-serving)|
|[📊 Industry-strength Visualisation](#industry-strength-visualisation)|[📅 Metadata Management](#metadata-management)|[📜 Model, Data & Experiment Management](#model-data-and-experiment-management)|
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|[🔩 Model Storage Optimisation](#model-storage-optimisation)|[🔏 Privacy & Safety](#privacy-and-safety)|[🏁 Training Orchestration](#training-orchestration)|
*[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.
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*[AgentScope](https://github.com/modelscope/agentscope) - AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models.
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*[AgentStack](https://github.com/AgentOps-AI/AgentStack) - AgentStack scaffolds your agent stack.
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*[AIOpsLab](https://github.com/microsoft/AIOpsLab) - AIOpsLab is a holistic framework to enable the design, development, and evaluation of autonomous AIOps agents..
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*[AutoGen](https://github.com/microsoft/autogen) - AutoGen is an open-source framework for building AI agent systems.
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*[Chidori](https://github.com/ThousandBirdsInc/chidori) - Chidori is a reactive runtime that supports building robust AI agents using languages like Node.js, Python, and Rust, with a focus on reactivity and observability in agent workflows.
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*[Composio](https://github.com/ComposioHQ/composio) - Composio equip's your AI agents & LLMs with 100+ high-quality integrations via function calling.
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*[CrewAI](https://github.com/crewAIInc/crewAI) - CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents.
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*[Eko](https://github.com/FellouAI/eko) - Eko is a production-ready JavaScript framework that enables developers to create reliable agents, from simple commands to complex workflows.
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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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*[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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*[OpenAGI](https://github.com/agiresearch/OpenAGI) - OpenAGI is used as the agent creation package to build agents for AIOS.
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*[PydanticAI](https://github.com/pydantic/pydantic-ai) - PydanticAI is a Python agent framework designed to make it less painful to build production grade applications with Generative AI.
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*[Swarm](https://github.com/openai/swarm) - Swarm is an educational framework exploring ergonomic, lightweight multi-agent orchestration.
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*[Swarms](https://github.com/kyegomez/swarms) - Swarms is an enterprise grade and production ready multi-agent collaboration framework that enables you to orchestrate many agents to work collaboratively at scale to automate real-world activities.
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*[TensorZero](https://github.com/tensorzero/tensorzero) - TensorZero is an open-source framework for building production-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluations, and experimentation.
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## AutoML
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*[AutoGluon](https://github.com/autogluon/autogluon) - Automated feature, model, and hyperparameter selection for tabular, image, and text data on top of popular machine learning libraries (Scikit-Learn, LightGBM, CatBoost, PyTorch, MXNet).
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*[Autokeras](https://github.com/keras-team/autokeras) - AutoML library for Keras based on ["Auto-Keras: Efficient Neural Architecture Search with Network Morphism"](https://arxiv.org/abs/1806.10282).
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