Showing 10 open source projects for "combine"

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  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

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  • 1
    DeerFlow

    DeerFlow

    Deep Research framework, combining language models with tools

    DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. “planner,” “searcher,” “coder,” “report generator”) that collaborate in a structured workflow, allowing tasks like literature reviews, data gathering, data analysis, code execution, and final report generation to be largely automated. ...
    Downloads: 159 This Week
    Last Update:
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  • 2
    Semantic Kernel

    Semantic Kernel

    Integrate cutting-edge LLM technology quickly and easily into your app

    Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds. To help developers build their own Copilot experiences on top of AI plugins, we have released Semantic Kernel, a lightweight open-source SDK that allows you to orchestrate AI plugins.
    Downloads: 0 This Week
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  • 3
    AI-Trader

    AI-Trader

    100% Fully-Automated Agent-Native Trading

    AI-Trader is an open-source AI-powered quantitative trading framework designed to combine financial analysis, machine learning, and autonomous trading workflows into a unified research platform. The project integrates large language models, financial indicators, market analysis pipelines, and automated decision-making systems to support strategy generation and market prediction tasks. It is built to help researchers and developers experiment with AI-assisted trading strategies using historical and real-time financial data. ...
    Downloads: 4 This Week
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  • 4
    Grok CLI

    Grok CLI

    An open-source AI agent that brings the power of Grok

    ...The CLI supports streaming responses, so outputs appear in real time as the Grok model generates them, making interactions feel responsive and fluid in terminal contexts. Grok CLI is designed to integrate with existing terminal habits—aliases, pipes, editors, and tooling—so you can combine AI assistance with native command-line workflows like grep, awk, and git. It also includes authentication support, configuration management, and caching options so frequent queries are efficient.
    Downloads: 13 This Week
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    MongoDB Atlas runs apps anywhere

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  • 5
    HybridClaw

    HybridClaw

    The enterprise operating layer for open agents

    HybridClaw is an emerging open-source framework focused on enabling hybrid AI agent systems that combine local execution, tool integration, and multi-agent orchestration into a cohesive development environment. It is designed to work alongside modern agent ecosystems such as OpenClaw, Claude Code, and similar agentic coding tools, providing a flexible infrastructure for managing agent behaviors, workflows, and capabilities. The project emphasizes modularity, allowing developers to define and compose “skills” or capabilities that agents can invoke dynamically, enabling more adaptive and context-aware automation. ...
    Downloads: 6 This Week
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  • 6
    Open Agents

    Open Agents

    An open source template for building cloud agents

    ...The project also includes examples and templates that demonstrate how to build and deploy agents for real-world applications. By prioritizing composability, it allows developers to combine simple components into more complex agent systems. Overall, open-agents serves as a playground for building and experimenting with next-generation AI agent architectures.
    Downloads: 1 This Week
    Last Update:
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  • 7
    Youtu-Agent

    Youtu-Agent

    A simple yet powerful agent framework that delivers with models

    ...The framework supports automated generation of agent components, enabling the system to synthesize prompts, tool interfaces, and workflow configurations automatically. Youtu-Agent also incorporates hybrid learning strategies that combine experience accumulation with reinforcement learning to improve agent performance over time. These learning mechanisms allow agents to refine their reasoning, coding, and search capabilities as they interact with environments and tasks.
    Downloads: 1 This Week
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  • 8
    MetaClaw

    MetaClaw

    Just talk to your agent

    ...The architecture suggests scalability, allowing the system to handle multiple agents or complex workflows simultaneously. It is likely designed for experimentation with next-generation agent systems that combine planning, learning, and execution. Overall, MetaClaw represents a research-driven effort to push the boundaries of intelligent agent coordination and adaptability.
    Downloads: 0 This Week
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  • 9
    TypeAgent Python

    TypeAgent Python

    Structured RAG: ingest, index, query

    ...Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language interactions into structured representations that can be processed by deterministic software components. This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. The repository is intended primarily as a research prototype and sample code rather than a production-ready framework, allowing developers to experiment with building AI agents that maintain structured memory and perform tasks through defined actions.
    Downloads: 0 This Week
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  • 10
    DevOpsGPT

    DevOpsGPT

    Multi agent system for AI-driven software development

    Welcome to the AI Driven Software Development Automation Solution, abbreviated as DevOpsGPT. We combine LLM (Large Language Model) with DevOps tools to convert natural language requirements into working software. This innovative feature greatly improves development efficiency, shortens development cycles, and reduces communication costs, resulting in higher-quality software delivery. The automated software development process significantly reduces delivery time, accelerating software deployment and iterations. ...
    Downloads: 0 This Week
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