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40 रिपॉजिटरी

Awesome GitHub RepositoriesModel Capability Extensions

Integrations and tools that enhance the functional output and performance of AI models.

Distinguishing note: Focuses on augmenting model capabilities through external tool integration.

Explore 40 awesome GitHub repositories matching artificial intelligence & ml · Model Capability Extensions. Refine with filters or upvote what's useful.

Awesome Model Capability Extensions GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • alishahryar1/free-claude-codeAlishahryar1 का अवतार

    Alishahryar1/free-claude-code

    34,843GitHub पर देखें↗

    This project is a multi-provider AI gateway and proxy server that intercepts and routes requests between AI clients and various large language model providers. It functions as an API protocol translator and model router, mapping incoming requests to specific upstream providers or local runners to provide a unified interface for multiple models. The system distinguishes itself by bridging chat platforms and command line interfaces, converting messages from chat services into managed command line sessions. It further optimizes traffic by executing certain web search and fetch requests locally a

    Provides standardized interfaces for integrating external AI service providers to handle multi-turn text and tool use.

    Python
    GitHub पर देखें↗34,843
  • gitlawb/openclaudeGitlawb का अवतार

    Gitlawb/openclaude

    28,988GitHub पर देखें↗

    OpenClaude is an LLM orchestration interface and multi-provider AI gateway that connects various AI providers and local models to an integrated tool suite. It functions as an agentic tool execution environment and a system for AI-powered code editor integration, enabling in-editor chat and automated coding tasks. The project provides a gRPC AI agent service that exposes model capabilities and file editing tools to external applications as a headless service. It also includes a configuration layer for managing provider credentials and routing specific agents to different model APIs. The syste

    Standardizes interaction with diverse cloud and local AI backends through a unified provider interface.

    TypeScriptaiai-agentai-tools
    GitHub पर देखें↗28,988
  • openai/openai-agents-pythonopenai का अवतार

    openai/openai-agents-python

    27,191GitHub पर देखें↗

    This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

    Enables advanced tool search and deferred loading capabilities for high-performance model interfaces.

    Pythonagentsaiframework
    GitHub पर देखें↗27,191
  • vercel-labs/aivercel-labs का अवतार

    vercel-labs/ai

    24,918GitHub पर देखें↗

    This project is a TypeScript SDK and application framework for integrating large language models into software. It provides a unified interface and multi-provider model wrapper to interact with various AI model providers through a single, consistent API. The toolkit includes a generative UI framework and an AI agent orchestrator. These tools enable the creation of autonomous agents capable of executing functions and the development of AI-driven user interfaces with specialized state management for streaming chatbot components. The framework covers broad capability areas including stream-base

    Offers a standardized interface for integrating and communicating with various AI model service providers.

    TypeScript
    GitHub पर देखें↗24,918
  • lilittlecat/awesome-free-chatgptLiLittleCat का अवतार

    LiLittleCat/awesome-free-chatgpt

    20,849GitHub पर देखें↗

    This repository serves as a comprehensive directory and resource hub for accessing, deploying, and optimizing artificial intelligence tools. It functions as a community-driven index that aggregates web portals, mirror sites, and alternative hosting platforms to provide users with free or alternative access to large language models and conversational assistants. The project distinguishes itself by offering a dual focus on both service discovery and self-hosting capabilities. It provides a curated collection of open-source templates and frameworks that enable users to deploy private, custom-tai

    Catalogs specialized conversational tools for tasks like document analysis and creative writing.

    Pythonawesomeawesome-listchat
    GitHub पर देखें↗20,849
  • microsoft/onnxruntimemicrosoft का अवतार

    microsoft/onnxruntime

    19,347GitHub पर देखें↗

    This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

    Integrates custom mathematical operations and specialized computational layers into the inference engine to support non-standard model architectures.

    C++ai-frameworkdeep-learninghardware-acceleration
    GitHub पर देखें↗19,347
  • google-gemini/gemini-fullstack-langgraph-quickstartgoogle-gemini का अवतार

    google-gemini/gemini-fullstack-langgraph-quickstart

    18,217GitHub पर देखें↗

    This project is an agentic workflow orchestrator designed for building and deploying autonomous systems that perform multi-step reasoning. It functions as a tool-augmented engine, enabling developers to chain model calls with external function execution to complete complex, user-defined tasks. By integrating large language models with persistent memory and stateful logic, the framework supports the creation of intelligent applications capable of independent operation. The platform distinguishes itself through graph-based state orchestration, which allows developers to define logic steps and t

    Enhances model performance by integrating external tools for structured data output and real-time communication.

    Jupyter Notebookgeminigemini-api
    GitHub पर देखें↗18,217
  • wasp-lang/waspwasp-lang का अवतार

    wasp-lang/wasp

    18,146GitHub पर देखें↗

    Wasp is a declarative full-stack web framework that enables developers to build and deploy applications by defining their architecture in a centralized configuration. By using a high-level specification, the framework automates the orchestration of frontend, backend, and database components, ensuring that infrastructure concerns like routing, authentication, and data modeling are handled consistently across the entire stack. The framework distinguishes itself through its compiler-driven approach, which translates declarative configurations into cohesive, production-ready codebases. It provide

    Enables dynamic generation of application specifications and routing structures through custom meta-programming logic.

    TypeScripthaskelljavascriptnode
    GitHub पर देखें↗18,146
  • mnielsen/neural-networks-and-deep-learningmnielsen का अवतार

    mnielsen/neural-networks-and-deep-learning

    17,721GitHub पर देखें↗

    This project is a comprehensive educational resource and curriculum designed to teach the mathematical foundations and practical implementation of neural networks. It provides a structured path for understanding how computers learn from data, covering core concepts such as gradient descent, backpropagation, and the biological inspiration behind artificial neurons. The platform distinguishes itself by combining theoretical proofs with hands-on implementation exercises. It demonstrates the universal approximation theorem through visual explanations and guides users in building various architect

    Implements sigmoid activation functions to ensure predictable, incremental changes in network outputs.

    Python
    GitHub पर देखें↗17,721
  • vercel/vercelvercel का अवतार

    vercel/vercel

    15,738GitHub पर देखें↗

    Vercel is a cloud platform for building, deploying, and scaling web applications. It provides a unified infrastructure that automates the build process by detecting project frameworks and distributing static and dynamic content through a global content delivery network. The platform executes application logic using serverless functions that scale automatically based on real-time traffic demand. The platform distinguishes itself through a centralized AI gateway that proxies requests to multiple model providers, enabling standardized authentication, observability, and cost tracking. It supports

    Delivers real-time AI model responses using server-sent events for immediate feedback.

    TypeScriptclicloudcommand
    GitHub पर देखें↗15,738
  • quarkusio/quarkusquarkusio का अवतार

    quarkusio/quarkus

    15,479GitHub पर देखें↗

    Quarkus is a Kubernetes-native Java framework designed for building high-performance, memory-efficient applications. It utilizes ahead-of-time native compilation to transform Java code into standalone, optimized binaries that eliminate the need for a virtual machine, enabling rapid startup and reduced memory consumption. By performing code augmentation during the build phase, it shifts heavy processing tasks away from runtime, ensuring that applications are optimized for cloud-native environments. The framework distinguishes itself through a unified approach to reactive and imperative program

    Develops modular runtime components that expose new features while leveraging build-time optimizations.

    Javacloud-nativehacktoberfestjava
    GitHub पर देखें↗15,479
  • getgrav/gravgetgrav का अवतार

    getgrav/grav

    15,395GitHub पर देखें↗

    Grav is a flat-file content management system that eliminates the need for a traditional database by storing site content and configuration in human-readable Markdown and YAML files. Built as a modular PHP web framework, it uses a hierarchical page routing system where the physical directory structure directly determines the site's URL paths. The platform is distinguished by its event-driven plugin architecture and a command-line interface that prioritizes system administration, deployment, and maintenance tasks. It utilizes a blueprint-driven system to generate administrative forms from stru

    Allow developers to register custom AI providers by implementing standardized interfaces for integration with the assistant, processor, and command-line tools.

    PHPcmscontentcontent-management
    GitHub पर देखें↗15,395
  • nesquena/hermes-webuinesquena का अवतार

    nesquena/hermes-webui

    14,912GitHub पर देखें↗

    Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

    Allows switching between different AI model providers at runtime via configuration without losing state.

    Pythonagentai-agentshermes
    GitHub पर देखें↗14,912
  • modsetter/surfsenseMODSetter का अवतार

    MODSetter/SurfSense

    14,816GitHub पर देखें↗

    SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implement

    Expands automated research abilities by defining custom functions that allow language models to interact with external data sources.

    Pythonaceternity-uiagentagents
    GitHub पर देखें↗14,816
  • andrewyng/aisuiteandrewyng का अवतार

    andrewyng/aisuite

    14,692GitHub पर देखें↗

    This project is a framework for managing generative AI services through a unified provider interface and adapter layer. It provides a standardized API for calling multiple cloud-based and locally hosted models, translating provider-specific parameters and responses into a uniform format. The system includes an agent orchestrator designed for long-running tasks, featuring state persistence for resuming runs and execution tracing to monitor decision-making processes. It integrates the Model Context Protocol to connect models to external servers and filesystems and employs a policy-based executi

    Offers a standardized interface for integrating both cloud-based and locally-hosted generative AI services.

    Python
    GitHub पर देखें↗14,692
  • jujumilk3/leaked-system-promptsjujumilk3 का अवतार

    jujumilk3/leaked-system-prompts

    14,134GitHub पर देखें↗

    This project is a research-oriented repository that serves as a centralized database for system-level prompts and internal behavioral instructions extracted from various large language models. Its primary purpose is to provide a transparent, accessible reference for researchers and developers to study how artificial intelligence models are configured, constrained, and governed. The repository distinguishes itself by cataloging the hidden directives and operational guidelines that define model personas and safety boundaries. By archiving these instruction sets, it enables comparative analysis

    Integrates model interfaces into generated artifacts to build intelligent applications.

    aidocumentllm
    GitHub पर देखें↗14,134
  • dask/daskdask का अवतार

    dask/dask

    13,746GitHub पर देखें↗

    Dask एक पैरेलल कंप्यूटिंग फ्रेमवर्क और डिस्ट्रीब्यूटेड टास्क शेड्यूलर है जिसे Python डेटा साइंस वर्कफ़्लो को सिंगल मशीनों से बड़े क्लस्टर्स तक स्केल करने के लिए डिज़ाइन किया गया है। यह एक क्लस्टर रिसोर्स मैनेजर के रूप में कार्य करता है जो कार्यों और उनकी डिपेंडेंसी को डायरेक्टेड एसाइक्लिक ग्राफ (DAGs) के रूप में प्रस्तुत करके कम्प्यूटेशनल लॉजिक को व्यवस्थित करता है। यह आर्किटेक्चर सिस्टम को जटिल निष्पादन आवश्यकताओं का प्रबंधन करते हुए उपलब्ध हार्डवेयर पर वर्कलोड के वितरण को स्वचालित करने की अनुमति देता है। यह प्रोजेक्ट एक लेज़ी इवैल्यूएशन इंजन के माध्यम से खुद को अलग करता है जो डेटा ऑपरेशन्स को तब तक स्थगित कर देता है जब तक कि उन्हें स्पष्ट रूप से अनुरोध न किया जाए, जिससे ग्लोबल ग्राफ ऑप्टिमाइज़ेशन और कुशल संसाधन आवंटन सक्षम होता है। इसमें उपलब्ध मेमोरी से अधिक डेटासेट को प्रोसेस करते समय सिस्टम क्रैश को रोकने के लिए मेमोरी-अवेयर डेटा स्पिलिंग शामिल है, और यह टास्क ग्राफ फ्यूजन का उपयोग ऑपरेशन्स के अनुक्रमों को एकल निष्पादन चरणों में संयोजित करने के लिए करता है, जिससे शेड्यूलिंग ओवरहेड और इंटर-नोड संचार कम हो जाता है। यह प्लेटफॉर्म बड़े पैमाने पर डेटा एनालिटिक्स के लिए एक व्यापक क्षमता सतह प्रदान करता है, जिसमें डिस्ट्रीब्यूटेड मशीन लर्निंग, उच्च-प्रदर्शन कंप्यूटिंग एकीकरण, और पैरेलल डेटा प्रोसेसिंग के लिए समर्थन शामिल है। यह क्लस्टर लाइफसाइकिल मैनेजमेंट, परफॉरमेंस प्रोफाइलिंग, और टास्क निष्पादन की रीयल-टाइम मॉनिटरिंग के लिए व्यापक उपकरण प्रदान करता है। उपयोगकर्ता इन वातावरणों को स्थानीय हार्डवेयर, क्लाउड प्रदाताओं, कंटेनरीकृत सिस्टम, और उच्च-प्रदर्शन कंप्यूटिंग क्लस्टर्स सहित विविध बुनियादी ढांचे पर तैनात कर सकते हैं।

    Integrates optional third-party libraries to enable specialized features like cloud storage access, advanced data formats, and machine learning.

    Pythondasknumpypandas
    GitHub पर देखें↗13,746
  • zai-org/chatglm3zai-org का अवतार

    zai-org/ChatGLM3

    13,764GitHub पर देखें↗

    ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a high-performance inference engine designed to support conversational AI, enabling developers to build interactive agents capable of multi-turn dialogue, autonomous code execution, and structured tool invocation. The project distinguishes itself through its focus on hardware-agnostic deployment and resource optimization. It supports distributed model parallelism across multiple graphics cards, paged key-value caching for concurrent request processing, and weight quantization t

    Registers custom functions as external tools that the model can invoke using function metadata.

    Python
    GitHub पर देखें↗13,764
  • lllyasviel/stable-diffusion-webui-forgelllyasviel का अवतार

    lllyasviel/stable-diffusion-webui-forge

    12,730GitHub पर देखें↗

    Stable Diffusion WebUI Forge is a web-based interface and inference engine designed for the generation of AI media. It functions as a platform for executing diffusion-based models, providing a centralized environment to manage image preprocessors, custom generation logic, and hardware-accelerated sampling. The project distinguishes itself through a neural network patching framework that allows for the modification of model layers and the application of spatial conditioning during inference. By injecting custom logic and adapters directly into the network, users can influence output behaviors

    Modifies model structures during inference using masked adapters and depth estimation to achieve custom output behaviors.

    Python
    GitHub पर देखें↗12,730
  • modelcontextprotocol/typescript-sdkmodelcontextprotocol का अवतार

    modelcontextprotocol/typescript-sdk

    12,674GitHub पर देखें↗

    This project provides a TypeScript software development kit for the Model Context Protocol, a standard designed to facilitate bidirectional communication between AI applications and external data sources or tools. It serves as a foundational framework for building both clients and servers, enabling language models to interact with external systems through a unified, decoupled interface. The SDK distinguishes itself by implementing a transport-agnostic connection layer that supports both local standard input-output streams and remote HTTP endpoints. It utilizes a JSON-RPC message bus to manage

    Provides specialized data and functional capabilities to models through standardized interfaces for external interaction.

    TypeScript
    GitHub पर देखें↗12,674
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सब-टैग एक्सप्लोर करें

  • AI Provider Interfaces3 सब-टैग्सStandardized interfaces for registering and integrating external artificial intelligence service providers into the application. **Distinct from Model Capability Extensions:** Focuses on the provider registration interface for AI services, whereas the parent focuses on general model capability extensions.
  • Generation Logic Extenders1 सब-टैगInterfaces for integrating custom image processing and control logic into the inference pipeline. **Distinct from Model Capability Extensions:** Distinct from Model Capability Extensions: focuses on extending the generation pipeline logic specifically, rather than general model performance augmentation.
  • Input Flatteners2 सब-टैग्सUtilities for reshaping input tensors within model architectures to simplify training loop data handling. **Distinct from Model Capability Extensions:** Focuses on tensor reshaping for model input compatibility, distinct from document flattening or UI input configuration.
  • Meta-Programming ExtensionsFrameworks for dynamically generating application specifications and routing structures using standard logic. **Distinct from Model Capability Extensions:** Distinct from Model Capability Extensions: focuses on meta-programming for framework configuration rather than augmenting AI model outputs.
  • Neural Model PatchersTools for modifying model structures during inference using adapters and specialized estimation techniques. **Distinct from Model Capability Extensions:** Distinct from Model Capability Extensions: focuses on structural modification of the model during inference rather than general functional augmentation.