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86 repositorios

Awesome GitHub RepositoriesReasoning Models

Language models optimized for complex logical deduction, chain-of-thought processing, and multi-step reasoning.

Distinguishing note: None of the candidates were provided; this distinguishes specialized reasoning models from general-purpose LLMs.

Explore 86 awesome GitHub repositories matching artificial intelligence & ml · Reasoning Models. Refine with filters or upvote what's useful.

Awesome Reasoning Models GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • comfyanonymous/comfyuiAvatar de comfyanonymous

    comfyanonymous/ComfyUI

    117,322Ver en GitHub↗

    ComfyUI is a modular generative AI workflow orchestrator and node-based GUI for designing and executing complex diffusion model pipelines. It functions as both a visual interface for building generative logic graphs and a programmable backend API that exposes diffusion model operations for external integration. The system distinguishes itself through a graph-based execution model that supports differential workflow execution, re-running only modified nodes to reduce computation. It features dynamic model offloading to manage memory between system RAM and GPU VRAM and utilizes metadata-embedde

    Uses reference images to prompt a model, allowing the visual characteristics of an image to influence the output.

    Python
    Ver en GitHub↗117,322
  • deepseek-ai/deepseek-r1Avatar de deepseek-ai

    deepseek-ai/DeepSeek-R1

    91,996Ver en GitHub↗

    DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes. The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions. The training framework incorporates group relative policy optimiz

    Optimizes reasoning performance by rewarding correct final answers to generate internal chain-of-thought sequences.

    Ver en GitHub↗91,996
  • cherryhq/cherry-studioAvatar de CherryHQ

    CherryHQ/cherry-studio

    47,419Ver en GitHub↗

    Cherry Studio is a cross-platform desktop application that serves as a centralized workspace for managing and interacting with multiple artificial intelligence models. It functions as a local-first orchestrator, prioritizing user privacy by storing all conversation history and knowledge bases directly on your device. By providing a unified interface for both cloud-based and local AI services, the platform simplifies API key management and allows for consistent model interaction across different operating systems. The application distinguishes itself through a robust retrieval-augmented genera

    Utilizes advanced models for complex reasoning, code generation, and agent-based tasks.

    TypeScriptai-agentclaude-codecode-agent
    Ver en GitHub↗47,419
  • microsoft/taskmatrixAvatar de microsoft

    microsoft/TaskMatrix

    34,079Ver en GitHub↗

    TaskMatrix is a visual language model orchestration framework and modular visual pipeline designed to coordinate disparate foundation models. It functions as a multi-model workflow coordinator that sequences visual and textual models through logic paths to handle image processing tasks without requiring additional training. The system integrates large language models with visual foundation models to enable the exchange of image data during interactive chat sessions. It utilizes template-based orchestration to chain specialized models together for complex visual tasks. The framework supports

    Provides a framework for chaining language models, prompts, and visual tools into multi-step logical workflows.

    Python
    Ver en GitHub↗34,079
  • sgl-project/sglangAvatar de sgl-project

    sgl-project/sglang

    29,079Ver en GitHub↗

    Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

    Extracts and separates internal thinking processes from final generated content to provide structured access to model reasoning.

    Pythonattentionblackwellcuda
    Ver en GitHub↗29,079
  • openai/openai-agents-pythonAvatar de openai

    openai/openai-agents-python

    27,191Ver en GitHub↗

    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

    Standardizes reasoning data output across different model implementations for consistent processing.

    Pythonagentsaiframework
    Ver en GitHub↗27,191
  • cinnamon/kotaemonAvatar de Cinnamon

    Cinnamon/kotaemon

    25,139Ver en GitHub↗

    Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

    Combine prompt templates, language models, and post-processing functions to transform input data into structured outputs.

    Pythonchatbotllmsopen-source
    Ver en GitHub↗25,139
  • vercel/aiAvatar de vercel

    vercel/ai

    21,885Ver en GitHub↗

    This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

    Retrieves internal reasoning steps generated by models to provide transparency into output generation.

    TypeScriptanthropicartificial-intelligencegemini
    Ver en GitHub↗21,885
  • openai/gpt-ossAvatar de openai

    openai/gpt-oss

    20,191Ver en GitHub↗

    gpt-oss is an open-weight large language model and reasoning engine designed for complex reasoning and agentic workflows. It functions as an AI agent framework and model serving API, allowing for local deployment and the hosting of standardized interfaces to expose model completions and internal reasoning processes. The project distinguishes itself as a quantized inference engine, utilizing tensor parallelism and weight quantization to run high-parameter models on limited hardware. It features a reasoning model that employs chain-of-thought processing to solve multi-step logical tasks. The s

    Provides an open-weight model optimized for complex logical deduction and multi-step reasoning.

    Python
    Ver en GitHub↗20,191
  • stitionai/devikaAvatar de stitionai

    stitionai/devika

    19,511Ver en GitHub↗

    Devika is an autonomous AI software engineering system designed to plan, write, and debug code from high-level natural language instructions. It functions as an agentic software engineer that decomposes complex objectives into actionable coding steps for autonomous execution. The system integrates cloud-based and self-hosted large language models through a provider-agnostic layer, allowing for multi-model reasoning and code completion. It distinguishes itself by combining these models with a sandboxed execution environment for running code across different operating systems and a web-browsing

    Integrates specialized reasoning models to perform multi-step logical deduction and combined code generation.

    Python
    Ver en GitHub↗19,511
  • huggingface/trlAvatar de huggingface

    huggingface/trl

    18,653Ver en GitHub↗

    This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities. The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficie

    Improves complex reasoning performance through two-stage reinforcement learning and offline value function estimation.

    Python
    Ver en GitHub↗18,653
  • alibaba-nlp/deepresearchAvatar de Alibaba-NLP

    Alibaba-NLP/DeepResearch

    18,251Ver en GitHub↗

    DeepResearch is an autonomous research agent framework designed to orchestrate multi-step information gathering and complex reasoning tasks. The platform functions as an agent orchestration system that manages the entire lifecycle of autonomous research, from initial planning and web navigation to the synthesis of evidence-backed reports. The framework distinguishes itself through a specialized training pipeline that supports the development and fine-tuning of autonomous models using reinforcement learning and structured knowledge graph synthesis. By employing parallel agent coordination, the

    Divides high-level research objectives into sequential reasoning steps to retrieve and synthesize data.

    Pythonagentalibabaartificial-intelligence
    Ver en GitHub↗18,251
  • google-gemini/gemini-fullstack-langgraph-quickstartAvatar de google-gemini

    google-gemini/gemini-fullstack-langgraph-quickstart

    18,217Ver en GitHub↗

    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

    Offers high-performance models optimized for low-latency reasoning and creative workflows.

    Jupyter Notebookgeminigemini-api
    Ver en GitHub↗18,217
  • emcie-co/parlantAvatar de emcie-co

    emcie-co/parlant

    18,119Ver en GitHub↗

    Parlant is an agentic workflow engine and orchestration framework designed for building conversational AI that adheres to strict behavioral guidelines. It provides a platform for managing multi-turn interactions through state-machine-based logic, allowing developers to define complex, hierarchical conversational flows that can adapt, skip, or revisit steps based on real-time user input. The framework distinguishes itself through its focus on behavioral governance and observability. It enables developers to define precise domain terminology and enforce instruction compliance through prioritize

    Exposes transparent reasoning paths generated during message composition to help troubleshoot agent decisions.

    Pythonai-agentsai-alignmentcustomer-service
    Ver en GitHub↗18,119
  • camel-ai/camelAvatar de camel-ai

    camel-ai/camel

    17,253Ver en GitHub↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Extracts and structures internal chain-of-thought reasoning steps from model outputs for high-quality synthetic datasets.

    Pythonagentai-societiesartificial-intelligence
    Ver en GitHub↗17,253
  • richards199999/thinking-claudeAvatar de richards199999

    richards199999/Thinking-Claude

    17,061Ver en GitHub↗

    Thinking-Claude is a browser extension and reasoning interface designed to compel large language models to perform deep inner monologues before delivering final responses. It functions as a thought process formatter that organizes internal AI reasoning into a structured format. The tool uses systematic reasoning prompts and instruction-based injection to force models through a multi-step verification process. This ensures the AI executes a recursive loop of reasoning and self-correction prior to emitting an answer. To manage this output, the project employs client-side response interception

    Guides the model through a thorough multi-step inner monologue and verification process.

    TypeScript
    Ver en GitHub↗17,061
  • vercel/vercelAvatar de vercel

    vercel/vercel

    15,738Ver en GitHub↗

    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

    Configures reasoning models to optimize performance for complex logical tasks.

    TypeScriptclicloudcommand
    Ver en GitHub↗15,738
  • kilo-org/kilocodeAvatar de Kilo-Org

    Kilo-Org/kilocode

    15,616Ver en GitHub↗

    Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments. The platform distinguishes itself through its federated task management and policy-based access control, which

    Utilizes specialized model logic to perform multi-step analysis and logical deduction before generating final code or responses.

    TypeScriptaiai-ageai-coding
    Ver en GitHub↗15,616
  • qwenlm/qwen3-coderAvatar de QwenLM

    QwenLM/Qwen3-Coder

    15,615Ver en GitHub↗

    Qwen3-Coder is a specialized large language model designed for software development, technical reasoning, and automated code synthesis. Built on transformer-based sequence modeling, it functions as a multilingual programming assistant capable of generating, completing, and debugging source code across more than one hundred programming languages. The model distinguishes itself through its capacity to process and maintain logical coherence across massive datasets, supporting context windows of up to one million tokens. This allows for repository-scale reasoning, enabling the model to analyze co

    Applies advanced logical deduction and mathematical reasoning to solve complex technical problems.

    Python
    Ver en GitHub↗15,615
  • google-ai-edge/galleryAvatar de google-ai-edge

    google-ai-edge/gallery

    15,162Ver en GitHub↗

    This project is a development framework for building edge-based AI agents that perform multimodal inference and system-level automation directly on mobile devices. By prioritizing local-first execution, the platform ensures data privacy and offline functionality, allowing developers to run large language models on hardware without requiring external server connectivity. The framework distinguishes itself through an integrated orchestration layer that connects language models to custom tools, scripts, and native device intents. It provides a structured registry for mapping natural language ins

    Extracts and displays internal model reasoning steps to provide transparency into decision-making processes.

    Kotlin
    Ver en GitHub↗15,162
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Explorar subetiquetas

  • Chain-of-Thought Enforcement1 sub-etiquetaMechanisms that force general-purpose models to emulate the multi-step reasoning behavior of specialized reasoning models. **Distinct from Reasoning Models:** Focuses on forcing the process via prompting/UI rather than the native architecture of the model itself.
  • Hybrid Reasoning EnginesSystems that combine semantic graph search with numerical calculation and symbolic logic. **Distinct from Reasoning Pipelines:** Integrates numerical and symbolic logic with semantic search, while reasoning pipelines focus on the orchestration of LLM steps.
  • Internal Monologue Modeling1 sub-etiquetaArchitectures that generate hidden thought tokens to coordinate complex reasoning before or during audible speech. **Distinct from Reasoning Models:** Distinct from general Reasoning Models by focusing on a hidden stream of thought specifically for coordinating speech output.
  • Model Switching InterfacesUser interfaces and shortcuts for dynamically changing active models or reasoning levels. **Distinct from Reasoning Models:** Focuses on the interface and mechanism for switching models rather than the properties of the reasoning models themselves.
  • Reasoning Capture UtilitiesTools for extracting and visualizing intermediate thought processes from reasoning-capable models. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on the observability and extraction of the reasoning process rather than the model architecture itself.
  • Reasoning Need DetectorsDetermines whether a query requires reasoning and applies expensive reasoning models only when beneficial. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on detecting when reasoning is needed, not the reasoning models themselves.
  • Reasoning Optimization1 sub-etiquetaEnhances complex reasoning performance through two-stage reinforcement learning and value estimation. **Distinct from Reasoning Models:** Focuses on the optimization methodology for reasoning, distinct from the models themselves.
  • Reasoning Parsers2 sub-etiquetasUtilities for extracting and structuring internal chain-of-thought reasoning steps from model outputs. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on the parsing and extraction of reasoning traces, not the model architecture itself.
  • Reasoning Pipelines4 sub-etiquetasFrameworks for chaining language models, prompts, and tools into multi-step logical workflows. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on the orchestration and chaining of reasoning steps rather than the underlying model architecture.
  • Reasoning Steering2 sub-etiquetasInserts tips and guided questions to steer a model's internal reasoning path. **Distinct from Reasoning Path Recording:** Distinct from recording a reasoning path; this is the proactive guidance of the path during prompting.
  • Reasoning Trace Retrievers1 sub-etiquetaUtilities for fetching structured thought processes from reasoning models. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on the retrieval of reasoning summaries rather than the model architecture itself.
  • Reasoning Transparency Interfaces1 sub-etiquetaUtilities for retrieving and exposing internal model reasoning steps or thought processes. **Distinct from Reasoning Models:** Distinct from Reasoning Models: focuses on the retrieval and exposure of reasoning data, rather than the model architecture itself.
  • Reasoning Vision-Language ModelsMultimodal models that integrate chain-of-thought and RL to solve complex visual-spatial tasks. **Distinct from Reasoning Models:** Combines the visual modality with reasoning capabilities, distinct from text-only reasoning models.
  • User-Based Model ResolutionLogic for determining the optimal model and reasoning effort based on user profiles and team defaults. **Distinct from Reasoning Models:** Focuses on the resolution logic based on user identity rather than the properties of the reasoning model itself.