Search Results for "reasoning" - Page 7
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Showing 606 open source projects for "reasoning"
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Ling
Ling is a MoE LLM provided and open-sourced by InclusionAI
...The project offers different sizes (Ling-lite, Ling-plus) and emphasizes flexibility and efficiency: being able to scale, adapt expert activation, and perform across a range of natural language/reasoning tasks. Example scripts, inference pipelines, and documentation. The codebase includes inference, examples, models, documentation, and model download infrastructure. As more developers and researchers engage with the platform, we can expect rapid advancements and improvements, leading to even more sophisticated applications. Model inference and API code (e.g. integration with Transformers). ... -
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best-claude-hud
Minimal Claude Code statusline HUD powered by Rust
best-claude-hud is a fast Rust-based status line for Claude Code terminal sessions. It displays the active model, available reasoning effort, launch directory, Git state, and context-window consumption in a compact interface. Optional segments can also show usage limits, cost, session details, output style, and update information. The npm package installs prebuilt native binaries, so users do not need a Rust toolchain. Its setup command updates Claude Code settings, preserves existing configuration, and creates a backup when replacing a previous status line. ... -
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OpenSage
An agent framework that enables AI to create their own agent
...Unlike traditional agent frameworks that require developers to manually define workflows, tools, and structures, OpenSage introduces a system where large language models can dynamically generate their own agent architectures, including sub-agents, toolchains, and execution strategies. The framework is built around the concept of an Agent Development Kit (ADK), providing structured components for memory, reasoning, and task decomposition while allowing agents to iteratively improve their own design. A key innovation is its hierarchical and graph-based memory system, which enables agents to store, retrieve, and organize information across complex workflows with improved efficiency and contextual awareness. -
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Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
CHA, or Conversational Health Agents, is an open-source framework designed to build intelligent healthcare assistants powered by large language models and external data sources. The system enables developers to create personalized AI agents that can interact with users through natural language while performing multi-step reasoning and task execution. It integrates orchestration capabilities that allow the agent to gather information from APIs, knowledge bases, and external services in order to generate more accurate and context-aware responses. The framework supports modular components such as planning, tool execution, and multimodal input processing, which makes it suitable for complex healthcare applications. ... -
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Page Agent
JavaScript in-page GUI agent. Control web interfaces
...Page Agent is designed to integrate seamlessly into existing web applications, making it possible to embed AI copilots into SaaS platforms without major backend changes. It supports a bring-your-own-LLM approach, allowing developers to connect their preferred language models to power the agent’s reasoning capabilities. -
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Rogue
AI Agent Evaluator & Red Team Platform
...The system allows developers to define specific scenarios, expected outcomes, and business rules so that the framework can verify whether an agent behaves according to required policies. During testing, Rogue records conversations and produces detailed reports that explain whether the agent passed or failed each scenario. These reports include reasoning and evidence, helping developers understand why a particular failure occurred. -
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MiroFlow
Agent framework that enables tool-use agent tasks
...This architecture allows agents to perform advanced reasoning tasks such as deep research, future event prediction, and multi-step knowledge analysis. The framework emphasizes reliability and scalability by incorporating robust workflow execution, concurrency management, and fault-tolerant design to handle unstable APIs or network conditions. -
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MING
A large-scale model of medical consultation in Chinese
...The project focuses on building a healthcare-focused conversational system capable of responding to medical questions, analyzing case descriptions, and guiding diagnostic reasoning. It is trained using medical instruction tuning so that the model can understand patient symptoms and respond with structured explanations and clinical suggestions. One of its primary goals is to simulate a multi-round medical consultation process, allowing the system to ask follow-up questions before offering diagnostic recommendations. ... -
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AgentEvolver
Towards Efficient Self-Evolving Agent System
...The system focuses on improving the efficiency and scalability of training autonomous agents by allowing them to generate tasks, explore environments, and refine strategies without heavy reliance on manually curated datasets. Its architecture combines reinforcement learning with LLM-driven reasoning mechanisms to guide exploration and learning. The framework introduces several key mechanisms, including self-questioning to create new learning tasks, self-navigating to improve exploration through experience reuse, and self-attributing to assign rewards based on the usefulness of actions. These mechanisms enable agents to continuously improve their capabilities while interacting with complex environments and tools. ... -
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WebGLM
An Efficient Web-enhanced Question Answering System
...The system is based on the General Language Model architecture and was designed to enable language models to interact directly with web information during the question-answering process. Instead of relying solely on knowledge stored in the model’s training data, the system retrieves relevant web content and integrates it into the reasoning process. WebGLM introduces several components that coordinate this process, including a retrieval module that selects relevant web documents, a generator that produces answers, and a scoring system that evaluates the quality of generated responses. The architecture aims to improve the reliability and usefulness of AI systems that answer questions about current or external knowledge sources. -
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AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
...Knowledge graphs organize information as networks of nodes and relationships, allowing applications to analyze connections between concepts, datasets, or real-world entities. By incorporating AI techniques such as natural language processing and semantic reasoning, the project enables systems to automatically extract relationships and insights from large volumes of data. These capabilities make knowledge graph platforms particularly useful for applications such as recommendation engines, enterprise knowledge management, and research data exploration. The system emphasizes structured data modeling and graph-based queries that allow users to explore relationships that would be difficult to identify using traditional relational databases. -
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Lagent
A lightweight framework for building LLM-based agents
Lagent is a lightweight open-source framework designed to help developers build autonomous agents powered by large language models. The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. ... -
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LangGraph.js
Framework to build resilient language agents as graphs
...Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports advanced capabilities such as branching logic, loops, and conditional execution, enabling developers to build sophisticated AI systems that can adapt to dynamic conditions. The framework integrates seamlessly with language models, tools, and external APIs, allowing agents to retrieve information and perform actions across different systems. ... -
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InternLM-XComposer-2.5
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System
...It incorporates visual understanding modules that allow the model to analyze images and integrate them into coherent narrative outputs. The framework also supports tasks such as image captioning, multimodal reasoning, and layout generation for structured visual documents. By combining language generation with visual composition capabilities, the system enables new forms of content creation that integrate written explanations with automatically generated visual components. -
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vLLM Semantic Router
System Level Intelligent Router for Mixture-of-Models at Cloud
Semantic Router is an open-source system designed to intelligently route requests across multiple large language models based on the semantic meaning and complexity of user queries. Instead of sending every prompt to the same model, the system analyzes the intent and reasoning requirements of the request and dynamically selects the most appropriate model to process it. This approach allows developers to combine multiple models with different strengths, such as lightweight models for simple queries and more advanced reasoning models for complex tasks. The router operates as an intelligent layer between users and model infrastructure, capturing signals from prompts, responses, and contextual data to improve decision-making. ... -
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NExT-GPT
Code and models for ICML 2024 paper, NExT-GPT
...This architecture allows the model to convert between modalities, such as generating images from text descriptions or producing audio or video outputs based on textual prompts. The project also introduces instruction-tuning strategies that enable the model to perform complex multimodal reasoning and generation tasks with minimal additional parameters. -
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AgenticSeek
Fully Local Manus AI. No APIs, No $200 monthly bills
...It runs entirely on the user’s hardware and can autonomously browse the web, write code, and plan multi-step tasks without sending data to external services. The system is optimized for local reasoning models and emphasizes zero cloud dependency to maintain full user control. AgenticSeek includes intelligent agent selection, allowing it to determine the best internal agent to handle a given request. It also supports hands-free workflows such as automated web form interaction and information extraction. Overall, the project functions as a self-hosted, multi-capability AI agent designed for users who prioritize autonomy, privacy, and local execution. -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
...The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. Its architecture highlights agent loops, tool calling, and stepwise execution, which are increasingly important patterns in modern AI automation. ... -
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Claude-Mem
Claude Code plugin that automatically captures everything Claude does
...It automatically captures Claude’s tool usage, observations, and decisions, then compresses them into semantic memories that carry forward into future sessions. By enabling long-term continuity, Claude-Mem helps Claude “remember” project history, past fixes, and prior reasoning even after restarts or reconnects. Its progressive disclosure approach intelligently injects only the most relevant context, balancing usefulness with token efficiency. Claude-Mem runs automatically in the background with no manual workflow changes required. Designed for serious developers, it transforms Claude Code into a continuously learning, project-aware coding assistant. -
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ML Ferret
Refer and Ground Anything Anywhere at Any Granularity
...In practice, this enables tasks like “find that small red icon next to the chart and describe it” where both the linguistic reference and the visual region are ambiguous without fine spatial reasoning. -
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TraceRoot
Find the Root Cause in Your Code's Trace
...Lightweight SDKs for Python and TypeScript enable seamless instrumentation using OpenTelemetry, with support for both self-hosted and cloud deployment. Human-in-the-loop interaction is central: developers can guide reasoning by selecting relevant spans or logs, then verify agent reasoning through traceable context. -
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NVIDIA Isaac GR00T
NVIDIA Isaac GR00T N1.5 is the world's first open foundation model
NVIDIA Isaac‑GR00T N1.5 is an open-source foundation model engineered for generalized humanoid robot reasoning and manipulation skills. It accepts multimodal inputs—such as language and images—and uses a diffusion transformer architecture built upon vision-language encoders, enabling adaptive robot behaviors across diverse environments. It is designed to be customizable via post-training with real or synthetic data. The vision-language model remains frozen during both pretraining and finetuning, preserving language understanding and improving generalization. ... -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. -
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OpenWork
An open-source alternative to Claude Cowork, powered by opencode
OpenWork is a framework for building decentralized collaborative work environments powered by AI and human contributions. At its core, the project enables contributors to define tasks, workflows, and goals that can be split, shared, and recombined across distributed nodes while agents and humans cooperate to advance progress. It offers structured templates for work items, decision logic for task allocation, and consensus mechanisms that let groups verify and validate results toward shared... -
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zhangxuefeng-skill
Zhang Xuefeng's cognitive operating system
...It emphasizes pragmatic, outcome-driven reasoning focused on employment prospects, income potential, and social constraints rather than abstract ideals. The architecture organizes knowledge into reproducible decision flows, enabling consistent outputs across different scenarios. It also demonstrates how human expertise can be translated into modular AI skills that can be reused and adapted.