Search Results for "reasoning models"
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Showing 11 open source projects for "reasoning models"
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AI Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs
AI Agents from Scratch is an educational repository designed to teach developers how to build autonomous AI agents using large language models and modern AI frameworks. The project walks through the process of constructing agents step by step, beginning with simple prompt-based interactions and gradually introducing more advanced capabilities such as planning, tool use, and memory. The repository provides example implementations that demonstrate how language models can interact with external systems, perform reasoning tasks, and execute structured workflows. ... -
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AG2
Framework for building and orchestrating multi-agent AI systems
...AG2 focuses on making multi-agent systems more accessible by simplifying how agents are configured, connected, and executed. It includes mechanisms for agent-to-agent interaction, task delegation, and iterative reasoning, which are essential for building advanced AI-driven applications. AG2 is intended for developers experimenting with autonomous systems, research prototypes, or production-grade agent pipelines. AG2 emphasizes flexibility, allowing users to integrate different models and customize behaviors depending on their use case. Overall, it serves as a foundation for building scalable and modular AI agent ecosystems. -
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Lemon AI
Full-stack Open-source Self-Evolving General AI Agent
LemonAI is an open-source full-stack framework for building autonomous AI agents capable of performing complex tasks such as research, programming, data analysis, and document processing. The platform is designed to run primarily on local infrastructure, providing a privacy-focused alternative to cloud-dependent agent platforms. It integrates with local large language models through tools such as Ollama, vLLM, and other model runtimes while also allowing optional connections to external... -
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Deep Research
Use any LLMs (Large Language Models) for Deep Research
...The project emphasizes privacy: processing and storage happen locally, avoiding server-side retention of your queries and notes. A simple web UI lets you enter topics and configure models, while the backend streams progress as sources are fetched and arguments are weighed. It offers MCP server support and SSE APIs, so IDEs and agent clients can drive the same workflow programmatically. The result is a repeatable process for scoping questions, collecting evidence, and producing a concise report with citations and reasoning steps. -
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Every Code
Local AI coding agent CLI with multi-agent orchestration tools
...Every Code enhances the traditional coding assistant model by introducing multi-agent orchestration, allowing multiple AI agents to collaborate, compare solutions, and refine outputs in parallel. It supports integration with various AI providers, enabling users to route tasks across different models depending on their needs. Every Code also includes browser integration and automation capabilities, extending its usefulness beyond simple code generation into more complex development tasks. Customization is a key focus, with support for theming, configurable settings, and reasoning controls that allow developers to fine-tune how the agent behaves. -
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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running... -
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super-agent-party
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Super Agent Party is an open-source experimental framework designed to demonstrate collaborative multi-agent AI systems interacting within a shared environment. The project explores how multiple specialized AI agents can coordinate to solve complex tasks by communicating with each other and sharing information. Instead of relying on a single monolithic model, the framework organizes agents with different roles or capabilities that cooperate to achieve goals. Each agent may handle different... -
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RuoYi AI
Enterprise AI platform for building, deploying, and managing apps
RuoYi AI is a full-stack enterprise-oriented AI development platform designed to help developers rapidly build, deploy, and manage intelligent applications using modern large language models and AI ecosystems. It provides a unified framework for integrating multiple AI models from different providers, allowing teams to switch or combine models through a consistent interface without vendor lock-in. RuoYi AI includes built-in support for retrieval-augmented generation, enabling organizations to create secure, private knowledge bases with high-accuracy search and reasoning capabilities. ... -
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LaVague
Framework for building AI agents that automate complex web tasks
...LaVague is centered around a World Model that analyzes the current webpage state and determines the next set of instructions, combined with an Action Engine that converts those instructions into executable automation code. It can use browser automation tools such as Selenium or Playwright to interact with websites programmatically. Developers can integrate various language models and configure the agent’s reasoning and execution behavior to suit different automation scenarios. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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ChatAnyLLM
Switch local model, cloud provider, or custom agent mid-conversation.
...Users may manually configure any OpenAI-compatible API endpoint, connecting third-party providers such as Groq or Cerebras. The application stores conversation history locally and saves api keys with system-level encryption. It supports reasoning models, multimodal inputs, and formatting for LaTeX and code. -
11
fe4ml-zh
Feature Engineering for Machine Learning
...The project explains techniques for creating, selecting, and transforming features in ways that improve model accuracy and robustness. It also discusses the role of domain knowledge, data preprocessing, and statistical reasoning in building effective machine learning models.
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