Search Results for "knowledge" - Page 6
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Showing 488 open source projects for "knowledge"
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Agentic Context Engine
Make your agents learn from experience
Agentic Context Engine (ACE) is an open-source framework designed to help AI agents improve their performance by learning from their own execution history. Instead of relying solely on model training or fine-tuning, the framework focuses on structured context engineering, allowing agents to accumulate knowledge from past successes and failures during task execution. The system treats context as a dynamic “playbook” that evolves over time through a process of generation, reflection, and curation, enabling agents to refine strategies across repeated tasks. In this workflow, one component generates solutions, another reflects on outcomes, and a third curates useful knowledge so it can be reused in future interactions. ... -
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LearnLLM.AI
Sharing knowledge about big models that everyone can understand
LLMForEverybody is an open-source educational repository designed to make large language model concepts accessible to a broad audience, including beginners, developers, and job candidates preparing for AI-related interviews. The project organizes knowledge about LLMs into a structured learning path that begins with foundational research papers and progresses through the evolution of modern model architectures. It covers a wide range of topics including attention mechanisms, tokenization strategies, training techniques, model optimization, and deployment approaches. The repository aims to provide intuitive explanations and practical examples so readers can understand both the theoretical and applied aspects of large language models. ... -
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AWS Agent Skills
AWS Skills for Agents
AWS Agent Skills is a repository that curates AWS-focused agent skills — capability modules that give AI assistants like Claude Code and Codex deep, practical knowledge across key Amazon Web Services domains. Instead of streaming giant documentation sets or relying on episodic web search, this project compresses AWS best practices, usage patterns, edge cases, and real-world engineering guides into pre-structured skill definitions that are token-efficient and tailored for reasoning. The skills cover critical AWS services such as IAM, Lambda, DynamoDB, S3, API Gateway, EKS, and many more, letting agents offer actionable advice on infrastructure as code, debugging, security configurations, and architectural workflows. ... -
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MagicTime
Time-lapse Video Generation Models as Metamorphic Simulators
...The main idea is to enhance the capacity of video generation models to accurately depict the real world through our proposed methods and dataset. Compared to general videos, metamorphic videos contain physical knowledge, long persistence, and strong variation, making them difficult to generate. -
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Hermes Agent Orange Book
From Beginner to Master · Orange Book Series
Hermes Agent Orange Book is a structured knowledge resource and guide for building and understanding Hermes-style autonomous agents. It compiles principles, workflows, and patterns used in agent-based systems into an organized format. The project focuses on explaining how agents manage memory, tools, and iterative reasoning processes. It serves as both a reference and a learning resource for developers working with autonomous AI systems. -
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Qwen3
Qwen3 is the large language model series developed by Qwen team
Qwen3 is a cutting-edge large language model (LLM) series developed by the Qwen team at Alibaba Cloud. The latest updated version, Qwen3-235B-A22B-Instruct-2507, features significant improvements in instruction-following, reasoning, knowledge coverage, and long-context understanding up to 256K tokens. It delivers higher quality and more helpful text generation across multiple languages and domains, including mathematics, coding, science, and tool usage. Various quantized versions, tools/pipelines provided for inference using quantized formats (e.g. GGUF, etc.). Coverage for many languages in training and usage, alignment with human preferences in open-ended tasks, etc. -
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FAY
Framework for building AI-powered interactive digital humans and agent
...Fay provides multiple interfaces for text, voice, and digital human control, enabling developers to build interactive assistants, virtual presenters, or automated service agents. It also supports custom knowledge bases and configurable behaviors so developers can tailor the personality and responses of the digital human. -
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AWS GenAI LLM Chatbot
A modular and comprehensive solution to deploy a Multi-LLM
...The project is built as a modular blueprint that helps organizations stand up a production-oriented chat experience rather than a simple demo, combining model access, knowledge retrieval, storage, security, and user interface components into one deployable system. It supports multiple model providers and endpoints, giving teams flexibility to work with Amazon Bedrock, SageMaker-hosted models, and additional model access patterns through related integrations. A major part of the design is its RAG layer, which enables the chatbot to pull contextual knowledge from connected data sources so responses can be grounded in enterprise content rather than relying only on model memory. -
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second-brain-ai-assistant-course
Learn to build your Second Brain AI assistant with LLMs
The Second Brain AI Assistant Course is an open-source educational project designed to teach developers how to build a personal AI assistant that interacts with a user’s knowledge base. The course provides a structured curriculum that walks learners through the architecture and implementation of a production-ready AI system powered by large language models. The concept of a “second brain” refers to a personal knowledge repository containing notes, research, and documents that can be queried and analyzed using AI. ... -
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Streamer-Sales
LLM Large Model of Selling Anchor
...By analyzing product characteristics and marketing information, the model can produce engaging explanations that emphasize benefits, features, and emotional appeal to encourage viewers to make purchasing decisions. The system integrates multiple AI technologies including retrieval-augmented generation to incorporate product knowledge, speech synthesis to convert generated scripts into voice output, and digital human generation to create virtual hosts. It also supports automatic speech recognition and agent-based tools that can retrieve additional information such as logistics or product details during live sessions. -
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NVIDIA Generative AI Examples
Generative AI reference workflows
...Many of the examples show how to deploy AI services using containerized environments, GPU acceleration, and microservices that can scale across modern infrastructure. Developers can explore sample chatbot applications, document question-answering systems, and knowledge-base pipelines that illustrate how generative AI can interact with external data sources. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
...It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. The goal is to demystify agent architectures like Claude Code by having learners build simplified versions themselves and observe how tools, memory management, planning constraints, and context isolation contribute to reliable agent behavior. Along the way, the project teaches fundamentals such as how to let models call external tools, maintain clean memory for long tasks, and inject domain expertise without retraining the model. -
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openbench
Provider-agnostic, open-source evaluation infrastructure
openbench is an open-source, provider-agnostic evaluation infrastructure designed to run standardized, reproducible benchmarks on large language models (LLMs), enabling fair comparison across different model providers. It bundles dozens of evaluation suites — covering knowledge, reasoning, math, code, science, reading comprehension, long-context recall, graph reasoning, and more — so users don’t need to assemble disparate datasets themselves. With a simple CLI interface (e.g. bench eval <benchmark> --model <model-id>), you can quickly evaluate any model supported by Groq or other providers (OpenAI, Anthropic, HuggingFace, local models, etc.). openbench also supports private/local evaluations: you can integrate your own custom benchmarks or data (e.g. internal test suites, domain-specific tasks) to evaluate models in a privacy-preserving way. -
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
...Alternatively one may use their current FEniCS installation. A low-level development API and provides some functionality to make directly dealing with the library a little bit easier, but still requires knowledge of FEniCS itself. Interfaces have been provided for the main functions and their attributes, and instructions to add further ones can be found here. A high-level API for usage with DifferentialEquations. An example can be seen in solving the heat equation with high-order adaptive time-stepping. Various gists/jupyter notebooks have been created to provide a brief overview of the overall functionality and of any differences between the pythonic FEniCS and the Julian wrapper. ... -
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LangChain.js
Building applications with LLMs through composability
...But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. This is built to integrate as seamlessly as possible with the LangChain Python package. Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages. -
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Graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw)
...The system likely supports dynamic updates, allowing graphs to evolve as data changes or new inputs are introduced. It is particularly useful in domains such as network analysis, knowledge graphs, and system architecture visualization. The architecture emphasizes flexibility, enabling users to customize how data is mapped and displayed. It may also include analytical features to explore patterns, clusters, or anomalies within the graph. Overall, Graphify serves as a bridge between raw data and visual insight. -
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Label Sleuth
Open source no-code system for text annotation and building of text
An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. -
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prompts.chat
Share, discover, and collect prompts
...The project emphasizes discoverability and collaboration by allowing community submissions that automatically synchronize with the repository. It can also be self-hosted, enabling organizations to maintain a private prompt knowledge base if needed. -
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Ian Xiaohei Illustrations
Chinese Little Black Weird Text Illustration Generation Skill
...Its default style uses a 16:9 white-background composition with rough hand-drawn lines and small red, orange, or blue annotation marks. It is especially useful for knowledge creators who want recognizable article visuals without designing from scratch. The project works as a reusable creative instruction layer for AI-assisted content production. -
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Laravel Boost
Laravel-focused MCP server for augmenting AI powered local development
...It ships opinionated, composable guidelines tuned for popular Laravel packages, which helps keep generated code idiomatic and consistent with framework norms. The package also curates a large body of vectorized Laravel ecosystem knowledge that’s scoped to what you’ve actually installed, improving retrieval precision and response quality. It’s designed to fit naturally into existing projects, supporting current Laravel releases and modern PHP runtimes with minimal setup. Rather than trying to replace your editor or framework, Boost acts like an intelligent layer that understands Laravel’s conventions and reduces the “explain my app to the AI” friction. -
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MCP Neo4j
Model Context Protocol with Neo4j
An implementation of the Model Context Protocol with Neo4j, enabling natural language interactions with Neo4j databases and facilitating operations such as schema retrieval and Cypher query execution. -
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ExtractThinker
ExtractThinker is a Document Intelligence library for LLMs
ExtractThinker is a tool designed to facilitate the extraction and analysis of information from various data sources, aiding in data processing and knowledge discovery. -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
...It integrates a latent-space audio encoder, discrete acoustic tokens, and reinforcement-learning–based training (CoT + RL) to enhance its ability to capture and reproduce voice styles, intonations, and subtle vocal cues. Moreover, Step-Audio2 supports tool-calling and retrieval-augmented generation (RAG), allowing it to access external knowledge sources or audio/text databases, thus reducing hallucinations and improving coherence in complex dialogues. -
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Mini Agent
A minimal yet professional single agent demo project
...The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during long, complex tasks. It includes a basic toolset for file-system operations and shell commands, plus integrations with MCP tools such as web search and knowledge graph access. Mini-Agent also comes with “Claude Skills”-style predefined skills for tasks like document processing, design work, and testing, packaged as reusable behaviors that can be invoked by the agent as needed. -
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Hivemind
Hivemind turns your traces into reusable skills across agents
Hivemind is a shared memory and skill system for AI coding agents. It captures agent sessions as structured traces, including prompts, tool calls, responses, and subagent activity. The system mines those traces for repeated patterns and turns them into reusable skills that can be shared across a team. It supports agents such as Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi. Hivemind uses hybrid lexical and semantic retrieval so agents can recall prior decisions, solved bugs,...
