Search Results for "knowledge" - Page 5
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Showing 488 open source projects for "knowledge"
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Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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History LLMs
Information hub for our project training the largest possible LLMs
History LLMs serves as the central information hub for a research project focused on training large language models exclusively on historical texts up to specified cutoff dates, essentially creating time-locked AI that speaks from within a particular era’s worldview. The History LLMs aim to be trained on massive curated datasets of time-stamped documents so that the resulting models can offer responses grounded only in the knowledge available before their cutoff, such as 1913, thereby avoiding hindsight contamination common in modern LLMs. This approach enables researchers in the humanities and social sciences to explore how people at different historical moments would have discussed world events, norms, and ideas without later developments influencing the model. ... -
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free-code
The free build of Claude Code
...It typically focuses on simplifying the development process by offering prebuilt components, example projects, or utilities that can be reused across different applications. The project is designed to encourage collaboration and knowledge sharing within the developer community, making it easier for beginners and experienced developers alike to access useful code snippets and frameworks. It may include a collection of curated resources that span multiple programming languages and use cases. Free-code emphasizes openness and accessibility, ensuring that users can freely explore, modify, and distribute the code. ... -
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ChatterBot
Machine learning, conversational dialog engine for creating chat bots
...The language independent design of ChatterBot allows it to be trained to speak any language. Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it’s own knowledge of possible responses as it interacts with humans and other sources of informative data. An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply increase. -
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Activepieces
Open Source AI Automation
Activepieces is an open-source automation tool designed to build workflows that connect different apps and services without requiring extensive programming knowledge. It’s tailored for technical and non-technical users alike, enabling teams to automate repetitive tasks using a visual editor and a large library of pre-built connectors. Activepieces can be self-hosted or used via a cloud deployment, making it flexible for teams of all sizes. It supports integrations with popular services like Slack, Google Sheets, and Discord, and allows users to create custom pieces to suit unique needs. ... -
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RAGFlow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data. -
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ByteRover CLI
The portable memory layer for autonomous coding agents
ByteRover CLI is a portable memory layer for autonomous coding agents. It gives developers a way to store, organize, and reuse project knowledge across coding tools and sessions. The project centers on a context tree that can capture important information about a codebase, decisions, patterns, and instructions. It can run as an interactive command-line experience and connect to an LLM of the user’s choice. ByteRover is useful when agents need persistent context instead of starting from scratch every time they enter a project. ... -
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Agent Learning Hub
A curated AI Agent learning roadmap
Agent Learning Hub is a curated learning roadmap and resource collection for understanding AI agents. It is maintained as a central README-style knowledge hub rather than a software runtime or framework. The project organizes high-quality articles, official blogs, papers, open-source projects, engineering experience, and learning paths into a sequence that users can follow. It is especially useful for beginners and intermediate learners who want a structured entry point into the fast-moving agent ecosystem. ... -
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OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. It also focuses on cost efficiency by reducing redundant computations and reusing successful workflows, significantly lowering token usage in repeated tasks. ... -
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pi-autoresearch
Autonomous experiment loop extension for pi
...Its architecture suggests a focus on autonomy, allowing it to run multi-step research pipelines that mimic human investigative processes. This makes it particularly useful for exploratory analysis, trend discovery, or generating structured knowledge from large information spaces. Overall, pi-autoresearch represents a step toward self-directed research agents capable of producing increasingly refined outputs over time. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Code-Graph-RAG
The ultimate RAG for your monorepo
Code-Graph-RAG is an advanced retrieval-augmented generation system designed specifically for understanding and interacting with large, multi-language codebases by transforming them into structured knowledge graphs. It uses Tree-sitter to parse source code into abstract syntax trees, extracting relationships between functions, classes, and modules to build a graph-based representation of the entire codebase. This structured approach enables more accurate and context-aware querying compared to traditional text-based search methods, allowing users to ask natural language questions about code structure and functionality. ... -
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Amurex
World's first AI meeting copilot
Amurex is an open-source AI-powered meeting copilot designed to act as an “invisible companion” that enhances productivity by automating meeting-related tasks and knowledge capture across professional workflows. It integrates directly into platforms such as Google Meet and Microsoft Teams, allowing it to operate in real time without disrupting the user’s existing environment. The system leverages advanced AI to generate live suggestions during meetings, helping participants respond more effectively and stay aligned with discussion goals. ... -
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Super Magic
All-in-one AI productivity platform with agents, workflows, and IM
...Alongside this, Magic includes a visual workflow engine that enables users to design complex AI processes using a drag-and-drop interface without requiring extensive coding knowledge. It also provides an enterprise-grade instant messaging system that integrates AI conversations with internal communication, allowing teams to collaborate while leveraging intelligent assistants. Its architecture is built using a microservices approach with containerized services. -
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RAGHub
A community-driven collection of RAG
...The repository is community-driven, meaning developers can contribute new tools, frameworks, or educational resources to keep the dRAGHub is an open-source directory and knowledge hub dedicated to organizing tools, frameworks, and research resources related to Retrieval-Augmented Generation systems. -
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MarkPDFDown
A high-quality PDF to Markdown tool based on large language model
...The project focuses on extracting text, formatting, and structural information from complex PDF documents and transforming that information into clean Markdown that preserves the original hierarchy of headings, paragraphs, tables, and lists. By producing Markdown rather than raw text, the tool makes it easier to integrate documents into knowledge bases, documentation systems, or language model pipelines that rely on structured input. The software is particularly useful for developers working with technical documents, academic papers, or reports that need to be indexed, summarized, or processed by downstream AI systems. -
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AI Engineering Academy
Mastering Applied AI, One Concept at a Time
AI-Engineering.academy is a community-driven educational repository that organizes practical knowledge and learning paths for applied AI engineering. The project aims to make complex AI concepts accessible by structuring them into progressive learning modules covering topics such as prompt engineering, retrieval-augmented generation, LLM deployment, and AI agents. Rather than focusing purely on theoretical explanations, the repository emphasizes hands-on understanding of how modern AI systems are designed, built, and deployed in real-world applications. ... -
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
Kernel Memory is an open-source reference architecture developed by Microsoft to help developers build memory systems for AI applications powered by large language models. The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using contextual information from private or enterprise datasets. Kernel Memory can ingest documents in multiple formats, process them into embeddings, and store them in searchable indexes. ... -
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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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OM1
Modular AI runtime for robots
OM1 is an open-source AI platform designed to build autonomous agents capable of interacting with digital environments and completing complex tasks. The project focuses on creating a modular architecture where language models can coordinate with external tools, APIs, and knowledge sources to accomplish multi-step objectives. Instead of operating as simple conversational systems, OM1 agents can plan actions, retrieve information, and execute tasks across different services. The framework integrates reasoning modules, planning strategies, and tool interfaces that allow agents to operate in dynamic environments. ... -
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Wiseflow
Enhance any agent's browser use skill
...This automated workflow helps reduce the noise associated with large information ecosystems and highlights the most important insights for users. Wiseflow can automatically categorize extracted content, assign tags, and upload processed results into databases or knowledge systems for further use. -
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Quint Code
Structured reasoning framework for Claude Code, Gemini, and Cursor
...Instead of accepting the first plausible answer generated by an AI assistant, Quint Code encourages generating multiple competing hypotheses, verifying them, and validating them against real evidence stored in a structured “knowledge base” within your project. It supports a cycle of abduction, deduction, and induction backed by CLI commands (like /q1-hypothesize, /q2-verify, /q3-validate, etc.) that create a persisting audit trail in a .quint/ directory. -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
...Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. Then, we establish graph topology and connect related knowledge clusters, enabling the LLM to "understand" the data. -
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Awesome-FL
Comprehensive and timely academic information on federated learning
A “awesome” curated list of federated learning (FL) academic resources: research papers, tools, frameworks, datasets, tutorials, and workshops. A hub for FL knowledge maintained by the academic community. -
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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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Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
...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. It also includes a web-based interface for interacting with the agent, making it accessible for testing and deployment in real-world scenarios. -
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Memori
SQL-native memory layer enabling persistent context for AI agents
Memori is an open source SQL-native memory engine designed to add persistent memory capabilities to AI applications, large language models, and multi-agent systems. It provides a memory layer that automatically captures conversations and interactions between users and AI models, allowing systems to retain knowledge across sessions instead of operating statelessly. It extracts structured information such as facts, preferences, rules, and summaries from interactions and stores them in standard SQL databases for later retrieval. By recalling relevant context during future model calls, Memori helps AI agents produce more consistent and context-aware responses while reducing the need to repeatedly provide background information. ...
