Search Results for "context"
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Showing 167 open source projects for "context"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Claude Context
Code search MCP for Claude Code
...It supports workflows such as retrieval-augmented generation, where external knowledge is dynamically incorporated into model responses. The project emphasizes scalability, allowing it to handle large datasets and complex queries efficiently. It also provides tools for organizing and managing context, making it easier to maintain structured knowledge bases. Overall, Claude-context acts as a bridge between raw data and AI models, improving the relevance and accuracy of generated outputs. -
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Model Context Protocol (MCP)
Specification and documentation for the Model Context Protocol
Model Context Protocol is an open protocol that standardizes how LLM applications connect to external data sources, tools, and runtime context. It separates the concern of providing context from the model interaction itself, allowing AI applications to access resources through a common interface. The project includes the specification, documentation, SDKs, maintained servers, and community infrastructure around the protocol. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. -
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Cherry Studio
Cherry Studio is a desktop client that supports for multiple LLMs
Cherry Studio is a cross-platform desktop client that integrates multiple large language model providers into a unified interface for creating and using AI assistants, supporting customization and multi-model conversations. Selection Assistant with smart content selection enhancement. Deep Research with advanced research capabilities. Memory System with global context awareness. Document Preprocessing with improved document handling. MCP Marketplace for Model Context Protocol ecosystem. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Claude Code
Claude Code is an agentic coding tool that lives in your terminal
...It helps developers code faster by executing routine tasks, explaining complex code snippets, and managing git workflows—all via natural language commands. Claude Code integrates seamlessly into your terminal, IDE, or GitHub by tagging @claude to interact with your code context. The tool is designed to simplify development by automating repetitive work and providing instant clarifications on code behavior. User feedback and usage data are collected responsibly, with strict privacy safeguards and limited retention, ensuring no feedback is used to train generative models. Claude Code is open and actively maintained with community-driven bug reporting and feature requests. ... -
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OpenViking
Context database designed specifically for AI Agents
OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. ... -
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agentmemory
#1 Persistent memory for AI coding agents
...It combines keyword search, vector search, confidence scoring, lifecycle management, and knowledge graph querying to retrieve useful memories without overloading the context window. The project also includes hooks, an API, import and export support, audit trails, and team sharing workflows. Overall, agentmemory is designed to make AI coding assistants more continuous, context-aware, and useful across long-running software projects. -
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Kanwas
Shared context board for teams and agents
Kanwas is an open-source shared context board built for teams and AI agents working together in the same workspace. It gives people and agents a common canvas where documents, evidence, decisions, notes, tasks, embeds, and outputs can live side by side. Instead of scattering context across chats, documents, and disconnected tools, Kanwas turns messy collaborative work into a shared visual environment that both humans and agents can read and update. -
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MineContext
MineContext is your proactive context-aware AI partner
...It is built around a context engineering framework that manages the full lifecycle of data, including capture, processing, storage, retrieval, and consumption. The platform emphasizes privacy through a local-first architecture, allowing users to keep their data stored and processed on their own device rather than relying on external cloud services. -
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WrenAI
Open-source SQL AI Agent for Text-to-SQL. Make Text2SQL Easy
Wren AI is a SQL AI Agent for data teams to get results and insights faster by asking business questions without writing SQL, and it's open-source. Wren AI has implemented a semantic engine architecture to provide the LLM context of your business; you can easily establish a logical presentation layer on your data schema that helps LLM learn more about your business context. With Wren AI, you can process metadata, schema, terminology, data relationships, and the logic behind calculations and aggregations with “Modeling Definition Language”, to generate accurate SQL queries with semantic context. ... -
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Harness Engineering
Field guide, and agent context bundle for harness engineering
Harness Engineering is a retrieval-optimized anthology, field guide, and agent context bundle for improving AI coding-agent performance. It treats the model and agent as fixed while strengthening the surrounding context, tools, constraints, and proof mechanisms. The repository organizes developed arguments, practical cases, source evidence, evaluations, and reusable playbooks into distinct layers. Its agent guide routes each task to the smallest relevant set of materials instead of loading the entire corpus. ... -
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CopilotKit
Build in-app AI chatbots, and AI-powered Text areas
A bridge between your copilot and your app. A programmable 2-way bridge between your copilot, and your application state (client & cloud). Supports 3rd party integrations (e.g. Salesforce, Zendesk, etc.). Plug-and-play, fully customizable, copilot infrastructure. Build in-app AI chatbots that can "see" the current app state + take action inside your app. The AI chatbot can talk to your app frontend & backend, and to 3rd party services (Salesforce, Dropbox, etc.) via plugins. Autocompletion +... -
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AnythingLLM
The all-in-one Desktop & Docker AI application with full RAG and AI
...A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean. -
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MiMo Code
Where Models and Agents Co-Evolve
...The tool includes multiple agent modes, including a build mode for development, a plan mode for read-only analysis, and a compose mode for structured workflows. Its persistent memory system stores project notes, checkpoints, scratch notes, and task progress so the assistant can resume work with context. It also supports subagents, goal checking, voice input, MCP connections, and custom provider configuration. MiMo-Code is useful for developers who want an autonomous coding assistant that combines terminal workflows, long-running task management, and project-aware memory. -
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Letta Code
The memory-first coding agent
Letta Code is a memory-first CLI coding agent built on the Letta platform that offers developers a persistent AI assistant capable of learning and improving over time rather than resetting state each session, giving agents a sense of continuity and context across coding tasks. Unlike traditional session-based coding tools, Letta Code attaches a long-lived agent to a working directory so that the agent accumulates memory about a project’s structure, preferences, and history, effectively acting as a collaborative partner rather than a stateless helper. Users can initialize and connect the agent to various models, including popular large language models, and issue commands, refactor code, or ask context-aware questions directly in the terminal, with memory retained across multiple interactions. -
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BlenderMCP
Blender Model Context Protocol Integration
BlenderMCP is a bridge that connects Blender, a 3D modeling and rendering software, with AI systems like Claude through the Model Context Protocol, enabling direct AI-driven interaction with 3D environments. It allows users to control Blender using natural language prompts, effectively turning AI into a co-creator for 3D modeling, scene construction, and asset manipulation. The system establishes a two-way communication channel between Blender and the AI, where commands can be sent and results retrieved in real time. ... -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. ... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. ... -
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Open Claude Cowork
Open Source version of Claude Cowork built with Claude Code
...It offers a native Electron-based interface for macOS, Windows, and Linux that feels familiar and modern, supporting persistent, multi-session conversations that maintain context across multiple turns and workflows while you focus on higher-level goals rather than low-level prompts. With support for over 500 integrated tools—including Gmail, Slack, GitHub, Google Drive, and more via the Composio Tool Router—Open Claude Cowork lets agents execute complex tasks that span multiple platforms and APIs, effectively acting as a cross-service productivity layer. -
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Paperclip
Open-source orchestration for zero-human companies
Paperclip is an open-source tool designed to help AI systems and developer tools access academic research papers through a standardized interface. The project implements a server based on the Model Context Protocol (MCP), a framework that allows large language models and AI agents to connect to external data sources and tools in a consistent way. By acting as a middleware layer, Paperclip aggregates multiple academic databases and exposes them through a single interface, allowing AI applications to search and retrieve scholarly papers without needing to integrate with each provider individually. ... -
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LobeHub
Workspace to find, build, and collaborate with AI agents
LobeHub is an all-in-one workspace designed to help humans and AI agents collaborate, grow, and evolve together. It treats AI agents as true teammates rather than one-off tools, enabling deeper context, continuity, and productivity. Users can build personalized agent teams that understand their workflows, preferences, and goals over time. LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. ... -
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Sol Advisor
Codex-native architect orchestration with Luna and Terra
...The primary Sol session owns requirements, architecture, specifications, verification, and final acceptance. Its default native workflow delegates implementation to a Terra agent and then requires a fresh Sol review in a separate context. An alternative Luna lane can create user-visible Codex application tasks when the user explicitly authorizes that mode. This separation is intended to reduce context contamination while preserving clear responsibility for quality and architectural decisions. The workflow can monitor implementation, inspect diffs, request corrections, coordinate dependent work, and control final approval. ... -
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Ralph
Autonomous AI agent loop that runs until all PRD items are complete
Ralph is an autonomous AI coding loop that repeatedly runs agentic development tools until every item in a product requirements document is complete. It is designed to work with Amp or Claude Code, launching a fresh AI instance on each iteration to avoid context overload. Instead of relying on one long conversation, Ralph keeps progress through git history, a structured prd.json file, and an append-only progress.txt memory file. Each run selects the highest-priority unfinished user story, implements it, runs quality checks, commits passing work, and updates the PRD status. The workflow encourages teams to split features into small, verifiable stories that fit inside a single context window. ... -
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Dify
One API for plugins and datasets, one interface for prompt engineering
...Unify your development process with one API for plugins and datasets integration, and streamline your operations using a single interface for prompt engineering, visual analytics, and continuous improvement. Out-of-the-box web sites supporting form mode and chat conversation mode A single API encompassing plugin capabilities, context enhancement, and more, saving you backend coding effort Visual data analysis, log review, and annotation for applications -
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Pal
A personal context-agent that learns how you work
...The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can answer questions, recall information, and assist with future tasks more effectively. The agent can perform web research, summarize information, and store insights so that useful discoveries are not lost across conversations or sessions. Over time, the agent learns from interactions, remembers patterns that worked well, and applies those learnings to similar tasks in the future, allowing it to improve without requiring additional model training.
