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    A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.
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    MIT
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    Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON
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    16
    MIT
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    Enables persistent local memory in VS Code Copilot Chat via MemPalace, using a ChromaDB 0.6.x backend with automatic setup and verification.
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    MIT
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    Enables multi-turn conversations with Google Gemini AI models, supporting file and image analysis, automatic model selection, deep thinking mode, and Google Search integration through the AIStudioProxyAPI backend.
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    MIT
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    AI-native open-source backend-as-a-service. Manage Postgres with RLS, auth, file storage, serverless TypeScript functions, durable objects, realtime, KV, AI gateway, and RAG via MCP, either HTTP at /mcp on a self-hosted instance or stdio with npx @butterbase/mcp.
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    2,766
    Apache 2.0
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    Enables AI assistants to search and query documentation with flexible backend configurations (ChromaDB, Xenova embeddings) and supports multiple chunking strategies via an MCP server.
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    6
    MIT
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    Local-first knowledge backend for AI agents that connects MCP hosts to an Obsidian-compatible vault with indexed retrieval, token-budgeted memory recall, and secure ingestion.
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    MIT
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    A self-hosted, MCP-native web-search backend for AI agents that provides meta-search, clean extraction, RAG with citations, and GitHub project selection.
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    MIT
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    A Python backend implementing the Model Context Protocol with Azure OpenAI integration, enabling applications to interact with LLMs through a standardized interface with streaming capabilities.
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    MIT
  • F
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    Integrates the OpenViking context database with Cloudwise SA Workflow Skills to enable semantic search and access to product catalogs or solution templates. It supports hierarchical context loading for optimized token usage and includes a mock mode for testing without a live backend.
    Last updated