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    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
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    MIT
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    A local MCP server that enables LLMs to securely search and read files within designated Google Drive folders. Supports Google Docs, Google Sheets, PDFs, and plain text with strict folder-scoping via service account authentication.
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    MIT
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    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
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    AGPL 3.0
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    Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.
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    MIT
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    Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.
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    MIT
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    Provides a persistent "second brain" for Claude featuring zero-latency hot caching, semantic cold storage, and automatic pattern mining from activity logs. It enables users to store, search, and automatically extract project facts and code patterns for enhanced contextual recall.
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    MIT
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    Persistent, searchable project memory for Claude Code. Each project has its own local DuckDB with vector embeddings, auto-loading context at session start.
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    Cognitive memory for AI agents. Implements Atkinson-Shiffrin multi-store memory (sensory → STM → LTM), semantic RAG with fastembed, Ebbinghaus forgetting curves, trust scoring, and metacognitive guard. 76+ MCP tools. 100% local, MIT licensed.
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    AGPL 3.0
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    A vendor-agnostic cognitive persistence layer for AI agents. Eliminate the "repetition tax" by transporting your context, preferences, and history across sessions. Features an auto-adaptation engine that syncs global instructions to ensure operational cohesion and optimize token usage across any LLM or multi-agent workflow.
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    Apache 2.0