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"Creating a Memory Database for LLM Chat Conversations" matching MCP servers:

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    MCP server that provides a live coordination layer for AI agents, including attributable handoffs, a shared event ledger, atomic work-claiming, and advisory file leases to prevent collisions.
    Last updated
    27
    6
    AGPL 3.0
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    A local-first, multi-provider cost meter for LLM usage, exposed as MCP tools. Captures every call into a local SQLite ledger and lets any coding agent query spend, compare providers, and get recommendations — no cloud, no account. First-class support for Chinese providers (Qwen, DeepSeek) alongside Anthropic and OpenAI.
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    7
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    MIT
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    A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.
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    MIT
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    Routes your AI tasks to the best available model across 20+ providers — automatically selecting based on task type, budget, and subscription pressure. Supports text, image, video, and audio with built-in cost optimization and fallback chains.
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    MIT
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    A fully local persistent memory layer for LLM coding agents (Claude Code, Codex, Gemini CLI, OpenCode). A shell wrapper intercepts tool invocations, fires hooks on every tool call, then runs a 3-layer pipeline (extract → compress to ≤500-token digest → merge into project memory doc) at session end. The next session gets prior context injected automatically.
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    6
    12
    MIT
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    Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md
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    3,509
    AGPL 3.0
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    A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.
    Last updated
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    MIT
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    Enables coding agents to watch and analyze videos by extracting scene-aware frames, transcribing speech, and generating shot timelines, all constrained by a token budget to fit LLM context limits.
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    MIT
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    Enables AI assistants to interact with and manage multiple database types (PostgreSQL, MySQL, SQLite, SQL Server, MongoDB, Redis) through natural language, supporting query analysis, schema management, data analysis, backup/restore, and security analysis.
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    MIT
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    A persistent, project-scoped memory layer for AI coding agents, providing 39 tools for capturing, searching, deduplicating, relating, and maintaining memory records across long-running coding sessions.
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    MIT
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    Provides AI agents with access to real-time cryptocurrency data from Coinbase's public API, including prices, market statistics, historical data, and technical analysis, plus simulated wallet transactions for educational purposes.
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    3
    MIT
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    Token cost math for LLM API calls: current per-million-token rates for 69 models across 17 providers, with local arithmetic for estimates, comparisons and monthly budgets. Rates are verified and date-stamped.
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    1
    MIT