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"Understanding how memory is stored or memory storage methods" matching MCP servers:

  • F
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    Provides persistent long-term memory for AI coding agents by storing entities, relations, and observations across different sessions. It enables users to manage and query structured knowledge like coding preferences, project patterns, and technical solutions via a graph-based storage system.
    Last updated
    1
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    An in-memory knowledge graph MCP server that gives coding agents structural and semantic recall over codebases by indexing Python source, ADR documents, and project configuration, exposing 7 tools for search, traversal, context retrieval, and natural-language Q&A.
    Last updated
  • A
    license
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    quality
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    maintenance
    A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.
    Last updated
    218
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Shared, code-grounded memory for developers and their coding agents. Capture a learning once and the whole team plus every agent recalls it; memory is grounded in your code and stored as git-tracked JSON reviewed in PRs, with citations validated on write and stale memory withheld from recall. Works with any MCP client.
    Last updated
    11
    30
    GPL 3.0
  • A
    license
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    quality
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    maintenance
    Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.
    Last updated
    1
    146,749
    Sleepycat
  • F
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    quality
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    maintenance
    Provides a persistent memory and governance layer that allows AI coding agents to query documented architecture rules and validate code against team standards. It enables agents to verify compliance across categories like security and testing before suggesting changes to ensure consistency across development sessions.
    Last updated
    3
    17
  • A
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    quality
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    maintenance
    Exposes the four Ejentum cognitive harnesses (reasoning, code, anti-deception, memory) as MCP tools any agentic client can call. Drop-in scaffolding that catches LLM failure modes like sycophancy, hallucination, and reasoning shortcuts.
    Last updated
    4
    222
    11
    MIT
  • A
    license
    A
    quality
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    maintenance
    Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.
    Last updated
    2
    11
    4,225
    AGPL 3.0
  • A
    license
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    quality
    F
    maintenance
    This MCP server is designed for planning with Claude Code, Cline, or Cursor and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. It uses the Qwen 3 Coder model for high-quality code generation and can be embedded in IDEs.
    Last updated
    1
    51
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Self-hosted decision memory for AI coding agents. Captures decisions with the alternatives you rejected, and warns before an agent re-proposes a rejected approach.
    Last updated
    4
    82
    Apache 2.0
  • A
    license
    A
    quality
    A
    maintenance
    Local-first, auditable memory for Codex, Claude Code, and MCP clients. It stores scoped user/project memory in SQLite or Postgres, serves read-only recall and inspection tools by default, and supports opt-in governed writeback with review and forget controls.
    Last updated
    8
    95
    8
    MIT