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Fix entry formatting
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Frank Fiegel
@@ -2199,7 +2199,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
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- [rushikeshmore/CodeCortex](https://github.com/rushikeshmore/CodeCortex) [](https://glama.ai/mcp/servers/@rushikeshmore/codecortex) 📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter native parsing (28 languages) and serves via MCP. 14 tools, ~85% token reduction. Works with Claude Code, Cursor, Codex, and any MCP client.
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- [rwnalds/engram](https://github.com/rwnalds/engram) [](https://glama.ai/mcp/servers/rwnalds/engram) 📇 🏠 - Second brain your agents read and write, over a git-backed markdown vault. Authority-aware search ranks superseded and archived notes below live ones (from frontmatter, no vector DB), so agents quote the current doc, not the dead one. Per-agent read-only or write tokens, a git audit trail of every change with diffs, a knowledge-graph dashboard, and Obsidian compatibility.
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- [s60yucca/mnemos](https://github.com/s60yucca/mnemos) [](https://glama.ai/mcp/servers/s60yucca/mnemos) 🏎️ 🏠 🍎 🪟 🐧 - Persistent memory engine for AI coding agents. Stores architecture decisions, bug root causes, and project conventions across sessions. Single Go binary with embedded SQLite, FTS5 search, context assembly within token budgets, and autopilot setup for Claude Code, Kiro, and Cursor.
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- [seanmarkwei/agentram-mcp](https://github.com/seanmarkwei/agentram-mcp) 📇 ☁️ [](https://glama.ai/mcp/servers/seanmarkwei/agentram-mcp) - Persistent memory for AI agents through a simple key-value HTTP API. No vector database or embeddings required. Store, retrieve, search, and share memory across agents with shared namespaces and TTL support. `npx -y agentram-mcp`
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- [seanmarkwei/agentram-mcp](https://github.com/seanmarkwei/agentram-mcp) [](https://glama.ai/mcp/servers/seanmarkwei/agentram-mcp) 📇 ☁️ - Persistent memory for AI agents through a simple key-value HTTP API. No vector database or embeddings required. Store, retrieve, search, and share memory across agents with shared namespaces and TTL support. `npx -y agentram-mcp`
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- [SagaPeak/artifacta-mcp](https://github.com/SagaPeak/artifacta-mcp) [](https://glama.ai/mcp/servers/SagaPeak/artifacta-mcp) 🎖️ 📇 🐍 ☁️ 🍎 🪟 🐧 - The artifact store for AI agents. Every output your agents produce — persisted, retrievable, shareable. Across runs, sessions, and tools. Session/agent metadata, content-hash dedup, expiring share links; 11 tools with path-confined uploads and destructive actions gated by default. TypeScript (`npx @artifacta-mcp/mcp`) and Python (`pipx run artifacta-mcp`).
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- [SAIHM-Admin/saihm-mcp](https://github.com/SAIHM-Admin/saihm-mcp) [](https://glama.ai/mcp/servers/SAIHM-Admin/saihm-mcp) 📇 ☁️ 🏠 🍎 🪟 🐧 - Sovereign, encrypted, persistent memory for AI agents. Eight MCP tools — remember, recall, forget (GDPR Art. 17 erasure), share. Anchored on COTI V2 mainnet. Apache-2.0. Install: `npx -y @saihm/mcp-server`.
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- [SaravananJaichandar/world-model-mcp](https://github.com/SaravananJaichandar/world-model-mcp) [](https://glama.ai/mcp/servers/SaravananJaichandar/world-model-mcp) 🐍 🏠 🍎 🪟 🐧 - Temporal knowledge graph for codebases. Captures decision traces, links test failures to code changes, learns co-edit patterns, predicts regression risk, and enforces learned constraints at the edit boundary via a PreToolUse hook. 22 MCP tools, 9 SQLite databases with FTS5, supports Python/TypeScript/JavaScript/Solidity/Go/Rust/Java. Install via `pip install world-model-mcp`.
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