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Add PIL to Knowledge & Memory

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Paul Poulose
2026-09-13 18:12:22 -04:00
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@@ -2839,6 +2839,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
- [dfrancislyondflabc-tech/agentic-recall](https://github.com/dfrancislyondflabc-tech/agentic-recall) [![dfrancislyondflabc-tech/agentic-recall MCP server](https://glama.ai/mcp/servers/dfrancislyondflabc-tech/agentic-recall/badges/score.svg)](https://glama.ai/mcp/servers/dfrancislyondflabc-tech/agentic-recall) 📇 🏠 🍎 🪟 🐧 - Long-term memory across sessions over a folder of plain markdown files. Hybrid retrieval (BM25F + dense vectors + phrase proximity) with an explicit absence verdict: when the corpus does not hold the answer it returns no results and names the query terms that appear nowhere, instead of ranking a least-bad match as though it were one. Embedding model runs locally, nothing leaves the machine. MIT. - [dfrancislyondflabc-tech/agentic-recall](https://github.com/dfrancislyondflabc-tech/agentic-recall) [![dfrancislyondflabc-tech/agentic-recall MCP server](https://glama.ai/mcp/servers/dfrancislyondflabc-tech/agentic-recall/badges/score.svg)](https://glama.ai/mcp/servers/dfrancislyondflabc-tech/agentic-recall) 📇 🏠 🍎 🪟 🐧 - Long-term memory across sessions over a folder of plain markdown files. Hybrid retrieval (BM25F + dense vectors + phrase proximity) with an explicit absence verdict: when the corpus does not hold the answer it returns no results and names the query terms that appear nowhere, instead of ranking a least-bad match as though it were one. Embedding model runs locally, nothing leaves the machine. MIT.
- [vishalbanwari26/mnemos](https://github.com/vishalbanwari26/mnemos) [![vishalbanwari26/mnemos MCP server](https://glama.ai/mcp/servers/vishalbanwari26/mnemos/badges/score.svg)](https://glama.ai/mcp/servers/vishalbanwari26/mnemos) 🐍 🏠 - Persistent memory with three swappable storage backends (Postgres/pgvector, Qdrant, Neo4j) behind one interface, benchmarked head-to-head for latency, not just claimed interchangeable. Retrieval weighting adapts per user via epsilon-greedy over real outcome feedback instead of a fixed formula. Reflection merges near-duplicate facts and decays/archives stale ones, audit-logged, never hard-deleted. Real before/after MCP eval: +60% answer accuracy with memory vs. without. `git clone` + `uv sync` (not yet on PyPI). - [vishalbanwari26/mnemos](https://github.com/vishalbanwari26/mnemos) [![vishalbanwari26/mnemos MCP server](https://glama.ai/mcp/servers/vishalbanwari26/mnemos/badges/score.svg)](https://glama.ai/mcp/servers/vishalbanwari26/mnemos) 🐍 🏠 - Persistent memory with three swappable storage backends (Postgres/pgvector, Qdrant, Neo4j) behind one interface, benchmarked head-to-head for latency, not just claimed interchangeable. Retrieval weighting adapts per user via epsilon-greedy over real outcome feedback instead of a fixed formula. Reflection merges near-duplicate facts and decays/archives stale ones, audit-logged, never hard-deleted. Real before/after MCP eval: +60% answer accuracy with memory vs. without. `git clone` + `uv sync` (not yet on PyPI).
- [pjpoulose/PIL](https://github.com/pjpoulose/PIL) [![pjpoulose/PIL MCP server](https://glama.ai/mcp/servers/pjpoulose/PIL/badges/score.svg)](https://glama.ai/mcp/servers/pjpoulose/PIL) 📇 🏠 🐍 🍎 🪟 🐧 - Personal Instagram Library: turns your Instagram saved posts into a private, AI-searchable knowledge base on your own machine. Vision-AI deep reads (summaries, key points, how-tos, links, tags) go into local SQLite, queried live through a read-only stdio MCP server by Cursor, Claude Code, and Claude Desktop — or as a static export for any AI tool. Code is shared, data stays home.
### ⚖️ <a name="legal"></a>Legal ### ⚖️ <a name="legal"></a>Legal
Access to legal information, legislation, and legal databases. Enables AI models to search and analyze legal documents and regulatory information. Access to legal information, legislation, and legal databases. Enables AI models to search and analyze legal documents and regulatory information.