MCP-Loci: Local Persistent Memory Server for Claude and MCP-Compatible AI

What MCP-Loci Does
MCP-Loci is a persistent memory server built to address Claude's limitation of forgetting context between sessions. Instead of re-explaining who you are, what you're working on, or copying context documents each time you start a new conversation, this tool maintains memory across sessions.
Key Features and Implementation
The server provides five tools:
- remember
- recall
- forget
- synthesize
- health
The recall functionality uses a hybrid approach combining:
- BM25 keyword matching via SQLite FTS5
- Local semantic embeddings using all-MiniLM-L6-v2
This hybrid query approach addresses the limitations of pure keyword search (which fails when you can't remember exact phrases) and pure semantic search (which can be slow and imprecise).
Technical Details
The system runs fully local with no API keys required for search. Installation involves:
- One pip install command
- Four lines of JSON configuration in your Claude Desktop config
The tool is compatible with Claude and any MCP-compatible AI.
📖 Read the full source: r/ClaudeAI
👀 See Also

Claude Code fails silently when ANTHROPIC_API_KEY is set in cloud environments
Setting ANTHROPIC_API_KEY in cloud environments causes Claude Code to malfunction and may incur unexpected API usage charges. Users report extra usage and unresponsive behavior.

Alibaba's $10 monthly coding plan offers high-volume access to multiple AI models for OpenClaw users
For $10 per month, Alibaba's plan provides access to Qwen3.5-Plus, Kimi-K2.5, GLM-5, and MiniMax-M2.5 models with quotas of 1,200 requests per 5 hours, 9,000 per week, and 18,000 per month.

Claude-Code v2.1.76 adds MCP elicitation, worktree optimizations, and numerous fixes
Claude-Code v2.1.76 introduces MCP elicitation support for structured input mid-task, adds worktree.sparsePaths for monorepo efficiency, and fixes 20+ issues including deferred tool schema loss, slash command problems, and Remote Control session stability.
Hugging Face's physics-intern: Multi-Agent Framework Doubles Gemini on CritPt Benchmark
Hugging Face released physics-intern, a multi-agent framework for theoretical physics research that doubles Gemini models' performance on the CritPt benchmark and sets a new SOTA vs GPT-5.5 Pro at lower cost.