⚡ Cut Claude token usage by 90%+ — free, open-source, local-first context compression for Claude Code. Hybrid RAG (BM25 + ONNX vectors), AST chunking, reranking. No API needed.

MCP plugin that saves 98% of your context window. Works with Claude Code, Gemini CLI, VS Code Copilot, OpenCode, and Codex CLI. Sandboxed code execution, FTS5 knowledge base, and intent-driven search.

Memory for coding agents — Claude Code, Codex, Cursor and 17 others. Indexes the sessions they already wrote to disk, including months from before you installed it, and recalls them in any of them. No LLM, no embeddings, one local Go binary.

Memory for coding agents — Claude Code, Codex, Cursor and 17 others. Indexes the sessions they already wrote to disk, including months from before you installed it, and recalls them in any of them. No LLM, no embeddings, one local Go binary.

Supercharge AI Agents, Safely

Self-learning vector memory for AI agents — single-file .rvf cognitive container with HNSW search, episodic Reflexion memory, causal graph + Cypher, 9 RL algorithms, Thompson Sampling bandit, 41 MCP tools, hybrid (BM25 + dense) retrieval, GNN attention. 1

Cross-session memory plugin for DeepSeek Harness: seven-layer SQLite store (soul/user/project/fact/lesson/topic/rules), BM25 retrieval, per-window dream consolidation. 跨会话七层长期记忆插件。

MCP server giving AI agents (Claude Code, Claude Desktop, Cursor, ChatGPT, Codex, OpenClaw) persistent long-term memory backed by your local Obsidian markdown vault. Hybrid retrieval (BM25 + ML embeddings + BGE reranker, RRF-fused), HNSW + int8 quantizati

Self-hosted semantic code search platform — Go server with web dashboard, CLI, and AI-agent skills. Search code by meaning, not text: hybrid BM25 + dense embeddings via llama.cpp.

本地优先、零普通运行时依赖的 DSH 项目记忆：有界会话冻结 Hot Memory、倒排 BM25 召回、生命周期与缓存命中优化、中英双语 GUI。 / Local-first DSH project memory with zero regular runtime dependencies: bounded session-frozen Hot Memory, inverted-index BM25 recall, lifecycle, cache-aware design, and a bilingual GUI.

Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.