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.

A temporal context graph, a memory API, retrieval primitives, and a multiple-platform integration mesh — designed to be embedded into any host process.

Automated harness evolution for AI agents. A Claude Code plugin that iteratively optimizes system prompts, routing, retrieval, and orchestration code using full-trace counterfactual diagnosis. Based on Meta-Harness (Lee et al., 2026).

Local-first memory, hybrid RAG, and agent personalization for AI coding agents (OpenCode, Claude Code, Codex, Gemini CLI, Antigravity). MCP server + CLI with persistent context, document ingestion, vector + SQLite FTS5 retrieval, sync, setup, and safe uni

Reflect2Evolve memory plugin: layered L1/L2/L3 memory, reflection-weighted value backprop, cross-task policy induction, skill crystallization, and three-tier retrieval for OpenClaw, Hermes Agent, and DeepSeek Harness.

Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, Cursor, GitHub Copilot, Gemini, etc.) and AI agents (Claude Desktop, OpenClaw, Hermes, etc.). Supports Serper, Tavily, and SearXNG.

Model Context Protocol (MCP) server that enables AI assistants to securely interact with Odoo ERP systems through standardized resources and tools for data retrieval and manipulation.

Provides read-only access to a China-region Garmin Connect account, enabling retrieval of daily summaries, sleep, heart rate, HRV, training readiness, body battery, and activity data.

Enables discovery, retrieval, summarization, comparison, methodology and limitation analysis, research gap identification, and local management of academic papers via arXiv and Gemini AI.

Enables deterministic, read-only retrieval of ServiceNow Knowledge articles, including search, article details, and binary attachments, via the ServiceNow Knowledge Management REST API.

Provides four MCP tools for neural retrieval: personalized user recommendations, item-to-item similarity, cold-start text search, and explainable recommendation scores, enabling LLM agents to query a trained two-tower recommender over Amazon Reviews data.

Enables document indexing and question answering with Retrieval-Augmented Generation, providing cited answers from user-supplied documents.