Context, Memory & RAG tools for Cursor

Persistent memory server for coding agents that retrieves only the top-K relevant observations via hybrid BM25 + vector + graph search instead of loading a whole memory file into context (~1,900 vs 22K+ tokens at 240 observations, 92% fewer). Reports 95.2% R@5 on LongMemEval-S with a local embedding model, plus a reproducible eval harness to check that claim. One server serves 20+ agents (Claude C

Turns agent-learned project knowledge into a repo-local, symbol-grounded wiki with drift detection and task-aware routing. The critical gap it fills: most agent memory stores facts, but mex keeps those facts connected to the exact code symbols they describe and flags when code changes invalidate them — making memory freshness a harness-level concern rather than a manual cleanup task

Self-improving executable context layer for data and analytics agents: it ingests warehouses, BI tools, and wikis to build a semantic layer with approved metrics, joinable columns, and resolved fan/chasm traps, then serves the result to Claude Code, Codex, and Cursor through MCP. Fills the gap where general-purpose agents invent metric logic on every question — it turns warehouse querying from a p

Repository-level operating harness that turns any software repo into an agent-ready workspace: structured AGENTS.md, HARNESS.md, and FEATURE_INTAKE.md give agents the missing project context — where to start, what the product contract says, how risky the change is, and which decisions future agents should inherit. The most concrete open-source implementation of "coding agents need better repositor

AI 应用开发、AI 编程实战与面试指南，涵盖 LLM、Agent、RAG、MCP、Claude Code、Codex 等核心技术与工程实践。

Open-source, Git-native memory for AI coding agents — deterministic local recall for Claude Code, Codex, Cursor, Gemini CLI, and compatible tools.

Letta's July 2026 library that normalizes native session transcripts from 15+ harnesses (Claude Code, Codex, Cursor, OpenCode, Pi, Gemini CLI, OpenHands, and more) into one validated, model-ready record format. Fills a real infrastructure gap — every runtime logs the same concepts (messages, reasoning, tool calls, results) in incompatible formats — and is explicitly designed for *agent* consumptio

Organization-as-code with brain-inspired memory

Memory component for agents that keeps each memory with its source evidence in one local SQLite file and retires memories recorded as depending on a source when that source is corrected; TypeScript SDK, stdio MCP server, CLI and HTTP API, with exact replay and bisect in the 3.0 pre-release. MIT

Engineering discipline layer for Claude Code -- 67 slash commands, 20 coding rules, 27 skills, 9 agents, quality gates between every step. 8 stack profiles (Go, Python, Java, Kotlin, React, etc.), agent memory across sessions. Install: npx @c0x12c/spartan-ai-toolkit@latest --local

CLI that benchmarks SDK, CLI, and MCP guidance docs (SKILL.md) against multiple LLMs and runs an iterative optimizer to rewrite them until every configured model meets a score floor

by Wolfe-Jam - Authors and versions CLAUDE.md and AGENTS.md from a repository's actual detected stack, so Claude Code's project context stays in sync with the code instead of drifting. One typed source file (project.faf, an IANA-registered media type) is the single origin for every assistant's context file; a deterministic score reports how complete that context is. The file lives in the repo and

Drop-in 12-rule CLAUDE.md + AGENTS.md baseline that closes common agent-orchestration failures (token spirals, silent partial failures, two-pattern pollution). Includes PRD generator prompt, browser-skill-graduation workflow, and 5 example skills. ~700 tokens total, MIT

Open-source linter for AI context files (CLAUDE.md, .cursorrules, copilot-instructions.md) that catches stale paths, wrong commands, and token waste by validating against the real codebase

One source of truth for AI coding rules across every IDE. Author rules, agents, and skills once in plain markdown, and the engine routes them into each tool's native format (Claude Code, Cursor, Copilot, Codex, Pi, OpenCode, AGENTS.md) with no duplication or drift. Zero dependencies, bash + awk, MIT

CLI and MCP server that generates AGENTS.md/CLAUDE.md from static analysis of codebase structure, git history, and convention detection. Surfaces hub files, co-change coupling, and blast radius. No LLM required

Persistent multi-tenant memory layer and MCP server for AI coding assistants with sub-50ms hybrid recall

Cross-agent registry and CLI for discovering and installing 220+ AI assets including skills, prompts, MCP configs, and workflows