Alternatives
Products that do what Code-Memory MCP Server does
Deterministic & high-precision code intelligence via MCP
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Hey HN! I'm José, and I built Recall to solve a problem that was driving me crazy. The Problem: I use Claude for coding daily, but every conversation starts from scratch. I'd explain my architecture, coding standards, past decisions... then hit the context limit and lose everything. Next session? Start over. The Solution: Recall is an MCP (Model Context Protocol) server that gives Claude persistent memory using Redis + semantic search. Think of it as long-term memory that survives context limits and session restarts. How it works: - Claude stores important context as "memories" during…
Oct 2025 · npmjs.com
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An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory
24d ago · github.com
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- 8PS
I got tired of playwright-mcp eating through Claude's 200K token limit, so I built this using the new Claude Skills system. Built it with Claude Code itself. Instead of sending accessibility tree snapshots on every action, Claude just writes Playwright code and runs it. You get back screenshots and console output. That's it. 314 lines of instructions vs a persistent MCP server. Full API docs only load if Claude needs them. Same browser automation, way less overhead. Works as a Claude Code plugin or manual install. Token limit issue:…
Oct 2025 · github.com
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I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc. We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state. Install in Claude Code: /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill /plugin install ensue-memory # restart Claude Code What it does: (1) persists context between sessions (2) semantic & temportal search (not just…
Dec 2025 · github.com
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Open-source MCP server. #5 on LoCoMo. 100% local. MIT.
May 2026 · totalmemory.dev
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Hi all! I normally work on the PyTorch project but I've been on baby leave for the past month, so I've been playing around with AI as a user rather than a framework implementor. I really liked the agent experience with Claude Code, but I couldn't really justify spending so many dollars on API costs for random side projects. I already pay for a Claude Pro subscription though, and it turns out you can simulate many of Claude Code's features with an MCP. If you have a Pro subscription, check this out! I think it really captures the Claude Code experience quite well, without forcing you to pay…
2025 · github.com
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Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode…
Feb 2026 · github.com
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We built CodeYam Memory because Claude Code kept making the same mistakes on our codebase. Our claude.md files quickly got stale and maintaining by hand or with Claude wasn’t sufficient. While digging into this we found that Claude has a native rules system that allowed us to target specific parts of our repo with path matching. This was ideal for our use case but trying to manage these rules by hand was already not working and would be even harder with more granular, targeted rules. CodeYam Memory uses a background agent to review your coding session transcripts, identifies confusion…
Mar 2026
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Hey HN! I built Retain as the evolution of claude-reflect (github.com/BayramAnnakov/claude-reflect). The original problem: I use Claude Code/Codex daily for coding, plus claude.ai and ChatGPT occasionally. Every conversation contains decisions, corrections, and patterns I forget existed weeks later. I kept re-explaining the same preferences. claude-reflect was a CLI tool that extracted learnings from Claude Code sessions. Retain takes this further with a native macOS app that: - Aggregates conversations from Claude Code, claude.ai, ChatGPT, and Codex CLI - Instant full-text…
Jan 2026 · github.com
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In the nature of Open Source, I am releasing something I'm actively working on but is insanely simple and will likely be made anyways. It is an SDK for scripting Claude Code. It's a lightweight (155 lines) and free wrapper around claude code This is a big deal because it seems that using claude code and cursor has become largly repitive. My workflow typically goes like this: Plan out my task into a file, then have claude code implement the plan into my code. I'm actively building a product with this, but still wanted to make it OSS! Use it now with `pip install codesys`
2025 · github.com
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Jun 2026 · github.com
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Hi, we're Sergey and Serafim. We've been building dev tools at 21st.dev and recently open-sourced 1Code (https://1code.dev), a local UI for Claude Code. Here's a video of the product: https://www.youtube.com/watch?v=Sgk9Z-nAjC0 Claude Code has been our go-to for 4 months. When Opus 4.5 dropped, parallel agents stopped needing so much babysitting. We started trusting it with more: building features end to end, adding tests, refactors. Stuff you'd normally hand off to a developer. We started running 3-4 at once. Then the CLI became annoying: too many terminals, hard to…
Jan 2026 · github.com
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The memory layer that decides what's worth remembering
13d ago · skynetlab-cortex.com
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Tired of AI coding tools that forget everything between sessions? Every time I open a new chat with Claude or fire up Copilot, I'm back to square one explaining my codebase structure. So I built something to fix this. It's called In Memoria. Its an MCP server that gives AI tools persistent memory. Instead of starting fresh every conversation, the AI remembers your coding patterns, architectural decisions, and all the context you've built up. The setup is dead simple: `npx in-memoria server` then connect your AI tool. No accounts, no data leaves your machine. Under the hood it's TypeScript +…
2025 · github.com
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