Alternatives
Products that do what Rhizome MCP does
A shared execution layer for coding agents
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13,000+ MCP servers, skills & plugins for AI coding agents
Jul 2026 · codexmarketplaces.com
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- 6PL
How it works: - Storage uses one SQLite database file, plus a local LanceDB index of vectors. No need for a server, cloud services, or any API keys. - Retrieval is a hybrid approach using BM25 (rank-bm25) and vector-based search (sentence-transformers) combined with a co-occurrence graph of entities, using reciprocal rank fusion. The idea is to find the right memory, not the closest one. - It plugs into the agent's lifecycle via MCP: before the agent responds, relevant memories are added to its input; after each turn, decisions and new learnings are automatically recorded. No need to…
Jun 2026 · github.com
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Smarter RAG with Agentic Retrieval & Context-Aware MCP
Sep 2025
- 8MM
Hi HN, for about a year now I've been experimenting with AI agents and building my own home ecosystem; from the start I set out with the idea of an agent that behaves like a member of the family, not as a personal agent, and this made me clash very early first with OpenClaw's builtin memory, then I tested dozens of memory plugins without ever finding one that fit my purpose, so like any good builder I made my own. First on OpenClaw, as a plugin, then the idea matured and since the beginning of this year the memory plugin has evolved into an agent agnostic MCP server. It has been running my…
Jul 2026 · github.com
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Store memories, auto-extract entities and relationships, search semantically. MCP server + REST API + SDKs. Self-hostable, cloud option, MIT license.
May 2026 · agentrecall.cloud
- 10CM
I built an MCP server that connects coding agents (Claude Code, Cursor, OpenCode, Codex) to a collaborative workspace where your team and other AI models can review what the agent is planning. The problem: When Claude Code creates an implementation plan, it lives in your terminal session. Nobody else sees it until it becomes a PR. If you want GPT to check the architecture or a teammate to flag issues, you're copy-pasting between windows. This MCP server fixes that. When your agent creates a plan, it gets shared as a collaborative thread in CoChat. Engineers comment on it, other AI models…
Feb 2026 · github.com
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I kept noticing the same pattern: my AI coding agents solve the same problems over and over across sessions. Coding problems, version specific bugs and general guidelines, solved once through multiple agent interactions and context windows and then forgotten by the next context window. So I built OpenHive, a shared knowledge base that agents contribute to and query from. The idea is simple: when an agent solves a problem, it posts a structured problem-solution pair. When another agent hits a similar issue, it searches the hive first. How it works: - REST API with semantic search (pgvector +…
May 2026 · openhivemind.vercel.app
- 12MM
Hey HN! I’m Gui from deco (decocms.com). We’ve been using this tool internally as the foundation for a few customer AI platforms, and today we’re open-sourcing it as MCP Mesh. MCP is quickly becoming the standard for agentic systems, but… once you go past a couple servers it turns into the same problems for every team: - M×N config sprawl (every client wired to every server, each with its own JSON + ports + retries) - Token + tool bloat (dumping tool definitions into every prompt doesn’t scale) - Credentials + blast radius (tokens scattered across clients, hard to audit, hard to revoke) - No…
Dec 2025 · github.com
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Hi HN -- Since Anthropic announced the Model Context Protocol (MCP) last week [1], I've been excited about giving Claude new capabilities through my custom servers. But while MCP is powerful, implementing the protocol correctly requires a lot of low-level boilerplate code. I found myself wanting something like FastAPI - a high-level framework that would let me focus on building features, not servers. After some hacking, I'm sharing FastMCP: a Pythonic framework for building MCP servers. FastMCP uses decorators to transform normal functions into MCP tools, resources, templates, and prompts,…
2024 · github.com
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I was tired of asking my claude code to reference my codex chats to get references to what decisions it made and why ; so I built Reference MCP It, whenever prompted establishes sessions to get direct access - been using it on my system for a bit and was super helpful so I made a repo :) Would love feedback!
Jun 2026 · github.com
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I’ve been building and using agents heavily lately. The Model Context Protocol ecosystem is growing insanely fast, but discovering and configuring new tools is still highly manual. Every time I needed to connect an agent to a new service, I had to browse registries, figure out the transport type, identify required env vars, and manually update "mcp.json" files. So I built MCPfinder. It aggregates servers from the official MCP registry, Glama, and Smithery (around 25,000 combined entries) into a deduplicated, ranked catalog. But the real twist is the DX: MCPfinder is itself an MCP server :D…
Apr 2026 · mcpfinder.dev
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I built a simple multi user, multi board, Task/Kanban MCP server. I have been looking for something like this to manage development agents, but I wasn't seeing anything that felt like what I wanted. So I set down and decided to vibe code an alternative. While it was an experiment at first I have been using it daily for my personal development projects and I really think there are others who might be looking for exactly this. It's 100% a WIP, but it is also very usable. I have a demo instance running at https://mootasks.dev. If you find this interesting I'd appreciate a star.…
Apr 2026 · github.com
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TLDR: OpenAPI-MCP is a Dockerized server that dynamically generates Model Context Protocol (MCP) tool definitions directly from your Swagger/OpenAPI documentation. It allows your AI agent to seamlessly access any API without additional coding, streamlining development and eliminating repetitive manual setup. For more details, code updates-----: GitHub: ckanthony/openapi-mcp Docker Hub: ckanthony/openapi-mcp
2025 · github.com
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Hey HN Community, I built MCP360 after spending weeks integrating APIs for an AI agent project. Each tool needed its own setup, auth, billing, and maintenance. When any APIs changed, my integrations broke. I got tired of it. MCP360 is a single gateway giving AI agents access to 100+ tools through one config block. Search engines, web scraping, SEO, e-commerce data, maps, domain tools, and more. Real example: Instead of managing Google Search, web scraping, SERP tracking, and keyword research as 4 separate subscriptions (4 bills, 4 auth systems, 4 points of failure), you connect once. Works…
Oct 2025 · mcp360.ai
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We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early
9d ago · twing.dev
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So I've been building ClawMem, an open-source context engine that gives AI coding agents persistent memory across sessions. It works with Claude Code (hooks + MCP) and OpenClaw (ContextEngine plugin + REST API), and both can share the same SQLite vault, so your CLI agent and your voice/chat agent build on the same memory without syncing anything. The retrieval architecture is a Frankenstein, which is pretty much always my process. I pulled the best parts from recent projects and research and stitched them together: [QMD](https://github.com/tobi/qmd) for the…
Mar 2026 · github.com
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I built MCPlexor to solve a token waste problem I kept running into with MCP-based agents. The Problem: MCP (Model Context Protocol) is great for giving LLMs access to external tools. But if you connect multiple servers (GitHub, Linear, Postgres, Slack), you end up with 40-50k tokens of tool definitions injected into every request – before the agent even does anything. On a 200k context model, that's 25% gone. On smaller models, it's worse. And most runs only use 1-2 tools. The Solution: MCPlexor sits between your agent and your MCP servers. Instead of loading all tool definitions upfront:…
Feb 2026 · mcplexor.com
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Shared execution memory for every AI agent in your org
30d ago · semelbase.com
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AgentRQ is a (optionally) human-in-the-loop, self learning closed loop task manager for agents. Agents can create and schedule tasks for themself and work on them on their own schedule. In high level it comes with one supervisor MCP that controls workspaces(worker agents) and unlimited number of isolated workspace MCPs (self learning agents). Each workspace/agent has a mission/persona for the agent. And self-learning-loop note. I am using it about 6 weeks in production, and completed more than 500 tasks. I just released the opensource version(as is in production) under Apache 2.0…
Apr 2026 · github.com
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Find the right API. Give your AI agents the right tools.
27d ago · reqistry.dev
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