Maccha – Cross Agent Brain for Antigravity, Claude Code, OpenCode etc.
Multi Agent Continuous Context Harness - MACCHA solves the problem that every AI coding session starts from zero. It combines a file-based 7-tier context architecture with a working memory engine (Memanto) that features vector embeddings, confidence decay, and semantic conflict detection — so Antigravity, OpenCode, and Claude Code all share the same persistent, self-improving brain. No 24/7 daemon needed.
Maccha – Cross Agent Brain for Antigravity, Claude Code, OpenCode etc. launched on June 20, 2026 with 5 votes, #2,466 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Turn Antigravity, OpenCode, Claude Code or any AI coding assistant into a fully contextualized, secure, self-improving real-world Digital Assistant - KarelTestSpecial/real-agent-setup
Does a similar job
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Harness by Autonomous7d ago · autonomous.ai · ▲292Every coding agent, every machine, one command center.
Google AntigravityNov 2025 · antigravity.google · ▲590Run and monitor several coding agents at once in an IDE
1CodeJan 2026 · github.com · ▲569Just to clarify the background a bit. This project wasn’t planned as a big standalone release at first. On January 16, Ollama added support for an Anthropic-compatible API, and I was curious how far this could be pushed in practice. I decided to try plugging local Ollama models directly into a Claude Code-style workflow and see if it would actually work end to end. Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude Technically, what I do is pretty straightforward: - Detect which local models are available in Ollama. - When…
Second Brain for AIMay 2026 · github.com · ▲286Persistent memory for Claude, ChatGPT & Cursor. Free.
Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Claude-MemDec 2025 · cmem.ai · ▲115AI agent memory, everywhere. claude-mem is open-source memory for Claude Code; cmem links it to any MCP client — Cursor, Codex, more — via one private link.
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