mainline-org

mainline-org / mainline

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Git-native memory for coding agents. Repo memory before the diff.

mainline.sh agent-context agent-memory ai-agents ai-code-review ai-coding
30
0
100% credibility
Found May 08, 2026 at 30 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Mainline adds a Git-native memory layer to code repositories so AI coding agents understand the historical reasons for code decisions before making changes.

How It Works

1
🌐 Discover Mainline

You find a helpful tool online that gives AI coding helpers a memory of why your code exists, preventing repeated mistakes.

2
📥 Get it set up

Download and install Mainline easily on your computer with a quick command.

3
🛠️ Add to your project

Run a simple setup in your code folder to connect Mainline to your work.

4
🧠 AI gains project memory

Link your AI coding tool, and it instantly sees the reasons behind your code before making changes.

5
💻 Code smarter together

As you build with AI, it checks history, notes risks, and suggests safe paths.

✅ Safer, faster projects

Your AI avoids old pitfalls, teams stay aligned, and your code evolves reliably.

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Star Growth

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AI-Generated Review

What is mainline?

Mainline adds Git-native memory to repos, feeding coding agents the historical "why" behind code before they generate diffs. It records decisions, abandoned approaches, constraints, and risks in durable Git refs and notes, accessible via a Go CLI with commands like `mainline context --current` for pre-edit retrieval or `hub open` for human browsing. Agents follow a protocol (`start`, `append`, `seal`) while hooks auto-inject repo state into tools like Cursor or Claude Code at session start.

Why is it gaining traction?

Unlike vendor-locked agent memory or RAG on code alone, mainline survives Git pushes, works cross-agent (Codex, Claude, Copilot), and surfaces constraints code can't—like superseded decisions or reviewer guards. Its evals prove intent-first agents commit zero violations on history-dependent tasks where code-first hits nine. Hooks and a skill doc make adoption seamless without workflow changes.

Who should use this?

Solo devs chaining AI agents across sessions, needing continuity to avoid rehashing abandoned ideas. Teams reviewing AI PRs, wanting intent summaries before diffs to catch overlaps early. Cursor or Claude Code users hitting prod breaks from forgotten legacy constraints.

Verdict

Try it if coding agents are core to your flow—solid CLI, hooks, and evals make a compelling case despite 30 stars and 1.0% credibility score signaling early alpha. Docs are thorough (English/Chinese), tests include property-based coverage, but expect polish as it hits v0.5. (187 words)

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