linora-u

linora-u / AgentLoom

Public

YAML-driven multi-agent orchestration framework for long-running, auditable AI automation workflows.

github.comlinora-uAgentLoom#readme agent-framework ai-agent automation cicd code-review
12
0
100% credibility
Found May 06, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

AgentLoom is a framework for orchestrating teams of AI agents using simple YAML plans to automate complex tasks like code reviews, test generation, and repository analysis.

How It Works

1
📰 Discover AgentLoom

You hear about a helpful tool that lets AI teams handle big jobs like checking code or mapping projects, so you grab it for your computer.

2
🔗 Connect your AI helper

Link it to your preferred AI service so the team can think and chat smartly.

3
📝 Describe your goal

Write a plain plan in simple words, like 'review my code for issues' or 'map my project structure' – no tech skills needed.

4
🚀 Launch the team

Start your AI crew with one easy command and watch them team up on the work.

5
👀 Follow the action

See your agents collaborating step by step, building reports or docs just like you asked.

✅ Get amazing results

Receive polished outputs like quality reports or project maps, ready to use and share.

Sign up to see the full architecture

4 more

Sign Up Free

Star Growth

See how this repo grew from 12 to 12 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is AgentLoom?

AgentLoom is a Python framework for YAML-driven multi-agent orchestration, letting you define auditable workflows for long-running AI automation tasks like code quality reviews or repo mapping. You assemble agents as building blocks in YAML configs, run them via simple CLI commands like `loom run `, and get resilient execution with checkpoints, resume support, and structured logs. It handles complex collaboration without constant oversight, producing traceable results for CI/CD or batch jobs.

Why is it gaining traction?

It stands out with declarative YAML setups that make workflows replayable and auditable, plus built-in safeguards like path controls, shell sandboxes, and execution environments (local, Docker, e2b). Developers love the visualization tools—a web UI for topology graphs and a TUI dashboard for monitoring—alongside 42+ tools, skills extensions, and MCP integration for ecosystem tools. The dual modes (structured tool calls or code execution) plus batch parallelism handle real production-scale automations better than chatty single-agent setups.

Who should use this?

Backend engineers automating code reviews, test generation, or bug fixes in large repos. DevOps teams embedding AI steps in CI/CD pipelines for 24/7 tasks. Solo devs or small teams needing controlled, long-running multi-agent flows without building orchestration from scratch.

Verdict

Promising alpha framework (12 stars, 1.0% credibility) with stellar docs, ready-to-run examples, and comprehensive tests—install via uv and try `loom run` on the ai_quality_analysis demo. Maturity is low, so expect bugs in edge cases, but it's a solid pick for YAML-driven agent workflows if you need auditability over raw speed.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.