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AutoMaxFix vs Legioni

AutoMaxFix and Legioni are both cli coding agents tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AutoMaxFix

AutoMaxFix

AutoMaxFix runs a detect-reproduce-repair loop: it watches for test failures or runtime drift, surfaces one ticket at a time, lets an AI agent propose a patch, and stops cold until a human approves it. That deliberate stop is the point. The vendor describes it explicitly as 'the boring opposite of an autonomous agent' — one ticket, one patch attempt, one approval, one report. Every fix is logged with provenance so you can trace what changed and why. The ceiling arrives fast: the tool handles one ticket per execution, so teams running parallel failure streams will need external orchestration to manage the queue.

Legioni

Legioni

The orchestrator receives a plain-language task in opencode, breaks it down, and hands it to a chain of specialist agents — architect, implementer, reviewer, test-strategist — in sequence. Each step feeds the next; the loop closes only when tests pass. The 'lesson promotion' mechanism lets teams encode what they learn into persistent agent behavior, so the same mistake doesn't resurface two projects later. The hard boundary: Legioni runs inside opencode, full stop. If your team is not already on opencode or cannot adopt it, the architecture is irrelevant — there is no standalone path and no API to route through a different runtime.

AttributeAutoMaxFixLegioni
PricingFreeFree
Free trialNoNo
Open sourceYesYes
Has APINoNo
Self-hosted optionYesYes
PlatformsLinux, macOS, Windows (Python 3.11+)npm, Node.js
Pros
  • Human approval gate is structural, not configurable — patches cannot merge without explicit sign-off, so teams using AI coding agents have a documented decision point for every change rather than discovering autonomous commits after the fact.
  • Fix provenance logging means every patch carries a record of what triggered it, what the agent proposed, and who approved it, so a post-incident audit does not require reconstructing context from git blame and Slack history.
  • Single-ticket, single-patch execution model keeps the blast radius of any one repair attempt contained — a bad patch attempt does not cascade into a queue of subsequent changes built on a broken base.
  • MIT-licensed and self-hosted, so the tool runs inside your existing infrastructure without routing code or failure telemetry through a third-party cloud, which matters when the codebase contains proprietary logic.
  • Test failure and runtime drift detection in one loop means the tool catches failures that show up after deployment — not just the ones CI catches before it — so drift that accumulates quietly in production is surfaced before it compounds.
  • Test-driven agent loop closes only when tests pass, so you are not manually checking whether the implementer's output actually works before shipping it to review.
  • Specialist agents handle discrete roles — architecture, implementation, review, test strategy — which means a single task does not collapse into one undifferentiated prompt that loses track of constraints halfway through.
  • Lesson promotion persists learned behaviors across sessions and projects, so teams stop re-encoding the same project rules every time they open a new task.
  • Zero-install path via npx means you can run `legioni init` in a new repo without touching your global Node environment, keeping adoption friction low for the first experiment.
  • MIT license and self-hosted operation mean the agents run entirely within your environment — no usage data leaves to a vendor API beyond whatever opencode itself sends.
Cons
  • Single-ticket-per-execution is a hard architectural limit: when multiple tests fail simultaneously or a deploy surfaces a cascade of issues, there is no built-in queue. Teams with parallel failure streams have to wrap the CLI in their own orchestration layer, which means they are now maintaining that glue code.
  • No hosted option, no webhook integration, and no multi-user approval UI means the approval gate is a local CLI prompt — functional for a solo developer or a small team running in the same terminal session, but not viable for a distributed team that needs asynchronous review. Teams that need a browser-based approval workflow or Slack-integrated sign-off will need to build that integration themselves or move to a different toolchain.
  • At 16 commits with pull requests still open, the documented integration surface is thin. Teams cannot assume the examples directory covers their CI/CD setup — expect to read source code to understand behavior at the edges, and expect the API surface to shift before it stabilizes.
  • Legioni is a hard dependency on opencode: every agent, every loop, every lesson runs inside opencode's runtime. Teams on Cursor, Cline, or any other AI coding environment cannot use this tool — the only path forward is adopting opencode first, which is a separate adoption decision with its own trade-offs.
  • The project's public repository shows five commits and a small star count, which means edge cases in the agent loop, lesson promotion conflicts, and stack detection failures are under-documented and under-reported. Teams hitting unexpected behavior have no community issue history to search and no support channel beyond filing a GitHub issue themselves.
  • Lesson promotion is a manual, explicit action — the agents do not automatically surface or apply learned behaviors without team intervention. Projects where nobody curates the promoted lessons see no compounding benefit across sessions, which makes the core differentiating feature opt-in rather than default.
Bottom line

AutoMaxFix and Legioni are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between AutoMaxFix and Legioni?

AutoMaxFix is Free and open source, while Legioni is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AutoMaxFix better than Legioni?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

AutoMaxFix vs Legioni: which should I pick?

Pick AutoMaxFix if its pricing model, openness, or platform fit matches your constraints; pick Legioni otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.