Hanesu vs Legioni

Hanesu 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.

Hanesu

The project borrows from Harness Engineering principles: work is broken into phases with task files, role handoffs, quality gates, and progress artifacts written to disk. Agents using runtimes like OpenCode, Codex, or Claude Code run through that structure rather than a monolithic prompt. The vendor explicitly flags this is not for small, obvious edits — a direct prompt is faster there. Where it earns its place is multi-step refactors, security-sensitive changes, or bugfix workflows where you need the agent to stop, surface what it found, and wait for your sign-off before proceeding.

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.

Attribute	Hanesu	Legioni
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	npm, GitHub	npm, Node.js
Pros	Phase-gated workflow structure means the agent stops and surfaces findings before writing code, so risky refactors don't reach your codebase without a checkpoint you signed off on. Artifact writing is built into each phase, so decision records and discovery outputs are committed alongside the code change — teams doing security audits or post-incident reviews have a documented trail rather than a reconstructed chat log. MIT license and local repo installation means no external service dependency and no data leaving your environment, which removes the approval friction that blocks adoption in regulated or air-gapped codebases. Works alongside existing agent runtimes rather than replacing them, so teams already invested in OpenCode, Codex, or Claude Code don't have to abandon their toolchain to add structured workflow control.	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	For tasks that are small or well-defined, the phase and artifact overhead slows the agent down relative to a single direct prompt — the tool's own docs acknowledge this, but teams will still burn time learning where that line sits in their specific codebase. The project carries 2 stars and 4 commits at the time of curation; there is no issue tracker activity and no documented community — teams hitting an undocumented edge case in the gate logic have no support channel beyond reading the source code themselves. Hanesu has no API and no integration surface beyond local repo files, so any team that needs to tie agent workflow state into CI pipelines, ticketing systems, or deployment gates will have to build that plumbing from scratch — at which point they are maintaining the workflow layer Hanesu provides plus the integration layer it doesn't, and a more mature agent orchestration platform becomes the faster path.	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

Hanesu 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 Hanesu and Legioni?

Hanesu 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 Hanesu 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.