Command Code vs Hanesu

Command Code and Hanesu 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.

Command Code

The agent runs in three modes — interactive CLI, headless with a prompt flag for scripted pipelines, and a background sandbox — so it fits scheduled jobs as well as live coding. Learned preferences compile into reusable skills automatically; no rules to write by hand. The team collaboration angle is real: one command pushes your taste profile, the whole team pulls it. Where the walls appear is less documented: open-model tool-calling support is a stated differentiator, but teams hitting complex multi-step agentic chains on open models will need to validate those claims against their specific stack before committing production workloads.

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.

Attribute	Command Code	Hanesu
Pricing	Paid	Free
Price	$1/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	CLI via npm	npm, GitHub
Pros	Continuous preference learning from accepts, rejects, and edits — so you stop re-correcting the same patterns every session and the agent converges on your actual coding style over time. Three distinct execution modes (interactive, headless, background sandbox), which means the same agent that assists during live coding can run unattended in a CI pipeline without a separate tool. Persistent `/memory` and custom `/agents` scoped to a project, so context you built yesterday is available tomorrow without pasting it back into the prompt. Team taste push/pull in a single command, so a lead's hard-won preference profile becomes the team's baseline instantly — replacing the undocumented tribal knowledge that causes style drift at scale. Vendor-stated open-model harness support, so teams running DeepSeek or MiniMax can access tool-calling capabilities those models lack natively, reducing lock-in to closed-model providers.	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.
Cons	The open-model tool-calling claim is the riskiest dependency: teams building multi-step agentic pipelines on open models have no published benchmark data to validate reliability under production load — only the vendor's stated architecture. Teams whose delivery timeline cannot absorb a harness failure mid-sprint will need to run their own stress tests before committing. The learning loop requires an accumulation period — early sessions before enough accept/reject signal has been gathered will produce generic output indistinguishable from any other agent, which means teams evaluating it on a one-day trial will not see the core differentiation. Complex branching agentic logic — tasks where the next step depends on what the previous step returned across four or more decision points — is not documented as a supported pattern. Teams with those requirements are more likely to move to an agent framework with explicit graph-based workflow control, at which point Command Code's taste layer becomes a side benefit rather than the primary system.	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.

Bottom line

Command Code is paid while Hanesu is free; Hanesu is open source; only Command Code exposes a public API; Command Code runs on CLI via npm; Hanesu on npm, GitHub. Pick the difference that actually blocks you.

Frequently asked questions

What is the difference between Command Code and Hanesu?

Command Code is Paid, while Hanesu is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Command Code better than Hanesu?

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.