Flightwake vs Legioni

Flightwake 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.

Flightwake

The framework installs into a git repository via npx and writes pure Markdown records that live in version control alongside the code. There are zero runtime dependencies — no sidecar process, no external service, no database to stand up. The design targets agents like Claude Code that run multi-step coding sessions where state bleeds across multiple handoffs; the records become the handoff. The ceiling appears fast for teams who want queryable logs, dashboards, or structured telemetry: Flightwake writes Markdown files, full stop. Teams who outgrow flat-file observability wire a separate log aggregation layer and end up maintaining both.

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	Flightwake	Legioni
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	CLI, Git, Markdown	npm, Node.js
Pros	Zero runtime dependencies and no external services required, which means setup is a single npx command and there is no infrastructure to break between sessions. Records live in git as plain Markdown, so every agent decision and trap is reviewable in a standard pull request — no separate tooling needed to audit what the agent did. Multi-session handoff is a first-class workflow, so a coding agent picking up a half-finished task has structured context from the prior session rather than starting cold. Sensitive-information self-check runs at session end, so the agent surfaces potential data leakage before the session closes rather than leaving it to a manual review. MIT license and self-hostable by default, so there is no vendor dependency and the records stay inside the team's own repository with no data leaving the environment.	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	There is no structured query layer: finding patterns across sessions — which files an agent modified most often, which decision types recur, which traps repeat — requires grepping Markdown files manually or scripting your own parser. Teams who need cross-session analytics hit this wall immediately and add a separate log aggregation step. The framework is built explicitly for strong coding agents in the Claude Code generation; teams running lighter agents or non-git workflows find the record obligations add friction with no corresponding payoff, and the docs describe no supported path for non-git environments. When a team needs alerting, dashboards, or integration with existing observability stacks, Flightwake has no native output format beyond Markdown — at that point teams either build a transform layer themselves or switch to an agent observability tool that emits structured JSON or OpenTelemetry traces.	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

Flightwake 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 Flightwake and Legioni?

Flightwake 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 Flightwake 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.