Legioni vs Naja-scope

Legioni and Naja-scope 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.

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.

Naja-scope

naja-scope runs as an MCP server that lets an agent like Claude interrogate an elaborated SystemVerilog design through targeted queries — what drives this signal, what's inside this module, where does this net terminate — and returns exact file-and-line answers instead of requiring the full source in context. The agent asks; the tool answers precisely. It's built on the najaeda netlist engine and installs via pip. The ceiling appears fast: there's no hosted API, no GUI, and the query surface is scoped narrowly to signal tracing and hierarchy exploration. Teams needing schematic visualization or integration into EDA toolchains will be stitching naja-scope to other systems themselves.

Attribute	Legioni	Naja-scope
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	npm, Node.js	Python, pip
Pros	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.	Query-driven design exploration rather than context-window dumps, so agents get precise file-and-line answers without burning tokens on thousands of RTL lines the model won't trace correctly anyway. Operates on the elaborated netlist via the najaeda engine, which means cross-hierarchy signal tracing returns structurally correct answers instead of guesses from partial source text. MCP-compatible server interface, so any agent that speaks MCP — including Claude — can connect without a custom integration layer being written by your team. Apache-2.0 licensed and self-hosted, which means your design IP never leaves your infrastructure and there's no vendor dependency for continued access. pip-installable with no build toolchain required, so a designer can add it to an existing Python environment without involving a DevOps team.
Cons	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.	The query surface covers signal tracing and hierarchy navigation only — teams that also need timing analysis, power domain queries, or constraint checking against the same design will find naja-scope answers one narrow set of questions and nothing adjacent, forcing a separate toolchain for everything else. No GUI or visual hierarchy browser exists; all interaction goes through the agent query interface, which means designers who want to explore a design spatially rather than by asking questions will hit a dead end immediately and need a different tool. The project has a small commit history and no documented third-party integrations or commercial support tier — teams with production uptime requirements on their AI-assisted design review flow are taking on an unmaintained-dependency risk that typically triggers a switch to a vendor-backed tool when the first blocking bug appears and a fix timeline is uncertain.

Bottom line

Legioni and Naja-scope 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 Legioni and Naja-scope?

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

Is Legioni better than Naja-scope?

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.