Memex vs Naja-scope

Memex 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.

Memex

Orbit runs as a local harness that pulls one dependency-ordered task at a time, hands it to whichever coding agent you configure, then runs your tests, lint, and type checks before recording the result. Every run writes structured JSON artifacts — what the agent returned, how the output scored against a rubric, and a human-readable recommendation to accept, iterate, or stop. The audit trail is durable and replayable without an API key, which makes it usable in air-gapped environments. The tooling is intentionally minimal, so teams building on top of it will write their own adapter glue for agents that do not speak the expected JSON contract. Orbit does not manage the agent itself — it manages what the agent must prove.

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	Memex	Naja-scope
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Python 3.7+	Python, pip
Pros	Validation gates block task completion until tests, lint, and type checks pass, which means you stop shipping agent output that looks correct but breaks the build. Durable, structured artifacts written after every run — including rubric scoring and a human-readable progress log — so you have an audit trail when a stakeholder asks what the agent actually did last Tuesday. Deterministic replay with no API key required, so you can rerun any recorded orbit in a local or air-gapped environment without incurring model costs or network dependencies. Agent-neutral adapter contract, so swapping Claude for Codex behind the same task backlog produces comparable JSON artifacts instead of anecdotal impressions about which agent performed better. Dependency-aware backlog sequencing, which means the harness advances tasks in the order your project actually requires rather than letting an agent jump to a task whose prerequisites are still failing.	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	Agents that do not return structured JSON output require a custom adapter before Orbit can score or validate them — that wrapper is yours to write and maintain, and the docs describe it as a contribution target rather than a solved problem. There is no hosted service, no web UI, and no managed execution layer; teams that need cloud-hosted runs, a visual dashboard, or multi-user access to the artifact store will build all of that infrastructure themselves or switch to a commercial agent orchestration platform that ships those layers. The harness is intentionally small, which means complex branching logic — tasks that conditionally fan out based on what a prior agent returned — is outside what Orbit models; teams with multi-path workflows end up scripting the branching outside Orbit and using the harness only for the leaf-level validation step.	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

Memex 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 Memex and Naja-scope?

Memex 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 Memex 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.