Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams that want one framework for generating, debugging, and shipping multi-surface agents instead of stitching together separate browser, memory, scheduling, and dashboard layers.
Who should use it
Who should skip it
Consider Negai-ai/AgentClaw lower priority if you already have a working solution in this category.
About this signal
Negai-ai/AgentClaw is tracked by RepoRadar as a agent framework in the Agents section. It was first seen on 2026-06-30 and last updated on 2026-06-30. The current verdict is 'try now' with a Gold tier and advanced setup difficulty. Across RepoRadar's eight signals, Negai-ai/AgentClaw is strongest on workflow potential (9.7) and open-source/build quality (8.4) and weakest on setup ease (4.2) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned Negai-ai/AgentClaw a composite score of 8.2 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
It can operate browsers, desktops, terminals, files, and external tools, so test with least-privilege permissions and non-production credentials first; The generated project writes runtime configuration for models, auth, storage, and schedulers into local files, so review those defaults before publishing an agent to other users.