Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for heavy coding-agent users who want memory that survives across sessions and tools, especially when context budgets and repeated re-explanation are becoming the bottleneck.
Who should use it
Who should skip it
Skip rohitg00/agentmemory if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
rohitg00/agentmemory is tracked by RepoRadar as a developer tool in the Agent Memory section. It was first seen on 2026-07-01 and last updated on 2026-07-01. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. rohitg00/agentmemory leads on workflow potential (9.8) and open-source/build quality (8.4); its lowest signal is setup ease (6.4), so factor that in before investing setup time. 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 rohitg00/agentmemory a composite score of 8.3 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 installs hooks, skills, and an MCP service into coding-agent environments, so first evaluation should happen on a disposable repo and agent profile rather than a production workspace; The recall, token-savings, and benchmark numbers are maintainer claims, so compare retrieval quality and context cost on your own codebase before standardizing on it.