Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams building agent runtimes that need durable memory with a real API surface, production Redis backing, and a cleaner path than inventing another bespoke memory stack inside every app.
Who should use it
Who should skip it
Skip redis/agent-memory-server if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
redis/agent-memory-server is tracked by RepoRadar as a memory service in the AI Infrastructure 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 moderate setup difficulty. Across RepoRadar's eight signals, redis/agent-memory-server is strongest on workflow potential (9.6) and practical usefulness (9.0) and weakest on setup ease (6.4) — 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 redis/agent-memory-server a composite score of 8.5 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
Stores working and long-term memory for agents, so first evaluation should use synthetic or low-sensitivity data until retention and deletion rules are clear; The README includes quickstarts with auth disabled, so shared or internet-reachable deployments should enable authentication before wider use.