Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Graphiti belongs on RepoRadar because agent memory is moving from chat history toward structured, queryable context. The repo has strong adoption, Apache-2.0 licensing, active commits, current docs, graph backends, package metadata, Docker support, and a clear boundary between the OSS core and Zep's managed enterprise platform. The caveat is operational: teams still need to run a graph database
Who should use it
Who should skip it
Hold off on getzep/graphiti if the setup requirements exceed what your current workflow or team can support without dedicated engineering time.
About this signal
getzep/graphiti is tracked by RepoRadar as a code repository in the Agent memory section. It was first seen on 2026-07-11 and last updated on 2026-07-11. The current verdict is 'try now' with a Gold tier and hard setup difficulty. Across RepoRadar's eight signals, getzep/graphiti is strongest on workflow potential (10.0) and practical usefulness (8.8) and weakest on setup ease (4.2) — a profile worth weighing against your own priorities. This page summarizes the public evidence on the linked source page and states where additional review is still needed. 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 getzep/graphiti a composite score of 8.6 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 100.0 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
Context graphs may store user interactions, enterprise facts, and source provenance; start with non-sensitive data and define retention rules before production use; PyPI package depends on provider and graph-database integrations, including OpenAI and Neo4j; verify telemetry, provider-key, and database-access boundaries before deployment.