memora
Give your AI agents persistent, collective memory, with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
Not yet testedsource: GitHubPythonMITcommit c5624e8303f0
Python, MIT licensed. The project labels itself: agent memory, ai agent, claude, claude code, cloudflare d1, codex, knowledge graph and llms.
memora has not been verified yet.
Measured by Argusic on a fresh machine every time. Every number links to its evidence.
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
No recorded runs.
Topics (from GitHub)
agent-memoryai-agentclaudeclaude-codecloudflare-d1codexknowledge-graphllmsmcpmcp-servermemorymodel-context-protocolragsemantic-searchsemantic-search-algorithmsqlite
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/memora)Questions
- Does memora run?
- memora has not been fully verified yet. No recorded run has produced a verdict yet.
- How did Argusic test memora?
- On a fresh, disposable machine, with every command recorded. 0 attempts are recorded, and the full method is on the methodology page.
- Where is the evidence for memora?
- All 0 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.