memlayer
Plug-and-play memory for LLMs in 3 lines of code. Add persistent, intelligent, human-like memory and recall to any model in minutes.
Not yet testedsource: GitHubhomepagePythonMITcommit 5e95f44061a8
Python, MIT licensed. The project labels itself: agent, ai, ai infrastructure, context management, developer tools, embedded, graph database and knowledge graph.
memlayer 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)
agentaiai-infrastructurecontext-managementdeveloper-toolsembeddedgraph-databaseknowledge-graphllmllm-memorymemoryopenaipersistent-memorypythonragretrievalretrieval-augmented-generationsemantic-searchtransformervector-database
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Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/memlayer)Questions
- Does memlayer run?
- memlayer has not been fully verified yet. No recorded run has produced a verdict yet.
- How did Argusic test memlayer?
- 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 memlayer?
- 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.