Lightweight agent memory systems
Paper-, article-, product-, or spec-grounded systems that are useful for the agent-memory-systems landscape but have no inspectable implementation repository to support a code-grounded review in ../reviews/.
Use this directory for agent-memory-system-review notes carrying source-tier: doc-grounded — when no reachable repository supports a code-grounded review in ../reviews/. They keep the same comparison elements but record a claim-level, doc-grounded evidence stance; the review spec's instructions are tier-neutral — see the source-tier field in ../types/agent-memory-system-review.md.
- AgeMem — RL-trained memory-management policy covered through source ingest and analysis notes.
- Fintool — production AI agent for professional investors; commercial-scale evidence for filesystem-first, covered from a founder's practitioner report with no public source.
- Incremental Self-Improvement — Schmidhuber's reward-gated self-modification paradigm for learning learning strategies; useful lineage for policy-level promotion, but lightweight coverage and not a modern KB system.
- Mnemosyne / IsaacCLupus mnemosyn spec — spec-first local semantic memory OS with schema/prototype evidence but no current reference implementation.
- Sig — macOS work-memory app covered from public release docs, local-file claims, and product README material rather than inspectable app source.
- Trajectory-Informed Memory Generation — trajectory-to-tip learning pipeline covered through source ingest.
TODO: OpenClaw-RL now has a reachable repository (Gen-Verse/OpenClaw-RL) and should get a repo-backed review rather than a lightweight note.
Complete file listing (generated at build time)