Special edition · 2026-06-06 · ranked by stars/day · every link verified live.
"Memory" has become the hottest word in agent tooling — but it spans everything from a JSON file of session notes to a distributed vector database. The repos below are the fastest-climbing projects that actually store and retrieve agent state, sorted from purpose-built memory layers down to the vector databases underneath them.
MemPalace/mempalace — ⭐54,074 · ↑872.2/day · Python
A free, open-source AI memory system built on ChromaDB and exposed over MCP, leading on public memory benchmarks. The benchmark claim is what separates it from the crowd of memory wrappers — it competes on recall accuracy, not just on having a tidy API, and the MCP surface means any agent can plug in without custom glue.
Who needs it: anyone giving an agent long-term memory who wants something they can measure rather than trust.
thedotmack/claude-mem — ⭐80,924 · ↑291.1/day · TypeScript
Persistent context across sessions for any agent: it captures what the agent does, compresses it with an LLM, and re-injects the relevant parts next session. The compression step is the real work — raw transcripts overflow the context window fast, so the value is in what it throws away.
Who needs it: developers tired of re-explaining the project every time an agent session resets.
rohitg00/agentmemory — ⭐21,496 · ↑212.8/day · TypeScript
Persistent memory aimed specifically at coding agents, positioned on real-world benchmark results. Narrower scope than a general memory layer — it's tuned for the code-context case (files touched, decisions made) rather than open-ended chat history.
Who needs it: people running coding agents that lose the thread across long refactors.
gastownhall/beads — ⭐24,383 · ↑102.9/day · Go
A "memory upgrade" for coding agents, shipped as a Go binary. The language choice matters for adoption: a single dependency-free binary is far easier to drop into a CI or local toolchain than a Python package with its own environment.
Who needs it: teams who want agent memory without adding a Python runtime to their stack.
mem0ai/mem0 — ⭐57,864 · ↑53.5/day · Python
A universal memory layer for agents and the most established name in this list. Velocity has cooled as it matures, but it remains the default many teams reach for — broad integrations and a stable API are worth more than raw growth rate once you're in production.
Who needs it: teams who want a proven, widely-integrated memory layer over the newest one.
qdrant/qdrant — ⭐31,851 · ↑14.5/day · Rust
The vector database under many of the layers above — high-performance ANN search in Rust, with hybrid (vector + keyword) retrieval. Low stars/day because it's infrastructure that already won its niche; you adopt it for stability, not novelty. The alternative path is pingcap/tidb (⭐40,137 · ↑10.2/day), which now bolts native vector search onto a transactional SQL database — one store for both your app data and your embeddings instead of running a separate vector service.
Who needs it: teams building retrieval at scale who want a dedicated vector engine (qdrant) or vectors inside their existing SQL database (tidb).
The two highest-velocity repos tagged into this bucket aren't memory infrastructure — track them as signals, not building blocks:
The pattern: "memory" is now a marketing tag attached to anything agentic. The genuinely reusable layers — MemPalace, claude-mem, mem0 — move slower than the all-in-one harnesses but are what you actually build on.
Live GitHub pull, bucketed by theme, verified not-archived and pushed recently, ranked by stars/day, curated for substance. Counts pulled at publish — they move daily.
*Autonomous AI Digest · catch acceleration, not stars · all editions*