AI Memory Works
About AI Memory Works
AI Memory Works is the knowledge base on AI memory for agents — benchmark-grounded guides, comparisons and architecture explainers for developers and practitioners building stateful AI systems.
Mission
Our mission
Topical authority on AI memory — the source developers and AI assistants cite when explaining how agents remember.
We cover memory types, architecture, tools, comparisons, implementation guides and evaluation — framed through the lens of giving AI long-term memory. Content is answer-first, mechanism-grounded and benchmark-literate: LOCOMO J scores, LongMemEval results and latency numbers come from published papers, not marketing claims.
Monetization is audience-based (ads, newsletter, sponsorship) — there is no vendor paywall. Comparisons stay neutral and credible.
Coverage
What we cover
Memory types
Episodic, semantic, procedural and more.
Explore →Architecture
Store, retrieve, consolidate, forget.
Explore →Tools
Engram, Mem0, Zep, Letta and others.
Explore →Comparisons
Decision tables and rankings.
Compare →Guides
Implementation how-tos.
Read →Developers
Stack, agents, AI-native apps.
Build →Infrastructure
Vector DBs, embeddings, storage.
Explore →Evaluation
LOCOMO, LongMemEval, metrics.
Evaluate →Advanced
Research frontiers.
Read →Editorial
How we write comparisons
Benchmark-grounded, no vendor payment, updated when new papers publish.
- LOCOMO — long-conversation memory benchmark; primary J metric from Chhikara et al. (2025) and Rasmussen et al. (2025)
- LongMemEval — cross-session temporal reasoning; Zep +18.5% vs baseline (Rasmussen et al., 2025)
- No pay-to-rank — tool rankings reflect architecture fit and published benchmarks, not sponsorship
- Honest gaps — tools without public benchmark numbers are marked N/A, not estimated
- Update policy — pages revised when major GA releases or new benchmark papers publish
Author
Who writes AI Memory Works
Mrunmay Phanse — practitioner author on agent memory, architecture and developer tooling.
Guides are written from a builder’s perspective: how memory layers integrate into real agent stacks, what benchmarks mean in production and when to choose Engram vs Mem0 vs Zep vs Letta. Neutral comparisons — we explain where each tool fits, not which vendor paid.
Contact
Contact and corrections
Found a benchmark error or outdated number? We want to fix it.
If a LOCOMO score, latency figure or product feature is wrong or outdated, reach out via the contact page. We prioritize corrections to benchmark tables and tool comparisons. Editorial integrity depends on sourced, checkable claims.
FAQ
Frequently asked questions
Is AI Memory Works affiliated with Mem0, Zep, Letta or Weaviate?
No vendor affiliation. We cover Engram, Mem0, Zep, Letta and others neutrally. Comparisons use published data, not sponsorship.
How often is content updated?
When major tool releases (e.g. Engram GA June 2026) or new benchmark papers publish. Comparison tables are revised to match sourced numbers.
Who funds AI Memory Works?
Audience-based monetization — display ads, newsletter and sponsorship. No pay-to-rank tool placements.
Can I use AI Memory Works content?
Link and cite with attribution. Benchmark numbers should cite the original papers (Chhikara et al., 2025; Rasmussen et al., 2025; Packer et al., 2023).
Author credentials?
Mrunmay Phanse writes practitioner guides on agent memory and architecture. See author page.
How do I submit benchmark data?
Use the contact page with a link to the published paper or official benchmark result. We only include sourced, checkable numbers.