Engram – Open-source agent memory that beats Mem0 by 20% on LOCOMO

Hacker News

I built Engram because every AI agent I worked with forgot everything between sessions. Existing solutions (Mem0, Zep) are Python-first and extraction-based. They aggressively compress conversations into facts at write time. Engram takes the opposite approach: store memories with rich metadata and invest intelligence at read time, when you actually know the query. TypeScript, SQLite, zero infrastructure. Ran the LOCOMO benchmark (same one Mem0 used to claim SOTA): Engram: 80.0% (10 conversations, 1,540 questions) Mem0 published: 66.9% 93.6% fewer tokens than full-context approaches Works as an MCP server, REST API, or embedded SDK. Supports Gemini, OpenAI, Ollama, Groq, and any OpenAI-compatible provider. npm install -g engram-sdk && engram init https://engram.fyi | https://github.com/tstockham96/engram

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, context · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, gemini, compatible · Missing: reddit linkedin, podcasting, created

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, llama · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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