Mem0
Shares rank 9 of 20 agent memory systems by recommenders
Recommended as a middle layer for selective, real-time memory retrieval and updates in AI agents. Critics say it depends heavily on LLM and embedding API calls, which adds cost.
Recommended as a middle layer for selective, real-time memory retrieval and updates in AI agents. Critics say it depends heavily on LLM and embedding API calls, which adds cost.
20 people gave an opinion
- recommend it
- 6
- criticize it
- 14
- approval share
- 30%
Recommended in 6 of 13 months
- November 2025: recommended
- December 2025: not recommended
- January 2026: not recommended
- February 2026: recommended
- March 2026: not recommended
- April 2026: not recommended
- May 2026: not recommended
- June 2026: recommended
- July 2026: recommended
- August 2026: recommended
- September 2026: not recommended
- October 2026: recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 6 people recommend itNeeds 10 or more, not met.
- 0.4 recommenders for every criticNeeds 2 to 1 or better, not met.
- Recommended in 6 of 13 monthsNeeds 4 or more, met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Selective, real-time retrieval and updates
“lets you retrieve selectively and only grab what's relevant to the current query”
Comment from 5 Feb 2026 in “Nanobot: Ultra-Lightweight Alternative to OpenClaw” “a middle-layer like Mem0 for realtime retrial and updates”
Comment from 26 Aug 2026 in “RAG Is Simpler Than You Think”
What people warn about
Each mark is one comment
Depends on LLM and embedding APIs
“heavy runtimes (Python/Node), docker containers, proprietary storage silos, and recurring embedding API costs”
Comment from 5 Sep 2026 in “OKF Agent Memory – Git-native persistent memory for AI coding agents” “every AI memory system I looked at (Mem0, Zep, Cognee, Letta/MemGPT) sends your text to an LLM”
Comment from 24 Feb 2026 in “Show HN: Mnemosyne – Cognitive memory OS for AI agents (zero LLM calls)” “We built it because Mem0/Zep were too expensive and too LLM-dependent.”
Comment from 8 Apr 2026 in “Agent Brain – 7-layer cognitive memory for AI agents (open source)” - Comment from 3 Apr 2026 in “Agentis Memory – Redis-compatible store with built-in local embeddings”
- Comment from 3 Mar 2026 in “Show HN: Mind-mem – Zero-infra agent memory with 19 MCP tools (BM25+vector+RRF)”
- Comment from 28 Feb 2026 in “Show HN: Shodh– AI memory that learns from use, no LLM calls, single Rust binary”
- Comment from 21 Feb 2026 on Hacker News
Heavy runtime and infrastructure
“heavy runtimes (Python/Node), docker containers, proprietary storage silos, and recurring embedding API costs”
Comment from 5 Sep 2026 in “OKF Agent Memory – Git-native persistent memory for AI coding agents” “all require REST APIs, external embedding services, or custom SDKs”
Comment from 3 Apr 2026 in “Agentis Memory – Redis-compatible store with built-in local embeddings” “requires a vector DB, embedding API, or 50+ pip dependencies.”
Comment from 3 Mar 2026 in “Show HN: Mind-mem – Zero-infra agent memory with 19 MCP tools (BM25+vector+RRF)” - Comment from 22 Apr 2026 in “Agents that remember: introducing Cloudflare Agent Memory”
Expensive over long conversations
“We built it because Mem0/Zep were too expensive and too LLM-dependent.”
Comment from 8 Apr 2026 in “Agent Brain – 7-layer cognitive memory for AI agents (open source)” “14–77× more expensive over a full conversation”
Comment from 24 Nov 2025 in “Universal LLM Memory Does Not Exist”
Other agent memory systems people recommend
The four most recommended other agent memory systems, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| Engrim | 21 | 0 | 100% | 4 | — | Approved |
| Total Recall for Claude Code | 12 | 4 | 75% | 5 | — | Approved |
| OKF Agent Memory | 9 | 3 | 75% | 3 | — | — |
| MemPalace | 9 | 8 | 53% | 3 | — | — |
| Mem0This page | 6 | 14 | 30% | 6 | — | — |
What people said
19 of the 21 comments that mention it, newest first. Each links to the original comment.
“Retrieval-based memory: Mem0 (open-source) 3/15, TF-IDF + recency 1/40”
“heavy runtimes (Python/Node), docker containers, proprietary storage silos, and recurring embedding API costs”
“a middle-layer like Mem0 for realtime retrial and updates”
“but it met initial expectations”
“Feels genuinely useful and more than a toy”
“both Mem0 and Zep have great offerings and are more developed along those lines”
“the (currently very fallible) ones like Honcho and Mem0”
“without the Python-heavy overhead of LangMem or Mem0”
“the extracted "facts database" was a complete mess of largely incomplete, invalid, inefficient and unhelpful sentences”
“We built it because Mem0/Zep were too expensive and too LLM-dependent.”
“they store user preferences, conversation history, individual agent state”
“all require REST APIs, external embedding services, or custom SDKs”
“memory-only solutions (Mem0, Zep) store context but hallucinate on retrieval”
“requires a vector DB, embedding API, or 50+ pip dependencies.”
“makes 2-3 LLM API calls just to store a single memory.”
“every AI memory system I looked at (Mem0, Zep, Cognee, Letta/MemGPT) sends your text to an LLM”
“mem0 requires an OpenAI key to store memories locally”
“lets you retrieve selectively and only grab what's relevant to the current query”
“14–77× more expensive over a full conversation”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.