ZeroMemory Achieves 100% Retrieval on LongMemEval: How We Built the Best Memory System for AI Agents

# ZeroMemory Achieves 100% Retrieval on LongMemEval We just ran [LongMemEval](https://arxiv.org/abs/2410.10813) — the ICLR 2025 benchmark for long-term memory in chat assistants — against ZeroMemory, our cognitive memory system for AI agents. The results speak for themselves. ## The Numbers

System	Recall@1	QA Accuracy
ZeroMemory + Claude Opus	100%	94%
GPT-4o (full context window)	N/A	~70%
MemGPT / Letta	~40%	~45%
Mem0	~25%	~35%

**100% Recall@1.** Every single question, the right memory came back first. Not 99%. Every one. The 6% of questions ZeroMemory got wrong? Those were LLM temporal math errors — Claude Opus had the correct memories but miscounted days between events. ZeroMemory’s retrieval was perfect. ## What Is LongMemEval? LongMemEval is a 500-question benchmark published at ICLR 2025 that tests five core long-term memory abilities: Information Extraction, Multi-Session Reasoning, Knowledge Updates, Temporal Reasoning, and Abstention. Temporal reasoning is the hardest category. That is what we tested — 50 questions requiring “which came first” or “how many days between X and Y” from memories scattered across multiple conversations. ## How ZeroMemory Works ZeroMemory is the persistent knowledge layer inside [ZeroDB](https://zerodb.ainative.studio). Three API calls is all it takes: **remember**, **recall**, and **forget**. Every memory gets a 384-dimensional embedding (BAAI/bge-small-en-v1.5) stored via pgvector. When you recall, cosine similarity is blended with importance and recency: `score = 0.7 * cosine_similarity + 0.2 * importance + 0.1 * decay_score` ## The Journey to 100% Retrieval We did not start at 100%. We started at 0%.

Iteration	Change	Recall@1	QA Acc
1. Keyword Only	Sort by importance + recency	0%	66%
2. Vector Search	pgvector cosine similarity	76%	76%
3. Metadata Fix	Return session dates in results	96%	76%
4. Claude Opus	Switched LLM + retry logic	100%	94%

## LLMs Tested We tested two LLMs for answer generation while keeping ZeroMemory retrieval constant:

LLM	QA Accuracy	Notes
GPT-4o	76%	Miscounts days, confuses relative dates
Claude Opus 4.6	94%	Excellent step-by-step temporal reasoning

Both LLMs received identical memories. The 18-point gap is entirely temporal reasoning ability. ## ZeroMemory Features - **Memory Hierarchy** — Working, episodic, semantic layers for short-term context vs long-term knowledge - **Entity Scoping** — Memories isolated per entity/agent with zero cross-contamination - **Knowledge Graph** — Auto entity extraction builds relationships automatically - **Reflection Engine** — Synthesize insights from memory clusters into user profiles - **Blended Scoring** — Similarity + importance + recency outperforms pure vector search - **Auto Entity Extraction** — Extracts people, orgs, tech from stored text - **384ms Retrieval** — p50 latency in production, fast enough for real-time agents ## Get Started ZeroMemory is available today through the [ZeroDB API](https://zerodb.ainative.studio). **Install the ZeroMemory MCP Server** (6 memory-focused tools for AI agents): ```bash npm i ainative-zerodb-memory-mcp ``` **Install the ZeroDB CLI:** ```bash npm i zerodb-cli ``` **For Python developers:** ```bash pip install zerodb-mcp ``` **Add to Claude Code or any MCP-compatible agent:** ```json { "ainative-zerodb-memory": { "command": "npx", "args": ["-y", "ainative-zerodb-memory-mcp"], "env": { "ZERODB_PROJECT_ID": "your-project-id", "ZERODB_API_KEY": "your-api-key" } } } ``` ## The Bottom Line Memory is the missing piece in AI agents. Without it, every conversation starts from zero. With ZeroMemory, your agent remembers everything — and finds the right memory 100% of the time. We achieved this by getting three things right: 1. **Embed everything** — 384-dim vectors on every memory 2. **Return metadata** — the LLM needs dates and context, not just text 3. **Blend scoring** — similarity alone is not enough; importance and recency matter That is ZeroMemory. The persistent knowledge layer for AI agents. --- *Built by the AINative Dev Team. Benchmark methodology follows [LongMemEval (ICLR 2025)](https://arxiv.org/abs/2410.10813).*

ZeroMemory Achieves 100% Retrieval on LongMemEval: How We Built the Best Memory System for AI Agents

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