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CueMap
Fast, accurate and inspectable context recall for AI agents
CueMap is an open-source memory engine for AI agents, built in Rust. It combines deterministic lexical and structural recall with a customized local MiniLM reranker—no external model calls required. At 1M memories, average reads take 2.63ms lexical or 8.36ms hybrid. Raw Hit@20 reaches 96.2% on LongMemEval, 96.1% on LoCoMo, 80.3% on BEAM 1M, and 67.0% on BEAM 10M. Use it through MCP, Python/TypeScript SDKs, Docker, or CLI, with source-backed results and inspectable traces.
Hello Product Hunt,
CueMap is an open-source memory layer for fast, accurate, and explainable context recall. It is written in Rust, runs locally, and makes no external model calls.
Candidate discovery stays deterministic. A natural-language query is decomposed into explicit cues, weighted facets, and query-plan labels. CueMap matches these signals against an inverted index to produce a bounded candidate set.
In hybrid mode, a bundled local MiniLM model reranks that shortlist. Semantic similarity improves the final ordering without controlling which memories are discovered. Original content and provenance remain attached, while inspectable traces expose how each result was retrieved and scored.
On 1M short Wikipedia passages, including HTTP:
- Lexical: 2.63ms average / 3.72ms P95
- Hybrid: 8.36ms average / 10.67ms P95
Raw retrieval Hit@20:
- LongMemEval: 96.2%
- LoCoMo: 96.1%
- BEAM 128K: 84.2%
- BEAM 1M: 80.3%
- BEAM 10M: 67.0%
These are retrieval measurements taken before an answer model sees or interprets the returned context.
CueMap is available through MCP, Python and TypeScript SDKs, Docker, and CLI. The engine, integrations, evaluation harnesses, benchmark methodology, and reports are open source.
It is built for developers who need predictable latency, source-backed results, and direct control over how context is discovered, ranked, expanded, and assembled.
Our longer-term goal is to make memory feel like a living system rather than a static search index. As CueMap moves toward v1.0, we’re working toward recall that is fast enough to sit beside inference loops, accurate enough to challenge leading memory systems, and controllable enough to become a multi-request workflow where agents can expand, reorder, and refine evidence before answering.
https://cuemap.dev
About CueMap on Product Hunt
“Fast, accurate and inspectable context recall for AI agents”
CueMap was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #29 on the daily leaderboard. CueMap is an open-source memory engine for AI agents, built in Rust. It combines deterministic lexical and structural recall with a customized local MiniLM reranker—no external model calls required. At 1M memories, average reads take 2.63ms lexical or 8.36ms hybrid. Raw Hit@20 reaches 96.2% on LongMemEval, 96.1% on LoCoMo, 80.3% on BEAM 1M, and 67.0% on BEAM 10M. Use it through MCP, Python/TypeScript SDKs, Docker, or CLI, with source-backed results and inspectable traces.
On the analytics side, CueMap competes within Open Source, Artificial Intelligence, GitHub, Database and OpenAI Day — topics that collectively have 591.6k followers on Product Hunt. The dashboard above tracks how CueMap performed against the three products that launched closest to it on the same day.
Who hunted CueMap?
CueMap was hunted by Kaan Demirel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CueMap including community comment highlights and product details, visit the product overview.
Kaan Demirel