Mireye vs supermemory

Agents such as Claude, ChatGPT, Gemini, Cursor or custom builds reach Mireye through one REST API and MCP tools, returning a replayable plan of the steps it ran.

Every returned field includes its source, fetch time and confidence level, so provenance can be forwarded to an end user, underwriter or auditor without extra work.

Attach authoritative parcel identity, ownership and attributes to a coordinate, or filter the US parcel universe by state, size and supported fields to get ranked candidates.

Ready-made presets cover flood risk, solar and wind siting, data center siting, grid interconnection, wildfire underwriting and terrain; hundreds of locations can be sent in one call.

A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.

Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.

Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.

Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.