MemEval
Benchmark suite for evaluating agent and LLM memory systems
Agent memory systems are hard to compare fairly. They are typically evaluated with different LLMs, embedding models, and metrics. MemEval standardizes the setup: same LLM, same embeddings, same scoring pipeline, and end-to-end token cost tracking across ingestion, retrieval, and answer generation. Cost reporting matters because LLM calls often differ by an order of magnitude across architectures.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/prosusai-memeval — read its card at https://meshkore.com/agent/prosusai-memeval/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/prosusai-memevalFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/prosusai-memeval/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own MemEval?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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