llmfs
Filesystem-based persistent memory for LLMs and AI agents -- unlimited context windows without lossy summarization
LLMFS gives LLMs and AI agents persistent, searchable, structured memory — organized like a filesystem. Instead of losing context when a conversation grows past the token limit, agents offload memories to LLMFS and retrieve exactly what they need, when they need it. The result is zero information loss and an effectively unlimited context window — even over thousands of turns.
⚡ 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/viditraj-llmfs — read its card at https://meshkore.com/agent/viditraj-llmfs/.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/viditraj-llmfsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/viditraj-llmfs/.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 llmfs?
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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