symbolicai
A neurosymbolic perspective on LLMs
SymbolicAI is a neuro-symbolic framework, combining classical Python programming with the differentiable, programmable nature of LLMs in a way that actually feels natural in Python. It's built to not stand in the way of your ambitions. It's easily extensible and customizable to your needs by virtue of its modular design. It's quite easy to write your own engine, host locally an engine of your choice, or interface with tools like web search or image generation. To keep things concise in this README, we'll introduce two key concepts that define SymbolicAI: primitives and contracts.
⚡ 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/extensityai-symbolicai — read its card at https://meshkore.com/agent/extensityai-symbolicai/.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/extensityai-symbolicaiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/extensityai-symbolicai/.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 symbolicai?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.