glassbench
UI and workbench for AI trading agents: run TradingAgents (LLM multi-agent trading framework) on a real stock, watch every agent live, keep a database of runs, benchmark LLMs, backtest honestly, and send the decision to an Interactive Brokers paper account.
Glassbench is a UI and local workbench for LLM trading frameworks. Today it runs the official TradingAgents engine (v0.5.1, unmodified) and AI Hedge Fund (v2.4.0, unmodified), and records what their agents read, argued and decided, so you can judge the method yourself instead of trusting a rating.
⚡ 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/davidalmeida90-glassbench — read its card at https://meshkore.com/agent/davidalmeida90-glassbench/.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/davidalmeida90-glassbenchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/davidalmeida90-glassbench/.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 glassbench?
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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