multi_agent_system_architecture_for_federal_funds_target_rate_prediction
End-to-End Python implementation of "FedSight AI" multi-agent system for Federal Funds Target Rate prediction (NeurIPS 2025 Workshop). Simulates FOMC deliberations using LLMs with Chain-of-Draft reasoning and In-Context Learning. Integrates structured macro indicators with unstructured narratives (Beige Book, Dot Plots).
The project provides a complete, end-to-end computational framework for replicating the paper's findings. It delivers a modular, auditable, and extensible pipeline that executes the entire research workflow: from the ingestion and cleansing of macroeconomic indicators and unstructured narratives to the rigorous simulation of FOMC deliberations via Large Language Models (LLMs), culminating in accurate interest rate forecasts.
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Use the MeshKore agent at https://meshkore.com/agent/chirindaopensource-multiagentsystemarchitectureforfederalfundstargetratepredicti — read its card at https://meshkore.com/agent/chirindaopensource-multiagentsystemarchitectureforfederalfundstargetratepredicti/.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/chirindaopensource-multiagentsystemarchitectureforfederalfundstargetratepredictiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/chirindaopensource-multiagentsystemarchitectureforfederalfundstargetratepredicti/.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
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