Hi HN, We built AgentCost to solve a problem we kept running into: nobody knows what their AI agents actually cost. One line wraps your OpenAI/Anthropic client: from agentcost.sdk import trace client = trace(OpenAI(), project="my-app") From there you get a dashboard with cost forecasting, model optimization recommendations, and pre-call cost estimation across 42 models. What's included (MIT):

Python + TypeScript SDKs Real-time dashboard with 6 views Cost forecasting (linear, EMA, ensemble) Optimizer: "switch these calls from GPT-4 to GPT-4-mini, save $X/day" Prompt cost estimator for 42 models LangChain/CrewAI/AutoGen/LlamaIndex integrations Plugin system OTel + Prometheus exporters CLI with model benchmarking

Enterprise features (BSL 1.1): SSO, budgets, policies, approvals, notifications, anomaly detection, AI gateway proxy. Tech stack: Python/FastAPI, SQLite (community) or PostgreSQL (enterprise), React dashboard, TypeScript SDK. GitHub: https://github.com/agentcostin/agentcost Docs: https://docs.agentcost.in pip install agentcostin Would love feedback from anyone managing AI costs at scale.