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.
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.