Hey HN,

I built AgentBudget after an AI agent loop cost me $187 in 10 minutes — GPT-4o retrying a failed analysis over and over. Existing tools (LangSmith, Langfuse) track costs after execution but don't prevent overspend.

AgentBudget is a Python SDK that gives each agent session a hard dollar budget with real-time enforcement. Integration is two lines:

    import agentbudget
    agentbudget.init("$5.00")

It monkey-patches the OpenAI and Anthropic SDKs (same pattern as Sentry/Datadog), so existing code works without changes. When the budget is hit, it raises BudgetExhausted before the next API call goes out.

How it works:

- Two-phase enforcement: estimates cost pre-call (input tokens + average completion), reconciles post-call with actual usage. Worst-case overshoot is bounded to one call. - Loop detection: sliding window over (tool_name, argument_hash, timestamp) tuples. Catches infinite retries even if budget remains. - Cost engine: pricing table for 50+ models across OpenAI, Anthropic, Google, Mistral, Cohere. Fuzzy matching for dated model variants. - Unified ledger: tracks both LLM calls and external tool costs (via track() or @track_tool decorator) in a single session.

Benchmarks: 3.5μs median overhead per enforcement check. Zero budget overshoot across all tested scenarios. Loop detection: 0 false positives on diverse workloads, catches pathological loops at exactly N+1 calls.

No infrastructure needed — it's a library, not a platform. No Redis, no cloud services, no accounts.

I also wrote a whitepaper covering the architecture and integration with Coinbase's x402 payment protocol (where agents make autonomous stablecoin payments): https://doi.org/10.5281/zenodo.18720464

1,300+ PyPI installs in the first 4 days, all organic. Apache 2.0.

Happy to answer questions about the design.