AgentBudget – Real-time dollar budgets for AI agents
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…
What it does
In the maker’s words, at launch
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.
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