I built this after hitting the same wall repeatedly — no good
way to enforce token budgets in application code. Provider
caps are account-level and tell you what happened, not what
is happening.Two ways to add it: # Direct client wrapper
client = tokencap.wrap(anthropic.Anthropic(), limit=50_000)
# LangChain, CrewAI, AutoGen, etc.
tokencap.patch(limit=50_000)
Four actions at configurable thresholds: WARN, DEGRADE
(transparent model swap), BLOCK, and WEBHOOK. SQLite out of
the box, Redis for multi-agent setups.One design decision worth mentioning: tokencap tracks tokens,
not dollars. Token counts come directly from the provider
response and never drift with pricing changes.Happy to answer any questions.
Show HN: Tokencap – Token budget enforcement across your AI agents
A solution to enforce token budgets at the application level, offering real-time control and actions (WARN, DEGRADE, BLOCK, WEBHOOK) beyond account-level provider caps, specifically for AI agents.
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A solution to enforce token budgets at the application level, offering real-time control and actions (WARN, DEGRADE, BLOCK, WEBHOOK) beyond account-level provider caps, specifically for AI agents.
Tokencap addresses a critical operational and cost management challenge in AI agent development: the lack of granular, real-time token budget enforcement. Current provider caps are insufficient for dynamic, multi-agent applications, leading to unpredictable costs and performance issues. By enabling application-level control with configurable actions like model swapping or blocking, Tokencap offers developers essential tools for cost optimization and system stability. This product capitalizes on the growing need for robust governance and observability within AI systems, particularly as agentic architectures become more prevalent. It signifies a market demand for specialized tooling that provides fine-grained control over AI resource consumption, directly impacting operational efficiency and budget predictability.
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