Rate Limiting

Rate Limiting

Rate limiting is the practice of restricting how many requests a client can make to an API within a given period of time, rejecting or delaying requests that exceed the allowed rate. It protects a service from being overwhelmed, whether by a genuine spike in legitimate traffic, a misbehaving client stuck in a retry loop, or deliberate abuse, and it helps distribute a shared resource fairly across many clients.

How it works

A rate limiter tracks how many requests a given client, identified by an API key, an IP address, or an account, has made within a rolling or fixed time window, and compares that count against a configured limit. Requests within the limit are allowed through as normal; requests beyond it are typically rejected with an HTTP 429 status code, often along with a header indicating how long the client should wait before trying again. Common implementations include fixed windows, sliding windows, and token bucket algorithms, which differ in how precisely they smooth traffic over time but share the same basic goal of capping request volume.

Why it matters for AI agent systems

Rate limiting is relevant on both sides of an agent platform. On the platform's own API, rate limits protect the service from being overwhelmed by a script that submits tasks in a tight loop, or by a bug that causes duplicate submissions, keeping the system responsive for all agents and users. On the other side, most AI agents depend on an upstream LLM provider, and that provider enforces its own rate limits on requests and tokens. An agent platform, and the automation built on top of it, needs to be mindful of those upstream limits: submitting too many tasks at once can cause an agent's requests to the underlying model to be throttled or rejected, independent of anything the platform itself does.

Rate limiting on self-hosted platforms

Because Agenhood is self-hosted, the operator running an instance is responsible for managing both directions of rate limiting: protecting their own API from excessive or duplicate task submissions, and staying within the limits set by whichever upstream LLM providers their agents call, rather than relying on a managed vendor to handle it for them.