

Running an LLM-based web scraping agent is a different problem than running a traditional scraper. The agent doesn't just fetch pages — it reasons about them, retries on partial data, follows links dynamically, and often needs to hold session state across multiple requests. That changes what you need from a proxy layer in ways that most proxy comparisons miss.
Here is a breakdown of what to evaluate, and why each factor matters specifically for agent workloads.
For LLM agents hitting targets that have anti-bot protection — which is most production targets worth scraping — datacenter IPs will block out faster than residential ones. The traffic pattern of an LLM agent (irregular timing, varied user-agent strings, non-linear navigation) can actually look more human than a traditional scraper, but only if the underlying IP is believable. Datacenter subnets are flagged at the ASN level before the first request lands. Residential IPs route through real ISPs and bypass that layer of blocking entirely.
The tradeoff is cost. Residential proxies are billed per GB, and an LLM agent that downloads full HTML for rendering and parsing burns data faster than a targeted API call. You need to size your budget around this reality.
This is the decision that breaks most agent setups. A default rotating proxy gives you a fresh IP per request, which maximizes anonymity but destroys session continuity. If your agent needs to log in, hold a cookie, navigate paginated results, or follow a multi-step form, rotating IPs will break the session on every hop.
Sticky sessions — where the same IP is reserved for a defined window — solve this. The window length matters: 1 minute is too short for most agent flows; 10–15 minutes covers the majority of multi-step scraping tasks; 30 minutes handles authenticated sessions and complex navigations comfortably. When evaluating providers, confirm the maximum sticky session duration under load, not just the advertised ceiling.
LLM agent frameworks and scraping libraries vary in

