Helicone alternative
Helicone rate-limits per user. RunGuard detects loop patterns per run.
Helicone is a proxy that logs your OpenAI calls, lets you cache responses, and applies per-user rate limits. If your agent enters a loop, Helicone's rate limit eventually fires — but only after N calls to the same user bucket, not when it detects a repeating tool-call pattern. RunGuard trips at the pattern, not the count.
Why teams look for a Helicone alternative
- Rate limiting is blunt; loop detection is pattern-aware. Helicone lets you set a maximum number of requests per user per time window. A looping agent from one user will hit that limit eventually — but not until it has already fired N requests, which could be $10 in LLM spend. RunGuard detects the repeating signature (same tool, same arguments, same context fragment, three times in 60 seconds) and trips at the third repetition, regardless of global rate limits.
- Proxy architecture adds network latency to every request. Helicone routes your OpenAI calls through its servers (you change
base_urlto Helicone's endpoint). Every request adds a network hop — typically 20–80ms per call. For an agent that makes 10 calls per run, that is an extra 200–800ms of latency. RunGuard runs in-process and adds under 1ms per call. - Helicone is OpenAI-first; RunGuard is model-agnostic. Helicone's proxy is built around the OpenAI API format. For Anthropic, Mistral, Cohere, or custom LLM endpoints, Helicone has separate proxy configurations. RunGuard wraps any async function and has no opinion about the underlying API format.
Feature comparison
| Helicone | RunGuard | |
|---|---|---|
| Pattern-aware loop detection (pre-call) | ✗ | ✓ |
| Per-run USD budget cap | ✗ (per-user only) | ✓ (per-run) |
| Context-window overrun guard | ✗ | ✓ |
| In-process (zero added network hop) | ✗ (proxy) | ✓ |
| Model-agnostic (Anthropic, Mistral, etc.) | Partial | ✓ |
| Response caching | ✓ | ✗ |
| Per-user rate limits | ✓ | ✗ |
| Post-call request logging and analytics | ✓ | ✗ |
| Starting price | Free (10k req/mo) / $20/mo | $0 trial / $19/mo Solo |
How RunGuard is different
Helicone's model is a network proxy: your agent sends requests to Helicone's servers, which forward them to OpenAI, log the response, apply any configured rules (rate limit, cache hit), and return the response. All decisions are made at the HTTP level after the request is routed.
RunGuard's model is an in-process wrapper: you call guard(fn) and RunGuard's wrapper runs before forwarding to fn. It hashes the call arguments into a signature, checks whether that signature has appeared N times in the current run window, and if so throws LoopDetectedError — synchronously, in your process, before any network request is made. No proxy, no routing, no added latency beyond an in-memory hash lookup.
The two tools are complementary: Helicone for multi-tenant rate limiting, caching, and aggregate cost analytics; RunGuard for per-run loop detection and budget caps on autonomous agents.
When Helicone is still the right choice
If your primary need is multi-tenant per-user rate limiting, OpenAI response caching to reduce costs on repeated prompts, or aggregate cost analytics across many users and API keys — Helicone is well-suited. Its proxy model makes per-user controls easy without code changes in your agent. If you are building a platform that serves many end users and need to cap each user's API consumption, Helicone's rate limiting is the right tool. RunGuard is not a rate limiter and does not help with multi-tenant access control.
FAQ
- Can I use RunGuard with Helicone?
- Yes. RunGuard wraps the function that calls your LLM API. Whether that function talks directly to OpenAI or through Helicone's proxy does not matter — RunGuard operates above the HTTP layer and has no awareness of the proxy. You get both Helicone's logging and caching and RunGuard's pattern-aware loop detection.
- Does RunGuard's loop detection work for Anthropic's Claude models?
- Yes. RunGuard wraps any async function — it has no dependency on OpenAI's API format. Wrap your
anthropic.messages.create()call withguard()and RunGuard detects loops in Anthropic-based agents the same way it does for OpenAI agents. - What happens when RunGuard's budget cap is hit?
- RunGuard tracks estimated token cost for each call using the model's published token prices (configurable). When the cumulative cost for the current run window exceeds
maxUsd, it throwsBudgetExceededErrorbefore the next call fires. Your agent code can catch this and handle it gracefully — log the partial result, alert on Slack, or retry with a cheaper model.