AgentYield vs Helicone
Both tools touch your LLM traffic. They solve very different problems. Helicone is Datadog for LLM calls. AgentYield is the SREs that actually go fix the things Datadog flagged — specifically for agents.
A waste-detection and optimization engine for autonomous agents. Find duplicated tool calls, oversized context, model mismatches, retry storms, and redundant reads — with dollar-figure savings and Claude-generated fixes.
General-purpose observability proxy for LLM requests. Logs every prompt/response, with caching, routing, and request-level analytics.
Feature comparison
Side-by-side on the things that matter for AI agent teams.
| Capability | AgentYield | Helicone |
|---|---|---|
| Primary use case | Find & fix agent waste | Log & inspect LLM requests |
| Unit of analysis | Run (full agent loop) | Individual request |
| Integration model | Fire-and-forget SDK | HTTP proxy (baseURL swap) |
| Hot-path latency risk | No | Yes |
| Duplicate tool-call detection | Yes | No |
| Oversized context detection | Yes | No |
| Model-mismatch alerts | Yes | No |
| Excessive-retry detection | Yes | No |
| Redundant file/read detection | Yes | No |
| Dollar-figure waste estimate | Yes | Spend totals only |
| AI-generated fix recommendations | Claude-generated | No |
| Request caching | No | Yes |
| Multi-provider routing | No | Yes |
| Prompt management / versioning | No | Yes |
| Raw request log explorer | Per-run timeline | Full HQL search |
The four differences that actually matter
Run-centric, not request-centric
Helicone treats every LLM call as an isolated event. AgentYield groups calls and tool invocations into Runs — the actual unit of work an agent performs — so we can spot patterns no single request can reveal: a tool called 5 times in one loop, context that grew unbounded, a retry storm.
Zero hot-path latency
Helicone proxies your LLM traffic. If their service slows down, your agent slows down. AgentYield uses a fire-and-forget SDK that ships telemetry asynchronously — your agent never waits on us, and we can never break a production call.
Opinionated waste categories
We detect five specific failure modes that cost agent teams real money: duplicate tool calls, oversized context, model mismatch, excessive retries, and redundant reads. Each finding comes with the exact events involved, the dollar amount wasted, and a Claude-generated fix.
Built for agents, not chatbots
Helicone serves any LLM workload — a customer-support chatbot is the same to them as a 50-step research agent. AgentYield is designed for the latter: long-running, tool-using, multi-step autonomous systems where waste compounds invisibly across the loop.
When to pick which (or both)
Pick AgentYield if…
- You're running autonomous, multi-step agents (research, coding, sales, support automation).
- Your monthly LLM bill is climbing and you can't tell why.
- You want a prioritized, dollar-ranked list of fixes — not raw logs.
- You can't risk adding latency or a proxy to your production hot path.
Pick Helicone if…
- You need a system of record for every individual LLM request.
- You want request caching, multi-provider routing, or prompt versioning at the proxy layer.
- Your workloads are mostly chatbots / single-shot completions, not agent loops.
- You want to write SQL-style queries (HQL) against your raw request data.
They actually compose well
- Use Helicone for full request-by-request audit trails and HQL queries across your entire LLM org.
- Use AgentYield to surface the wasted spend inside agent runs and get prioritized fixes.
- They're not mutually exclusive — many teams use both: Helicone as the system of record, AgentYield as the optimizer.
See what your agents are wasting
Drop in a log file. Get a Waste Score, a dollar-figure savings estimate, and ranked fixes in seconds. No signup required.