How AgentYield compares
You're evaluating LLM tooling. Here's where AgentYield fits next to the observability platforms you've probably already heard of — and why most teams end up using both.
Helicone, LangSmith, and Langfuse are observability tools — they show you what your LLM did.
AgentYield is a waste detector — it shows you where your agent is bleeding money and how to stop it.
These tools compose well. Most teams who care about cost end up running both an observability layer and AgentYield on top.
Teams running these tools use AgentYield on top to detect waste patterns across agent runs — not as a replacement, but as a cost optimization layer.
The tools we get compared to
Click through for a full side-by-side feature comparison on each.
AgentYield vs Helicone
Datadog for LLM requests
General-purpose observability proxy. Logs every prompt/response, with caching, routing, and request-level analytics.
What Helicone does well
Full request-by-request audit trail, HQL queries, caching, multi-provider routing, prompt versioning at the proxy layer.
Where AgentYield wins
Helicone treats every LLM call as an isolated event. AgentYield groups calls into Runs and detects patterns — duplicate tool calls, retry storms, context bloat — that no single request reveals. Plus: zero hot-path latency (no proxy).
Best for: Teams who want a system of record for every LLM request and need request-level proxy features.
AgentYield vs LangSmith
Tracing & evals for LangChain
Tracing, debugging, and evaluation platform built by the LangChain team. Deeply integrated with LangChain/LangGraph.
What LangSmith does well
Step-by-step trace inspection, prompt playground, offline evals, datasets, and human annotation queues — all native to LangChain.
Where AgentYield wins
LangSmith shines if you're on LangChain. AgentYield is framework-agnostic and prescriptive — it doesn't just show you traces, it ranks the wasteful ones with dollar amounts and Claude-generated fixes.
Best for: LangChain/LangGraph teams who need eval pipelines, prompt iteration, and trace debugging as their daily workflow.
AgentYield vs Langfuse
Open-source LLM observability
Self-hostable traces, prompt management, evaluations, and analytics dashboards across any LLM workflow.
What Langfuse does well
Open source, self-hostable for compliance, prompt versioning, LLM-as-judge evals, full observability for any LLM workload.
Where AgentYield wins
Langfuse hands you the raw material; you decide what's wasteful. AgentYield runs five waste detectors automatically and gives you a ranked list of fixes. No Postgres/ClickHouse/Redis to operate.
Best for: Teams who need open-source/self-hosted observability and a prompt ops layer across all their LLM apps.
What's true across every comparison
The four things that make AgentYield structurally different from observability tools.
Run-centric, not request-centric
We model your data as Runs — the full agent loop — because waste patterns only emerge at that level.
Prescriptive, not just observational
Every finding comes with a dollar amount, the exact events involved, and a Claude-generated fix.
Fire-and-forget SDK
No proxy, no hot-path risk. Telemetry ships asynchronously — your agent never waits on us.
Five named waste categories
Duplicate tool calls, oversized context, model mismatch, excessive retries, redundant reads. Opinionated by design.
Quick decision guide
Start with AgentYield if…
- You're running multi-step autonomous agents.
- Your LLM bill is climbing and you can't tell why.
- You want ranked, dollar-figure fixes — not raw traces.
- You want a 5-minute install with zero hot-path risk.
Start with an observability tool if…
- You need a system of record for every individual LLM request.
- Your workloads are mostly chatbots / single-shot completions.
- You want prompt versioning, eval pipelines, or human annotation.
- You're heavily invested in LangChain (LangSmith) or need self-hosting (Langfuse).
The honest answer: most serious agent teams end up running both an observability layer and AgentYield. The observability tool is the system of record; AgentYield is the optimizer that turns that data into dollar savings.
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.