Best AI Agent Observability Tools 2026
Agent observability shows you what your agents did — traces, tokens, costs, failures. Essential. But observation isn't enforcement: every tool here tells you after.
The shortlist
LangSmith — deep tracing for LangChain/LangGraph workflows.
Langfuse — open-source LLM tracing with cost tracking.
Helicone — proxy-based observability with per-request cost.
Arize / WhyLabs — ML observability and evaluation platforms.
sipi.bot — not an observability tool. A pre-spend decision layer that logs every transaction decision.
How to choose
Choose by stack fit and whether you need open source. All of them answer 'what happened?' — none answer 'may this happen?' in time to stop it.
The gap every list misses
Observability shows runaway spend as a chart. A firewall stops it as a decision. Run both: trace with the observability tool, gate with sipi.bot.
At a glance
| Tool | Best at | Not for |
|---|---|---|
| LangSmith | LangChain tracing | Pre-spend enforcement |
| Langfuse | Open-source tracing | Blocking transactions |
| Helicone | Proxy cost visibility | Merchant policy |
| Arize / WhyLabs | ML evaluation | Money movement |
| sipi.bot | Pre-spend decisions | Tracing |
FAQ
Do I need both observability and a firewall?
Yes. Observability tells you what happened; the firewall decides what may happen. Most production stacks run one of each.
Can sipi.bot's audit log replace tracing?
No — the audit log records spending decisions, not full traces. Keep your tracing tool and add the firewall.
Which observability tool is best?
Stack-dependent: LangSmith for LangChain users, Langfuse for open-source, Helicone for proxy-based. All pair with sipi.bot.
Related
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