Observability for AI Agents
Tracing, evaluation, and monitoring tools for AI agent systems in production
| Tool | Type | Pricing | OSS | Llm Tracing | Cost Tracking | Evaluation | Prompt Management | Real Time Monitoring | Verified |
|---|---|---|---|---|---|---|---|---|---|
| Datadog | cloud | Free tier (5 hosts)$15/host/mo Infrastructure$31/host/mo APMCustom Enterprise | 2026-04-28 | ||||||
| Langfuse | hybrid | Free (self-hosted)Free cloud (50k observations)$59/mo ProCustom Enterprise | 2026-04-28 | ||||||
| Helicone | hybrid | Free (100k requests)$20/mo GrowthCustom Enterprise | 2026-04-28 | ||||||
| LangSmith | cloud | Free (5k traces)$39/seat/mo PlusCustom Enterprise | 2026-04-28 | ||||||
| Grafana | hybrid | Free (self-hosted OSS)Free cloud (10k metrics)$29/mo ProCustom Enterprise | 2026-04-28 |
Supported Not supported Unverified
What do these features mean?
- Llm Tracing — Trace LLM calls, tool invocations, and agent reasoning steps end-to-end
- Cost Tracking — Track token usage and cost per request, per agent run, and per model
- Evaluation — Score agent outputs against test datasets with automated evaluators
- Prompt Management — Version, manage, and A/B test prompts in production
- Real Time Monitoring — Live dashboards and alerting for agent performance metrics
Missing a tool in this category? Use the add-tool skill to generate the file, then open a PR.
Observability for AI Agents
Observability for AI agents is a different problem than traditional APM. You're not just tracking request latency and error rates — you need to trace multi-step agent reasoning, measure token costs, evaluate output quality, and debug tool-calling chains that can branch unpredictably.
The tools in this category range from purpose-built LLM observability platforms (Langfuse, Helicone, LangSmith) to general-purpose monitoring tools that have added AI-specific capabilities (Datadog, Grafana).
What matters for agent observability:
- Tracing — follow an agent's execution across LLM calls, tool invocations, and retrieval steps. Most purpose-built tools capture this automatically with SDK decorators or middleware.
- Cost tracking — token usage adds up fast in agentic workflows. Knowing cost per agent run, per tool call, and per model helps optimize before the bill surprises you.
- Evaluation — automated scoring of agent outputs against test datasets. LangSmith and Langfuse both offer evaluation frameworks; Datadog and Grafana don't.
- Prompt management — versioning and A/B testing prompts in production. Langfuse includes this natively; others require separate tooling.
- Framework integration — how well the tool plugs into your agent framework (LangChain, LlamaIndex, Vercel AI, OpenAI Agents). Tighter integration means less instrumentation code.
The choice often comes down to: do you want a dedicated LLM observability tool, or do you want LLM visibility inside an existing monitoring stack? Purpose-built tools go deeper on AI-specific features. General tools give you one pane of glass across your entire infrastructure.