Feature Comparison
| Tool | Best for | Agent-specific strengths | Pricing and cost watch |
|---|---|---|---|
| Langfuse | Open-source LLM and agent observability | Traces, spans, prompts, datasets, evals, and self-hosting options for teams that want control. | Cloud or self-hosted cost; watch event volume, retention, and storage. |
| AgentOps | Agent run monitoring and debugging | Session timelines, tool calls, errors, cost, and agent-centric dashboards. | Pricing can scale with events or seats; estimate by runs and tool-call volume. |
| Braintrust | Eval-first AI product development | Experiments, datasets, scorers, prompt/version comparison, and production feedback loops. | Good when eval volume is planned; watch experiment and trace retention. |
| Helicone | LLM proxy, gateway logging, and spend tracking | Request logs, prompt metadata, latency, cache, user attribution, and model cost reporting. | Proxy or gateway usage can grow with every model call; review retention and rate limits. |
| OpenTelemetry | Vendor-neutral trace plumbing | Portable spans for agent steps, tools, retries, and service boundaries. | Free standard, but storage and backend costs move to your collector or observability vendor. |
| Datadog | Enterprise production monitoring | LLM and app traces can sit beside infra, logs, dashboards, alerts, and incident workflows. | Usually the highest governance fit, but control indexed logs, spans, and retention carefully. |
Direct Answer
The best agent observability stack captures every agent run as a trace: user goal, model calls, prompts, tool calls, retrieved context, eval results, latency, token cost, errors, and retention policy. Start with Langfuse or AgentOps, add Braintrust for evals, and export critical traces through OpenTelemetry when production monitoring matters.
What Agent Observability Must Capture
AI agent observability is different from basic LLM logging because the failure often appears across several steps. A useful trace should show the plan, intermediate tool calls, approvals, retries, retrieved documents, and final action.
- Trace tree: user request, agent plan, model spans, tool spans, retries, and final response.
- Prompt and context history: prompt version, system messages, retrieved snippets, and redaction status.
- Tool-call audit: tool name, input schema, output summary, approval state, latency, and error class.
- Eval signals: pass/fail graders, human feedback, regression datasets, and release comparisons.
- Cost metrics: tokens, model, cache hit rate, per-run cost, per-user cost, and budget alerts.
Trace, Eval, Prompt, Tool-Call, And Retention Checklist
Use this checklist before adopting an observability vendor or rolling your own OpenTelemetry spans.
[ ] Trace every agent run with parent and child spans [ ] Store prompt version and model version [ ] Capture tool name, input summary, output summary, and approval state [ ] Redact secrets, PII, customer data, and raw credentials [ ] Add automated evals for task success and safety boundaries [ ] Link human feedback to traces and prompt versions [ ] Set log and trace retention by data class [ ] Track cost per run, user, model, and workflow [ ] Export critical spans to the production monitoring stack
Pricing And Cost Model
Agent observability cost is usually driven by event volume, retention, seats, eval runs, and storage. The cheapest tool during a prototype may become expensive once every tool call, retry, and retrieved chunk becomes a retained event.
- Prototype: cap retention, sample low-value traces, and keep only summarized tool outputs.
- Team rollout: estimate traces per user per day, average spans per trace, and eval cadence.
- Production: separate hot debugging retention from longer compliance or audit retention.
- Enterprise: compare vendor cost with OpenTelemetry plus existing Datadog or log storage.
How To Choose
Pick by the dominant workflow. AgentOps and Langfuse fit teams debugging agent runs; Braintrust fits teams shipping eval-driven AI features; Helicone fits teams that first need gateway logs and cost; OpenTelemetry and Datadog fit platform teams standardizing observability across services.
FAQ
What is agent observability?
Agent observability is the practice of tracing and evaluating multi-step AI agent runs, including prompts, model calls, tool calls, retrieved context, errors, latency, cost, and user feedback.
How is agent observability different from LLM observability?
LLM observability often starts with model requests and responses. Agent observability adds the full workflow: planning, tools, approvals, retries, state, and task success.
Which agent observability tool should I start with?
Start with Langfuse or AgentOps if you need agent traces quickly, Braintrust if evals are the core workflow, Helicone if gateway logs and spend tracking are first, and OpenTelemetry if vendor portability matters.
Should I store full prompts and tool outputs?
Only when policy allows it. Redact secrets and sensitive data, summarize high-risk tool outputs, and set retention rules before sending production traffic.