AI agents fail in ways that traditional software doesn’t. They hallucinate, loop infinitely, use excessive tokens, and produce inconsistent outputs. Without observability, you’re flying blind.
Here are the platforms that give you visibility into what your agents are actually doing.
What agent observability means
Agent observability is not just logging. It’s understanding:
- What the agent decided (reasoning traces)
- Why it decided it (prompt context, tool calls)
- How much it cost (tokens, API calls, time)
- Whether it succeeded (task completion, output quality)
- Where it failed (errors, loops, hallucinations)
Traditional APM tools (Datadog, New Relic) don’t capture this. You need agent-specific observability.
The platforms
1. LangSmith
Best for: LangChain/LangGraph teams.
LangSmith is the official observability platform for LangChain. It captures every step of an agent’s execution: prompts, tool calls, model responses, and costs.
Key features:
- Step-by-step trace visualization
- Cost tracking per chain/agent/step
- Evaluation datasets for testing agents
- Human annotation for output quality
- Debugging tools for failed runs
Pricing: Free tier (5K traces/month), $39/month developer, $399/month team.
Best for: Teams building agents with LangChain or LangGraph. Deep integration gives visibility that generic tools can’t match.
2. Langfuse
Best for: Self-hosted observability.
Langfuse is open-source and can be self-hosted. It provides traces, cost tracking, and evaluation tools without sending your data to a third party.
Key features:
- Open source (MIT license)
- Self-hostable (Docker, Kubernetes)
- Trace visualization
- Cost per trace/generation
- Evaluation and scoring
- Prompt management
Pricing: Free (self-hosted), $50/month cloud, $500/month enterprise.
Best for: Teams that need data privacy or want to customize their observability stack.
3. Helicone
Best for: Quick setup across all providers.
Helicone works as a proxy. Change your API base URL, and it logs every request with cost data, latency, and error rates.
Key features:
- One-line setup (proxy)
- Per-request cost tracking
- Budget alerts
- User-level attribution
- Request/response logging
Pricing: Free tier, $20/month pro, $200/month team.
Best for: Developers who want quick observability without code changes. Works with any provider.
4. Braintrust
Best for: Evaluation-driven development.
Braintrust focuses on evaluating AI agent outputs. It lets you create test datasets, run evaluations, and track quality over time.
Key features:
- Evaluation datasets
- Automated scoring
- A/B testing for prompts
- Cost tracking
- Trace logging
Pricing: Free tier, $99/month team.
Best for: Teams that prioritize output quality and want systematic evaluation.
5. Arize Phoenix
Best for: ML observability with agent support.
Arize is an ML observability platform that added agent tracing. It provides drift detection, performance monitoring, and root cause analysis.
Key features:
- Agent trace visualization
- Drift detection
- Performance monitoring
- Root cause analysis
- Embedding visualization
Pricing: Free tier, custom enterprise pricing.
Best for: Teams that need ML-specific observability alongside agent tracing.
Comparison
| Platform | Setup | Self-Hosted | Agent-Specific | Cost Tracking | Evaluation | Price |
|---|---|---|---|---|---|---|
| LangSmith | LangChain | No | Yes | Yes | Yes | $39/mo |
| Langfuse | SDK | Yes | Yes | Yes | Yes | $50/mo |
| Helicone | Proxy | No | Limited | Yes | No | $20/mo |
| Braintrust | SDK | No | Yes | Yes | Yes | $99/mo |
| Arize Phoenix | SDK | Yes | Yes | Yes | Yes | Custom |
What to observe
Traces
Every agent execution should produce a trace: the sequence of prompts, tool calls, model responses, and decisions. Traces let you debug failures and understand agent behavior.
Costs
Track tokens, API calls, and cost per task. Set budget alerts. Monitor cost per outcome (not just total spend).
Quality
Evaluate outputs against expected results. Track quality metrics over time. Use human annotation for subjective quality.
Latency
Measure time-to-completion for agent tasks. Identify bottlenecks (slow models, excessive retries, tool call latency).
Errors
Log every error, timeout, and retry. Categorize errors by type (model error, tool error, timeout, rate limit).
My take
For most teams, start with Langfuse (self-hosted) or Helicone (proxy). Both give you the basics: traces, costs, and error tracking.
For LangChain teams, LangSmith is the obvious choice. The deep integration is worth the vendor lock-in.
For teams focused on output quality, add Braintrust for evaluation. The automated scoring and A/B testing help you optimize agent performance systematically.
For production deployments, use Langfuse (self-hosted) for privacy-sensitive data and Helicone for everything else. The combination gives you comprehensive observability without sending sensitive data to third parties.
FAQ
What’s the difference between observability and monitoring?
Monitoring tells you if something is wrong (alerts, dashboards). Observability tells you why it’s wrong (traces, logs, context). For AI agents, you need both.
Can I use traditional APM tools for agents?
Partially. Datadog and New Relic can track API calls and latency, but they don’t capture agent-specific data (reasoning traces, tool calls, prompt context). Use agent-specific tools for the AI layer.
How much does agent observability cost?
Free tiers are available for all major tools. Paid plans start at $20/month. Self-hosted Langfuse is free. For production, budget $50-200/month per team.
Do I need observability for development or just production?
Both. Development observability helps you debug and optimize agents. Production observability helps you monitor costs, detect failures, and ensure quality. Start with development, add production monitoring as you scale.
What’s the most important thing to observe?
Cost per completed task. Total spend is meaningless without knowing what you got for it. Track cost per PR merged, per issue resolved, per document processed.
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