See how your AI agents run.
Inspect recorded prompts, model calls, tool use and failures. Follow conversations across runs, compare latency, and understand estimated spend.
Free plan, no card. Node SDK setup in the quickstart.
- run_8fa21cHTTP 500 · create_ticket
- run_71b0e4Timeout · retrieve_docs
- run_5c9d13Rate limited · gpt-4.1
import { init, trace } from "@tracehatch/sdk"
init({ agent: "support" })
await trace("answer question", run)Example · set TRACEHATCH_API_KEY and TRACEHATCH_BASE_URL first.
- Status
- Failed
- Error
- HTTP 500
- Duration
- 12.9s
- Cost
- $0.0062
Total spend
$2,462.75
+18.2% on last month
- gpt-4.148%
- claude-sonnet24%
- gpt-4.1-mini16%
- other12%
Records agent runs from
- TypeScript
- Node.js 18+
- OpenAI
- Anthropic
- REST ingest API
Understand each step, not just the answer.
Execution traces
Follow instrumented steps in a waterfall, inspect logs and retry events, and open failed runs on the root-cause span.
Prompts and tool calls
Inspect captured prompts, model outputs, tool inputs and results alongside reported token usage and timing.
Search and filter runs
Search trace names, errors and step names. Narrow results by status, duration, cost, environment and time.
Sessions and conversations
Group runs by session and end user, read retained conversation turns, and open the trace behind each turn.
Understand your agents’ cost and latency.
Recorded cost estimates
See estimated spend and tokens per run and model call. Unknown prices stay visibly unpriced.
Spend breakdownsPro
Split spend by model, provider, agent and environment over your selected range, then inspect recent expensive runs.
Model performancePro
Compare model usage, cost, failures and latency percentiles, with first-token timing when reported by the model call.
Tool reliabilityPro
Compare tool calls, failures, retry attempts and latency. Follow recent errors into the exact trace step.
- Total spend
- $18,420
- Avg / run
- $0.038
- Tokens
- 24.8M
Spend by model
Find failures and slow steps.
Project health
Requests, failure rate, latency and spend for the range you pick, compared with the period before it.
Failure triage
Recent failures list the error and open the run behind it in one click.
Cross-trace span explorerPro
Filter recorded steps by kind, model, tool or duration. Explore latency distributions and jump into matching traces.
Usage and retention
Track your monthly trace allowance and sampling status. See your plan’s retention window and keep aggregate trends after traces expire.
- Traces
- 12,480
- Failures
- 1.3%
- P95 duration
- 1.84s
From prompt to production. Tracehatch records the run.
import { init, trace } from "@tracehatch/sdk"
init({ agent: "support" })
await trace("run", agent)Example · set TRACEHATCH_API_KEY and TRACEHATCH_BASE_URL first.
01Connect
Open project setup and run the guided installer in your existing app. Review the detected framework and file changes, then connect your development environment.
- prompt86ms
- tool call221ms
- response121ms
02Capture
Trigger an AI action in your app. Supported model calls are captured automatically; setup opens the recorded run so you can check the result.
- gpt-4.1
- claude-sonnet
- gpt-4.1-mini
03Analyze
Inspect traces, find slow steps, break spend down by model and resolve failures.
+18% P95 duration vs previous period
3 failed runs · support-triage
04Monitor
Review failure rate, latency and cost by environment, and compare the selected period with the one before it.
Built for the people who keep agents running.
One trace, read three ways — by the engineer who wrote the agent, the team that runs it, and the lead who pays for it.
AI engineers
Instrument your agent, then inspect failed runs step by step, including captured prompts, tool results and logs.
Platform and SRE teams
Track failure rate, latency and throughput by environment, and investigate changes across time periods.
Engineering leads
Understand estimated spend within each project, with breakdowns by model, provider, agent and environment on Pro.
Connect with your existing AI stack.
Capture supported OpenAI and Anthropic calls automatically in Node.js. Other server languages can use the gateway or send model calls to the log endpoint.
OpenAI
04- Chat Completions
- Responses
- Embeddings
- Streaming
Anthropic
02- Messages
- Streaming
Node.js SDK
04- TypeScript
- Node.js 18+
- Custom spans
- Tool retries
Developer tools
03- REST ingest API
- Project API keys
- Personal access tokens
Install the versioned Node SDK using the quickstart.
Start with the quickstartPricing grows with your AI apps.
Start free with traces, sessions, the dashboard and cost totals. Pro and Enterprise include deeper analytics and longer retention.
Starter
Free
For developers exploring AI observability.
- 10,000 traces per month
- 3 projects
- Dashboard, traces and sessions
- Cost totals and token usage
- 7-day retention
Pro
Advanced analyticsPreviewaccess
For deeper cost and performance analysis.
- Unlimited metered traces
- Unlimited projects
- Cost, model, tool and span analytics
- 90-day retention
Enterprise
Custom
For projects that need longer trace history.
- All Pro analytics
- No plan-based trace expiry
- Unlimited projects
- Usage tracking
Plan changes and payments are not available in the app during preview. Contact us about Pro or Enterprise access. Compare every plan.
Frequently asked questions.
What engineering teams ask before instrumenting their first agent.
Create a project, open setup and run the guided installer in the server app that makes your AI calls. It detects your framework, connects through your browser and previews the configuration changes. Restart your app and trigger an existing AI action, then open its recorded run. Manual and AI-assisted instructions are also available.
Stop guessing why your agents fail.
Follow recorded runs from model calls to tool results. Find failures, inspect slow steps, and understand estimated spend.