Stop flying blind
with your AI agents.
TraceHawk gives you full-stack observability for AI agents — span waterfall, decision trees, cost attribution, and real-time alerts. In 3 lines of code.
Free · 50K spans/month · 7-day retention
Works with the tools you already use
Your agents fail in ways
you can't see.
Without observability, every production issue is a fire drill.
Without TraceHawk: Agent crashes at 3am. You find out when users complain.
With TraceHawk: Real-time error alerts via Slack, email, or webhook.
Without TraceHawk: $4,200 LLM bill with zero breakdown of where it went.
With TraceHawk: Per-agent, per-model cost attribution with budget limits.
Without TraceHawk: Agent picked the wrong tool. No idea why.
With TraceHawk: Decision tree view shows every branch and choice made.
How it works
Up and running in minutes.
Install the SDK
pip install tracehawk
# or
npm install @tracehawk/sdkOne package. Works with Python and TypeScript agents.
Add one decorator
import tracehawk
tracehawk.init(api_key="th-...")
@tracehawk.observe
def my_agent(prompt):
# Your existing code
return openai_client.chat(...)Wrap your agent. Every LLM call, tool use, and decision is captured automatically.
See everything
# Open TraceHawk dashboard
# → Full span waterfall
# → Decision tree view
# → Cost per agent/model
# → Real-time error alertsLive in seconds. No infrastructure to manage, no SDK rewrites.
Comparison
Purpose-built for AI agents.
Langfuse and LangSmith are great — for LangChain. TraceHawk is framework-agnostic.
Datadog omitted — starts at $15/host/month, no native MCP or LLM cost tracking.
"We had no idea our research agent was spending $200/day on embeddings until TraceHawk flagged it. Saved us before the bill hit."
"The decision tree view is the killer feature. Finally I can show my team exactly why the agent chose to call that tool."
"Migrated from LangSmith in an afternoon. Works with our custom agent framework — no LangChain required."
Ship agents with
confidence.
Free forever for solo developers. Start in minutes.