Track and optimize coding agent usage
Use case / coding agents
Track and optimizecoding agentusage
Coding agents make engineers faster, but they also make AI spend unpredictable. Govern it centrally. See spend by team and by developer. Trace sessions to improve token efficiency.
Total spend
$1,540
Sessions
4,812
Budget left
28%
Set up
Via gateways
Govern AI spend from a central place
Give teams and departments a spend envelope on the gateway, then see what actually landed in Langfuse. A developer cannot disable a gateway the way they can a local hook.
What is an LLM gateway? →A gateway as the routing layer
Via hooks
Dive deep into session details
Log tool calls, retries, skill usage, and model turns. Reconstruct what the agent did so you can debug loops, bloated context, and skills that need a rewrite.
Tracing coding agents →Gateway integrations
Any gateway,one place for LLM calls
Route coding-agent traffic through LiteLLM or OpenRouter today. Langfuse Gateway is coming soon: virtual keys, access control, and tracing built in.
LLM gateways
Hooks integrations
Your coding agent,fully traced
Claude Code, Codex, OpenCode, and more. Log tool calls, token spend, and skill usage without changing how engineers work.
Learn how to trace and govern coding agents
Start with hooks or a gateway, then use traces to cut wasted tokens and turn repeated work into shared skills.
What is an LLM gateway?
When a gateway is the right enforcement layer for model access, budgets, and tracing.
Evaluating AI agent skills
Measure whether skills actually improve quality and reduce wasted context.
Optimizing an AI skill with Autoresearch
Iterate on skills with traces and experiments so token spend actually drops.
Ready to govern coding agent usage?
Use a gateway or hooks to trace sessions, attribute spend, and improve token efficiency with Langfuse.
No credit card required · Free tier available · Self-hosting option