Laminar MCP Server

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When an AI agent misbehaves in production, the hard part is usually finding out why. Laminar traces agent and LLM application runs so you can inspect exactly where something went wrong. Connected to Neotask, you can pull up traces, dig into failures, manage evaluation datasets, and debug agent behavior over time without leaving the conversation to dig through a separate observability dashboard.

• Traces agent and LLM application runs for inspection
• Surfaces failures so you can see exactly where a run broke down
• Manages evaluation datasets used to test agent behavior

What you can automate

Laminar MCP Server

Trace agent and LLM application runs, inspect failures, manage evaluation datasets, and debug agent behavior over time in Laminar.

Real workflows

Debug a failed agent run

Ask Neotask to pull the trace for a specific failed run. It queries Laminar for the trace details so you can see exactly where the agent broke down.

Review eval dataset coverage

Ask what's in your current evaluation dataset. Neotask pulls it from Laminar so you can check coverage before adding new test cases.

Frequently asked questions

Who is this for?

Teams building AI agents who need observability into why a run failed or behaved unexpectedly.

Does it fix the agent for me?

No — it gives you the trace and debugging context; you (or Neotask, separately) make the fix.