Agentiqa vs Langfuse

Short answer: Langfuse is an open source platform for agent tracing, evals and prompt management. You can self-host it or use its cloud. Agentiqa is an AI Quality System. Its release gates run your agent's key tasks before each release and check what changed in the connected system. Langfuse shows what your agent did and how its outputs score. Agentiqa checks whether the next version still writes the right records.

At a glance

"—" means we did not find it on the Langfuse pages listed in Sources. Langfuse facts checked on 31 Oct 2026.

What Langfuse does

Langfuse calls itself "Open Source Agent Evals & Observability". Its site lists:

• Tracing. Every LLM call, tool invocation and retrieval step, with cost and latency dashboards. It works with any language through OpenTelemetry.
• Evaluation. LLM-as-a-judge, heuristic functions and human review, on production data or in experiments on datasets.
• Prompt management and a playground for testing prompt changes.
• Annotation queues for human review.

Langfuse can be self-hosted under the MIT license, or used as a cloud service with a free Hobby plan.

What Agentiqa does

Agentiqa is built for AI agents that write data into business systems: ERP and procurement suites, CRM, billing.

• Release gates run the agent's key tasks before each release. You describe each task in plain words. The gate runs it in a real browser with a test account and grades each check.
• The record, not only the reply. After each task, the gate checks what changed in the connected system.
• Confirmed regressions. When you rerun the failed tasks, AI Quality Manager reports a step as a regression only if it fails again. Unconfirmed failures are listed separately.
• AI Quality Manager compares each release with the previous one, shows regression candidates, and proposes test updates and new tests. A person on your team approves them.

Each failure comes with a trace, the recording and steps to reproduce.

Where they differ

Langfuse records and scores what happened: in production, or in an experiment on a dataset. A release gate runs a task end to end before release and then reads the connected system. Some failures only show up there:

• Wrong data: the record exists, with a wrong value. See wrong data.
• A false "done": the agent reports success, but the write failed. See hallucinated success.
• Partial writes: the order header is saved, the lines are not. See partial writes.

A trace records the call. It does not prove the record exists with the right values.

When to pick which

• Pick Langfuse if you want open source tracing and evals you can self-host, with prompt management in the same tool.
• Pick Agentiqa if your agent changes records for customers and you want each release checked on its key tasks before it ships.
• Use both if you need both. Langfuse for traces, prompts and evals; Agentiqa for the release check on the riskiest write tasks.

Sources

Langfuse: langfuse.com, langfuse.com/pricing, langfuse.com/compare/langsmith, checked 31 Oct 2026. Agentiqa: agentiqa.com, Enterprise, Community Edition, llms.txt.