CortexOps vs Langfuse: Open Source AI Observability Compared

Both CortexOps and Langfuse are open-source AI observability platforms. If you are evaluating them, the choice comes down to a few key differences: framework support, evaluation methodology, and whether you need a CI/CD deployment gate.

What They Are

Langfuse is an open-source LLM engineering platform focused on tracing, prompt management, and evaluation. It has a strong Python and TypeScript SDK, a hosted cloud option, and a popular self-hosted deployment. Over 6 million SDK downloads per month.

CortexOps is an open-source AI agent observability platform focused specifically on agentic systems. It supports 12 agent frameworks via a unified instrumentation layer, provides LLM-as-judge evaluation, and ships a CI/CD deployment gate CLI designed to block regressions before they reach production.

Feature Comparison

The Key Difference: LLM Tracing vs Agent Tracing

Langfuse traces LLM calls — the individual model invocations that happen inside your application. This is valuable for prompt engineering and cost monitoring.

CortexOps traces agent execution — the full graph of nodes, tool calls, state transitions, and conditional branches that make up an agent run. This distinction matters when you are debugging:

With Langfuse you see:

LLM call #1 → input tokens: 342, output tokens: 89, latency: 1.2s

LLM call #2 → input tokens: 218, output tokens: 45, latency: 0.8s

With CortexOps you see:

agent_run (4.3s)

  └── classify_intent (1.2s) ✓

  └── check_refund_policy (0.9s) ✓

  └── process_refund (2.1s) ✗ FAILED

       └── tool: lookup_order (0.3s) ✓

       └── tool: issue_refund (1.8s) ✗ timeout

The agent-level trace tells you which node failed, which tool call timed out, and what the execution path was — without that, debugging a multi-node agent is guesswork.

The CI/CD Gate

This is where CortexOps has a clear advantage for production teams.

# Block the merge if task_completion drops below 90%

cortexops eval run \

  --dataset datasets/my_agent.yaml \

  --judge \

  --fail-on "task_completion < 0.90"

Combined with the GitHub Action:

- uses: ashishodu2023/cortexops-eval-action@v1

  with:

    dataset: datasets/my_agent.yaml

    fail-on: "task_completion < 0.90"

    cortexops-api-key: ${{ secrets.CORTEXOPS_API_KEY }}

Every pull request shows an eval report as a PR comment. The merge is blocked if quality drops. Langfuse has evaluation capabilities but does not ship a first-class CI/CD gate pattern.

When to Choose Langfuse

• You are optimising LLM calls and prompts more than agent behaviour


• You need TypeScript SDK support


• You have an existing Langfuse deployment


• You want the largest open-source community in this space

When to Choose CortexOps

• You are building and operating LLM agents specifically


• You need agent-level traces (nodes, tools, state) not just LLM call logs


• You want a CI/CD gate that blocks regressions automatically


• You use multiple agent frameworks

Try Both

Both are open source, both have free tiers. The fastest way to decide is to instrument one agent run with each and compare the trace data you get back.

pip install cortexops — 3 lines to your first agent trace.

Links:

• CortexOps: getcortexops.com | github.com/ashishodu2023/cortexops


• Langfuse: langfuse.com | github.com/langfuse/langfuse

Ashish Verma is a Senior AI Engineer at PayPal and co-founder of CortexOps.