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.
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 | Langfuse | CortexOps |
|---|---|---|
| Open source | ✓ MIT | ✓ MIT |
| Self-hostable | ✓ Yes | ✓ Yes |
| Cloud hosted | ✓ Yes | ✓ Yes |
| Tracing | ✓ LLM calls | ✓ Agent execution (nodes, tools, state) |
| Agent frameworks | Via SDK wrappers | ✓ 12 native integrations |
| OpenTelemetry | ✓ Partial | ✓ OTLP native |
| LLM-as-judge | ✓ Yes | ✓ Yes |
| CI/CD eval gate CLI | ✗ | ✓ cortexops eval run |
| GitHub Actions | ✗ | ✓ cortexops-eval-action |
| PII redaction | ✓ | ✓ |
| Free tier | ✓ | ✓ 5,000 traces/month |
| Pro pricing | Usage-based | $49/month flat |
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.
This is where CortexOps has a clear advantage for production teams.
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.
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:
Ashish Verma is a Senior AI Engineer at PayPal and co-founder of CortexOps.