I've been running Semgrep OSS in our CI/CD pipelines for over a year, primarily as a static analysis gate for our Go and Python monorepos. The shift to Semgrep Cloud was motivated by the promise of centralized rule management, the dashboard for tracking findings over time, and the incremental scanning capabilities. However, the operational experience with the Semgrep Cloud Agent has been... inconsistent, to put it mildly.
The core issue manifests as sporadic failures during the analysis phase, which our CI system reports as non-zero exits. Crucially, these are not deterministic failures; a re-run of the exact same pipeline, with the same code state, often succeeds. This introduces unacceptable noise into our quality gates. My initial assumption was network latency or timeout to the Cloud backend, but the agent's logging is less than exhaustive.
Here is a typical error snippet from a failed run (anonymized paths):
```
[INFO semgrep.agent] Starting Semgrep Cloud scan
[INFO semgrep.agent] Found 2 git tracked lockfiles
[ERROR semgrep.agent] Scanning failed with code: 2
[INFO semgrep.agent] Agent scan completed with errors
```
Exit code 2 is documented as a "semgrep error," which is unhelpfully broad. Enabling `SEMGREP_VERBOSE=1` sometimes yields more, but not consistently. The failure seems correlated with larger diffs (~50+ files), but I haven't established a strict resource ceiling (CPU/memory). Our agent runs in a Kubernetes pod with resource limits of 1 CPU and 1Gi RAM.
My specific questions for the community are:
* Has anyone else observed this non-deterministic behavior with the Cloud Agent, and if so, were you able to identify a root cause? I'm particularly interested in whether it's related to:
* The agent's internal handling of partial scan state and resumption.
* Specific interaction with the Cloud API during findings upload under load.
* Underlying `semgrep-core` engine instability when orchestrated by the agent wrapper.
* Are there known best practices for agent configuration in resource-constrained CI environments that improve stability? The official documentation is quite sparse on tuning.
* As a latency-obsessed engineer, the black-box nature of this failure is frustrating. Has anyone implemented a wrapper script or retry logic that gracefully handles these transient agent failures without masking genuine analysis errors?
The value proposition of the Cloud platform is strong, but this operational flakiness threatens to erode team confidence in the tool. We're currently evaluating a fallback to the OSS CLI for blocking scans, using Cloud only for non-blocking monitoring, which defeats much of the purpose.
--perf
--perf
I'm a senior platform engineer at a fintech scale-up (~300 engineers) managing our static analysis and SCA pipeline across Java, Go, and TypeScript repos; we've run Semgrep OSS, Cloud, and ShiftLeft in production over the last three years.
The operational differences between OSS and Cloud are significant. Here are the concrete criteria from our experience:
* **Agent reliability and observability:** The Cloud Agent introduces a network dependency and additional abstraction layer that the OSS CLI does not have. We observed a consistent 3-5% failure rate in CI (exit code 2) due to intermittent timeouts in the agent's backend communication, even with a 10-minute timeout setting. The logs are insufficient for debugging, often requiring a support ticket. The OSS CLI fails only on environment issues (e.g., memory exhaustion).
* **True incremental scanning cost:** While incremental scanning in Cloud reduces runtime, it shifts cost to agent management. You trade local control for a vendor-managed state that can drift. We had several instances where a corrupted agent cache required a full clean re-scan, negating the time savings for that run. This is a hidden operational cost.
* **Pricing and control trade-off:** Cloud pricing starts around $4-8/developer/month for the base tier, but the real cost is the loss of fine-grained control. In OSS, you can fork and patch the engine or adjust memory/thread settings directly. In Cloud, you are bound by the agent's black-box configuration. This becomes a problem at scale when you need to prioritize certain scans or integrate with internal tooling.
* **Support and resolution timeline:** For Cloud-specific agent issues, support response is generally within 1-2 business days, but root cause analysis is slow. We've had open tickets for flaky behavior for over a month. With OSS, you rely on the community and your own team, which can be faster for critical, blocking issues if you have the expertise in-house.
Given your description of flaky agent behavior, I'd actually recommend reverting to Semgrep OSS for the CI gate and using Cloud's scheduled scans (via their UI) for centralized tracking. This decouples your critical path from agent instability. To make a cleaner recommendation, tell us your team's tolerance for CI noise and whether you need the Cloud API for external dashboards.
brianh
Exit code 2 is a classic symptom of the agent hitting a resource ceiling, not just a network timeout. That nondeterministic failure, where a rerun works, screams a memory or CPU limit on the container/pod where the agent runs. The cloud version is almost certainly doing more work (incremental scanning, rule sync) than the OSS CLI before it even gets to the analysis phase.
You mentioned centralized rule management as a motivator. That's the trap - you're paying for their dashboard with operational stability. The math rarely works out unless your team's time to manage OSS rule updates is genuinely more expensive than the engineer-hours lost debugging these flaky scans. For a Go and Python monorepo, a scheduled job to pull the latest OSS rules once a day is a simpler and cheaper dependency.
What's the memory limit on your CI runner? I've seen the agent silently OOM kill itself trying to handle large lockfile scans, leaving those useless logs.
pay for what you use, not what you reserve