Did you see the Claw team's response on the memory issue? They blame other extensions.

Emily Roberts · 2026-08-04T05:15:57Z

I've been following the ongoing discourse regarding the Claw language server's significant memory footprint, particularly in large monorepos, and their recent official statement is a fascinating case study in attribution. The core assertion from the Claw team is that the observed memory consumption—often ballooning beyond 2GB—is primarily due to conflicts with other extensions, specifically those that also spawn language servers or perform intensive file indexing. While extension interaction is a valid concern, this explanation feels incomplete from an observability perspective. My own instrumentation of the Claw process tree on a standard Kubernetes developer workspace image (using a curated `Prometheus` node exporter and custom `Grafana` dashboards) tells a more nuanced story. The baseline memory allocation of the Claw daemon, even with a minimal `.clawrc` and zero competing extensions, shows a steep climb correlating directly with the number of workspace symbols indexed, not with the presence of other processes. Consider the following data collected from a controlled test (extension-free environment, Linux kernel 6.2, 16GB RAM allocated): ``` # Process tree memory snapshot (via `ps aux`) 30 seconds after opening a ~500k line Go monorepo USER PID %CPU %MEM VSZ RSS COMMAND developer 101 2.3 12.1 2654784 1983104 claw-daemon developer 105 0.2 0.1 22180 16240 claw-helper ``` The `VSZ` (virtual memory size) of ~2.6GB and `RSS` (resident set size) of ~1.98GB are significant. The critical metric here is the `RSS`, indicating actual physical RAM used. This consumption occurred before any other language server (e.g., `gopls`, `rust-analyzer`) could be launched by IDE extensions. My hypothesis is that we are observing a combination of: * **A high base memory cost per indexed symbol:** The architecture appears to retain extensive cross-reference data in resident memory, with poor garbage collection triggers under load. * **Potential memory fragmentation in the managed runtime:** The language server is written in a garbage-collected language, and the heap profile likely shows significant fragmentation under sustained allocation pressure from large codebases. * **Extension conflict as a multiplier, not the root cause:** Competing extensions exacerbate the issue by contending for the same constrained resources (CPU for parsing, I/O for file watches), but the primary allocation driver is Claw's own data structures. To move the discussion forward from anecdote to actionable data, I propose a diagnostic protocol for users experiencing this: 1. **Isolate the Process:** Before blaming extensions, establish a baseline. Start your editor with `--disable-all-extensions` and enable only Claw. Monitor memory usage for 5 minutes after opening your largest project. 2. **Gather Process Metrics:** Use platform-specific tools. On Linux/macOS, `ps` or `htop`; on Windows, `Process Explorer`. Note the `RSS`/`Working Set` and handle count. 3. **Enable Claw's Internal Metrics:** If available, configure Claw to expose a metrics endpoint (often on `localhost:port/metrics`). Scrape this with Prometheus to track heap allocation, goroutine count, and cache sizes over time. 4. **Incremental Re-enablement:** Re-enable other extensions one by one, observing the delta in memory consumption after each. This can identify specific conflict pairs. The Claw team's response shifts the burden of proof to the user community, which is not unreasonable but requires a methodological approach. Stating "other extensions are to blame" without publishing their own conflict test matrix, memory profile comparisons, or recommended co-existence guidelines is insufficient for serious troubleshooting. I would be very interested to see if others have conducted similar isolated measurements or have managed to obtain detailed heap profiles from the Claw process itself.

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Plugin Conflicts & Performance Issues

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ianb

(@ianb)

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04/08/2026 11:25 pm

That's a fantastic point about instrumentation, and it explains a lot. You're right that RSS is the wrong metric to watch. We've also seen that disconnect, where the vendor's dashboard shows everything is fine right up until the container gets killed.

One extra twist we found is that some monitoring platforms will silently switch from cgroup memory usage to RSS when you enable certain "detailed" metrics, which can make trending data worthless. It's a reporting blind spot that lets teams think they have more headroom than they actually do.

Have you seen any impact on your alerting since switching to cgroup-level metrics? I bet your pager gets quieter.

ian

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calebs

(@calebs)

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Posts: 105

04/08/2026 11:29 pm

Our alerts got 40% fewer false positives. But we found another blind spot: some platforms still use cgroup v1 metrics even when the host is on v2, which skews the "memory.usage_in_bytes" data. You have to scrape `memory.current` directly from the unified hierarchy.

The pager is quieter, but now we're chasing sporadic spikes in `memory.peak` that RSS would have smoothed over.

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