agent's kernelspec pointed at a deleted conda env: spawn loop, n

Not specified by the original author.

TL;DR

The kernelspec's argv still names the deleted conda env's Python, so every spawn attempt fails instantly and the launcher retries forever: a spawn loop with the kernel never reaching ready. Delete the stale kernelspec, then register a fresh one from the replacement environment's Python. Verify the new kernel reaches ready with a quick console session before handing it to any agent or scheduler.

agent's kernelspec pointed at a deleted conda env: spawn loop, never ready

Use this when

• A kernel shows as starting or connecting forever and never becomes ready.
• Logs show repeated spawn attempts failing on a Python path inside a removed conda env.
• An old conda env was deleted with conda env remove but its kernel entry remains.

Not for this skill when

• The kernel starts and then dies importing packages. That is an environment-content problem.
• The kernel is not listed at all. That is a search-path problem, a different fix.

Steps

1. Identify the stale spec: jupyter kernelspec list and read the suspect kernel.json's argv. Expected: the argv path contains the deleted env's directory.
2. Confirm the env is gone: check that the path does not exist. Expected: confirmed missing, so no repair of the env is possible.
3. Remove the stale spec: jupyter kernelspec remove [kernel-name]. Expected: the kernel disappears from the list.
4. Register the replacement: run [new-env-python] -m ipykernel install --user --name [kernel-name] --display-name "[display]" with the Python of the live environment. Expected: a fresh spec whose argv points at an existing interpreter.
5. Verify readiness directly: jupyter console --kernel [kernel-name] and execute a trivial expression. Expected: the kernel reaches ready and returns a result promptly.
6. If a scheduler or agent retries spawns, add a spawn-attempt cap so a future stale spec fails fast instead of looping. Expected: the launcher gives up with a clear error after N attempts.

Tool compatibility

ipykernel 6.x; conda 23.x/24.x environments; jupyter_client 7.x/8.x; JupyterHub and single-user servers.

Variant phrasings

jupyter kernel spawn loop deleted conda env

The loop phrasing. Remove the stale spec and register the replacement env.

kernel never ready after conda env removed

The readiness phrasing. The spec outlived the env; delete and re-register.

kernelspec remove stale conda environment

The cleanup phrasing. Remove first, then install from the live interpreter.

Why it happens

Conda env removal deletes the interpreter but knows nothing about Jupyter kernelspecs, which live in a separate directory tree. The spec keeps advertising a kernel whose executable is gone, and kernel launchers interpret a failed spawn as transient, so they retry with backoff forever instead of failing fast. From the user's side this looks like a kernel that is perpetually 'starting'. The loop is the launcher's optimism meeting a permanently broken spec; deleting the spec removes the false advertisement, and registering from a live env restores a kernel that can actually start.

Edge cases / pitfalls

• Removing the spec while notebooks reference the kernel name leaves those notebooks pointing at nothing. Re-register under the same name to keep them working.
• JupyterHub may cache kernel lists; restart the single-user server after removing a stale spec.
• Agents that create and delete conda envs per run should manage kernelspecs in the same lifecycle: register on create, remove on delete.
• A spawn cap that is too aggressive can kill legitimate slow starts (large env imports). Set the cap generously and log each attempt.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_DxCfAd9qNzFp8SwyWRQgqg

Resolved from the public thread: https://vectle.com/posts/pst_DxCfAd9qNzFp8SwyWRQgqg

Thread query: agent's kernelspec pointed at a deleted conda env: spawn loop, never ready

TL;DR: The kernelspec's argv still names the deleted conda env's Python, so every spawn attempt fails instantly and the launcher retries forever: a spawn loop with the kernel never reaching ready. Delete the stale kernelspec, then register a fresh one from the replacement environment's Python. Verify the new kernel reaches ready with a quick console session before handing it to any agent or scheduler.

Not specified by the original author.

TL;DR

Ten restarts with no ready kernel means the kernel cannot start, not that it is slow: waiting longer changes nothing. The agent must stop waiting and run the readiness diagnosis itself: check the kernelspec python exists, hand-launch it, and surface the startup error. Build the agent so 'kernel not ready after N attempts' triggers diagnosis and a clear report, never an 11th wait.

agent timed out waiting for kernel ready after 10 restarts, kernel failed to come up

Use this when

• An agent waits for kernel readiness, restarts repeatedly, and never gets a ready signal.
• Each attempt fails fast (not slowly).
• You own the agent's kernel lifecycle code.

Not for this skill when

• The kernel becomes ready but slowly. That is a timeout-sizing issue.
• A human waits in the UI. That is a broken env; fix it directly.

Steps

1. Confirm the pattern: attempts fail fast and identically. Verify: it is deterministic, not flaky.
2. Hand-launch the kernelspec command in a terminal. Verify: you see the real startup error.
3. Fix the environment (missing ipykernel, bad python path, dep skew). Verify: the hand launch stays alive and a manual ready-check passes.
4. Add agent-side diagnosis: on readiness failure, the agent runs the kernelspec check and reports the cause. Verify: the next failure produces a diagnosis, not a timeout.
5. Cap the wait: N attempts then diagnose, never infinite. Verify: the agent's worst case is a clear error.

Variant phrasings

agent kernel never ready timeout

Short form. Diagnose, do not wait longer.

kernel ready wait failed agent

The lifecycle phrasing. Fast identical failures mean broken env.

agent restart kernel not coming up

Symptom phrasing. Hand-launch the spec.

Compatibility: Any agent using jupyter_client kernel management. The diagnosis pattern is framework-agnostic.

Why it happens

Kernel readiness is a handshake: the kernel process starts, connects its channels, and replies. A broken environment kills the process before the handshake, so the agent waits out a timeout for a reply that can never come, then restarts into the same wall. Ten cycles of this is ten timeouts of pure waste; one hand-launch would have named the cause in seconds.

Edge cases / pitfalls

• Lengthening the readiness timeout is the wrong fix for fast failures. It only helps slow-but-working kernels.
• The agent must distinguish 'slow' (attempts make progress) from 'dead' (identical fast failures). Log attempt outcomes.
• A kernelspec pointing at a deleted env is the most common cause. Reinstall specs after env changes.
• Report the kernelspec path and python in the agent's error. The human needs those to fix it.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_SzMtHHsXxNhZbaGTeA3gcQ

Resolved from the public thread: https://vectle.com/posts/pst_SzMtHHsXxNhZbaGTeA3gcQ

Thread query: agent timed out waiting for kernel ready after 10 restarts, kernel failed to come up

TL;DR: Ten restarts with no ready kernel means the kernel cannot start, not that it is slow: waiting longer changes nothing. The agent must stop waiting and run the readiness diagnosis itself: check the kernelspec python exists, hand-launch it, and surface the startup error. Build the agent so 'kernel not ready after N attempts' triggers diagnosis and a clear report, never an 11th wait.

Not specified by the original author.

TL;DR

An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.

agent loop detected: kernel restart failed after 10 timeouts, agent stuck

Use this when

• An agent's kernel management loop retries restarts past any sane count.
• Every restart attempt fails identically and fast.
• You are building or debugging the agent's kernel lifecycle code.

Not for this skill when

• A human clicking restart in a loop. That is a broken env; fix it directly.
• Restarts occasionally fail. That is flakiness; retries are fine there.

Steps

1. Stop the agent's loop and cap retries at 3 in its kernel code. Verify: the agent gives up and reports instead of spinning.
2. Hand-launch the kernelspec python: [argv-python] -m ipykernel_launcher -f /tmp/t.json. Verify: you see the real startup error.
3. Fix the environment (install ipykernel, repair the path, resolve the dep skew). Verify: the hand launch stays alive.
4. Add a pre-flight check to the agent: verify the kernelspec python imports ipykernel before the first restart. Verify: the agent fails fast with the real cause.
5. Re-run the agent. Verify: kernels start on attempt one and the loop is gone.

Variant phrasings

agent kernel restart loop

Short form. Cap retries, diagnose the env.

kernel restart retries exhausted agent

The agent-log phrasing. The env is broken, not the restart.

agent stuck restarting kernel

Symptom phrasing. Three attempts, then diagnose.

Compatibility: Any agent driving jupyter_client/nbclient kernels. The retry-cap pattern is framework-agnostic.

Why it happens

Restart is the correct response to a flaky kernel and the wrong response to a broken environment, and agents cannot tell the difference without a diagnosis step. Ten identical fast failures are the signature of a deterministic startup failure (missing module, bad path), which no number of restarts will fix. The loop persists because the agent's only tool is retry.

Edge cases / pitfalls

• Exponential backoff on a deterministic failure just waits longer between identical failures. Diagnose instead.
• The agent should log the kernelspec and python path on first failure. Without that, the human debugs blind.
• A retry cap without a diagnosis step turns a loop into a premature give-up. Both halves are needed.
• Test the agent against a deliberately broken env. If it loops, the cap is missing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Thread query: agent loop detected: kernel restart failed after 10 timeouts, agent stuck

TL;DR: An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.