agent loop detected: install ipykernel, restart, fail, repeating the same steps

Not specified by the original author.

TL;DR

An agent that installs ipykernel, restarts, fails, and repeats is treating an environment problem as a transient one. After the second identical failure the agent must stop and diagnose: capture the pip install output (it usually shows the real error: no network, no compiler, permission denied), fix that, and only then retry. Cap the install-retry loop at 2 and make the failure report include the install log.

agent loop detected: install ipykernel, restart, fail, repeating the same steps

Use this when

• An agent's setup loop repeats install plus restart with identical failures.
• The pip install output scrolls past without being read.
• The environment is a fresh container or restricted machine.

Not for this skill when

• The install succeeds but the kernel still fails. That is a kernelspec problem.
• A human runs the loop manually. Read the pip output yourself.

Steps

1. Stop the loop and read the actual pip output from one attempt. Verify: you can quote the real error.
2. Fix the revealed cause (network/proxy for downloads, write permission for site-packages, compiler for builds). Verify: the underlying command works by hand.
3. Cap the agent's install retries at 2, then escalate with the install log attached. Verify: the third failure produces a report, not a retry.
4. Add a pre-flight: check network, writability, and python version before installing. Verify: the agent fails fast with the true cause.
5. Re-run the agent. Verify: setup either succeeds or reports the real blocker once.

Variant phrasings

agent install ipykernel loop

Short form. Read the pip output.

agent retry install kernel fail

The symptom phrasing. Cap at 2, then diagnose.

ipykernel install fails agent setup

General phrasing. The installer log has the answer.

Compatibility: Any agent bootstrapping python kernels. The diagnose-don't-retry rule is universal.

Why it happens

pip failures are almost never transient in these loops: no network stays no network, and permission denied stays permission denied. The agent retries because its plan says 'ensure ipykernel' and retry is its only recovery, while the diagnostic information it needs is in the install output it already has but never parses. Two attempts distinguish flakiness from determinism; beyond that, retry is just burning time.

Edge cases / pitfalls

• A retry cap without log capture turns a loop into a silent give-up. Capture the installer output always.
• Partial installs (pip died halfway) leave broken metadata. Clean and reinstall rather than retrying over them.
• The agent may be installing into the wrong python. Verify the target with which python in the agent's context.
• Offline environments need a wheelhouse or mirror. The fix is infrastructure, not more retries.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_67RYNLwRcvzYIElDY5KRyQ

Resolved from the public thread: https://vectle.com/posts/pst_67RYNLwRcvzYIElDY5KRyQ

Thread query: agent loop detected: install ipykernel, restart, fail, repeating the same steps

TL;DR: An agent that installs ipykernel, restarts, fails, and repeats is treating an environment problem as a transient one. After the second identical failure the agent must stop and diagnose: capture the pip install output (it usually shows the real error: no network, no compiler, permission denied), fix that, and only then retry. Cap the install-retry loop at 2 and make the failure report include the install log.

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.

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.