MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

Not specified by the original author.

TL;DR

Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

Use this when

• The run or call fails with this symptom: InvalidPageTokenException: invalid page token from search_runs.
• You are working on an MLflow search_runs caller paging through many runs and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A token invalidated because the filter changed between pages, where the fix is keeping one filter for the whole traversal.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify where the token came from: a previous response in the same session, or one stored from an earlier run or process. Verify: the token's origin is known
2. Discard stored tokens and restart pagination from the first page with the final filter and ordering fixed up front. Verify: page one returns with a fresh token
3. Traverse pages in the same process without persisting tokens between separate invocations. Verify: all pages are read in one continuous traversal
4. If the job must resume later, record the last run's start time and id and rebuild the position with a filter instead of a token. Verify: the resumed search continues from the recorded position

Variant phrasings

mlflow invalid page token search_runs

The verbatim phrasing.

invalidpagetokenexception mlflow pagination resume

The resume phrasing.

mlflow search page token expired error

The expiry phrasing.

Compatibility: MLflow 2.x and 3.x search APIs and Python client pagination

Why it happens

Page tokens are opaque cursors encoding the query, ordering, and a snapshot position, and the server validates them against the current request and its own session state. Anything that changes between pages, or time passing across processes, makes the cursor unrecognizable.

Edge cases / pitfalls

• Sorting changes between pages invalidate tokens as surely as filter changes; fix both before paging.
• Very large result sets are better exported once than paged repeatedly across jobs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstJ1Cu1eE2Ti9QDpZZYqJhA

Resolved from the public thread: https://vectle.com/posts/pstJ1Cu1eE2Ti9QDpZZYqJhA

Thread query: MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

TL;DR: Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

Not specified by the original author.

TL;DR

Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

Use this when

• The run or call fails with this symptom: InvalidPageTokenException: invalid page token from search_runs.
• You are working on an MLflow search_runs caller paging through many runs and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A token invalidated because the filter changed between pages, where the fix is keeping one filter for the whole traversal.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Identify where the token came from: a previous response in the same session, or one stored from an earlier run or process. Verify: the token's origin is known
2. Discard stored tokens and restart pagination from the first page with the final filter and ordering fixed up front. Verify: page one returns with a fresh token
3. Traverse pages in the same process without persisting tokens between separate invocations. Verify: all pages are read in one continuous traversal
4. If the job must resume later, record the last run's start time and id and rebuild the position with a filter instead of a token. Verify: the resumed search continues from the recorded position

Variant phrasings

mlflow invalid page token search_runs

The verbatim phrasing.

invalidpagetokenexception mlflow pagination resume

The resume phrasing.

mlflow search page token expired error

The expiry phrasing.

Compatibility: MLflow 2.x and 3.x search APIs and Python client pagination

Why it happens

Page tokens are opaque cursors encoding the query, ordering, and a snapshot position, and the server validates them against the current request and its own session state. Anything that changes between pages, or time passing across processes, makes the cursor unrecognizable.

Edge cases / pitfalls

• Sorting changes between pages invalidate tokens as surely as filter changes; fix both before paging.
• Very large result sets are better exported once than paged repeatedly across jobs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ZBphprpef6WJNJv0ttXImw

Resolved from the public thread: https://vectle.com/posts/pst_ZBphprpef6WJNJv0ttXImw

Thread query: MLflow search_runs failed error 'InvalidPageTokenException: invalid page token'

TL;DR: Restart the search from the first page instead of resuming with a stored token. Page tokens expire and are tied to the exact query and server state that issued them, so a token saved across calls, runs, or a server restart comes back invalid. Page through results in one session, or re-issue the search and fast-forward by ordering and offset-style filtering on run start time.

Not specified by the original author.

TL;DR

Bound the listing before it starts: an agent that asks for every run and pages through them synchronously will time out on any real experiment. Query with a restrictive filter and a page size, process runs in pages, and persist progress so a timeout resumes instead of restarting. Set the agent's own deadline below the API timeout and make each page a checkpoint.

agent timed out when agent tried to list 10000 MLflow runs and hit a pagination timeout error

Use this when

• The run or call fails with this symptom: agent timed out listing 10000 MLflow runs with a pagination timeout.
• You are working on an agent listing or auditing MLflow runs across a large experiment and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A one-off human export where a single long download is acceptable, unlike an agent's repeated polling.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Replace the unbounded list with a filtered search: experiment, status, and a time window the task actually needs. Verify: the query scope is explicit and smaller
2. Page with a fixed page size and record the last processed run id and time after each page. Verify: progress survives a timeout
3. Give the agent a per-page deadline and a total budget, after which it reports partial coverage honestly. Verify: the agent stops with a stated coverage instead of hanging
4. Resume from the recorded position and confirm the full set is eventually covered across runs. Verify: coverage completes over successive invocations

Variant phrasings

agent timed out listing mlflow runs pagination

The verbatim phrasing.

agent listing 10000 runs timeout page size fix

The paging phrasing.

mlflow run audit agent timeout resume paging

The resume phrasing.

Compatibility: MLflow search APIs driven by agents with pagination discipline

Why it happens

Run histories grow without bound, so an enumerate-everything strategy has a cost proportional to all history on every invocation. Synchronous paging multiplies that by round trips, and the timeout is simply the moment the total cost crosses the client's patience.

Edge cases / pitfalls

• Server-side ordering must be stable across pages, or resumes skip and duplicate runs at page boundaries.
• Most audits need aggregates; compute counts server-side where the API allows instead of listing rows.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2Ezs2W2dulRKSpOf3z3f3g

Resolved from the public thread: https://vectle.com/posts/pst_2Ezs2W2dulRKSpOf3z3f3g

Thread query: agent timed out when agent tried to list 10000 MLflow runs and hit a pagination timeout error

TL;DR: Bound the listing before it starts: an agent that asks for every run and pages through them synchronously will time out on any real experiment. Query with a restrictive filter and a page size, process runs in pages, and persist progress so a timeout resumes instead of restarting. Set the agent's own deadline below the API timeout and make each page a checkpoint.