* fix(query): bound per-turn latency growth in long REPL sessions (#1949)
Addresses the progressive latency regression where consecutive prompts in a
single session grow non-linearly (2nd prompt ~10s, 3rd 10+ min) due to
unbounded message accumulation with no proactive compaction and no per-prompt
turn cap on the main thread.
- Cap the interactive REPL main thread at 50 turns per prompt (DEFAULT_REPL_MAX_TURNS).
Headless/print mode and the SDK are unchanged (--max-turns flag / SDK callers
still control it), preserving the SDK API contract.
- Default maxMessagesCompactionThreshold to '200' so message-count compaction
runs well before the context window fills, instead of 'off'.
- Lower the auto-compact threshold buffer from 13k -> 30k so compaction fires
earlier with less accumulated history. The effective-context floor buffer is
kept at 13k and getAutoCompactThreshold() falls back to it for small-context
models, so the threshold can never go negative (no #635 regression).
Test updates: isolate the hard-cap override test from the new 200-message
default, and correct an outdated constant reference in the autoCompact test.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(query): repair REPL latency guard
* fix(query): cover resume and default guard paths
* fix(query): enforce cap across interactive paths
* docs(compaction): clarify disabled message limits
* fix(query): retain explicit message thresholds
* fix(query): enforce explicit threshold recovery
* fix(query): honor legacy active-message limit
* fix(doctor): report effective message compaction limit
* fix(config): share message threshold validation
* test(doctor): cover disabled message compaction
* fix(compact): preserve latency guard coverage
* test(repl): exercise turn cap defaults
* fix(compact): honor disabled default message guard
* fix(swarm): honor disabled auto compaction
---------
Co-authored-by: Claude <noreply@anthropic.com>
* feat(api): rotate OpenAI credential pools
* fix(api): align pooled credential discovery
* fix(cache-probe): preserve GitHub credential precedence
* fix(provider): honor pooled OpenAI fallbacks
* fix(provider): validate pooled profile credential labels
* fix(api): harden OpenAI credential pool handling
Reject placeholder values in pooled OpenAI credentials before requests, discovery, diagnostics, and profile generation can use them.
Normalize pooled credentials to a single usable key for model discovery, runtime cache partitions, cache probing, and NVIDIA NIM cache lookups.
Preserve documented profile precedence by letting live shell credentials override saved pools, carrying OpenCode fallback pools through launch, and redacting individual pool members in profile display.
Add regression coverage for pooled credential validation, profile launch/rebuild behavior, discovery/cache callers, diagnostics, provider autodetect, and shim failover semantics.
* fix(provider): cover pooled key recommendation path
Import the pooled OpenAI credential validator in provider-recommend and split invalid credentials from unset credentials in user guidance.
Add a script-level regression that runs the OpenAI recommendation path with OPENAI_API_KEYS so the ts-nocheck script cannot regress with runtime ReferenceErrors.
Scrub pooled OpenAI keys before xAI OAuth profile env construction and loosen the invalid-pool discovery test to assert auth header absence instead of exact header shape.
* fix(tests): stabilize rebased provider checks
* fix(provider): address pooled credential review findings
* test(api): cover opencode go credential failover
* fix(provider): share OpenAI credential usability checks
* fix(provider): respect pooled credential precedence
* fix(model): preserve pooled discovery credential precedence
* fix(model): fall back from unusable pooled discovery keys
JATMN
Bogdan
nehanandCopilot
Christian Schimetschka