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feat(core)!: redesign auto-compaction thresholds with three-tier ladder (#4345)
* feat(core)!: redesign auto-compaction thresholds with three-tier ladder

Replaces the single 70% proportional threshold with a three-tier ladder
(warn/auto/hard) that combines proportional fallback with absolute
reservation. Large-window models (>=128K) now reserve ~33K instead of
30% of the window, freeing tens of thousands of context tokens that the
old formula wasted.

Other improvements bundled in the same redesign:

- Compression sideQuery now disables thinking and caps maxOutputTokens
  at 20K, matching claude-code so the buffer math is predictable across
  providers (Anthropic/OpenAI/Gemini handle thinking budgets
  inconsistently)
- Failure handling upgraded from one-shot permanent lock to a 3-strike
  circuit breaker; reactive overflow still latches immediately
- New estimatePromptTokens helper closes the lag-by-one-turn and
  first-send-is-0 gaps in lastPromptTokenCount
- Hard-tier rescue pulls reactive overflow recovery forward to before
  the API call, saving an oversized round-trip
- /context command displays the three-tier ladder + current tier
- tipRegistry's context-* tips track the new thresholds instead of
  fixed 50/80/95 percentages

BREAKING CHANGE: chatCompression.contextPercentageThreshold setting is
removed. Settings files containing the field log a one-line deprecation
warning at startup and the value is ignored; behaviour is now controlled
by built-in thresholds via the new computeThresholds() function.

Design: docs/design/auto-compaction-threshold-redesign.md
Plan: docs/plans/2026-05-14-auto-compaction-threshold-redesign.md

* test(core): fix leftover hasFailedCompressionAttempt option in compress test

A pre-existing test case at chatCompressionService.test.ts:678 still
passed `hasFailedCompressionAttempt: false` in the CompressOptions
shape; rebasing onto current main surfaced this as a typecheck error
because the field was renamed to `consecutiveFailures` (Task 7 of the
three-tier ladder migration). Update to `consecutiveFailures: 0` —
semantically equivalent, the test asserts the side-query is called
when `force: true`, no other behaviour change.

* fix(core): drop compaction summary when output hits maxOutputTokens cap

Adds a defensive guard in ChatCompressionService.compress() that detects
when the side-query summary hit COMPACT_MAX_OUTPUT_TOKENS (20K). In that
case the summary is likely truncated mid-content, so we drop it and
return NOOP rather than persist a half-summary. The next send re-tries;
reactive overflow still catches the catastrophic case where the API
rejects the next request as too large.

Documented in the design doc as risk #2; the bot reviewer on PR #4168
correctly pushed for it to land alongside the threshold redesign rather
than as a follow-up since the new 20K cap is what makes truncation
likely in the first place.

* fix(cli): render three-tier thresholds in /context TUI view

The Task 11 redesign updated the non-interactive text formatter
(formatContextUsageText) but left ContextUsage.tsx — the interactive
React component that real /context users see — unchanged. As a result
the TUI still showed the old single "Autocompact buffer" line and none
of the new warn/auto/hard ladder.

Adds a "Compaction thresholds" section after the per-category breakdown:
  - Effective window
  - Warn / Auto / Hard threshold rows with a ▶ marker on the row the
    current usage has crossed
  - Current tier label coloured by severity (safe→green, warn/auto→
    yellow, hard→red)

The existing progress bar legend (Used / Free / Autocompact buffer)
is preserved because it's tied to the three-segment progress bar
visualisation; the new section adds the absolute numbers + tier badge
on top of that.

Caught by the tmux e2e test (PR #4168 ci-monitor follow-up). Pre-fix
the assertion 'Compaction thresholds' missed completely from the TUI;
post-fix the new section renders correctly for fresh and live sessions
on 1M / 200K / 128K windows.

* fix(core,cli): address PR #4168 review batch 4

Behavior fixes:
- MAX_TOKENS truncation guard now returns COMPRESSION_FAILED_EMPTY_SUMMARY
  instead of NOOP so the consecutive-failure breaker actually trips after
  repeated max-length summaries (R1.1).
- Reactive overflow failure increments consecutiveFailures by 1 instead
  of latching to MAX in one shot, so a transient network blip doesn't
  permanently disable auto-compaction. The hard-tier rescue resets the
  counter, which remains the designated recovery path (R1.2).
- /context current-tier classification uses rawOverhead (system + tools +
  memory + skills) as the tier input when API data is not yet available,
  rather than 0 — large inherited contexts no longer silently show 'safe'
  (R2.2).

Performance:
- sendMessageStream computes effectiveTokens ONCE and passes it through
  TryCompressOptions.precomputedEffectiveTokens, so the cheap-gate inside
  service.compress doesn't redo the estimation. Also fixes the
  imageTokenEstimate inconsistency between the rescue and cheap-gate
  paths (R1.3 + R1.4).
- Steady-state path (lastPromptTokenCount > 0) skips the costly
  getHistory(true) clone — estimatePromptTokens only needs the user
  message in that branch.

Code hygiene:
- BYTES_PER_TOKEN → CHARS_PER_TOKEN (inputs are char counts, not byte
  counts; CJK text would mislead under the old name) (R3.1).
- Drop dead getContextUsagePercent helper + index re-export — no callers
  in source after the threshold rewire (R1.5).
- Add a comment on estimatePromptTokens' first-send fallback documenting
  the ~15-20K under-estimate (system prompt + tools + skills) and that
  reactive overflow is the safety net (R3.3).

Tests:
- New CLI ContextUsage.test.tsx exercises the React renderer for the
  three-tier section: section presence, ▶ marker placement per tier,
  current-tier label coloring (R1.6).
- New chatCompressionService.test.ts case pins that a stale
  contextPercentageThreshold: 0 value in user settings no longer
  short-circuits compaction (R2.1).
- New tokenEstimation.test.ts case covers functionResponse (distinct
  nested-parts branch from functionCall) (R3.5).
- New geminiChat.test.ts integration test exercises the real
  ChatCompressionService — not a mock — for the first-send-after-
  inherited-history scenario where lastPromptTokenCount=0 and only the
  full-history estimate can cross the auto threshold (R3.4).

Declined: R3.2 (change `>=` to `>` on the MAX_TOKENS guard). The current
operator catches the at-cap case as suspicious, which is intentional —
landing exactly at the output cap is far more likely truncation than
clean stop given p99.99 ≈ 17K. With R1.1 in place, persistent truncations
trip the breaker after MAX_CONSECUTIVE_FAILURES so the worst case is
bounded.

* fix(core,cli): address PR #4168 review batch 5

- R5.1: tighten /context tier comment + TODO. The rawOverhead-based fix
  doesn't cover `--continue` restores with many history messages (since
  rawOverhead excludes messagesTokens). UI may still show 'safe' for one
  render until the first send. Documented inline and added a TODO to plumb
  chat history into collectContextData for same-source-of-truth as the
  cheap-gate.
- R5.2a: add TODO(finish_reason) at the truncation guard. The `>= cap`
  heuristic false-positives on legitimate at-cap summaries; the proper
  signal is finish_reason which runSideQuery doesn't surface today.
- R5.2b: split telemetry — new CompressionStatus.COMPRESSION_FAILED_OUTPUT_TRUNCATED
  enum value. Distinct from EMPTY_SUMMARY so logs/telemetry can tell
  prompt-quality failures (tune prompt / splitter) from capacity failures
  (raise cap / shrink splitter input). isCompressionFailureStatus()
  treats both as failures so the breaker behavior is unchanged.
- R5.3: expand consecutiveFailures JSDoc to clarify it tracks
  "non-force, non-hard-rescue consecutive failures" — hard-rescue resets
  the counter and force=true skips increments, so the counter is the
  "regular path" health signal only; reactive overflow is the real
  safety net for the force-only paths.
- R5.4: document the CompressOptions field rename
  (hasFailedCompressionAttempt: boolean → consecutiveFailures: number)
  as an SDK breaking change in the design doc with migration guide.

* fix(core): disambiguate hard-rescue from manual /compress orphan-strip

Self-review (dual reviewer / pr-triage round 1) caught a correctness
regression in the hard-rescue path:

`sendMessageStream` calls `tryCompress(force=true)` from inside the
pre-push window when `effectiveTokens >= hard`. The service's
orphan-strip predicate at `chatCompressionService.ts:426-429` gated on
`force` alone, which conflated two distinct call shapes:

  - manual `/compress` (force=true, trigger='manual'): user-initiated
    between turns; trailing model funcCall IS orphaned because no
    funcResponse is coming
  - hard-rescue (force=true, trigger='auto'): automatic mid-turn;
    trailing model funcCall is ACTIVE because its matching funcResponse
    is sitting in the pending `userContent` waiting to be pushed

The strip fired for both, so a hard-rescue triggered mid tool-use loop
would drop the active funcCall. After compression returned and
`userContent` (the funcResponse) was pushed, the next API request
carried tool_result with no matching tool_use → provider validation
error.

The in-code comment at L422-424 already documented this exact
constraint for the auto-compress case (`force=false`), but reusing
`force=true` for hard-rescue silently violated the same constraint.

Fix:
- Gate `hasOrphanedFuncCall` on `compactTrigger === 'manual'` instead
  of `force`. The trigger field already disambiguates intent.
- `sendMessageStream` hard-rescue now passes `trigger: 'auto'`
  explicitly (without it, `force=true` defaults to `trigger='manual'`
  via the `?? (force ? 'manual' : 'auto')` resolver).

Sibling audit for "force=true non-manual callsites":
- `GeminiClient.tryCompressChat` (manual /compress): correct — manual
- `sendMessageStream` hard-rescue: fixed in this commit
- `sendMessageStream` reactive overflow catch: already passes
  trigger='auto'; runs AFTER API call (userContent in history), so if
  it observes a trailing funcCall it IS orphaned but findCompressSplitPoint
  handles the case without needing the strip

RED-first regression test added:
`preserves trailing model+funcCall under hard-rescue (force=true + trigger=auto)`
in `chatCompressionService.test.ts`. Failed against pre-fix code (the
strip dropped the funcCall); passes against the fix.

Adjacent fixes from the same triage round:

- `docs/users/configuration/settings.md`: the
  `chatCompression.contextPercentageThreshold` row still said "use 0
  to disable compression entirely" — code has ignored the value since
  the removal commit. Marked the row REMOVED with migration guidance
  pointing at the design doc.
- `packages/core/src/config/config.ts`: the deprecation warning now
  tells users how to silence it (remove the key) and where to read
  current behavior, instead of just announcing the removal.
- `docs/design/auto-compaction-threshold-redesign.md`: closed Open
  Question 2 (small-window hard/auto collapse) — decision is to NOT
  annotate `/context`, with rationale on file.

Tests: 2395 core tests passing, typecheck clean.

* docs(core): fix tier-collapse direction in auto-compaction design doc

Self-review on the 50bac974b commit caught a direction error in the
M2a Open Question 2 closure note: said `currentTier` skips `'hard'`
and goes to `'auto'` on collapsed windows, which is backwards.

`contextCommand.ts:43-44` checks `tokens >= thresholds.hard` first
(no `hard > auto` guard — that fix lives in a separate follow-up), so
when `hard === auto` the `'hard'` branch matches first and the
`'auto'` band is the empty one. Updated the rationale to describe the
actual collapse direction and cite the source-of-truth file:line.

Conclusion of the open question (don't annotate `/context`) is
unchanged — only the explanation is corrected.

* refactor(core): extract shared in-flight funcCall fixture in compression tests

The auto-compress and hard-rescue tests for "trailing funcCall is
active, not orphaned" shared a byte-identical 4-message history and
mock setup. Pull both into setupInFlightFuncCallFixture() inside the
describe block so each test only contains the scenario name, the
compress() call shape, and its own assertions.

Net -29 LOC, no behavior change.

* fix(core,cli): address PR #4345 round-2 review feedback

- geminiChat: remove pre-call consecutiveFailures reset in hard-rescue.
  force=true already bypasses the breaker check in chatCompressionService;
  the pre-reset was redundant on success (post-call L614 already handles it)
  and *broke* the breaker on failure paths — hard-rescue failures don't
  increment via tryCompress (force=true skips that branch), only the
  reactive overflow path at L992 explicitly increments. With the pre-reset
  the counter oscillated 0↔1 every send and MAX_CONSECUTIVE_FAILURES=3 was
  unreachable. Wrote a RED test asserting the forwarded counter is the
  latched value, not zero; the test failed against the old code and passes
  with the reset removed.

- geminiChat: log hard-tier-rescue triggers via debugLogger.warn including
  effectiveTokens, hard, and the current consecutiveFailures so operators
  debugging "compaction stopped working" have a breadcrumb.

- chatCompressionService: clamp effectiveWindow to >= 0 in computeThresholds
  so the value surfaced in /context stays meaningful for tiny windows
  (window < SUMMARY_RESERVE). auto/warn/hard outputs are unaffected because
  each is Math.max(proportional, absolute) and the proportional branch
  dominates whenever the absolute branch goes negative.

- turn.ts: rewrite COMPRESSION_FAILED_OUTPUT_TRUNCATED docstring. Drop the
  misleading "compression succeeded" framing (the summary is dropped and
  isCompressionFailureStatus returns true) and reference the full enum name
  COMPRESSION_FAILED_EMPTY_SUMMARY instead of the abbreviation.

- contextCommand.test.ts: reword the no-API-data-session test comment.
  collectContextData classifies estimated sessions against rawOverhead;
  with default fixtures rawOverhead lands in `safe`, but heavy
  system-prompt / skill / MCP loads can push it into warn/auto/hard.

- design doc Background: prepend a blockquote clarifying the section
  describes pre-redesign behavior and that the inline file:line references
  point at code before PR #4345 (which removes them).

- ui/types: replace the duplicated ContextThresholds interface with a
  type alias to the core's CompactionThresholds. Field-by-field copy in
  contextCommand.ts becomes a direct spread. ContextUsage.tsx keeps its
  CompactionThresholds React component name — the alias avoids the
  collision a direct import would have caused.

- contextCommand: interpolate the actual reserve value into the
  "(window − 20K reserve)" annotation so SUMMARY_RESERVE retuning doesn't
  leave the text stale.

* fix(core): address PR #4345 round-3 + round-4 review feedback

R3-1: rewrite the stale "Hard-tier rescue resets the counter" comment in
the reactive-overflow path. The R2 commit removed the pre-call reset
from hard-rescue; the only counter-reset path is now the post-call
COMPRESSED branch in tryCompress. Two contradicting comments in the
same file would mislead a future maintainer tracing the lifecycle.

R3-2: rewrite the JSDoc on CompactionThresholds.hard. The "(resets
failure counter)" phrasing was true under the pre-R2 design; after R2
the hard threshold force-triggers compaction and bypasses the breaker,
but does not reset the counter (which only happens on COMPRESSED
success via the post-call branch). The type is consumed by both
geminiChat and the CLI UI (via ContextThresholds alias), so the
authoritative description had to match the actual contract.

R3-3: add a Step 3 to the hard-rescue regression test. The test title
claims "success recovers via the post-call branch" but the original
Steps 1-2 only verified the latched counter was forwarded INTO the
call. Step 3 follows up with a below-hard send and asserts the
forwarded counter is 0 — proving geminiChat.ts:614 ran on the
COMPRESSED result.

R3-4: assert effectiveWindow === 0 on the existing extreme-small-window
test and add a separate zero-window edge case. The Math.max(0, ...)
clamp from R2 was previously unasserted; a regression that removed
the clamp would go undetected.

R4-1: forward originalTokenCount on the breaker-NOOP path in
chatCompressionService.compress() to match the adjacent
threshold-NOOP path (L368-369). Returning {originalTokenCount: 0,
newTokenCount: 0} masked "breaker tripped at N tokens" as
"empty session" in telemetry dashboards.

R4-2a: add debugLogger.warn at the two consecutiveFailures increment
sites (cheap-gate path L586 and reactive-overflow path L955) when
the counter reaches MAX_CONSECUTIVE_FAILURES. The breaker is one of
the PR's headline safety features but, prior to this round, had zero
observability when it tripped. Required importing MAX_CONSECUTIVE_FAILURES
into geminiChat.ts.

R4-3: programmatically link tokenEstimation.ts's CHARS_PER_TOKEN to
compactionInputSlimming.ts's TOKEN_TO_CHAR_RATIO. Both are 4 today
and represent the same generic char/token conversion. Exporting from
compactionInputSlimming and aliasing in tokenEstimation eliminates
the silent-drift hazard the JSDoc already warned about.

Declined (round-weighted bar at round 4):
- R3-5: debugLogger test for hard-rescue trigger — observability test
  coverage is overthinking at round 3+; the log is informational.
- R4-2b: expose breaker state in /context — new feature; out of scope.
- R4-4: render test for auto-tier marker — test coverage gap on
  working code, defer to follow-up PR per round-weighted bar.
- R4-5a: extract makeFakeChat/makeFakeConfig shared factory — pure
  test refactor at round 4, not a fix.
- R4-5b: direct unit test for precomputedEffectiveTokens — exercised
  indirectly via hard-rescue path tests in geminiChat.test.ts.
- R4-6: truncation-guard fallback test for missing candidatesTokenCount
  — code already has a TODO acknowledging the heuristic is imperfect
  (chatCompressionService.ts:549-553); defer.

* fix(core): address PR #4345 round-5 review feedback

R5-1: assert breaker-NOOP forwards originalTokenCount. R4-1 changed the
breaker-NOOP return from `{0, 0}` to `{originalTokenCount, originalTokenCount}`
so telemetry can distinguish "breaker tripped at N tokens" from
"empty session", but the existing test only checked compressionStatus
and newHistory. Now seeds a non-zero originalTokenCount (120K) and
asserts both fields forward it.

R5-2: forward originalTokenCount on the empty-history NOOP. This was
sibling drift on R4-1 — I fixed the cited breaker-NOOP site but missed
the empty-history NOOP. Of 5 NOOP return sites in chatCompressionService,
4 now forward originalTokenCount (breaker, threshold-gate, post-split,
min-compression-fraction) and 1 (this one) was still returning `{0, 0}`,
breaking the project-wide invariant. Now consistent.

R5-3: replace 10 stale line-number references with semantic anchors.
After the R3+R4 push, the line refs in my R2/R3 comments (`geminiChat.ts:614`,
`chatCompressionService.ts:339`, `line 992`, `L627`, `line 944`) no longer
pointed at their original targets — `geminiChat.ts:614` now points at
`setSystemInstruction`'s body, completely unrelated to compaction. The
pattern itself is fragile; semantic phrasing ("the post-call reset in
tryCompress's COMPRESSED handler") doesn't drift when lines shift.

347/347 affected core tests passing locally; typecheck clean.

* fix(core): address PR #4345 round-6 review feedback (R6 sweep)

R6-1: rewrite the stale JSDoc bullet on `consecutiveFailures` (the
"Hard-tier rescue failures" bullet). The old wording said "the counter
is reset to 0 BEFORE the rescue call" — that contradicted R5 which
explicitly removed the pre-call reset. Now the bullet matches the
actual behavior: counter is NOT pre-reset, force=true bypasses the
breaker, post-call COMPRESSED handler resets on success, reactive
overflow is the explicit-increment safety net.

My R5 stale-comment sweep only grep'd inline `//` comments; this JSDoc
on the field declaration slipped through. Re-audited "reset to 0
BEFORE" / "pre-reset" across both packages — single site remaining.

R6-7: assert `passedOpts.trigger === 'auto'` in the hard-rescue test.
This field is the orphan-strip safety wire added by the C1 fix (the
service's `compactTrigger === 'manual'` check would otherwise strip
the trailing active funcCall mid tool-loop). The test asserted force
and pendingUserMessage but not the trigger; a refactor dropping the
'auto' from `trigger: shouldForceFromHard ? 'auto' : undefined` would
silently break orphan-strip safety. Now regression-guarded with a
single-line expect.

164/164 affected core tests passing locally.

Declined per round-weighted bar (round 6 defaults Suggestion / Test
coverage / Style to overthinking):
- R6-2/3/6: test-coverage gaps on working code — defer to follow-up
- R6-4: redundant truthy guard on always-set fields — style nit
- R6-5: text-vs-UI inconsistency on /context — existing test enforces
  current behavior; treat as design decision (offer follow-up if
  reviewer escalates)
- R6-8 (tipRegistry small-window context-high): explicitly closed in
  design doc's Open Question 2 — small windows have empty context-high
  band by design; UI work is out-of-scope for this PR
- R6-9: wasted clone on rare fallback path — Suggestion-level perf
- R6-10 (CompressionMessage missing case): file not in this PR's diff;
  reviewer themselves proposed it as follow-up
2026-05-25 21:11:08 +08:00
.github ci: split Aliyun OSS sync into a separate post-release workflow (#4492) 2026-05-25 19:34:21 +08:00
.husky Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
.qwen docs(agents,pr-template): add Working Principles and restructure PR template (#4496) 2026-05-25 19:15:35 +08:00
.vscode Merge branch 'main' into feat/sandbox-config-improvements 2026-03-06 14:38:39 +08:00
docs feat(core)!: redesign auto-compaction thresholds with three-tier ladder (#4345) 2026-05-25 21:11:08 +08:00
docs-site feat: update docs 2025-12-15 09:47:03 +08:00
eslint-rules pre-release commit 2025-07-22 23:26:01 +08:00
integration-tests feat(core): add NotebookEdit tool for Jupyter notebooks 2026-05-21 00:06:15 +08:00
packages feat(core)!: redesign auto-compaction thresholds with three-tier ladder (#4345) 2026-05-25 21:11:08 +08:00
scripts ci: split Aliyun OSS sync into a separate post-release workflow (#4492) 2026-05-25 19:34:21 +08:00
.dockerignore fix(cli): skip stdin read for ACP mode 2026-03-27 11:47:01 +00:00
.editorconfig pre-release commit 2025-07-22 23:26:01 +08:00
.gitattributes feat(installer): add standalone hosted install and uninstall flow (#3828) 2026-05-21 11:57:10 +08:00
.gitignore feat(cli): do not append trailing space for directory completions (#4092) (#4288) 2026-05-23 23:37:23 +08:00
.npmrc chore: remove google registry 2025-08-08 20:45:54 +08:00
.nvmrc chore(deps): upgrade ink 6.2.3 → 7.0.2 + bump Node engine to 22 (#3860) 2026-05-11 17:29:50 +08:00
.prettierignore Merge branch 'main' into feat/add-vscode-settings-json-schema 2026-03-03 11:21:57 +08:00
.prettierrc.json pre-release commit 2025-07-22 23:26:01 +08:00
.yamllint.yml Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
AGENTS.md docs(agents,pr-template): add Working Principles and restructure PR template (#4496) 2026-05-25 19:15:35 +08:00
CONTRIBUTING.md chore(deps): upgrade ink 6.2.3 → 7.0.2 + bump Node engine to 22 (#3860) 2026-05-11 17:29:50 +08:00
Dockerfile chore(deps): upgrade ink 6.2.3 → 7.0.2 + bump Node engine to 22 (#3860) 2026-05-11 17:29:50 +08:00
esbuild.config.js fix(build): tree-shake React reconciler dev build to prevent PerformanceMeasure leak (#4462) 2026-05-23 21:00:32 +08:00
eslint.config.js feat(cli,sdk): qwen serve daemon (Stage 1) (#3889) 2026-05-13 14:47:47 +08:00
LICENSE Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
Makefile feat: update docs 2025-12-22 21:11:33 +08:00
package-lock.json chore(release): v0.16.1 [skip ci] 2026-05-23 23:09:48 +08:00
package.json chore(release): v0.16.1 [skip ci] 2026-05-23 23:09:48 +08:00
README.md feat(cli,sdk): qwen serve daemon (Stage 1) (#3889) 2026-05-13 14:47:47 +08:00
SECURITY.md fix: update security vulnerability reporting channel 2026-02-24 14:22:47 +08:00
tsconfig.json # 🚀 Sync Gemini CLI v0.2.1 - Major Feature Update (#483) 2025-09-01 14:48:55 +08:00
vitest.config.ts test(channels): add comprehensive test suites for channel adapters 2026-03-27 15:26:39 +00:00

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Copy the prompt below and paste it into your agent:

Use Qwen Code as a coding agent. First, fetch and read this skill reference:
https://raw.githubusercontent.com/QwenLM/qwen-code/refs/heads/main/.qwen/skills/qwen-code-claw/SKILL.md
Then follow the instructions inside to install, authenticate, and use Qwen Code via acpx for all coding tasks.

Authentication

Qwen Code supports the following authentication methods:

  • API Key (recommended): use an API key from Alibaba Cloud Model Studio (Beijing / intl) or any supported provider (OpenAI, Anthropic, Google GenAI, and other compatible endpoints).
  • Coding Plan: subscribe to the Alibaba Cloud Coding Plan (Beijing / intl) for a fixed monthly fee with higher quotas.

⚠️ Qwen OAuth was discontinued on April 15, 2026. If you were previously using Qwen OAuth, please switch to one of the methods above. Run qwen and then /auth to reconfigure.

Use an API key to connect to Alibaba Cloud Model Studio or any supported provider. Supports multiple protocols:

  • OpenAI-compatible: Alibaba Cloud ModelStudio, ModelScope, OpenAI, OpenRouter, and other OpenAI-compatible providers
  • Anthropic: Claude models
  • Google GenAI: Gemini models

The recommended way to configure models and providers is by editing ~/.qwen/settings.json (create it if it doesn't exist). This file lets you define all available models, API keys, and default settings in one place.

Quick Setup in 3 Steps

Step 1: Create or edit ~/.qwen/settings.json

Here is a complete example:

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "description": "Qwen3-Coder via Dashscope",
        "envKey": "DASHSCOPE_API_KEY"
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Step 2: Understand each field

Field What it does
modelProviders Declares which models are available and how to connect to them. Keys like openai, anthropic, gemini represent the API protocol.
modelProviders[].id The model ID sent to the API (e.g. qwen3.6-plus, gpt-4o).
modelProviders[].envKey The name of the environment variable that holds your API key.
modelProviders[].baseUrl The API endpoint URL (required for non-default endpoints).
env A fallback place to store API keys (lowest priority; prefer .env files or export for sensitive keys).
security.auth.selectedType The protocol to use on startup (openai, anthropic, gemini, vertex-ai).
model.name The default model to use when Qwen Code starts.

Step 3: Start Qwen Code — your configuration takes effect automatically:

qwen

Use the /model command at any time to switch between all configured models.

More Examples
Coding Plan (Alibaba Cloud ModelStudio) — fixed monthly fee, higher quotas
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.6-plus from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY"
      },
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.5-plus with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "glm-4.7",
        "name": "glm-4.7 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "glm-4.7 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "kimi-k2.5",
        "name": "kimi-k2.5 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "kimi-k2.5 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "BAILIAN_CODING_PLAN_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Subscribe to the Coding Plan and get your API key at Alibaba Cloud ModelStudio(Beijing) or Alibaba Cloud ModelStudio(intl).

Multiple providers (OpenAI + Anthropic + Gemini)
{
  "modelProviders": {
    "openai": [
      {
        "id": "gpt-4o",
        "name": "GPT-4o",
        "envKey": "OPENAI_API_KEY",
        "baseUrl": "https://api.openai.com/v1"
      }
    ],
    "anthropic": [
      {
        "id": "claude-sonnet-4-20250514",
        "name": "Claude Sonnet 4",
        "envKey": "ANTHROPIC_API_KEY"
      }
    ],
    "gemini": [
      {
        "id": "gemini-2.5-pro",
        "name": "Gemini 2.5 Pro",
        "envKey": "GEMINI_API_KEY"
      }
    ]
  },
  "env": {
    "OPENAI_API_KEY": "sk-xxxxxxxxxxxxx",
    "ANTHROPIC_API_KEY": "sk-ant-xxxxxxxxxxxxx",
    "GEMINI_API_KEY": "AIzaxxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "gpt-4o"
  }
}
Enable thinking mode (for supported models like qwen3.5-plus)
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (thinking)",
        "envKey": "DASHSCOPE_API_KEY",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.5-plus"
  }
}

Tip: You can also set API keys via export in your shell or .env files, which take higher priority than settings.jsonenv. See the authentication guide for full details.

Security note: Never commit API keys to version control. The ~/.qwen/settings.json file is in your home directory and should stay private.

Local Model Setup (Ollama / vLLM)

You can also run models locally — no API key or cloud account needed. This is not an authentication method; instead, configure your local model endpoint in ~/.qwen/settings.json using the modelProviders field.

Set generationConfig.contextWindowSize inside the matching provider entry and adjust it to the context length configured on your local server.

Ollama setup
  1. Install Ollama from ollama.com
  2. Pull a model: ollama pull qwen3:32b
  3. Configure ~/.qwen/settings.json:
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3:32b",
        "name": "Qwen3 32B (Ollama)",
        "baseUrl": "http://localhost:11434/v1",
        "description": "Qwen3 32B running locally via Ollama",
        "generationConfig": {
          "contextWindowSize": 131072
        }
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3:32b"
  }
}
vLLM setup
  1. Install vLLM: pip install vllm
  2. Start the server: vllm serve Qwen/Qwen3-32B
  3. Configure ~/.qwen/settings.json:
{
  "modelProviders": {
    "openai": [
      {
        "id": "Qwen/Qwen3-32B",
        "name": "Qwen3 32B (vLLM)",
        "baseUrl": "http://localhost:8000/v1",
        "description": "Qwen3 32B running locally via vLLM",
        "generationConfig": {
          "contextWindowSize": 131072
        }
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "Qwen/Qwen3-32B"
  }
}

Usage

As an open-source terminal agent, you can use Qwen Code in five primary ways:

  1. Interactive mode (terminal UI)
  2. Headless mode (scripts, CI)
  3. IDE integration (VS Code, Zed)
  4. SDKs (TypeScript, Python, Java)
  5. Daemon mode — qwen serve exposes ACP over HTTP+SSE so multiple clients share one agent (experimental)

Interactive mode

cd your-project/
qwen

Run qwen in your project folder to launch the interactive terminal UI. Use @ to reference local files (for example @src/main.ts).

Headless mode

cd your-project/
qwen -p "your question"

Use -p to run Qwen Code without the interactive UI—ideal for scripts, automation, and CI/CD. Learn more: Headless mode.

IDE integration

Use Qwen Code inside your editor (VS Code, Zed, and JetBrains IDEs):

Daemon mode (qwen serve, experimental)

cd your-project/
qwen serve
# → qwen serve listening on http://127.0.0.1:4170 (mode=http-bridge)

Run Qwen Code as a local HTTP daemon so IDE plugins, web UIs, CI scripts and custom CLIs all share one agent session over HTTP+SSE — instead of each spawning their own subprocess. Loopback bind has no auth by default (set QWEN_SERVER_TOKEN to enable bearer auth even on loopback); remote binds (--hostname 0.0.0.0) require a token — boot refuses without one. See:

SDKs

Build on top of Qwen Code with the available SDKs:

Python SDK example:

import asyncio

from qwen_code_sdk import is_sdk_result_message, query


async def main() -> None:
    result = query(
        "Summarize the repository layout.",
        {
            "cwd": "/path/to/project",
            "path_to_qwen_executable": "qwen",
        },
    )

    async for message in result:
        if is_sdk_result_message(message):
            print(message["result"])


asyncio.run(main())

Commands & Shortcuts

Session Commands

  • /help - Display available commands
  • /clear - Clear conversation history
  • /compress - Compress history to save tokens
  • /stats - Show current session information
  • /bug - Submit a bug report
  • /exit or /quit - Exit Qwen Code

Keyboard Shortcuts

  • Ctrl+C - Cancel current operation
  • Ctrl+D - Exit (on empty line)
  • Up/Down - Navigate command history

Learn more about Commands

Tip: In YOLO mode (--yolo), vision switching happens automatically without prompts when images are detected. Learn more about Approval Mode

Configuration

Qwen Code can be configured via settings.json, environment variables, and CLI flags.

File Scope Description
~/.qwen/settings.json User (global) Applies to all your Qwen Code sessions. Recommended for modelProviders and env.
.qwen/settings.json Project Applies only when running Qwen Code in this project. Overrides user settings.

The most commonly used top-level fields in settings.json:

Field Description
modelProviders Define available models per protocol (openai, anthropic, gemini, vertex-ai).
env Fallback environment variables (e.g. API keys). Lower priority than shell export and .env files.
security.auth.selectedType The protocol to use on startup (e.g. openai).
model.name The default model to use when Qwen Code starts.

See the Authentication section above for complete settings.json examples, and the settings reference for all available options.

Benchmark Results

Terminal-Bench Performance

Agent Model Accuracy
Qwen Code Qwen3-Coder-480A35 37.5%
Qwen Code Qwen3-Coder-30BA3B 31.3%

Ecosystem

Looking for a graphical interface?

  • AionUi A modern GUI for command-line AI tools including Qwen Code
  • Gemini CLI Desktop A cross-platform desktop/web/mobile UI for Qwen Code

Troubleshooting

If you encounter issues, check the troubleshooting guide.

Common issues:

  • Qwen OAuth free tier was discontinued on 2026-04-15: Qwen OAuth is no longer available. Run qwen/auth and switch to API Key or Coding Plan. See the Authentication section above for setup instructions.

To report a bug from within the CLI, run /bug and include a short title and repro steps.

Connect with Us

Acknowledgments

This project is based on Google Gemini CLI. We acknowledge and appreciate the excellent work of the Gemini CLI team. Our main contribution focuses on parser-level adaptations to better support Qwen-Coder models.