When a Python process exits uncleanly (SIGKILL, crash, gateway restart
via hermes update), in-memory _active_sessions tracking is lost but the
agent-browser node daemons and their Chromium child processes keep
running indefinitely. On a long-running system this causes unbounded
memory growth — 24 orphaned sessions consumed 7.6 GB on a production
machine over 9 days.
Add _reap_orphaned_browser_sessions() which scans the tmp directory for
agent-browser-{h_*,cdp_*} socket dirs on cleanup thread startup. For
each dir not tracked by the current process, reads the daemon PID file
and sends SIGTERM if the daemon is still alive. Handles edge cases:
dead PIDs, corrupt PID files, permission errors, foreign processes.
The reaper runs once on thread startup (not every 30s) to avoid races
with sessions being actively created by concurrent agents.
When sending multi-chunk responses, individual chunks can fail due to
transient iLink API errors. Previously a single failure would abort the
entire message. Now each chunk is retried with linear backoff before
giving up, and the same client_id is reused across retries for
server-side deduplication.
Configurable via config.yaml (platforms.weixin.extra) or env vars:
- send_chunk_delay_seconds (default 0.35s) — pacing between chunks
- send_chunk_retries (default 2) — max retry attempts per chunk
- send_chunk_retry_delay_seconds (default 1.0s) — base retry delay
Replaces the hardcoded 0.3s inter-chunk delay from #7903.
Salvaged from PR #7899 by @corazzione. Fixes#7836.
WeCom AI Bot sends file attachments with msgtype="appmsg", not
msgtype="file". Previously only file content was discarded while
the text title reached the agent.
Changes:
- _extract_text(): Extract appmsg title (filename) for display
- _extract_media(): Handle appmsg type with file/image content
Fixes#7750
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The test expected content=None to immediately trigger thinking-exhaustion,
but PR #7738 correctly gates that check on _has_think_tags. Without think
tags, the agent falls through to normal continuation retry (3 attempts).
Cherry-picked from PR #7747 with follow-up fixes:
- Narrowed suspend_all_active() to suspend_recently_active() — only
suspends sessions updated within the last 2 minutes (likely in-flight),
not all sessions which would unnecessarily reset idle users
- /stop with no running agent no longer suspends the session; only
actual force-stops mark the session for reset
Models that do not use <think> tags (e.g. GLM-4.7 on NVIDIA Build,
minimax) may return content=None or empty string when truncated. The
previous _thinking_exhausted check treated any None/empty content as
thinking-budget exhaustion, causing these models to always show the
'Thinking Budget Exhausted' error instead of attempting continuation.
Fix: gate the exhaustion check on _has_think_tags — only trigger the
exhaustion path when the model actually produced reasoning blocks
(<think>, <thinking>, <reasoning>, <REASONING_SCRATCHPAD>). Models
without think tags now fall through to the normal continuation retry
logic (up to 3 attempts).
Fixes#7729
When API routers rewrite finish_reason from "length" to "tool_calls",
truncated JSON arguments bypassed the length handler and wasted 3
retry attempts in the generic JSON validation loop. Now detects
truncation patterns in tool call arguments regardless of finish_reason.
Fixes#7680
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Background process watchers (notify_on_complete, check_interval) created
synthetic SessionSource objects without user_id/user_name. While the
internal=True bypass (1d8d4f28) prevented false pairing for agent-
generated notifications, the missing identity caused:
- Garbage entries in pairing rate limiters (discord:None, telegram:None)
- 'User None' in approval messages and logs
- No user identity available for future code paths that need it
Additionally, platform messages arriving without from_user (Telegram
service messages, channel forwards, anonymous admin actions) could still
trigger false pairing because they are not internal events.
Fix:
1. Propagate user_id/user_name through the full watcher chain:
session_context.py → gateway/run.py → terminal_tool.py →
process_registry.py (including checkpoint persistence/recovery)
2. Add None user_id guard in _handle_message() — silently drop
non-internal messages with no user identity instead of triggering
the pairing flow.
Salvaged from PRs #7664 (kagura-agent, ContextVar approach),
#6540 (MestreY0d4-Uninter, tests), and #7709 (guang384, None guard).
Closes#6341, #6485, #7643
Relates to #6516, #7392
Two-phase design so the warning fires before the user's first message
on every platform:
Phase 1 (__init__):
_check_compression_model_feasibility() runs during agent construction.
Resolves the auxiliary compression model (same chain as call_llm with
task='compression'), compares its context length to the main model's
compression threshold. If too small, emits via _emit_status() (prints
for CLI) and stores the warning in _compression_warning.
Phase 2 (run_conversation, first call):
_replay_compression_warning() re-sends the stored warning through
status_callback — which the gateway wires AFTER construction. The
warning is then cleared so it only fires once.
This ensures:
- CLI users see the warning immediately at startup (right after the
context limit line)
- Gateway users (Telegram, Discord, Slack, WhatsApp, Signal, Matrix,
Mattermost, Home Assistant, DingTalk, etc.) receive it via
status_callback('lifecycle', ...) on their first message
- logger.warning() always hits agent.log regardless of platform
Also warns when no auxiliary LLM provider is configured at all.
Entire check wrapped in try/except — never blocks startup.
11 tests covering: core warning logic, boundary conditions, exception
safety, two-phase store+replay, gateway callback wiring, and
single-delivery guarantee.
The Weixin adapter was splitting responses at every top-level newline,
causing notification spam (up to 70 API calls for a single long markdown
response). This salvages the best aspects of six contributor PRs:
Compact mode (new default):
- Messages under the 4000-char limit stay as a single bubble even with
multiple lines, paragraphs, and code blocks
- Only oversized messages get split at logical markdown boundaries
- Inter-chunk delay (0.3s) between chunks prevents WeChat rate-limit drops
Legacy mode (opt-in):
- Set split_multiline_messages: true in platforms.weixin.extra config
- Or set WEIXIN_SPLIT_MULTILINE_MESSAGES=true env var
- Restores the old per-line splitting behavior
Salvaged from PRs #7797 (guantoubaozi), #7792 (luoxiao6645),
#7838 (qyx596), #7825 (weedge), #7784 (sherunlock03), #7773 (JnyRoad).
Core fix unanimous across all six; config toggle from #7838; inter-chunk
delay from #7825.
Independent halving of width and height caused aspect ratio distortion
for extreme dimensions (e.g. 8000x200 panoramas). When one axis hit the
64px floor, the other kept shrinking — collapsing the ratio toward 1:1.
Use proportional scaling instead: when either dimension hits the floor,
derive the effective scale factor and apply it to both axes.
Add tests for extreme panorama (8000x200) and tall narrow (200x6000)
images to verify aspect ratio preservation.
hermes claw migrate now always shows a full dry-run preview before
making any changes. The user reviews what would be imported, then
confirms to proceed. --dry-run stops after the preview. --yes skips
the confirmation prompt.
This matches the existing setup wizard flow (_offer_openclaw_migration)
which already did preview-then-confirm.
Docs updated across both docs/migration/openclaw.md and
website/docs/guides/migrate-from-openclaw.md to reflect:
- New preview-first UX flow
- workspace-main/ fallback paths
- accounts.default channel token layout
- TTS edge/microsoft rename
- openclaw.json env sub-object as API key source
- Hyphenated provider API types
- Matrix accessToken field
- SecretRef file/exec warnings
- Skills session restart note
- WhatsApp re-pairing note
- Archive cleanup step
Consolidates fixes from PRs #7869, #7860, #7861, #7862, #7864, #7868.
OpenClaw restructured several internal paths and config schemas that the
migration tool was reading from stale locations:
- workspace/ renamed to workspace-main/ (and workspace-{agentId} for
multi-agent). source_candidate() now checks fallback paths.
- Channel tokens moved from channels.*.botToken to
channels.*.accounts.default.botToken. New _get_channel_field() checks
both flat and accounts.default layout.
- TTS provider 'edge' renamed to 'microsoft'. Migration now checks both
and normalizes back to 'edge' for Hermes.
- API keys stored in openclaw.json 'env' sub-object (env.<KEY> or
env.vars.<KEY>) are now discovered as an additional key source.
- Provider apiType values now hyphenated (openai-completions,
anthropic-messages, google-generative-ai). thinkingDefault expanded
with minimal, xhigh, adaptive.
- Matrix uses accessToken field, not botToken.
- SecretRef file/exec sources now warn instead of silently skipping.
- Migration notes now mention skills requiring session restart and
WhatsApp requiring QR re-pairing.
Co-authored-by: SHL0MS <SHL0MS@users.noreply.github.com>
The auxiliary client previously checked env vars (AUXILIARY_{TASK}_PROVIDER,
AUXILIARY_{TASK}_MODEL, etc.) before config.yaml's auxiliary.{task}.* section.
This violated the project's '.env is for secrets only' policy — these are
behavioral settings, not API keys.
Flipped the resolution order in _resolve_task_provider_model():
1. Explicit args (always win)
2. config.yaml auxiliary.{task}.* (PRIMARY)
3. Env var overrides (backward-compat fallback only)
4. 'auto' (full auto-detection chain)
Env var reading code is kept for backward compatibility but config.yaml
now takes precedence. Updated module docstring and function docstring.
Also removed AUXILIARY_VISION_MODEL from _EXTRA_ENV_KEYS in config.py.
Cherry-picked from PR #7702 by kshitijk4poor.
Adds Xiaomi MiMo as a direct provider (XIAOMI_API_KEY) with models:
- mimo-v2-pro (1M context), mimo-v2-omni (256K, multimodal), mimo-v2-flash (256K, cheapest)
Standard OpenAI-compatible provider checklist: auth.py, config.py, models.py,
main.py, providers.py, doctor.py, model_normalize.py, model_metadata.py,
models_dev.py, auxiliary_client.py, .env.example, cli-config.yaml.example.
Follow-up: vision tasks use mimo-v2-omni (multimodal) instead of the user's
main model. Non-vision aux uses the user's selected model. Added
_PROVIDER_VISION_MODELS dict for provider-specific vision model overrides.
On failure, falls back to aggregators (gemini flash) via existing fallback chain.
Corrects pre-existing context lengths: mimo-v2-pro 1048576→1000000,
mimo-v2-omni 1048576→256000, adds mimo-v2-flash 256000.
36 tests covering registry, aliases, auto-detect, credentials, models.dev,
normalization, URL mapping, providers module, doctor, aux client, vision
model override, and agent init.
The summary model used for context compaction must have a context window
at least as large as the main agent model. If it's smaller, the
summarization API call fails and middle turns are dropped without a
summary, silently losing conversation context.
Promoted the existing note in configuration.md to a visible warning
admonition, and added a matching warning in the developer guide's
context compression page.
Cherry-picked from PR #7749 by kshitijk4poor with modifications:
- Raise hard image limit from 5 MB to 20 MB (matches most restrictive provider)
- Send images at full resolution first; only auto-resize to 5 MB on API failure
- Add _is_image_size_error() helper to detect size-related API rejections
- Auto-resize uses Pillow (soft dep) with progressive downscale + JPEG quality reduction
- Fix get_model_capabilities() to check modalities.input for vision support
- Increase default vision timeout from 30s to 120s (matches hardcoded fallback intent)
- Applied retry-with-resize to both vision_analyze_tool and browser_vision
Closes#7740
Matrix gateway: fix sync loop never dispatching events (#5819)
- _sync_loop() called client.sync() but never called handle_sync()
to dispatch events to registered callbacks — _on_room_message was
registered but never fired for new messages
- Store next_batch token from initial sync and pass as since= to
subsequent incremental syncs (was doing full initial sync every time)
- 17 comments, confirmed by multiple users on matrix.org
Feishu docs: add interactive card configuration for approvals (#6893)
- Error 200340 is a Feishu Developer Console configuration issue,
not a code bug — users need to enable Interactive Card capability
and configure Card Request URL
- Added required 3-step setup instructions to feishu.md
- Added troubleshooting entry for error 200340
- 17 comments from Feishu users
Copilot provider drift: detect GPT-5.x Responses API requirement (#3388)
- GPT-5.x models are rejected on /v1/chat/completions by both OpenAI
and OpenRouter (unsupported_api_for_model error)
- Added _model_requires_responses_api() to detect models needing
Responses API regardless of provider
- Applied in __init__ (covers OpenRouter primary users) and in
_try_activate_fallback() (covers Copilot->OpenRouter drift)
- Fixed stale comment claiming gateway creates fresh agents per message
(it caches them via _agent_cache since the caching was added)
- 7 comments, reported on Copilot+Telegram gateway
* fix(matrix): pass required args to MemoryCryptoStore for mautrix ≥0.21
MemoryCryptoStore.__init__() now requires account_id and pickle_key
positional arguments as of mautrix 0.21. The migration from matrix-nio
(commit 1850747) didn't account for this, causing E2EE initialization
to fail with:
MemoryCryptoStore.__init__() missing 2 required positional arguments:
'account_id' and 'pickle_key'
Pass self._user_id as account_id and derive pickle_key from the same
user_id:device_id pair already used for the on-disk HMAC signature.
Update the test stub to accept the new parameters.
Fixes#7803
* fix: use consistent fallback for pickle_key derivation
Address review: _pickle_key now uses _acct_id (which has the 'hermes'
fallback) instead of raw self._user_id, so both values stay consistent
when user_id is empty.
---------
Co-authored-by: Hermes Agent <hermes@nousresearch.com>
The _PROVIDER_MODELS['openai-codex'] static list was a manually maintained
duplicate of DEFAULT_CODEX_MODELS in codex_models.py. They drifted — the
static list was missing gpt-5.3-codex-spark (and previously gpt-5.4).
Replace the hardcoded list with _codex_curated_models() which calls
DEFAULT_CODEX_MODELS + _add_forward_compat_models() from codex_models.py.
Now both the CLI 'hermes model' flow and the gateway /model picker derive
from the same source of truth. New models added to DEFAULT_CODEX_MODELS
or _FORWARD_COMPAT_TEMPLATE_MODELS automatically appear everywhere.
Telegram flood control during streaming caused messages to be cut off
mid-response. The old behavior permanently disabled edits after a single
flood-control failure, losing the remainder of the response.
Changes:
- Adaptive backoff: on flood-control edit failures, double the edit interval
instead of immediately disabling edits. Only permanently disable after 3
consecutive failures (_MAX_FLOOD_STRIKES).
- Cursor strip: when entering fallback mode, best-effort edit to remove the
cursor (▉) from the last visible message so it doesn't appear stuck.
- Fallback send retry: _send_fallback_final retries each chunk once on
flood-control failures (3s delay) before giving up.
- Default edit_interval increased from 0.3s to 1.0s. Telegram rate-limits
edits at ~1/s per message; 0.3s was virtually guaranteed to trigger flood
control on any non-trivial response.
- _send_or_edit returns bool so the overflow split loop knows not to
truncate accumulated text when an edit fails (prevents content loss).
Fixes: messages cutting/stopping mid-response on Telegram, especially
with streaming enabled.
Based on PR #7285 by @kshitijk4poor.
Two bugs affecting Qwen OAuth users:
1. Wrong context window — qwen3-coder-plus showed 128K instead of 1M.
Added specific entries before the generic qwen catch-all:
- qwen3-coder-plus: 1,000,000 (corrected from PR's 1,048,576 per
official Alibaba Cloud docs and OpenRouter)
- qwen3-coder: 262,144
2. Random stopping — max_tokens was suppressed for Qwen Portal, so the
server applied its own low default. Reasoning models exhaust that on
thinking tokens. Now: honor explicit max_tokens, default to 65536
when unset.
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
* feat: add watch_patterns to background processes for output monitoring
Adds a new 'watch_patterns' parameter to terminal(background=true) that
lets the agent specify strings to watch for in process output. When a
matching line appears, a notification is queued and injected as a
synthetic message — triggering a new agent turn, similar to
notify_on_complete but mid-process.
Implementation:
- ProcessSession gets watch_patterns field + rate-limit state
- _check_watch_patterns() in ProcessRegistry scans new output chunks
from all three reader threads (local, PTY, env-poller)
- Rate limited: max 8 notifications per 10s window
- Sustained overload (45s) permanently disables watching for that process
- watch_queue alongside completion_queue, same consumption pattern
- CLI drains watch_queue in both idle loop and post-turn drain
- Gateway drains after agent runs via _inject_watch_notification()
- Checkpoint persistence + crash recovery includes watch_patterns
- Blocked in execute_code sandbox (like other bg params)
- 20 new tests covering matching, rate limiting, overload kill,
checkpoint persistence, schema, and handler passthrough
Usage:
terminal(
command='npm run dev',
background=true,
watch_patterns=['ERROR', 'WARN', 'listening on port']
)
* refactor: merge watch_queue into completion_queue
Unified queue with 'type' field distinguishing 'completion',
'watch_match', and 'watch_disabled' events. Extracted
_format_process_notification() in CLI and gateway to handle
all event types in a single drain loop. Removes duplication
across both CLI drain sites and the gateway.
The _PROVIDER_MODELS['openai-codex'] list was missing gpt-5.4 and gpt-5.4-mini,
causing them to not appear in the /model picker for ChatGPT OAuth users.
codex_models.py already had these models in DEFAULT_CODEX_MODELS, but the
curated list that feeds the Telegram/Discord /model picker was never updated.
Reported by @chongdashu
The System Overview ASCII diagram had inconsistent box widths:
- Entry Points box bottom border was 73 chars instead of 71
This caused the docs-site-checks CI to fail on every docs-only PR
due to pre-existing errors in the diagram.
Fix: normalize Entry Points bottom border to 71 characters,
matching the top border width.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Cover all public functions with 50 test cases:
- managed_nous_tools_enabled() feature flag toggling
- normalize_browser_cloud_provider() coercion and defaults
- coerce_modal_mode() / normalize_modal_mode() validation
- has_direct_modal_credentials() env vars and config file detection
- resolve_modal_backend_state() full backend selection matrix
- resolve_openai_audio_api_key() priority chain and edge cases
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Follow-up fixes for cherry-pick conflicts:
- Removed test_context_keeps_pending_approval test that referenced
pop_pending() which doesn't exist on current main
- Added headers attribute to FakeResponse in vision test (needed
after #6949 added Content-Length check)
Three fixes for vision_analyze returning cryptic 400 "Invalid request data":
1. Pre-flight base64 size check — base64 inflates data ~33%, so a 3.8 MB
file exceeds the 5 MB API limit. Reject early with a clear message
instead of letting the provider return a generic 400.
2. Handle file:// URIs — strip the scheme and resolve as a local path.
Previously file:///path/to/image.png fell through to the "invalid
image source" error since it matched neither is_file() nor http(s).
3. Separate invalid_request errors from "does not support vision" errors
so the user gets actionable guidance (resize/compress/retry) instead
of a misleading "model does not support vision" message.
Closes#6677
vision_tools.py: _download_image() loads the full HTTP response body into
memory via response.content (line 190) with no Content-Length check and no
max file size limit. An attacker-hosted multi-gigabyte file causes OOM.
Add a 50 MB hard cap: check Content-Length header before download, and
verify actual body size before writing to disk.
hermes_parser.py: tc_data["name"] at line 57 raises KeyError when the LLM
outputs a tool call JSON without a "name" field. The outer except catches
it silently, causing the entire tool call to be lost with zero diagnostics.
Add "name" field validation before constructing the ChatCompletionMessage.
mistral_parser.py: tc["name"] at line 101 has the same KeyError issue in
the pre-v11 format path. The fallback decoder (line 112) already checks
"name" correctly, but the primary path does not. Add validation to match.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
process_registry.py: _reader_loop() has process.wait() after the try-except
block (line 380). If the reader thread crashes with an unexpected exception
(e.g. MemoryError, KeyboardInterrupt), control exits the except handler but
skips wait() — leaving the child as a zombie process. Move wait() and the
cleanup into a finally block so the child is always reaped.
cron/scheduler.py: _run_job_script() only redacts secrets in stdout on the
SUCCESS path (line 417-421). When a cron script fails (non-zero exit), both
stdout and stderr are returned WITHOUT redaction (lines 407-413). A script
that accidentally prints an API key to stderr during a failure would leak it
into the LLM context. Move redaction before the success/failure branch so
both paths benefit.
skill_commands.py: _build_skill_message() enumerates supporting files using
rglob("*") but only checks is_file() (line 171) without filtering symlinks.
PR #6693 added symlink protection to scan_skill_commands() but missed this
function. A malicious skill can create symlinks in references/ pointing to
arbitrary files, exposing their paths (and potentially content via skill_view)
to the LLM. Add is_symlink() check to match the guard in scan_skill_commands.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
YAML parses bare numeric keys (e.g. `12306:`) as int, causing
TypeError when sorted() is called on mixed int/str collections.
Changes:
- Normalize toolset_names entries to str in _get_platform_tools()
- Cast MCP server name to str(name) when building enabled_mcp_servers
- Add regression test
When the stream consumer has sent at least one message (already_sent=True),
the gateway skips sending the final response to avoid duplicates. But this
also suppressed error messages when the agent failed mid-loop — rate limit
exhaustion, context overflow, compression failure, etc.
The user would see the last streamed content and then nothing: no error
message, no explanation. The agent appeared to 'stop responding.'
Fix: check the 'failed' flag at both the producer (_run_agent marks
already_sent) and consumer (_handle_message_with_agent checks it) sites.
Error messages are always delivered regardless of streaming state.
async_call_llm (and call_llm) can return non-OpenAI objects from
custom providers or adapter shims, crashing downstream consumers
with misleading AttributeError ('str' has no attribute 'choices').
Add _validate_llm_response() that checks the response has the
expected .choices[0].message shape before returning. Wraps all
return paths in call_llm, async_call_llm, and fallback paths.
Fails fast with a clear RuntimeError identifying the task, response
type, and a preview of the malformed payload.
Closes#7264
`resolve_provider_client()` already drops OpenRouter-format model slugs
(containing "/") when the resolved provider is not OpenRouter (line 1097).
However, `_get_cached_client()` returns `model or cached_default` directly
on cache hits, bypassing this check entirely.
When the main provider is openai-codex, the auto-detection chain (Step 1
of `_resolve_auto`) caches a CodexAuxiliaryClient. Subsequent auxiliary
calls for different tasks (e.g. compression with `summary_model:
google/gemini-3-flash-preview`) hit the cache and pass the OpenRouter-
format model slug straight to the Codex Responses API, which does not
understand it and returns an empty `response.output`.
This causes two user-visible failures:
- "Invalid API response shape" (empty output after 3 retries)
- "Context length exceeded, cannot compress further" (compression itself
fails through the same path)
Add `_compat_model()` helper that mirrors the "/" check from
`resolve_provider_client()` and call it on the cache-hit return path.
Four fixes to auxiliary_client.py:
1. Respect explicit provider as hard constraint (#7559)
When auxiliary.{task}.provider is explicitly set (not 'auto'),
connection/payment errors no longer silently fallback to cloud
providers. Local-only users (Ollama, vLLM) will no longer get
unexpected OpenRouter billing from auxiliary tasks.
2. Eliminate model='default' sentinel (#7512)
_resolve_api_key_provider() no longer sends literal 'default' as
model name to APIs. Providers without a known aux model in
_API_KEY_PROVIDER_AUX_MODELS are skipped instead of producing
model_not_supported errors.
3. Add payment/connection fallback to async_call_llm (#7512)
async_call_llm now mirrors sync call_llm's fallback logic for
payment (402) and connection errors. Previously, async consumers
(session_search, web_tools, vision) got hard failures with no
recovery. Also fixes hardcoded 'openrouter' fallback to use the
full auto-detection chain.
4. Use accurate error reason in fallback logs (#7512)
_try_payment_fallback() now accepts a reason parameter and uses
it in log messages. Connection timeouts are no longer misleadingly
logged as 'payment error'.
Closes#7559Closes#7512
The auxiliary client always calls client.chat.completions.create(),
ignoring the api_mode config flag. This breaks codex-family models
(e.g. gpt-5.3-codex) on direct OpenAI API keys, which need the
/v1/responses endpoint.
Changes:
- Expand _resolve_task_provider_model to return api_mode (5-tuple)
- Read api_mode from auxiliary.{task}.api_mode config and env vars
(AUXILIARY_{TASK}_API_MODE)
- Pass api_mode through _get_cached_client to resolve_provider_client
- Add _needs_codex_wrap/_wrap_if_needed helpers that wrap plain OpenAI
clients in CodexAuxiliaryClient when api_mode=codex_responses or
when auto-detection finds api.openai.com + codex model pattern
- Apply wrapping at all custom endpoint, named custom provider, and
API-key provider return paths
- Update test mocks for the new 5-tuple return format
Users can now set:
auxiliary:
compression:
model: gpt-5.3-codex
base_url: https://api.openai.com/v1
api_mode: codex_responses
Closes#6800
Refactor hardcoded color constants throughout the CLI to resolve from
the active skin engine, so custom themes fully control the visual
appearance.
cli.py:
- Replace _GOLD constant with _ACCENT (_SkinAwareAnsi class) that
lazily resolves response_border from the active skin
- Rename _GOLD_DEFAULT to _ACCENT_ANSI_DEFAULT
- Make _build_compact_banner() read banner_title/accent/dim from skin
- Make session resume notifications use _accent_hex()
- Make status line use skin colors (accent_color, separator_color,
label_color instead of cryptic _dim_c/_dim_c2/_accent_c/_label_c)
- Reset _ACCENT cache on /skin switch
agent/display.py:
- Replace hardcoded diff ANSI escapes with skin-aware functions:
_diff_dim(), _diff_file(), _diff_hunk(), _diff_minus(), _diff_plus()
(renamed from SCREAMING_CASE _ANSI_* to snake_case)
- Add reset_diff_colors() for cache invalidation on skin switch
