Seed qwen-oauth credentials from resolve_qwen_runtime_credentials() in
_seed_from_singletons(). Users who authenticate via 'qwen auth qwen-oauth'
store tokens in ~/.qwen/oauth_creds.json which the runtime resolver reads
but the credential pool couldn't detect — same gap pattern as copilot.
Uses refresh_if_expiring=False to avoid network calls during discovery.
Seed copilot credentials from resolve_copilot_token() in the credential
pool's _seed_from_singletons(), alongside the existing anthropic and
openai-codex seeding logic. This makes copilot appear in the /model
provider picker when the user authenticates solely through gh auth token.
Cherry-picked from PR #9767 by Marvae.
Add ctx.register_skill() API so plugins can ship SKILL.md files under
a 'plugin:skill' namespace, preventing name collisions with built-in
Hermes skills. skill_view() detects the ':' separator and routes to
the plugin registry while bare names continue through the existing
flat-tree scan unchanged.
Key additions:
- agent/skill_utils: parse_qualified_name(), is_valid_namespace()
- hermes_cli/plugins: PluginContext.register_skill(), PluginManager
skill registry (find/list/remove)
- tools/skills_tool: qualified name dispatch in skill_view(),
_serve_plugin_skill() with full guards (disabled, platform,
injection scan), bundle context banner with sibling listing,
stale registry self-heal
- Hoisted _INJECTION_PATTERNS to module level (dedup)
- Updated skill_view schema description
Based on PR #9334 by N0nb0at. Lean P1 salvage — omits autogen shim
(P2) for a simpler first merge.
Closes#8422
- Rename platform from 'qq' to 'qqbot' across all integration points
(Platform enum, toolset, config keys, import paths, file rename qq.py → qqbot.py)
- Add PLATFORM_HINTS for QQBot in prompt_builder (QQ supports markdown)
- Set SUPPORTS_MESSAGE_EDITING = False to skip streaming on QQ
(prevents duplicate messages from non-editable partial + final sends)
- Add _send_qqbot() standalone send function for cron/send_message tool
- Add interactive _setup_qq() wizard in hermes_cli/setup.py
- Restore missing _setup_signal/email/sms/dingtalk/feishu/wecom/wecom_callback
functions that were lost during the original merge
* Add hermes debug share instructions to all issue templates
- bug_report.yml: Add required Debug Report section with hermes debug share
and /debug instructions, make OS/Python/Hermes version optional (covered
by debug report), demote old logs field to optional supplementary
- setup_help.yml: Replace hermes doctor reference with hermes debug share,
add Debug Report section with fallback chain (debug share -> --local -> doctor)
- feature_request.yml: Add optional Debug Report section for environment context
All templates now guide users to run hermes debug share (or /debug in chat)
and paste the resulting paste.rs links, giving maintainers system info,
config, and recent logs in one step.
* feat: add openrouter/elephant-alpha to curated model lists
- Add to OPENROUTER_MODELS (free, positioned above GPT models)
- Add to _PROVIDER_MODELS["nous"] mirror list
- Add 256K context window fallback in model_metadata.py
The generic 'gpt-5' fallback was set to 128,000 — which is the max
OUTPUT tokens, not the context window. GPT-5 base and most variants
(codex, mini) have 400,000 context. This caused /model to report
128k for models like gpt-5.3-codex when models.dev was unavailable.
Added specific entries for GPT-5 variants with different context sizes:
- gpt-5.4, gpt-5.4-pro: 1,050,000 (1.05M)
- gpt-5.4-mini, gpt-5.4-nano: 400,000
- gpt-5.3-codex-spark: 128,000 (reduced)
- gpt-5.1-chat: 128,000 (chat variant)
- gpt-5 (catch-all): 400,000
Sources: https://developers.openai.com/api/docs/models
Port two improvements inspired by Kilo-Org/kilocode analysis:
1. Error classifier: add context overflow patterns for vLLM, Ollama,
and llama.cpp/llama-server. These local inference servers return
different error formats than cloud providers (e.g., 'exceeds the
max_model_len', 'context length exceeded', 'slot context'). Without
these patterns, context overflow errors from local servers are
misclassified as format errors, causing infinite retries instead
of triggering compression.
2. MCP initial connection retry: previously, if the very first
connection attempt to an MCP server failed (e.g., transient DNS
blip at startup), the server was permanently marked as failed with
no retry. Post-connect reconnection had 5 retries with exponential
backoff, but initial connection had zero. Now initial connections
retry up to 3 times with backoff before giving up, matching the
resilience of post-connect reconnection.
(Inspired by Kilo Code's MCP server disappearing fix in v1.3.3)
Tests: 6 new error classifier tests, 4 new MCP retry tests, 1
updated existing test. All 276 affected tests pass.
Adds Arcee AI as a standard direct provider (ARCEEAI_API_KEY) with
Trinity models: trinity-large-thinking, trinity-large-preview, trinity-mini.
Standard OpenAI-compatible provider checklist: auth.py, config.py,
models.py, main.py, providers.py, doctor.py, model_normalize.py,
model_metadata.py, setup.py, trajectory_compressor.py.
Based on PR #9274 by arthurbr11, simplified to a standard direct
provider without dual-endpoint OpenRouter routing.
- Use isinstance() with try/except import for CopilotACPClient check
in _to_async_client instead of fragile __class__.__name__ string check
- Restore accurate comment: GPT-5.x models *require* (not 'often require')
the Responses API on OpenAI/OpenRouter; ACP is the exception, not a
softening of the requirement
- Add inline comment explaining the ACP exclusion rationale
Cherry-picked from PR #7637 by hcshen0111.
Adds kimi-coding-cn provider with dedicated KIMI_CN_API_KEY env var
and api.moonshot.cn/v1 endpoint for China-region Moonshot users.
The v11→v12 migration converts custom_providers (list) into providers
(dict), then deletes the list. But all runtime resolvers read from
custom_providers — after migration, named custom endpoints silently stop
resolving and fallback chains fail with AuthError.
Add get_compatible_custom_providers() that reads from both config schemas
(legacy custom_providers list + v12+ providers dict), normalizes entries,
deduplicates, and returns a unified list. Update ALL consumers:
- hermes_cli/runtime_provider.py: _get_named_custom_provider() + key_env
- hermes_cli/auth_commands.py: credential pool provider names
- hermes_cli/main.py: model picker + _model_flow_named_custom()
- agent/auxiliary_client.py: key_env + custom_entry model fallback
- agent/credential_pool.py: _iter_custom_providers()
- cli.py + gateway/run.py: /model switch custom_providers passthrough
- run_agent.py + gateway/run.py: per-model context_length lookup
Also: use config.pop() instead of del for safer migration, fix stale
_config_version assertions in tests, add pool mock to codex test.
Co-authored-by: 墨綠BG <s5460703@gmail.com>
Closes#8776, salvaged from PR #8814
resolve_vision_provider_client() computed resolved_api_mode from config
but never passed it to downstream resolve_provider_client() or
_get_cached_client() calls, causing custom providers with
api_mode: anthropic_messages to crash when used for vision tasks.
Also remove the for_vision special case in _normalize_aux_provider()
that incorrectly discarded named custom provider identifiers.
Fixes#8857
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove the backward-compat code paths that read compression provider/model
settings from legacy config keys and env vars, which caused silent failures
when auto-detection resolved to incompatible backends.
What changed:
- Remove compression.summary_model, summary_provider, summary_base_url from
DEFAULT_CONFIG and cli.py defaults
- Remove backward-compat block in _resolve_task_provider_model() that read
from the legacy compression section
- Remove _get_auxiliary_provider() and _get_auxiliary_env_override() helper
functions (AUXILIARY_*/CONTEXT_* env var readers)
- Remove env var fallback chain for per-task overrides
- Update hermes config show to read from auxiliary.compression
- Add config migration (v16→17) that moves non-empty legacy values to
auxiliary.compression and strips the old keys
- Update example config and openclaw migration script
- Remove/update tests for deleted code paths
Compression model/provider is now configured exclusively via:
auxiliary.compression.provider / auxiliary.compression.model
Closes#8923
_query_local_context_length was checking model_info.context_length
(the GGUF training max) before num_ctx (the Modelfile runtime override),
inverse to query_ollama_num_ctx. The two helpers therefore disagreed on
the same model:
hermes-brain:qwen3-14b-ctx32k # Modelfile: num_ctx 32768
underlying qwen3:14b GGUF # qwen3.context_length: 40960
query_ollama_num_ctx correctly returned 32768 (the value Ollama will
actually allocate KV cache for). _query_local_context_length returned
40960, which let ContextCompressor grow conversations past 32768 before
triggering compression — at which point Ollama silently truncated the
prefix, corrupting context.
Swap the order so num_ctx is checked first, matching query_ollama_num_ctx.
Adds a parametrized test that seeds both values and asserts num_ctx wins.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
auxiliary_client.py had its own regex mirroring _strip_think_blocks
but was missing the <thought> variant. Also adds test coverage for
<thought> paired and orphaned tags.
The previous wording ('If one clearly matches') set too high a threshold,
and 'If none match, proceed normally' was an easy escape hatch for lazy
models. Now:
- Lowered threshold: 'matches or is even partially relevant'
- Added MUST directive and 'err on the side of loading' guidance
- Replaced permissive closer with 'only proceed without if genuinely none
are relevant'
This should reduce cases where the agent skips loading relevant skills
unless explicitly forced.
When running inside WSL (Windows Subsystem for Linux), inject a hint into
the system prompt explaining that the Windows host filesystem is mounted
at /mnt/c/, /mnt/d/, etc. This lets the agent naturally translate Windows
paths (Desktop, Documents) to their /mnt/ equivalents without the user
needing to configure anything.
Uses the existing is_wsl() detection from hermes_constants (cached,
checks /proc/version for 'microsoft'). Adds build_environment_hints()
in prompt_builder.py — extensible for Termux, Docker, etc. later.
Closes the UX gap where WSL users had to manually explain path
translation to the agent every session.
OpenAI OAuth refresh tokens are single-use and rotate on every refresh.
When Hermes refreshes a Codex token, it consumed the old refresh_token
but never wrote the new pair back to ~/.codex/auth.json. This caused
Codex CLI and VS Code to fail with 'refresh_token_reused' on their
next refresh attempt.
This mirrors the existing Anthropic write-back pattern where refreshed
tokens are written to ~/.claude/.credentials.json via
_write_claude_code_credentials().
Changes:
- Add _write_codex_cli_tokens() in hermes_cli/auth.py (parallel to
_write_claude_code_credentials in anthropic_adapter.py)
- Call it from _refresh_codex_auth_tokens() (non-pool refresh path)
- Call it from credential_pool._refresh_entry() (pool happy path + retry)
- Add tests for the new write-back behavior
- Update existing test docstring to clarify _save_codex_tokens vs
_write_codex_cli_tokens separation
Fixes refresh token conflict reported by @ec12edfae2cb221
The previous wording ('If one clearly matches') set too high a threshold,
and 'If none match, proceed normally' was an easy escape hatch for lazy
models. Now:
- Lowered threshold: 'matches or is even partially relevant'
- Added MUST directive and 'err on the side of loading' guidance
- Replaced permissive closer with 'only proceed without if genuinely none
are relevant'
This should reduce cases where the agent skips loading relevant skills
unless explicitly forced.
- Add openai/openai-codex -> openai mapping to PROVIDER_TO_MODELS_DEV
so context-length lookups use models.dev data instead of 128k fallback.
Fixes#8161.
- Set api_mode from custom_providers entry when switching via hermes model,
and clear stale api_mode when the entry has none. Also extract api_mode
in _named_custom_provider_map(). Fixes#8181.
- Convert OpenAI image_url content blocks to Anthropic image blocks when
the endpoint is Anthropic-compatible (MiniMax, MiniMax-CN, or any URL
containing /anthropic). Fixes#8147.
Users whose credentials exist only in external files — OpenAI Codex
OAuth tokens in ~/.codex/auth.json or Anthropic Claude Code credentials
in ~/.claude/.credentials.json — would not see those providers in the
/model picker, even though hermes auth and hermes model detected them.
Root cause: list_authenticated_providers() only checked the raw Hermes
auth store and env vars. External credential file fallbacks (Codex CLI
import, Claude Code file discovery) were never triggered.
Fix (three parts):
1. _seed_from_singletons() in credential_pool.py: openai-codex now
imports from ~/.codex/auth.json when the Hermes auth store is empty,
mirroring resolve_codex_runtime_credentials().
2. list_authenticated_providers() in model_switch.py: auth store + pool
checks now run for ALL providers (not just OAuth auth_type), catching
providers like anthropic that support both API key and OAuth.
3. list_authenticated_providers(): direct check for anthropic external
credential files (Claude Code, Hermes PKCE). The credential pool
intentionally gates anthropic behind is_provider_explicitly_configured()
to prevent auxiliary tasks from silently consuming tokens. The /model
picker bypasses this gate since it is discovery-oriented.
After compression, models (especially Kimi 2.5) would sometimes respond
to questions from the summary instead of the latest user message. This
happened ~30% of the time on Telegram.
Root cause: the summary's 'Next Steps' section read as active instructions,
and the SUMMARY_PREFIX didn't explicitly tell the model to ignore questions
in the summary. When the summary merged into the first tail message, there
was no clear separator between historical context and the actual user message.
Changes inspired by competitor analysis (Claude Code, OpenCode, Codex):
1. SUMMARY_PREFIX rewritten with explicit 'Do NOT answer questions from
this summary — respond ONLY to the latest user message AFTER it'
2. Summarizer preamble (shared by both prompts) adds:
- 'Do NOT respond to any questions' (from OpenCode's approach)
- 'Different assistant' framing (from Codex) to create psychological
distance between summary content and active conversation
3. New summary sections:
- '## Resolved Questions' — tracks already-answered questions with
their answers, preventing re-answering (from Claude Code's
'Pending user asks' pattern)
- '## Pending User Asks' — explicitly marks unanswered questions
- '## Remaining Work' replaces '## Next Steps' — passive framing
avoids reading as active instructions
4. merge-summary-into-tail path now inserts a clear separator:
'--- END OF CONTEXT SUMMARY — respond to the message below ---'
5. Iterative update prompt now instructs: 'Move answered questions to
Resolved Questions' to maintain the resolved/pending distinction
across multiple compactions.
Adds an optional focus topic to /compress: `/compress database schema`
guides the summariser to preserve information related to the focus topic
(60-70% of summary budget) while compressing everything else more aggressively.
Inspired by Claude Code's /compact <focus>.
Changes:
- context_compressor.py: focus_topic parameter on _generate_summary() and
compress(); appends FOCUS TOPIC guidance block to the LLM prompt
- run_agent.py: focus_topic parameter on _compress_context(), passed through
to the compressor
- cli.py: _manual_compress() extracts focus topic from command string,
preserves existing manual_compression_feedback integration (no regression)
- gateway/run.py: _handle_compress_command() extracts focus from event args
and passes through — full gateway parity
- commands.py: args_hint="[focus topic]" on /compress CommandDef
Salvaged from PR #7459 (CLI /compress focus only — /context command deferred).
15 new tests across CLI, compressor, and gateway.
Switch estimate_tokens_rough(), estimate_messages_tokens_rough(), and
estimate_request_tokens_rough() from floor division (len // 4) to
ceiling division ((len + 3) // 4). Short texts (1-3 chars) previously
estimated as 0 tokens, causing the compressor and pre-flight checks to
systematically undercount when many short tool results are present.
Also replaced the inline duplicate formula in run_conversation()
(total_chars // 4) with a call to the shared
estimate_messages_tokens_rough() function.
Updated 4 tests that hardcoded floor-division expected values.
Related: issue #6217, PR #6629
Three root causes of the 'agent stops mid-task' gateway bug:
1. Compression threshold floor (64K tokens minimum)
- The 50% threshold on a 100K-context model fired at 50K tokens,
causing premature compression that made models lose track of
multi-step plans. Now threshold_tokens = max(50% * context, 64K).
- Models with <64K context are rejected at startup with a clear error.
2. Budget warning removal — grace call instead
- Removed the 70%/90% iteration budget warnings entirely. These
injected '[BUDGET WARNING: Provide your final response NOW]' into
tool results, causing models to abandon complex tasks prematurely.
- Now: no warnings during normal execution. When the budget is
actually exhausted (90/90), inject a user message asking the model
to summarise, allow one grace API call, and only then fall back
to _handle_max_iterations.
3. Activity touches during long terminal execution
- _wait_for_process polls every 0.2s but never reported activity.
The gateway's inactivity timeout (default 1800s) would fire during
long-running commands that appeared 'idle.'
- Now: thread-local activity callback fires every 10s during the
poll loop, keeping the gateway's activity tracker alive.
- Agent wires _touch_activity into the callback before each tool call.
Also: docs update noting 64K minimum context requirement.
Closes#7915 (root cause was agent-loop termination, not Weixin delivery limits).
* fix(tools): neutralize shell injection in _write_to_sandbox via path quoting
_write_to_sandbox interpolated storage_dir and remote_path directly into
a shell command passed to env.execute(). Paths containing shell
metacharacters (spaces, semicolons, $(), backticks) could trigger
arbitrary command execution inside the sandbox.
Fix: wrap both paths with shlex.quote(). Clean paths (alphanumeric +
slashes/hyphens/dots) are left unmodified by shlex.quote, so existing
behavior is unchanged. Paths with unsafe characters get single-quoted.
Tests added for spaces, $(command) substitution, and semicolon injection.
* fix: is_local_endpoint misses Docker/Podman DNS names
host.docker.internal, host.containers.internal, gateway.docker.internal,
and host.lima.internal are well-known DNS names that container runtimes
use to resolve the host machine. Users running Ollama on the host with
the agent in Docker/Podman hit the default 120s stream timeout instead
of the bumped 1800s because these hostnames weren't recognized as local.
Add _CONTAINER_LOCAL_SUFFIXES tuple and suffix check in
is_local_endpoint(). Tests cover all three runtime families plus a
negative case for domains that merely contain the suffix as a substring.
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.
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
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>
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>
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
Aligns MiniMax provider with official API documentation. Fixes 6 bugs:
transport mismatch (openai_chat -> anthropic_messages), credential leak
in switch_model(), prompt caching sent to non-Anthropic endpoints,
dot-to-hyphen model name corruption, trajectory compressor URL routing,
and stale doctor health check.
Also corrects context window (204,800), thinking support (manual mode),
max output (131,072), and model catalog (M2 family only on /anthropic).
Source: https://platform.minimax.io/docs/api-reference/text-anthropic-api
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
_is_oauth_token() returned True for any key not starting with 'sk-ant-api',
which means MiniMax and Alibaba API keys were falsely treated as Anthropic
OAuth tokens. This triggered the Claude Code compatibility path:
- All tool names prefixed with mcp_ (e.g. mcp_terminal, mcp_web_search)
- System prompt injected with 'You are Claude Code' identity
- 'Hermes Agent' replaced with 'Claude Code' throughout
Fix: Make _is_oauth_token() positively identify Anthropic OAuth tokens by
their key format instead of using a broad catch-all:
- sk-ant-* (but not sk-ant-api-*) -> setup tokens, managed keys
- eyJ* -> JWTs from Anthropic OAuth flow
- Everything else -> False (MiniMax, Alibaba, etc.)
Reported by stefan171.
GPT-5+ models (except gpt-5-mini) are only accessible via the Responses
API on Copilot. When these models were configured as the compression
summary_model (or any auxiliary task), the plain OpenAI client sent them
to /chat/completions which returned a 400 error:
model "gpt-5.4-mini" is not accessible via the /chat/completions endpoint
resolve_provider_client() now checks _should_use_copilot_responses_api()
for the copilot provider and wraps the client in CodexAuxiliaryClient
when needed, routing calls through responses.stream() transparently.
Adds tests for both the wrapping (gpt-5.4-mini) and non-wrapping
(gpt-4.1-mini) paths.
