Kimi's gateway selects the correct temperature server-side based on the
active mode (thinking -> 1.0, non-thinking -> 0.6). Sending any
temperature value — even the previously "correct" one — conflicts with
gateway-managed defaults.
Replaces the old approach of forcing specific temperature values (0.6
for non-thinking, 1.0 for thinking) with an OMIT_TEMPERATURE sentinel
that tells all call sites to strip the temperature key from API kwargs
entirely.
Changes:
- agent/auxiliary_client.py: OMIT_TEMPERATURE sentinel, _is_kimi_model()
prefix check (covers all kimi-* models), _fixed_temperature_for_model()
returns sentinel for kimi models. _build_call_kwargs() strips temp.
- run_agent.py: _build_api_kwargs, flush_memories, and summary generation
paths all handle the sentinel by popping/omitting temperature.
- trajectory_compressor.py: _effective_temperature_for_model returns None
for kimi (sentinel mapped), direct client calls use kwargs dict to
conditionally include temperature.
- mini_swe_runner.py: same sentinel handling via wrapper function.
- 6 test files updated: all 'forces temperature X' assertions replaced
with 'temperature not in kwargs' assertions.
Net: -76 lines (171 added, 247 removed).
Inspired by PR #13137 (@kshitijk4poor).
Extract 12 Codex Responses API format-conversion and normalization functions
from run_agent.py into agent/codex_responses_adapter.py, following the
existing pattern of anthropic_adapter.py and bedrock_adapter.py.
run_agent.py: 12,550 → 11,865 lines (-685 lines)
Functions moved:
- _chat_content_to_responses_parts (multimodal content conversion)
- _summarize_user_message_for_log (multimodal message logging)
- _deterministic_call_id (cache-safe fallback IDs)
- _split_responses_tool_id (composite ID splitting)
- _derive_responses_function_call_id (fc_ prefix conversion)
- _responses_tools (schema format conversion)
- _chat_messages_to_responses_input (message format conversion)
- _preflight_codex_input_items (input validation)
- _preflight_codex_api_kwargs (API kwargs validation)
- _extract_responses_message_text (response text extraction)
- _extract_responses_reasoning_text (reasoning extraction)
- _normalize_codex_response (full response normalization)
All functions are stateless module-level functions. AIAgent methods remain
as thin one-line wrappers. Both module-level helpers are re-exported from
run_agent.py for backward compatibility with existing test imports.
Includes multimodal inline image support (PR #12969) that the original PR
was missing.
Based on PR #12975 by @kshitijk4poor.
Follow-up for PR #12252 salvage:
- Extract 75-line inline repair block to _repair_tool_call_arguments()
module-level helper for testability and readability
- Remove redundant 'import re as _re' (re already imported at line 33)
- Bound the while-True excess-delimiter removal loop to 50 iterations
- Add 17 tests covering all 6 repair stages
- Add sirEven to AUTHOR_MAP in release.py
Cherry-picked from PR #12252 by @sirEven.
Models like GLM-5.1 via Ollama can produce malformed tool_call arguments
(truncated JSON, trailing commas, Python None). The existing except
Exception: pass silently passes broken args to the API, which rejects
them with HTTP 400, crashing the session.
Adds a multi-stage repair pipeline at the pre-send normalization point:
1. Empty/whitespace-only → {}
2. Python None literal → {}
3. Strip trailing commas
4. Auto-close unclosed brackets
5. Remove excess closing delimiters
6. Last resort: replace with {} (logged at WARNING)
Cherry-picked from PR #12481 by @Sanjays2402.
Reasoning models (GLM-5.1, QwQ, DeepSeek R1) inflate completion_tokens
with internal thinking tokens. The compression trigger summed
prompt_tokens + completion_tokens, causing premature compression at ~42%
actual context usage instead of the configured 50% threshold.
Now uses only prompt_tokens — completion tokens don't consume context
window space for the next API call.
- 3 new regression tests
- Added AUTHOR_MAP entry for @Sanjays2402
Closes#12026
OpenAI-compatible clients (Open WebUI, LobeChat, etc.) can now send vision
requests to the API server. Both endpoints accept the canonical OpenAI
multimodal shape:
Chat Completions: {type: text|image_url, image_url: {url, detail?}}
Responses: {type: input_text|input_image, image_url: <str>, detail?}
The server validates and converts both into a single internal shape that the
existing agent pipeline already handles (Anthropic adapter converts,
OpenAI-wire providers pass through). Remote http(s) URLs and data:image/*
URLs are supported.
Uploaded files (file, input_file, file_id) and non-image data: URLs are
rejected with 400 unsupported_content_type.
Changes:
- gateway/platforms/api_server.py
- _normalize_multimodal_content(): validates + normalizes both Chat and
Responses content shapes. Returns a plain string for text-only content
(preserves prompt-cache behavior on existing callers) or a canonical
[{type:text|image_url,...}] list when images are present.
- _content_has_visible_payload(): replaces the bare truthy check so a
user turn with only an image no longer rejects as 'No user message'.
- _handle_chat_completions and _handle_responses both call the new helper
for user/assistant content; system messages continue to flatten to text.
- Codex conversation_history, input[], and inline history paths all share
the same validator. No duplicated normalizers.
- run_agent.py
- _summarize_user_message_for_log(): produces a short string summary
('[1 image] describe this') from list content for logging, spinner
previews, and trajectory writes. Fixes AttributeError when list
user_message hit user_message[:80] + '...' / .replace().
- _chat_content_to_responses_parts(): module-level helper that converts
chat-style multimodal content to Responses 'input_text'/'input_image'
parts. Used in _chat_messages_to_responses_input for Codex routing.
- _preflight_codex_input_items() now validates and passes through list
content parts for user/assistant messages instead of stringifying.
- tests/gateway/test_api_server_multimodal.py (new, 38 tests)
- Unit coverage for _normalize_multimodal_content, including both part
formats, data URL gating, and all reject paths.
- Real aiohttp HTTP integration on /v1/chat/completions and /v1/responses
verifying multimodal payloads reach _run_agent intact.
- 400 coverage for file / input_file / non-image data URL.
- tests/run_agent/test_run_agent_multimodal_prologue.py (new)
- Regression coverage for the prologue no-crash contract.
- _chat_content_to_responses_parts round-trip coverage.
- website/docs/user-guide/features/api-server.md
- Inline image examples for both endpoints.
- Updated Limitations: files still unsupported, images now supported.
Validated live against openrouter/anthropic/claude-opus-4.6:
POST /v1/chat/completions → 200, vision-accurate description
POST /v1/responses → 200, same image, clean output_text
POST /v1/chat/completions [file] → 400 unsupported_content_type
POST /v1/responses [input_file] → 400 unsupported_content_type
POST /v1/responses [non-image data URL] → 400 unsupported_content_type
Closes#5621, #8253, #4046, #6632.
Co-authored-by: Paul Bergeron <paul@gamma.app>
Co-authored-by: zhangxicen <zhangxicen@example.com>
Co-authored-by: Manuel Schipper <manuelschipper@users.noreply.github.com>
Co-authored-by: pradeep7127 <pradeep7127@users.noreply.github.com>
Previously, /steer text was only injected after an entire tool batch
completed (_execute_tool_calls_sequential/concurrent returned). If the
batch had a long-running tool (delegate_task, terminal build), the
steer waited for ALL tools to finish before landing — functionally
identical to /queue from the user's perspective.
Now _apply_pending_steer_to_tool_results() is called after EACH
individual tool result is appended to messages, in both the sequential
and concurrent paths. A steer arriving during Tool 1 lands in Tool 1's
result before Tool 2 starts executing.
Also handles leftover steers in the gateway: if a steer arrives during
the final API call (no tool batch to drain into), it's now delivered as
the next user turn instead of being silently dropped.
Fixes user report from Utku.
Context compression silently failed when the auxiliary compression model's
context window was smaller than the main model's compression threshold
(e.g. GLM-4.5-air at 131k paired with a 150k threshold). The feasibility
check warned but the session kept running and compression attempts errored
out mid-conversation.
Two changes in _check_compression_model_feasibility():
1. Hard floor: if detected aux context < MINIMUM_CONTEXT_LENGTH (64k),
raise ValueError so the session refuses to start. Mirrors the existing
main-model rejection at AIAgent.__init__ line 1600. A compression model
below 64k cannot summarise a full threshold-sized window.
2. Auto-correct: when aux context is >= 64k but below the computed
threshold, lower the live compressor's threshold_tokens to aux_context
(and update threshold_percent to match so later update_model() calls
stay in sync). Warning reworded to say what was done and how to
persist the fix in config.yaml.
Only ValueError re-raises; other exceptions in the check remain swallowed
as non-fatal.
Third-party gateways that speak the native Anthropic protocol (MiniMax,
Zhipu GLM, Alibaba DashScope, Kimi, LiteLLM proxies) now work end-to-end
with the same feature set as direct api.anthropic.com callers. Synthesizes
eight stale community PRs into one consolidated change.
Five fixes:
- URL detection: consolidate three inline `endswith("/anthropic")`
checks in runtime_provider.py into the shared _detect_api_mode_for_url
helper. Third-party /anthropic endpoints now auto-resolve to
api_mode=anthropic_messages via one code path instead of three.
- OAuth leak-guard: all five sites that assign `_is_anthropic_oauth`
(__init__, switch_model, _try_refresh_anthropic_client_credentials,
_swap_credential, _try_activate_fallback) now gate on
`provider == "anthropic"` so a stale ANTHROPIC_TOKEN never trips
Claude-Code identity injection on third-party endpoints. Previously
only 2 of 5 sites were guarded.
- Prompt caching: new method `_anthropic_prompt_cache_policy()` returns
`(should_cache, use_native_layout)` per endpoint. Replaces three
inline conditions and the `native_anthropic=(api_mode=='anthropic_messages')`
call-site flag. Native Anthropic and third-party Anthropic gateways
both get the native cache_control layout; OpenRouter gets envelope
layout. Layout is persisted in `_primary_runtime` so fallback
restoration preserves the per-endpoint choice.
- Auxiliary client: `_try_custom_endpoint` honors
`api_mode=anthropic_messages` and builds `AnthropicAuxiliaryClient`
instead of silently downgrading to an OpenAI-wire client. Degrades
gracefully to OpenAI-wire when the anthropic SDK isn't installed.
- Config hygiene: `_update_config_for_provider` (hermes_cli/auth.py)
clears stale `api_key`/`api_mode` when switching to a built-in
provider, so a previous MiniMax custom endpoint's credentials can't
leak into a later OpenRouter session.
- Truncation continuation: length-continuation and tool-call-truncation
retry now cover `anthropic_messages` in addition to `chat_completions`
and `bedrock_converse`. Reuses the existing `_build_assistant_message`
path via `normalize_anthropic_response()` so the interim message
shape is byte-identical to the non-truncated path.
Tests: 6 new files, 42 test cases. Targeted run + tests/run_agent,
tests/agent, tests/hermes_cli all pass (4554 passed).
Synthesized from (credits preserved via Co-authored-by trailers):
#7410 @nocoo — URL detection helper
#7393 @keyuyuan — OAuth 5-site guard
#7367 @n-WN — OAuth guard (narrower cousin, kept comment)
#8636 @sgaofen — caching helper + native-vs-proxy layout split
#10954 @Only-Code-A — caching on anthropic_messages+Claude
#7648 @zhongyueming1121 — aux client anthropic_messages branch
#6096 @hansnow — /model switch clears stale api_mode
#9691 @TroyMitchell911 — anthropic_messages truncation continuation
Closes: #7366, #8294 (third-party Anthropic identity + caching).
Supersedes: #7410, #7367, #7393, #8636, #10954, #7648, #6096, #9691.
Rejects: #9621 (OpenAI-wire caching with incomplete blocklist — risky),
#7242 (superseded by #9691, stale branch),
#8321 (targets smart_model_routing which was removed in #12732).
Co-authored-by: nocoo <nocoo@users.noreply.github.com>
Co-authored-by: Keyu Yuan <leoyuan0099@gmail.com>
Co-authored-by: Zoee <30841158+n-WN@users.noreply.github.com>
Co-authored-by: sgaofen <135070653+sgaofen@users.noreply.github.com>
Co-authored-by: Only-Code-A <bxzt2006@163.com>
Co-authored-by: zhongyueming <mygamez@163.com>
Co-authored-by: Xiaohan Li <hansnow@users.noreply.github.com>
Co-authored-by: Troy Mitchell <i@troy-y.org>
Bedrock rejects ``global-anthropic-claude-opus-4-7`` with ``HTTP 400:
The provided model identifier is invalid`` because its inference
profile IDs embed structural dots
(``global.anthropic.claude-opus-4-7``) that ``normalize_model_name``
was converting to hyphens. ``AIAgent._anthropic_preserve_dots`` did
not include ``bedrock`` in its provider allowlist, so every Claude-on-
Bedrock request through the AnthropicBedrock SDK path shipped with
the mangled model ID and failed.
Root cause
----------
``run_agent.py:_anthropic_preserve_dots`` (previously line 6589)
controls whether ``agent.anthropic_adapter.normalize_model_name``
converts dots to hyphens. The function listed Alibaba, MiniMax,
OpenCode Go/Zen and ZAI but not Bedrock, so when a user set
``provider: bedrock`` with a dotted inference-profile model the flag
returned False and ``normalize_model_name`` mangled every dot in the
ID. All four call sites in run_agent.py
(``build_anthropic_kwargs`` + three fallback / review / summary paths
at lines 6707, 7343, 8408, 8440) read from this same helper.
The bug shape matches #5211 for opencode-go, which was fixed in commit
f77be22c by extending this same allowlist.
Fix
---
* Add ``"bedrock"`` to the provider allowlist.
* Add ``"bedrock-runtime."`` to the base-URL heuristic as
defense-in-depth, so a custom-provider-shaped config with
``base_url: https://bedrock-runtime.<region>.amazonaws.com`` also
takes the preserve-dots path even if ``provider`` isn't explicitly
set to ``"bedrock"``. This mirrors how the code downstream at
run_agent.py:759 already treats either signal as "this is Bedrock".
Bedrock model ID shapes covered
-------------------------------
| Shape | Preserved |
| --- | --- |
| ``global.anthropic.claude-opus-4-7`` (reporter's exact ID) | ✓ |
| ``us.anthropic.claude-sonnet-4-5-20250929-v1:0`` | ✓ |
| ``apac.anthropic.claude-haiku-4-5`` | ✓ |
| ``anthropic.claude-3-5-sonnet-20241022-v2:0`` (foundation) | ✓ |
| ``eu.anthropic.claude-3-5-sonnet`` (regional inference profile) | ✓ |
Non-Claude Bedrock models (Nova, Llama, DeepSeek) take the
``bedrock_converse`` / boto3 path which does not call
``normalize_model_name``, so they were never affected by this bug
and remain unaffected by the fix.
Narrow scope — explicitly not changed
-------------------------------------
* ``bedrock_converse`` path (non-Claude Bedrock models) — already
correct; no ``normalize_model_name`` in that pipeline.
* Provider aliases (``aws``, ``aws-bedrock``, ``amazon``,
``amazon-bedrock``) — if a user bypasses the alias-normalization
pipeline and passes ``provider="aws"`` directly, the base-URL
heuristic still catches it because Bedrock always uses a
``bedrock-runtime.`` endpoint. Adding the aliases themselves to the
provider set is cheap but would be scope creep for this fix.
* No other places in ``agent/anthropic_adapter.py`` mangle dots, so
the fix is confined to ``_anthropic_preserve_dots``.
Regression coverage
-------------------
``tests/agent/test_bedrock_integration.py`` gains three new classes:
* ``TestBedrockPreserveDotsFlag`` (5 tests): flag returns True for
``provider="bedrock"`` and for Bedrock runtime URLs (us-east-1 and
ap-northeast-2 — the reporter's region); returns False for non-
Bedrock AWS URLs like ``s3.us-east-1.amazonaws.com``; canary that
Anthropic-native still returns False.
* ``TestBedrockModelNameNormalization`` (5 tests): every documented
Bedrock model-ID shape survives ``normalize_model_name`` with the
flag on; inverse canary pins that ``preserve_dots=False`` still
mangles (so a future refactor can't decouple the flag from its
effect).
* ``TestBedrockBuildAnthropicKwargsEndToEnd`` (2 tests): integration
through ``build_anthropic_kwargs`` shows the reporter's exact model
ID ends up unmangled in the outgoing kwargs.
Three of the new flag tests fail on unpatched ``origin/main`` with
``assert False is True`` (preserve-dots returning False for Bedrock),
confirming the regression is caught.
Validation
----------
``source venv/bin/activate && python -m pytest
tests/agent/test_bedrock_integration.py tests/agent/test_minimax_provider.py
-q`` -> 84 passed (40 new bedrock tests + 44 pre-existing, including
the minimax canaries that pin the pattern this fix mirrors).
CI-aligned broad suite: 12827 passed, 39 skipped, 19 pre-existing
baseline failures (all reproduce on clean ``origin/main``; none in
the touched code path).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Follow up salvaged PR #12668 by threading base_url through the
remaining direct-call sites so kimi-k2.5 uses temperature=1.0 on
api.moonshot.ai and keeps 0.6 on api.kimi.com/coding. Add focused
regression tests for run_agent, trajectory_compressor, and
mini_swe_runner.
- only use the native adapter for the canonical Gemini native endpoint
- keep custom and /openai base URLs on the OpenAI-compatible path
- preserve Hermes keepalive transport injection for native Gemini clients
- stabilize streaming tool-call replay across repeated SSE events
- add follow-up tests for base_url precedence, async streaming, and duplicate tool-call chunks
- add a native Gemini adapter over generateContent/streamGenerateContent
- switch the built-in gemini provider off the OpenAI-compatible endpoint
- preserve thought signatures and native functionResponse replay
- route auxiliary Gemini clients through the same adapter
- add focused unit coverage plus native-provider integration checks
The cherry-picked salvage (admin28980's commit) added codex headers only on the
primary chat client path, with two inaccuracies:
- originator was 'hermes-agent' — Cloudflare whitelists codex_cli_rs,
codex_vscode, codex_sdk_ts, and Codex* prefixes. 'hermes-agent' isn't on
the list, so the header had no mitigating effect on the 403 (the
account-id header alone may have been carrying the fix).
- account-id header was 'ChatGPT-Account-Id' — upstream codex-rs auth.rs
uses canonical 'ChatGPT-Account-ID' (PascalCase, trailing -ID).
Also, the auxiliary client (_try_codex + resolve_provider_client raw_codex
branch) constructs OpenAI clients against the same chatgpt.com endpoint with
no default headers at all — so compression, title generation, vision, session
search, and web_extract all still 403 from VPS IPs.
Consolidate the header set into _codex_cloudflare_headers() in
agent/auxiliary_client.py (natural home next to _read_codex_access_token and
the existing JWT decode logic) and call it from all four insertion points:
- run_agent.py: AIAgent.__init__ (initial construction)
- run_agent.py: _apply_client_headers_for_base_url (credential rotation)
- agent/auxiliary_client.py: _try_codex (aux client)
- agent/auxiliary_client.py: resolve_provider_client raw_codex branch
Net: -36/+55 lines, -25 lines of duplicated inline JWT decode replaced by a
single helper. User-Agent switched to 'codex_cli_rs/0.0.0 (Hermes Agent)' to
match the codex-rs shape while keeping product attribution.
Tests in tests/agent/test_codex_cloudflare_headers.py cover:
- originator value, User-Agent shape, canonical header casing
- account-ID extraction from a real JWT fixture
- graceful handling of malformed / non-string / claim-missing tokens
- wiring at all four insertion points (primary init, rotation, both aux paths)
- non-chatgpt base URLs (openrouter) do NOT get codex headers
- switching away from chatgpt.com drops the headers
Add ChatGPT-Account-Id and originator headers when using chatgpt.com
backend-api endpoint. Matches official codex-rs CLI behavior to prevent
Cloudflare JavaScript challenges on non-residential IPs (VPS, Mac Mini,
always-on servers).
Applied in AIAgent.__init__ and _update_base_url_headers to cover both
initial setup and credential rotation paths.
When creating httpx.Client with a custom transport for TCP keepalive,
proxy environment variables (HTTP_PROXY, HTTPS_PROXY) were ignored because
httpx only auto-reads them when transport=None.
Add _get_proxy_from_env() to explicitly read proxy settings and pass them
to httpx.Client, ensuring providers like kimi-coding-cn work correctly
when behind a proxy.
Fixes connection errors when HTTP_PROXY/HTTPS_PROXY are set.
Live test with timeout_seconds: 0.5 on claude-sonnet-4.6 proved the
initial wiring was insufficient: run_agent.py was overriding the
client-level timeout on every call via hardcoded per-request kwargs.
Root cause: run_agent.py had two sites that pass an explicit timeout=
kwarg into chat.completions.create() — api_kwargs['timeout'] at line
7075 (HERMES_API_TIMEOUT=1800s default) and the streaming path's
_httpx.Timeout(..., read=HERMES_STREAM_READ_TIMEOUT=120s, ...) at line
5760. Both override the per-provider config value the client was
constructed with, so a 0.5s config timeout would silently not enforce.
This commit:
- Adds AIAgent._resolved_api_call_timeout() — config > HERMES_API_TIMEOUT env > 1800s default.
- Uses it for the non-streaming api_kwargs['timeout'] field.
- Uses it for the streaming path's httpx.Timeout(connect, read, write, pool)
so both connect and read respect the configured value when set.
Local-provider auto-bump (Ollama/vLLM cold-start) only applies when
no explicit config value is set.
- New test: test_resolved_api_call_timeout_priority covers all three
precedence cases (config, env, default).
Live verified: 0.5s config on claude-sonnet-4.6 now triggers
APITimeoutError at ~3s per retry, exhausts 3 retries in ~15s total
(was: 29-47s success with timeout ignored). Positive case (60s config
+ gpt-4o-mini) still succeeds at 1.3s.
Follow-up on top of mvanhorn's cherry-picked commit. Original PR only
wired request_timeout_seconds into the explicit-creds OpenAI branch at
run_agent.py init; router-based implicit auth, native Anthropic, and the
fallback chain were still hardcoded to SDK defaults.
- agent/anthropic_adapter.py: build_anthropic_client() accepts an optional
timeout kwarg (default 900s preserved when unset/invalid).
- run_agent.py: resolve per-provider/per-model timeout once at init; apply
to Anthropic native init + post-refresh rebuild + stale/interrupt
rebuilds + switch_model + _restore_primary_runtime + the OpenAI
implicit-auth path + _try_activate_fallback (with immediate client
rebuild so the first fallback request carries the configured timeout).
- tests: cover anthropic adapter kwarg honoring; widen mock signatures
to accept the new timeout kwarg.
- docs/example: clarify that the knob now applies to every transport,
the fallback chain, and rebuilds after credential rotation.
Adds optional providers.<id>.request_timeout_seconds and
providers.<id>.models.<model>.timeout_seconds config, resolved via a new
hermes_cli/timeouts.py helper and applied where client_kwargs is built
in run_agent.py. Zero default behavior change: when both keys are unset,
the openai SDK default takes over.
Mirrors the existing _get_task_timeout pattern in agent/auxiliary_client.py
for auxiliary tasks - the primary turn path just never got the equivalent
knob.
Cross-project demand: openclaw/openclaw#43946 (17 reactions) asks for
exactly this config - specifically calls out Ollama cold-start hanging
the client.
Commit 4a9c3565 added a reference to `self.config` in
`_check_compression_model_feasibility()` to pass the user-configured
`auxiliary.compression.context_length` to `get_model_context_length()`.
However, `AIAgent` never stores the loaded config dict as an instance
attribute — the config is loaded into a local variable `_agent_cfg` in
`__init__()` and discarded after init.
This causes an `AttributeError: 'AIAgent' object has no attribute
'config'` on every session start when compression is enabled, caught by
the try/except and logged as a non-fatal DEBUG message.
Fix: store the loaded config as `self._config` in `__init__()` and
update the reference in the feasibility check to use `self._config`.
Follow-up for the helix4u easy-fix salvage batch:
- route remaining context-engine quiet-mode output through
_should_emit_quiet_tool_messages() so non-CLI/library callers stay
silent consistently
- drop the extra senderAliases computation from WhatsApp allowlist-drop
logging and remove the now-unused import
This keeps the batch scoped to the intended fixes while avoiding
leaked quiet-mode output and unnecessary duplicate work in the bridge.
Inline reasoning tags in an assistant message's content field leak to every downstream consumer: messaging platforms (#8878, #9568), API replay of prior turns, session transcript, CLI recap, generated session titles, and context compression. _extract_reasoning() already captures the reasoning text into msg['reasoning'] separately, so the raw tags in content are redundant.
Stripping once at the storage boundary in _build_assistant_message() cleans the content for every downstream path in one place — no per-platform or per-path stripper needed. Measured impact on a real MiniMax M2.7-highspeed session (per @luoyejiaoe-source, #9306): 55% of assistant messages started with <think> blocks, 51/100 session titles were polluted, 16% content-size reduction.
3 new regression tests in TestBuildAssistantMessage: closed-pair strip with reasoning capture, no-think-tag passthrough, and unterminated-block strip.
Resolves#8878 and #9568.
Originally proposed as PR #9250.
Providers served via NIM (MiniMax M2.7, some Moonshot/DeepSeek proxies) sometimes drop the closing </think> tag, leaving raw reasoning in the assistant's content field. _strip_think_blocks()'s closed-pair regex is non-greedy so it only matches complete blocks — any orphan <think>...EOF survived the stripper and leaked to users (#8878, #9568, #10408).
Adds an unterminated-tag pass that fires when an open reasoning tag sits at a block boundary (start of text or after a newline) with no matching close. Everything from that tag to end of string is stripped. The block-boundary check mirrors gateway/stream_consumer.py's filter so models that mention <think> in prose are not over-stripped.
Also makes the closed-pair regexes consistently case-insensitive so <THINK>...</THINK> and <Thinking>...</Thinking> are handled uniformly — previously the mixed-case open tag would bypass the closed-pair pass and be caught by the unterminated-tag pass, taking trailing visible content with it.
6 new regression tests in TestStripThinkBlocks covering: unterminated <think>, unterminated <thought>, multi-line unterminated, line-start orphan with preserved prefix, prose-mention non-regression, mixed-case closed pairs.
The implementation is inspired by @luinbytes's PR #10408 report of the NIM/MiniMax symptom. This commit does not include the 💭/🧠 emoji regexes from that PR — those glyphs are Hermes CLI display decorations, not model content markers.
Anthropic migrated their developer console from console.anthropic.com
to platform.claude.com. Two user-facing display URLs were still pointing
to the old domain:
- hermes_cli/main.py — API key prompt in the Anthropic model flow
- run_agent.py — 401 troubleshooting output
The OAuth token refresh endpoint was already migrated in PR #3246
(with fallback).
Spotted by @LucidPaths in PR #3237.
(Salvage of #3758 — dropped the setup.py hunk since that section was
refactored away and no longer contains the stale URL.)
Based on #12152 by @LVT382009.
Two fixes to run_agent.py:
1. _ephemeral_max_output_tokens consumption in chat_completions path:
The error-recovery ephemeral override was only consumed in the
anthropic_messages branch of _build_api_kwargs. All chat_completions
providers (OpenRouter, NVIDIA NIM, Qwen, Alibaba, custom, etc.)
silently ignored it. Now consumed at highest priority, matching the
anthropic pattern.
2. NVIDIA NIM max_tokens default (16384):
NVIDIA NIM falls back to a very low internal default when max_tokens
is omitted, causing models like GLM-4.7 to truncate immediately
(thinking tokens exhaust the budget before the response starts).
3. Progressive length-continuation boost:
When finish_reason='length' triggers a continuation retry, the output
budget now grows progressively (2x base on retry 1, 3x on retry 2,
capped at 32768) via _ephemeral_max_output_tokens. Previously the
retry loop just re-sent the same token limit on all 3 attempts.
Based on #11984 by @maxchernin. Fixes#8259.
Some providers (MiniMax M2.7 via NVIDIA NIM) resend the full function
name in every streaming chunk instead of only the first. The old
accumulator used += which concatenated them into 'read_fileread_file'.
Changed to simple assignment (=), matching the OpenAI Node SDK, LiteLLM,
and Vercel AI SDK patterns. Function names are atomic identifiers
delivered complete — no provider splits them across chunks, so
concatenation was never correct semantics.
* feat(steer): /steer <prompt> injects a mid-run note after the next tool call
Adds a new slash command that sits between /queue (turn boundary) and
interrupt. /steer <text> stashes the message on the running agent and
the agent loop appends it to the LAST tool result's content once the
current tool batch finishes. The model sees it as part of the tool
output on its next iteration.
No interrupt is fired, no new user turn is inserted, and no prompt
cache invalidation happens beyond the normal per-turn tool-result
churn. Message-role alternation is preserved — we only modify an
existing role:"tool" message's content.
Wiring
------
- hermes_cli/commands.py: register /steer + add to ACTIVE_SESSION_BYPASS_COMMANDS.
- run_agent.py: add _pending_steer state, AIAgent.steer(), _drain_pending_steer(),
_apply_pending_steer_to_tool_results(); drain at end of both parallel and
sequential tool executors; clear on interrupt; return leftover as
result['pending_steer'] if the agent exits before another tool batch.
- cli.py: /steer handler — route to agent.steer() when running, fall back to
the regular queue otherwise; deliver result['pending_steer'] as next turn.
- gateway/run.py: running-agent intercept calls running_agent.steer(); idle-agent
path strips the prefix and forwards as a regular user message.
- tui_gateway/server.py: new session.steer JSON-RPC method.
- ui-tui: SessionSteerResponse type + local /steer slash command that calls
session.steer when ui.busy, otherwise enqueues for the next turn.
Fallbacks
---------
- Agent exits mid-steer → surfaces in run_conversation result as pending_steer
so CLI/gateway deliver it as the next user turn instead of silently dropping it.
- All tools skipped after interrupt → re-stashes pending_steer for the caller.
- No active agent → /steer reduces to sending the text as a normal message.
Tests
-----
- tests/run_agent/test_steer.py — accept/reject, concatenation, drain,
last-tool-result injection, multimodal list content, thread safety,
cleared-on-interrupt, registry membership, bypass-set membership.
- tests/gateway/test_steer_command.py — running agent, pending sentinel,
missing steer() method, rejected payload, empty payload.
- tests/gateway/test_command_bypass_active_session.py — /steer bypasses
the Level-1 base adapter guard.
- tests/test_tui_gateway_server.py — session.steer RPC paths.
72/72 targeted tests pass under scripts/run_tests.sh.
* feat(steer): register /steer in Discord's native slash tree
Discord's app_commands tree is a curated subset of slash commands (not
derived from COMMAND_REGISTRY like Telegram/Slack). /steer already
works there as plain text (routes through handle_message → base
adapter bypass → runner), but registering it here adds Discord's
native autocomplete + argument hint UI so users can discover and
type it like any other first-class command.
When streaming died after text was already delivered to the user but
before a tool-call's arguments finished streaming, the partial-stream
stub at the end of _interruptible_streaming_api_call silently set
`tool_calls=None` on the returned message and kept `finish_reason=stop`.
The agent treated the turn as complete, the session exited cleanly with
code 0, and the attempted action was lost with zero user-facing signal.
Live-observed Apr 2026 with MiniMax M2.7 on a ~6-minute audit task:
agent streamed 'Let me write the audit:', started emitting a write_file
tool call, MiniMax stalled for 240s mid-arguments, the stale-stream
detector killed the connection, the stub fired, session ended, no file
written, no error shown.
Fix: the streaming accumulator now records each tool-call's name into
`result['partial_tool_names']` as soon as the name is known. When the
stub builder fires after a partial delivery and finds any recorded tool
names, it appends a human-visible warning to the stub's content — and
also fires it as a live stream delta so the user sees it immediately,
not only in the persisted transcript. The next turn's model also sees
the warning in conversation history and can retry on its own. Text-only
partial streams keep the original bare-recovery behaviour (no warning).
Validation:
| Scenario | Before | After |
|---------------------------------------------|---------------------------|---------------------------------------------|
| Stream dies mid tool-call, text already sent | Silent exit, no indication | User sees ⚠ warning naming the dropped tool |
| Text-only partial stream | Bare recovered text | Unchanged |
| tests/run_agent/test_streaming.py | 24 passed | 26 passed (2 new) |
* fix(interrupt): propagate to concurrent-tool workers + opt-in debug trace
interrupt() previously only flagged the agent's _execution_thread_id.
Tools running inside _execute_tool_calls_concurrent execute on
ThreadPoolExecutor worker threads whose tids are distinct from the
agent's, so is_interrupted() inside those tools returned False no matter
how many times the gateway called .interrupt() — hung ssh / curl / long
make-builds ran to their own timeout.
Changes:
- run_agent.py: track concurrent-tool worker tids in a per-agent set,
fan interrupt()/clear_interrupt() out to them, and handle the
register-after-interrupt race at _run_tool entry. getattr fallback
for the tracker so test stubs built via object.__new__ keep working.
- tools/environments/base.py: opt-in _wait_for_process trace (ENTER,
per-30s HEARTBEAT with interrupt+activity-cb state, INTERRUPT
DETECTED, TIMEOUT, EXIT) behind HERMES_DEBUG_INTERRUPT=1.
- tools/interrupt.py: opt-in set_interrupt() trace (caller tid, target
tid, set snapshot) behind the same env flag.
- tests: new regression test runs a polling tool on a concurrent worker
and asserts is_interrupted() flips to True within ~1s of interrupt().
Second new test guards clear_interrupt() clearing tracked worker bits.
Validation: tests/run_agent/ all 762 pass; tests/tools/ interrupt+env
subset 216 pass.
* fix(interrupt-debug): bypass quiet_mode logger filter so trace reaches agent.log
AIAgent.__init__ sets logging.getLogger('tools').setLevel(ERROR) when
quiet_mode=True (the CLI default). This would silently swallow every
INFO-level trace line from the HERMES_DEBUG_INTERRUPT=1 instrumentation
added in the parent commit — confirmed by running hermes chat -q with
the flag and finding zero trace lines in agent.log even though
_wait_for_process was clearly executing (subprocess pid existed).
Fix: when HERMES_DEBUG_INTERRUPT=1, each traced module explicitly sets
its own logger level to INFO at import time, overriding the 'tools'
parent-level filter. Scoped to the opt-in case only, so production
(quiet_mode default) logs stay quiet as designed.
Validation: hermes chat -q with HERMES_DEBUG_INTERRUPT=1 now writes
'_wait_for_process ENTER/EXIT' lines to agent.log as expected.
* fix(cli): SIGTERM/SIGHUP no longer orphans tool subprocesses
Tool subprocesses spawned by the local environment backend use
os.setsid so they run in their own process group. Before this fix,
SIGTERM/SIGHUP to the hermes CLI killed the main thread via
KeyboardInterrupt but the worker thread running _wait_for_process
never got a chance to call _kill_process — Python exited, the child
was reparented to init (PPID=1), and the subprocess ran to its
natural end (confirmed live: sleep 300 survived 4+ min after SIGTERM
to the agent until manual cleanup).
Changes:
- cli.py _signal_handler (interactive) + _signal_handler_q (-q mode):
route SIGTERM/SIGHUP through agent.interrupt() so the worker's poll
loop sees the per-thread interrupt flag and calls _kill_process
(os.killpg) on the subprocess group. HERMES_SIGTERM_GRACE (default
1.5s) gives the worker time to complete its SIGTERM+SIGKILL
escalation before KeyboardInterrupt unwinds main.
- tools/environments/base.py _wait_for_process: wrap the poll loop in
try/except (KeyboardInterrupt, SystemExit) so the cleanup fires
even on paths the signal handlers don't cover (direct sys.exit,
unhandled KI from nested code, etc.). Emits EXCEPTION_EXIT trace
line when HERMES_DEBUG_INTERRUPT=1.
- New regression test: injects KeyboardInterrupt into a running
_wait_for_process via PyThreadState_SetAsyncExc, verifies the
subprocess process group is dead within 3s of the exception and
that KeyboardInterrupt re-raises cleanly afterward.
Validation:
| Before | After |
|---------------------------------------------------------|--------------------|
| sleep 300 survives 4+ min as PPID=1 orphan after SIGTERM | dies within 2 s |
| No INTERRUPT DETECTED in trace | INTERRUPT DETECTED fires + killing process group |
| tests/tools/test_local_interrupt_cleanup | 1/1 pass |
| tests/run_agent/test_concurrent_interrupt | 4/4 pass |
Replace the hardcoded 'kimi-for-coding' string check with the helper
from auxiliary_client so there is one source of truth for the list of
models with fixed-temperature contracts. Adding a new entry to
_FIXED_TEMPERATURE_MODELS now automatically covers flush_memories too.
Byte-level reasoning models (xiaomi/mimo-v2-pro, kimi, glm) can emit lone
surrogates in reasoning output. The proactive sanitizer walked content/
name/tool_calls but not extra fields like reasoning or the nested
reasoning_details array. Surrogates in those fields survived the
proactive pass, crashed json.dumps() in the OpenAI SDK, and the recovery
block's _sanitize_messages_surrogates(messages) call also didn't check
those fields — so 'found' was False, no retry happened, and after 3
attempts the user saw:
API call failed after 3 retries. 'utf-8' codec can't encode characters
in position N-M: surrogates not allowed
Changes:
- _sanitize_messages_surrogates: walk any extra string fields (reasoning,
reasoning_content, etc.) and recurse into nested dict/list values
(reasoning_details). Mirrors _sanitize_messages_non_ascii coverage
added in PR #10537.
- _sanitize_structure_surrogates: new recursive walker, mirror of
_sanitize_structure_non_ascii but for surrogate recovery.
- UnicodeEncodeError recovery block: also sanitize api_messages,
api_kwargs, and prefill_messages (not just the canonical messages
list — the API-copy carries reasoning_content transformed from
reasoning and that's what the SDK actually serializes). Always
retry on detected surrogate errors, not only when we found
something to strip — gate on error type per PR #10537's pattern.
Tests: extended tests/cli/test_surrogate_sanitization.py with
coverage for reasoning, reasoning_content, reasoning_details (flat
and deeply nested), structure walker, and an integration case that
reproduces the exact api_messages shape that was crashing.
The 'Thinking Budget Exhausted' user-facing error message advised users to
'set model.max_tokens in config.yaml'. That config key is documented but
intentionally not wired through to the API call in CLI/gateway paths — we
omit max_tokens by default so the inference server uses its full output
budget (llama-server -1=infinity, vLLM max_model_len-prompt_len, etc.).
Users followed the suggestion, saw no change, and kept filing bugs (see
closed#4404, #10917, #6955 and PRs #5001/#6080/#6446/#6707/#7075/#8804/
#10924/#11173/#11268 — all reporting the same misdirection).
Replace the misleading suggestion with an actionable one: switch models
via /model. Lowering reasoning effort remains the primary remediation.
* fix(gateway): bound _agent_cache with LRU cap + idle TTL eviction
The per-session AIAgent cache was unbounded. Each cached AIAgent holds
LLM clients, tool schemas, memory providers, and a conversation buffer.
In a long-lived gateway serving many chats/threads, cached agents
accumulated indefinitely — entries were only evicted on /new, /model,
or session reset.
Changes:
- Cache is now an OrderedDict so we can pop least-recently-used entries.
- _enforce_agent_cache_cap() pops entries beyond _AGENT_CACHE_MAX_SIZE=64
when a new agent is inserted. LRU order is refreshed via move_to_end()
on cache hits.
- _sweep_idle_cached_agents() evicts entries whose AIAgent has been idle
longer than _AGENT_CACHE_IDLE_TTL_SECS=3600s. Runs from the existing
_session_expiry_watcher so no new background task is created.
- The expiry watcher now also pops the cache entry after calling
_cleanup_agent_resources on a flushed session — previously the agent
was shut down but its reference stayed in the cache dict.
- Evicted agents have _cleanup_agent_resources() called on a daemon
thread so the cache lock isn't held during slow teardown.
Both tuning constants live at module scope so tests can monkeypatch
them without touching class state.
Tests: 7 new cases in test_agent_cache.py covering LRU eviction,
move_to_end refresh, cleanup thread dispatch, idle TTL sweep,
defensive handling of agents without _last_activity_ts, and plain-dict
test fixture tolerance.
* tweak: bump _AGENT_CACHE_MAX_SIZE 64 -> 128
* fix(gateway): never evict mid-turn agents; live spillover tests
The prior commit could tear down an active agent if its session_key
happened to be LRU when the cap was exceeded. AIAgent.close() kills
process_registry entries for the task, tears down the terminal
sandbox, closes the OpenAI client (sets self.client = None), and
cascades .close() into any active child subagents — all fatal if
the agent is still processing a turn.
Changes:
- _enforce_agent_cache_cap and _sweep_idle_cached_agents now look at
GatewayRunner._running_agents and skip any entry whose AIAgent
instance is present (identity via id(), so MagicMock doesn't
confuse lookup in tests). _AGENT_PENDING_SENTINEL is treated
as 'not active' since no real agent exists yet.
- Eviction only considers the LRU-excess window (first size-cap
entries). If an excess slot is held by a mid-turn agent, we skip
it WITHOUT compensating by evicting a newer entry. A freshly
inserted session (zero cache history) shouldn't be punished to
protect a long-lived one that happens to be busy.
- Cache may therefore stay transiently over cap when load spikes;
a WARNING is logged so operators can see it, and the next insert
re-runs the check after some turns have finished.
New tests (TestAgentCacheActiveSafety + TestAgentCacheSpilloverLive):
- Active LRU entry is skipped; no newer entry compensated
- Mixed active/idle excess window: only idle slots go
- All-active cache: no eviction, WARNING logged, all clients intact
- _AGENT_PENDING_SENTINEL doesn't block other evictions
- Idle-TTL sweep skips active agents
- End-to-end: active agent's .client survives eviction attempt
- Live fill-to-cap with real AIAgents, then spillover
- Live: CAP=4 all active + 1 newcomer — cache grows to 5, no teardown
- Live: 8 threads racing 160 inserts into CAP=16 — settles at 16
- Live: evicted session's next turn gets a fresh agent that works
30 tests pass (13 pre-existing + 17 new). Related gateway suites
(model switch, session reset, proxy, etc.) all green.
* fix(gateway): cache eviction preserves per-task state for session resume
The prior commits called AIAgent.close() on cache-evicted agents, which
tears down process_registry entries, terminal sandbox, and browser
daemon for that task_id — permanently. Fine for session-expiry (session
ended), wrong for cache eviction (session may resume).
Real-world scenario: a user leaves a Telegram session open for 2+ hours,
idle TTL evicts the cached AIAgent, user returns and sends a message.
Conversation history is preserved via SessionStore, but their terminal
sandbox (cwd, env vars, bg shells) and browser state were destroyed.
Fix: split the two cleanup modes.
close() Full teardown — session ended. Kills bg procs,
tears down terminal sandbox + browser daemon,
closes LLM client. Used by session-expiry,
/new, /reset (unchanged).
release_clients() Soft cleanup — session may resume. Closes
LLM client only. Leaves process_registry,
terminal sandbox, browser daemon intact
for the resuming agent to inherit via
shared task_id.
Gateway cache eviction (_enforce_agent_cache_cap, _sweep_idle_cached_agents)
now dispatches _release_evicted_agent_soft on the daemon thread instead
of _cleanup_agent_resources. All session-expiry call sites of
_cleanup_agent_resources are unchanged.
Tests (TestAgentCacheIdleResume, 5 new cases):
- release_clients does NOT call process_registry.kill_all
- release_clients does NOT call cleanup_vm / cleanup_browser
- release_clients DOES close the LLM client (agent.client is None after)
- close() vs release_clients() — semantic contract pinned
- Idle-evicted session's rebuild with same session_id gets same task_id
Updated test_cap_triggers_cleanup_thread to assert the soft path fires
and the hard path does NOT.
35 tests pass in test_agent_cache.py; 67 related tests green.
Re-land of #10933, now guarded by the tests in #11266.
When a provider drops a TCP connection mid-stream, the socket can enter
CLOSE-WAIT and ''epoll_wait'' may never fire — no data or error signal
arrives, so the httpx read timeout never triggers and the agent hangs
indefinitely. The other defenses (''_force_close_tcp_sockets'', stale
stream detector) all ride on the socket layer reporting the dead
connection, which it never does without probes.
Inject ''SO_KEEPALIVE'' + ''TCP_KEEPIDLE''/''KEEPINTVL''/''KEEPCNT''
into the httpx transport. Kernel probes after 30s idle, retries every
10s, gives up after 3 → dead peer detected within ~60s instead of
hanging forever. Platform-aware: ''TCP_KEEPIDLE'' on Linux,
''TCP_KEEPALIVE'' on macOS. Silent no-op on Windows or anywhere
the socket options aren't available.
The original land (#10933) mutated ''client_kwargs'' in place when it
injected the ''httpx.Client''. Since callers pass ''self._client_kwargs''
by reference, the injected client leaked into the instance state. After
the first request, the OpenAI SDK closed its ''http_client'' — including
the injected one. The next ''_create_openai_client'' call re-read the
now-closed ''httpx.Client'' from ''self._client_kwargs'' and every
subsequent chat raised ''APIConnectionError'' with cause ''RuntimeError:
Cannot send a request, as the client has been closed'' (AlexKucera's
Discord report, 2026-04-16).
The defensive ''client_kwargs = dict(client_kwargs)'' copy already on
main (taeuk178's #10978) means this injection only lands in the
per-call local copy. Each ''_create_openai_client'' invocation gets
its OWN fresh ''httpx.Client'' whose lifetime is tied to the paired
''OpenAI'' client. When that ''OpenAI'' client is closed (rebuild,
teardown, credential rotation), its ''httpx.Client'' closes with it
and the next call constructs a fresh one — no stale closed transport
can be reused.
Full 4-test matrix all green (unit + live with real OpenRouter round
trips, HERMES_LIVE_TESTS=1):
tests/run_agent/test_create_openai_client_kwargs_isolation.py PASS
tests/run_agent/test_create_openai_client_reuse.py PASS (2)
tests/run_agent/test_sequential_chats_live.py PASS
Socket options verified on the live httpx transport:
_socket_options: [(1, 9, 1), (6, 4, 30), (6, 5, 10), (6, 6, 3)]
= (SO_KEEPALIVE=1, TCP_KEEPIDLE=30s, TCP_KEEPINTVL=10s, TCP_KEEPCNT=3)
Sequential-chat reproduction of the #10933 failure was explicitly
run against this patch — the defensive copy on main prevents the
closed transport from leaking back into ''self._client_kwargs'', so
every rebuild constructs a fresh transport.
Closes#10324
PR #4918 fixed the double-/v1 bug at fresh agent init by stripping the
trailing /v1 from OpenCode base URLs when api_mode is anthropic_messages
(so the Anthropic SDK's own /v1/messages doesn't land on /v1/v1/messages).
The same logic was missing from the /model mid-session switch path.
Repro: start a session on opencode-go with GLM-5 (or any chat_completions
model), then `/model minimax-m2.7`. switch_model() correctly sets
api_mode=anthropic_messages via opencode_model_api_mode(), but base_url
passes through as https://opencode.ai/zen/go/v1. The Anthropic SDK then
POSTs to https://opencode.ai/zen/go/v1/v1/messages, which returns the
OpenCode website 404 HTML page (title 'Not Found | opencode').
Same bug affects `/model claude-sonnet-4-6` on opencode-zen.
Verified upstream: POST /v1/messages returns clean JSON 401 with x-api-key
auth (route works), while POST /v1/v1/messages returns the exact HTML 404
users reported.
Fix mirrors runtime_provider.resolve_runtime_provider:
- hermes_cli/model_switch.py::switch_model() strips /v1 after the OpenCode
api_mode override when the resolved mode is anthropic_messages.
- run_agent.py::AIAgent.switch_model() applies the same strip as
defense-in-depth, so any direct caller can't reintroduce the double-/v1.
Tests: 9 new regression tests in tests/hermes_cli/test_model_switch_opencode_anthropic.py
covering minimax on opencode-go, claude on opencode-zen, chat_completions
(GLM/Kimi/Gemini) keeping /v1 intact, codex_responses (GPT) keeping /v1
intact, trailing-slash handling, and the agent-level defense-in-depth.
All 61 TUI-related tests green across 3 consecutive xdist runs.
tests/tui_gateway/test_protocol.py:
- rename `get_messages` → `get_messages_as_conversation` on mock DB (method
was renamed in the real backend, test was still stubbing the old name)
- update tool-message shape expectation: `{role, name, context}` matches
current `_history_to_messages` output, not the legacy `{role, text}`
tests/hermes_cli/test_tui_resume_flow.py:
- `cmd_chat` grew a first-run provider-gate that bailed to "Run: hermes
setup" before `_launch_tui` was ever reached; 3 tests stubbed
`_resolve_last_session` + `_launch_tui` but not the gate
- factored a `main_mod` fixture that stubs `_has_any_provider_configured`,
reused by all three tests
tests/test_tui_gateway_server.py:
- `test_config_set_personality_resets_history_and_returns_info` was flaky
under xdist because the real `_write_config_key` touches
`~/.hermes/config.yaml`, racing with any other worker that writes
config. Stub it in the test.
