When override_acp_command was passed to _build_child_agent, it failed to
override effective_provider to 'copilot-acp' and effective_api_mode to
'chat_completions'. This caused the child AIAgent to inherit the parent's
native API configuration (e.g. Anthropic) and attempt real HTTP requests
using the parent's API key, leading to HTTP 401 errors and completely
bypassing the ACP subprocess.
Ensure that if an ACP command override is provided, the child agent
correctly routes through CopilotACPClient.
Refs #2653
_normalize_custom_provider_entry silently drops the models field when it's
a list. Hand-edited configs (and the shape used by older Hermes versions)
still write models as a plain list of ids, so after the normalize pass the
entry reaches list_authenticated_providers() with no models and /model
shows the provider with (0) models — even though the underlying picker
code handles lists fine.
Convert list-format models into the empty-value dict shape the rest of
the pipeline already expects. Dict-format entries keep passing through
unchanged.
Repro (before the fix):
custom_providers:
- name: acme
base_url: https://api.example.com/v1
models: [foo, bar, baz]
/model shows "acme (0)"; bypassing normalize in list_authenticated_providers
returns three models, confirming the drop happens in normalize.
Adds four unit tests covering list→dict conversion, dict pass-through,
filtering of empty/non-string entries, and the empty-list case.
Adds MiniMax-AI/cli to the default taps list so the mmx-cli skill
is discoverable and installable out of the box via /skills browse
and /skills install. The skill definition lives upstream at
github.com/MiniMax-AI/cli/skill/SKILL.md, keeping updates decoupled.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When user_name is stored as None (e.g. Telegram users without a
display name), dict.get('user_name', '') returns None because the
key exists — the default is only used for missing keys. This causes
a TypeError when the format specifier :<20 is applied to None.
Use `or ''` to coerce None to an empty string.
Fixes#7392
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
NormalizedResponse and ToolCall now have backward-compat properties
so the agent loop can read them directly without the shim:
ToolCall: .type, .function (returns self), .call_id, .response_item_id
NormalizedResponse: .reasoning_content, .reasoning_details,
.codex_reasoning_items
This eliminates the 35-line shim and its 4 call sites in run_agent.py.
Also changes flush_memories guard from hasattr(response, 'choices')
to self.api_mode in ('chat_completions', 'bedrock_converse') so it
works with raw boto3 dicts too.
WS1 items 3+4 of Cycle 2 (#14418).
3-layer chain (transport → v2 → v1) was collapsed to 2-layer in PR 7.
This collapses the remaining 2-layer (transport → v1 → NR mapping in
transport) to 1-layer: v1 now returns NormalizedResponse directly.
Before: adapter returns (SimpleNamespace, finish_reason) tuple,
transport unpacks and maps to NormalizedResponse (22 lines).
After: adapter returns NormalizedResponse, transport is a
1-line passthrough.
Also updates ToolCall construction — adapter now creates ToolCall
dataclass directly instead of SimpleNamespace(id, type, function).
WS1 item 1 of Cycle 2 (#14418).
Replace direct normalize_anthropic_response() call in
_AnthropicCompletionsAdapter.create() with
AnthropicTransport.normalize_response() via get_transport().
Before: auxiliary_client called adapter v1 directly, bypassing
the transport layer entirely.
After: auxiliary_client → get_transport('anthropic_messages') →
transport.normalize_response() → adapter v1 → NormalizedResponse.
The adapter v1 function (normalize_anthropic_response) now has
zero callers outside agent/anthropic_adapter.py and the transport.
This unblocks collapsing v1 to return NormalizedResponse directly
in a follow-up (the remaining 2-layer chain becomes 1-layer).
WS1 item 2 of Cycle 2 (#14418).
When run_conversation returns a non-dict value (e.g. an int under
error conditions), the subsequent result.get("final_response", "")
raises an opaque "'int' object has no attribute 'get'" AttributeError.
Add a type guard that converts this into a clear RuntimeError, which
is properly caught by the outer except Exception handler that marks
the job as failed and delivers the error message.
FixesNousResearch/hermes-agent#9433
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
In WeCom group chats, messages sent as "@BotName /command" arrive with
the @mention prefix intact. This causes is_command() to return False
since the text does not start with "/".
Strip the leading @mention in group messages before creating the
MessageEvent, mirroring the existing behavior in the Telegram adapter.
- Replace hardcoded 'fr' default with DEFAULT_LOCAL_STT_LANGUAGE ('en')
— removes locale leak, matches other providers
- Drop redundant default=True on is_truthy_value (dict .get already defaults)
- Update auto-detect comment to include 'xai' in the chain
- Fix docstring: 21 languages (match PR body + actual xAI API)
- Update test_sends_language_and_format to set HERMES_LOCAL_STT_LANGUAGE=fr
explicitly, since default is no longer 'fr'
All 18 xAI STT tests pass locally.
* feat(agent): add PLATFORM_HINTS for matrix, mattermost, and feishu
These platform adapters fully support media delivery (send_image,
send_document, send_voice, send_video) but were missing from
PLATFORM_HINTS, leaving agents unaware of their platform context,
markdown rendering, and MEDIA: tag support.
Salvaged from PR #7370 by Rutimka — wecom excluded since main already
has a more detailed version.
Co-Authored-By: Marco Rutsch <marco@rutimka.de>
* test: add missing Markdown assertion for feishu platform hint
---------
Co-authored-by: Marco Rutsch <marco@rutimka.de>
iOS auto-corrects -- to — (em dash) and - to – (en dash), causing
commands like /model glm-4.7 —provider zai to fail with
'Model names cannot contain spaces'. Normalize at get_command_args().
The documentation claimed fallback activates 'at most once per session',
but the actual implementation restores the primary model at the start of
every run_conversation() call via _restore_primary_runtime().
Relevant source: run_agent.py lines 1666-1694 (snapshot), 6454-6517
(restore), 8681-8684 (called each turn).
Updated the One-Shot info box and the summary table to accurately
describe the per-turn restoration behavior.
