Preserve the platform CDN URL in Image.url alongside base64 data,
enabling plugins to use ContentElement.from_image_url() for direct
vision API access without redundant local download.
- aiocqhttp: use msg_data["data"]["url"] and msg.data["url"]
- discord: use attachment.url
- telegram: use file.file_path
- slack: use pic_url
- wecom: use picurl
- qqofficial: use pic_url
Satori adapter already follows this pattern (satori.py:168).
The change is purely additive — base64 is preserved for backward
compatibility, and get_bytes() priority (url → base64 → path)
ensures plugins can choose the optimal path.
Closes#2355
Co-authored-by: douxt <8429023+douxt@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Previously, MCP tool calls via call_tool() had no timeout, so a hung MCP
server would block the entire session indefinitely (exacerbated by
concurrency.session=1). This wraps the call in asyncio.timeout(30) and
raises an Exception on expiry, letting the LLM recover gracefully.
Closes#2339
When the LLM (e.g. MiniMax-M3) returns multiple rounds after tool calls, the _StreamAccumulator was initialized with initial_content=first_content, causing every subsequent round to repeat the entire first message. Remove the re-seeding so each round starts with a clean accumulator.
Add regression test verifying multi-round tool call content is not duplicated.
The Telegram (EBA) manifest was missing spec.categories, so it fell
into the uncategorized/protocol bucket. Restore popular + global to
match the legacy telegram adapter.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The 12 old adapters that now have an EBA replacement are tagged
`spec.legacy: true` in their source manifests. Principle: don't delete,
de-emphasize.
- sources/*.yaml (aiocqhttp, dingtalk, discord, kook, lark,
officialaccount, qqofficial, slack, telegram, wecom, wecombot,
wecomcs): add spec.legacy: true
- Adapter / IChooseAdapterEntity types: add optional legacy flag
- BotForm adapter Select: split legacy adapters into a collapsed,
grayscale group at the bottom with an explanatory hint; auto-expand
when the bot already uses a legacy adapter
- Wizard platform picker: same collapsed legacy section
- i18n: legacyAdapters / legacyAdaptersHint (zh-Hans, en-US)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Replace legacy pipeline binding card + RoutingRulesEditor with unified
EventBindingsEditor; remove use_pipeline_uuid/pipeline_routing_rules
from bot form schema and API update handler
- Add _augment_event_data() to botmgr for filter virtual fields
(message_text, message_element_types, chat_type)
- Add alembic migration 0009: migrate use_pipeline_uuid and
pipeline_routing_rules into event_bindings on first run
- Fix command.tsx: data-[disabled] -> data-[disabled=true] so cmdk 1.x
items (data-disabled=false) are not pointer-events:none
- EventBindingsEditor: onSelect on CommandItems, filter conditions panel,
disabled bindings section, dnd reorder
- i18n: add filter/condition keys for zh-Hans and en-US
- Update tests to match new bot service behavior
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Drop the PluginToolLoader.get_tool() override that returned a raw
ComponentManifest, so every loader's get_tool() now returns a uniform
resource_tool.LLMTool (PluginToolLoader.get_tools() already did this
conversion). This removes the only source of tool-shape heterogeneity.
- ToolManager.get_tool_schema(): drop the ComponentManifest-vs-LLMTool branch
- ToolManager.get_tool_detail(): new host-level shape {name, description,
human_desc, parameters}
- handler.py GET_TOOL_DETAIL: call tool_mgr.get_tool_detail(); delete the
handler-local _build_tool_detail + _i18n_to_dict/_i18n_to_text adapters and
the litellm TODO
- ToolLookupResult is now just LLMTool
The dropped label/spec fields were not consumed by any runner (local-agent
build_llm_tool and external harnesses use only name/description/parameters).
Extract the AgentRunner Protocol v1 host-side surface from the giant
RuntimeConnectionHandler.__init__ into sibling modules using a registration-
function pattern (behavior-preserving; @h.action == @self.action):
- agent_run_support.py: shared constants + authorization/scope/projection helpers
- agent_pull_actions.py: register(h) for history/event pull APIs
- agent_runner_actions.py: register(h) for run/runtime/stats/claim lifecycle
- agent_state_actions.py: register(h) for steering/state APIs
__init__ now calls the three register(self) functions. handler.py keeps the
pre-existing plugin/llm/vector/knowledge handlers, get_prompt/call_tool/
get_tool_detail (coupled to retained helpers), shared helpers, and outbound
methods; it re-imports _validate_agent_run_session so external imports keep
working. handler.py: 4066 -> 1871 lines.
test_state_api_auth.py: repoint get_session_registry patch targets to
agent_run_support (the lookup moved modules). 385 agent unit tests pass; ruff clean.
Expose skill tools (activate/register_skill/native exec) like native tools
instead of gating them behind the skill_authoring capability:
- toolmgr.get_all_tools drops include_skill_authoring; SkillToolLoader
self-gates on sandbox + skill_mgr
- preproc drops the include_skill_authoring branch; pipeline-bound skills
and the skills resource gate on skill_mgr presence
Persist activated skills into host.activated_skills conversation state so
they survive across runs (host writes at activate; last-write-wins); drop
the dead restore_activated_skills helper.
Prefill ToolResource.parameters host-side (tool_mgr.get_tool_schema) so
runners build LLM tools without per-tool get_tool_detail round-trips.
Align agent-runner-pluginization design docs to the all-tool model.
huanghuoguoguo