Core no longer assembles a whole trace locally. Every workflow node is uploaded
the moment it completes as its own `execution_node` record, and each execution
closes with exactly one `execution_chain` record; all records of one execution
carry the same `event_id`, so Space groups them back into a single chain.
- every execution starts at an inbound event: event-less Pipeline lanes and
WebUI/API debug runs synthesize their virtual inbound event at the boundary
and are marked debug=true, so a Runner is never a trace origin
- sampling is decided once per event from a stable hash of the event id (the
previous `int(id[:8], 16)` raised on every `platform:`-prefixed id and so
dropped every successful inbound-event chain) and is reported on each record
as sample{mode,denominator}; the default mode is now `all`
- the local trace buffer, the 120s TTL, the per-process trace cap and the shared
open-node mirror are gone, which also removes the truncated-chain upload and
the re-buffered-closed-trace leak they caused
- a broken chain is always uploaded, and its reason falls back to the first
failed node so the chain explains itself
- flush keeps records until the sender accepts them and counts what it drops;
telemetry health replaces the unused send-task queue
- heartbeat reports bound Agent / plugin-processor / event-binding counts
- bound Pipeline and Agent resources (plugins, MCP servers, knowledge bases,
tools with their Host source, skills, models and the Runner schema selectors
that picked them) are reported on the chain record
- failure text is bounded and redacted instead of passing exception text through
- the dead per-query feature counters and their call sites are removed
Wire contract: schema 2 (`execution_node` / `execution_chain`).
scan_skill_directory is disabled for Cloud-managed sandboxes, but the
register_skill tool was still advertised to the model whenever a backend was
available, so every call failed closed without a usable explanation. Advertise
register_skill only when the runtime may scan sandbox directories, and keep
activate available in both modes.
The workspace file tools (read/write/edit/glob/grep) ran a script through
`python`, which a sandbox built from a host rootfs may not provide: those hosts
ship `python3` only, and a read-only /usr prevents creating a `python` shim
inside the jail. Every file tool then failed with `/bin/sh: 1: python: not
found`. Resolve the interpreter with `command -v python3 || command -v python`,
matching the interpreter the Box service already uses for its own scripts.
* feat(mcp): surface OAuth-required server tests
* fix(mcp): show connection failure details in status cards
---------
Co-authored-by: RockChinQ <[email protected]>
execute_func_call returns list[ContentElement] for MCP tools, but the
runner assigned that list directly to the tool-message content. The
OpenAI chat-completions spec requires tool-message content to be a
string, so OpenAI-compatible endpoints return HTTP 500 when the raw
list is sent.
Serialize the list to a string before building the tool message, using
ContentElement.__str__ which returns the text payload for text elements
and a human-readable placeholder for images and files. Fixes#2457.
SessionManager clears image_base64 on past turns to save memory, and
exclude_none serialization drops the hollowed field entirely, so a
replayed history part can arrive as {'type': 'image_base64'} with no
payload. The converter accessed the missing key unconditionally and
raised KeyError on every turn after an image was sent.
Prefer the base64 payload when present, fall back to an image_url that
survived on the same element, and drop hollow parts otherwise (same
strategy as the existing file-part handling). Fixes#2469.
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.
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.
* fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models
MiniMax-M3 (and other OpenAI-compatible providers) emit chain-of-thought
reasoning directly in the content field wrapped in tags, instead
of using a separate reasoning_content field or the legacy CRETIRE_REASONING
markers. The existing remove_think logic only handled CRETIRE_* tags, so
think blocks leaked into user-visible output even when remove_think was enabled.
- Add _ThinkStripState: a stateful filter that correctly handles tags
split across streaming chunk boundaries.
- Add _strip_think classmethod with regex patterns for both and
CRETIRE_* tags.
- Wire think_state into invoke_llm_stream so deltas are filtered before
reaching the accumulator.
- Add remove_think safety net in _StreamAccumulator so the final message
from tool-call rounds also gets stripped.
- Fix remove_think resolution to use defensive nested .get() so
pipelines missing output.misc don't raise AttributeError.
* fix(litellmchat): add missing _CLOSE_TAG class attribute on _ThinkStripState
* fix(provider): handle think stripping across LiteLLM paths
---------
Co-authored-by: WangCham <[email protected]>
RockChinQ