run_turn takes its tools, system prompt and tool caller; the chat passes the
MCP tools as before. The assistant node offers one tool per wired target,
reads its other inputs as context, streams its reply a sentence at a time,
warms the provider on `listening` and says so when the provider is not there.
Completions share the per-server HTTP client. wren replies when nothing reads
`unhandled`, stt shows a note when nothing was heard. /ai/control lists one
conversation per entry node with the messages to watch.
transcribe and speak share an httpx client per speech server with a 300 s
keep-alive, and run on the engine's loop. stt warms it with GET /v1/models
when an utterance opens, so a reply's first sentence finds the connection
open: a new one over a lossy link cost up to a second (openpanel V4).
- `stt`: one realtime transcription session per utterance on the speech
server's url, opened at `listening` true or the first chunk, committed at
false; a whole clip goes to /v1/audio/transcriptions. Returns at once and
publishes `partial` and `text` when the server answers.
- `tts`: one /v1/audio/speech call per sentence, cached by voice and text in
node state; a reference is marked `append` while earlier ones still play.
- wren: `reply` says what was applied, `unhandled` carries the prompt when
nothing was. The install hint names the git pin (PyPI's `wren` is another
package).
- ai/client: transcribe, speak and transcribe_stream beside stream_completion.
- A device socket publishes scalar `in` ports as {"type": "publish"} text,
in order with its binary frames.
- Pipeline.publish walks only what its values reach: a node downstream of a
consumer that published nothing no longer runs again (wren re-acted on
every microphone chunk).
ai/client.py streams the completion and puts the chunks back together
(Assembler), covering OpenAI's piecewise tool-call arguments, Ollama's whole
calls, OpenRouter's comment lines, unnumbered calls and in-stream errors.
The turn emits a delta event per piece and a message per round as before,
and the chat grows the last answer as deltas arrive.
A provider set under Settings (OpenAI-compatible; key encrypted in
flow-data/ai.json) turns on an Assistant panel in the sidebar. Each turn runs
the FastMCP tools in-process as the person chatting, streams the rounds over
SSE and keeps one transcript per user. The panel floats on any screen and
shares the editor's lane: a settings panel keeps the top half, the chat the
bottom. The MCP client is now built once, for the endpoint and the chat.
Melvin Strobl
stroblme