1000411eb2
Frame Engine (v3-framed): - Tick-based deterministic pipeline: frames advance on completion, not timers - FrameRecord/FrameTrace dataclasses for structured per-message tracing - /api/frames endpoint: queryable frame trace history (last 20 messages) - frame_trace HUD event with full pipeline visibility - Reflex=2F, Director=4F, Director+Interpreter=5F deterministic frame counts Expert Architecture (v4-eras): - PA node (pa_v1): routes to domain experts, holds user context - ExpertNode base: stateless executor with plan+execute two-LLM-call pattern - ErasExpertNode: eras2_production DB specialist with DESCRIBE-first discipline - Schema caching: DESCRIBE results reused across queries within session - Progress streaming: PA streams thinking message, expert streams per-tool progress - PARouting type for structured routing decisions UI Controls Split: - Separate thinker_controls from machine controls (current_controls is now a property) - Machine buttons persist across Thinker responses - Machine state parser handles both dict and list formats from Director - Normalized button format with go/payload field mapping WebSocket Architecture: - /ws/test: dedicated debug socket for test runner progress - /ws/trace: dedicated debug socket for HUD/frame trace events - /ws (chat): cleaned up, only deltas/controls/done/cleared - WS survives graph switch (re-attaches to new runtime) - Pipeline result reset on clear Test Infrastructure: - Live test streaming: on_result callback fires per check during execution - Frontend polling fallback (500ms) for proxy-buffered WS - frame_trace-first trace assertion (fixes stale perceived event bug) - action_match supports "or" patterns and multi-pattern matching - Trace window increased to 40 events - Graph-agnostic assertions (has X or Y) Test Suites: - smoketest.md: 12 steps covering all categories (~2min) - fast.md: 10 quick checks (~1min) - fast_v4.md: 10 v4-eras specific checks - expert_eras.md: eras domain tests (routing, DB, schema, errors) - expert_progress.md: progress streaming tests Other: - Shared db.py extracted from thinker_v2 (reused by experts) - InputNode prompt: few-shot examples, history as context summary - Director prompt: full tool signatures for add_state/reset_machine/destroy_machine - nginx no-cache headers for static files during development - Cache-busted static file references Scores: v3 smoketest 39/40, v4-eras fast 28/28, expert_eras 23/23 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
633 lines
28 KiB
Python
633 lines
28 KiB
Python
"""Frame Engine: tick-based deterministic pipeline execution.
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Replaces the imperative handle_message() with a frame-stepping model:
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- Each frame dispatches all nodes that have pending input
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- Frames advance on completion (not on a timer)
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- 0ms when idle, engine awaits external input
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- Deterministic ordering: reflex=2 frames, thinker=3-4, interpreter=5
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Works with any graph definition (v1, v2, v3). Node implementations unchanged.
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"""
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import asyncio
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import json
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import logging
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import time
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from dataclasses import dataclass, field, asdict
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from .types import Envelope, Command, InputAnalysis, ThoughtResult, DirectorPlan, PARouting
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log = logging.getLogger("runtime")
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# --- Frame Trace ---
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@dataclass
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class FrameRecord:
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"""One frame's execution record."""
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frame: int
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node: str # which node ran ("input", "director", ...)
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started: float = 0.0 # time.monotonic()
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ended: float = 0.0
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duration_ms: float = 0.0
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input_summary: str = "" # what the node received
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output_summary: str = "" # what the node produced
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route: str = "" # where output goes next ("director", "output+ui", ...)
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condition: str = "" # if a condition was evaluated ("reflex=True", ...)
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error: str = "" # if the node failed
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@dataclass
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class FrameTrace:
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"""Complete trace of one message through the pipeline."""
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message: str = ""
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graph: str = ""
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total_frames: int = 0
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total_ms: float = 0.0
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started: float = 0.0
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path: str = "" # "reflex", "director", "director+interpreter"
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frames: list = field(default_factory=list) # list of FrameRecord
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def to_dict(self) -> dict:
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return {
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"message": self.message[:100],
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"graph": self.graph,
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"total_frames": self.total_frames,
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"total_ms": round(self.total_ms, 1),
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"path": self.path,
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"frames": [
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{
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"frame": f.frame,
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"node": f.node,
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"duration_ms": round(f.duration_ms, 1),
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"input": f.input_summary[:200],
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"output": f.output_summary[:200],
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"route": f.route,
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"condition": f.condition,
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"error": f.error,
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}
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for f in self.frames
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],
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}
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class FrameEngine:
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"""Tick-based engine that steps through graph nodes frame by frame."""
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def __init__(self, graph: dict, nodes: dict, sink, history: list,
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send_hud, sensor, memorizer, ui_node, identity: str = "unknown",
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channel: str = "unknown", broadcast=None):
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self.graph = graph
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self.nodes = nodes
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self.sink = sink
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self.history = history
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self._send_hud = send_hud
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self.sensor = sensor
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self.memorizer = memorizer
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self.ui_node = ui_node
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self.identity = identity
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self.channel = channel
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self._broadcast = broadcast or (lambda e: None)
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self.frame = 0
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self.bus = {}
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self.conditions = graph.get("conditions", {})
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self.edges = [e for e in graph.get("edges", []) if e.get("type") == "data"]
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self.has_director = "director" in nodes and hasattr(nodes.get("director"), "decide")
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self.has_interpreter = "interpreter" in nodes
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self.has_pa = "pa" in nodes and hasattr(nodes.get("pa"), "route")
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# Discover available experts in this graph
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self._experts = {}
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for role, node in nodes.items():
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if role.startswith("expert_"):
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expert_name = role[7:] # "expert_eras" → "eras"
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self._experts[expert_name] = node
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if self.has_pa and self._experts:
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nodes["pa"].set_available_experts(list(self._experts.keys()))
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log.info(f"[frame] PA with experts: {list(self._experts.keys())}")
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# Frame trace — last message's complete trace, queryable via API
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self.last_trace: FrameTrace = FrameTrace()
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# History of recent traces (last 20 messages)
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self.trace_history: list[dict] = []
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self.MAX_TRACE_HISTORY = 20
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# --- Frame lifecycle helpers ---
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def _begin_frame(self, frame_num: int, node: str, input_summary: str = "") -> FrameRecord:
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"""Start a new frame. Returns the record to fill in."""
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self.frame = frame_num
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rec = FrameRecord(
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frame=frame_num,
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node=node,
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started=time.monotonic(),
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input_summary=input_summary,
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)
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self.last_trace.frames.append(rec)
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return rec
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def _end_frame(self, rec: FrameRecord, output_summary: str = "",
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route: str = "", condition: str = ""):
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"""Complete a frame record with output and timing."""
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rec.ended = time.monotonic()
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rec.duration_ms = (rec.ended - rec.started) * 1000
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rec.output_summary = output_summary
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rec.route = route
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rec.condition = condition
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log.info(f"[frame] F{rec.frame} {rec.node} "
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f"{rec.duration_ms:.0f}ms -> {route or 'done'}")
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def _begin_trace(self, text: str) -> FrameTrace:
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"""Start a new message trace."""
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trace = FrameTrace(
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message=text,
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graph=self.graph.get("name", "unknown"),
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started=time.monotonic(),
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)
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self.last_trace = trace
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self.frame = 0
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return trace
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def _end_trace(self, path: str):
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"""Finalize the trace and emit as HUD event."""
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t = self.last_trace
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t.total_frames = self.frame
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t.total_ms = (time.monotonic() - t.started) * 1000
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t.path = path
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# Store in history
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self.trace_history.append(t.to_dict())
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if len(self.trace_history) > self.MAX_TRACE_HISTORY:
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self.trace_history = self.trace_history[-self.MAX_TRACE_HISTORY:]
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log.info(f"[frame] trace: {path} {t.total_frames}F {t.total_ms:.0f}ms")
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async def _emit_trace_hud(self):
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"""Emit the completed frame trace as a single HUD event."""
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t = self.last_trace
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await self._send_hud({
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"node": "frame_engine",
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"event": "frame_trace",
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"trace": t.to_dict(),
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})
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# --- Main entry point ---
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async def process_message(self, text: str, dashboard: list = None) -> dict:
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"""Process a message through the frame pipeline.
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Returns {response, controls, memorizer, frames, trace}."""
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self._begin_trace(text)
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# Handle ACTION: prefix
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if text.startswith("ACTION:"):
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return await self._handle_action(text, dashboard)
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# Setup
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envelope = Envelope(
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text=text, user_id=self.identity,
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session_id="test", timestamp=time.strftime("%Y-%m-%d %H:%M:%S"),
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)
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self.sensor.note_user_activity()
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if dashboard is not None:
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self.sensor.update_browser_dashboard(dashboard)
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self.history.append({"role": "user", "content": text})
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# --- Frame 1: Input ---
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mem_ctx = self._build_context(dashboard)
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rec = self._begin_frame(1, "input", input_summary=text[:100])
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command = await self.nodes["input"].process(
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envelope, self.history, memory_context=mem_ctx,
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identity=self.identity, channel=self.channel)
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a = command.analysis
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cmd_summary = f"intent={a.intent} language={a.language} tone={a.tone} complexity={a.complexity}"
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# Check reflex condition
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is_reflex = self._check_condition("reflex", command=command)
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if is_reflex:
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self._end_frame(rec, output_summary=cmd_summary,
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route="output (reflex)", condition="reflex=True")
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await self._send_hud({"node": "runtime", "event": "reflex_path",
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"detail": f"{a.intent}/{a.complexity}"})
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return await self._run_reflex(command, mem_ctx)
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else:
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next_node = "pa" if self.has_pa else ("director" if self.has_director else "thinker")
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self._end_frame(rec, output_summary=cmd_summary,
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route=next_node, condition=f"reflex=False")
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# --- Frame 2+: Pipeline ---
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if self.has_pa:
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return await self._run_expert_pipeline(command, mem_ctx, dashboard)
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elif self.has_director:
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return await self._run_director_pipeline(command, mem_ctx, dashboard)
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else:
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return await self._run_thinker_pipeline(command, mem_ctx, dashboard)
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# --- Pipeline variants ---
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async def _run_reflex(self, command: Command, mem_ctx: str) -> dict:
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"""Reflex: Input(F1) → Output(F2)."""
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rec = self._begin_frame(2, "output", input_summary="reflex passthrough")
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thought = ThoughtResult(response=command.source_text, actions=[])
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response = await self._run_output_and_ui(thought, mem_ctx)
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self.history.append({"role": "assistant", "content": response})
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await self.memorizer.update(self.history)
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self._trim_history()
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self._end_frame(rec, output_summary=f"response[{len(response)}]")
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self._end_trace("reflex")
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await self._emit_trace_hud()
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return self._make_result(response)
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async def _run_expert_pipeline(self, command: Command, mem_ctx: str,
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dashboard: list = None) -> dict:
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"""Expert pipeline: Input(F1) → PA(F2) → Expert(F3) → [Interpreter(F4)] → Output."""
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a = command.analysis
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# Frame 2: PA routes
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rec = self._begin_frame(2, "pa",
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input_summary=f"intent={a.intent} topic={a.topic}")
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routing = await self.nodes["pa"].route(
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command, self.history, memory_context=mem_ctx,
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identity=self.identity, channel=self.channel)
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route_summary = f"expert={routing.expert} job={routing.job[:60]}"
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self._end_frame(rec, output_summary=route_summary,
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route=f"expert_{routing.expert}" if routing.expert != "none" else "output")
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# Stream thinking message to user
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if routing.thinking_message:
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await self.sink.send_delta(routing.thinking_message + "\n\n")
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# Direct PA response (no expert needed)
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if routing.expert == "none":
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rec = self._begin_frame(3, "output+ui",
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input_summary=f"pa_direct: {routing.response_hint[:80]}")
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thought = ThoughtResult(response=routing.response_hint, actions=[])
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response = await self._run_output_and_ui(thought, mem_ctx)
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self.history.append({"role": "assistant", "content": response})
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await self.memorizer.update(self.history)
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self._trim_history()
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self._end_frame(rec, output_summary=f"response[{len(response)}]")
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self._end_trace("pa_direct")
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await self._emit_trace_hud()
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return self._make_result(response)
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# Frame 3: Expert executes
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expert = self._experts.get(routing.expert)
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if not expert:
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log.error(f"[frame] expert '{routing.expert}' not found")
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thought = ThoughtResult(response=f"Expert '{routing.expert}' not available.")
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rec = self._begin_frame(3, "output+ui", input_summary="expert_not_found")
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response = await self._run_output_and_ui(thought, mem_ctx)
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self.history.append({"role": "assistant", "content": response})
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self._end_frame(rec, output_summary="error", error=f"expert '{routing.expert}' not found")
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self._end_trace("expert_error")
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await self._emit_trace_hud()
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return self._make_result(response)
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rec = self._begin_frame(3, f"expert_{routing.expert}",
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input_summary=f"job: {routing.job[:80]}")
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# Wrap expert's HUD to stream progress to user
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original_hud = expert.send_hud
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expert.send_hud = self._make_progress_wrapper(original_hud, routing.language)
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try:
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thought = await expert.execute(routing.job, routing.language)
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finally:
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expert.send_hud = original_hud
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thought_summary = (f"response[{len(thought.response)}] tool={thought.tool_used or 'none'} "
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f"actions={len(thought.actions)}")
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has_tool = bool(thought.tool_used and thought.tool_output)
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# Interpreter (conditional)
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if self.has_interpreter and has_tool:
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self._end_frame(rec, output_summary=thought_summary,
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route="interpreter", condition="has_tool_output=True")
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rec = self._begin_frame(4, "interpreter",
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input_summary=f"tool={thought.tool_used} output[{len(thought.tool_output)}]")
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interpreted = await self.nodes["interpreter"].interpret(
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thought.tool_used, thought.tool_output, routing.job)
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thought.response = interpreted.summary
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self._end_frame(rec, output_summary=f"summary[{len(interpreted.summary)}]", route="output+ui")
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rec = self._begin_frame(5, "output+ui",
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input_summary=f"interpreted: {interpreted.summary[:80]}")
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path = "expert+interpreter"
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else:
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self._end_frame(rec, output_summary=thought_summary,
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route="output+ui",
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condition="has_tool_output=False" if not has_tool else "")
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rec = self._begin_frame(4, "output+ui",
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input_summary=f"response: {thought.response[:80]}")
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path = "expert"
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# Clear progress text, render final response
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self.sink.reset()
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response = await self._run_output_and_ui(thought, mem_ctx)
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self.history.append({"role": "assistant", "content": response})
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await self.memorizer.update(self.history)
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self._trim_history()
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controls_count = len(self.ui_node.current_controls)
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self._end_frame(rec, output_summary=f"response[{len(response)}] controls={controls_count}")
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self._end_trace(path)
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await self._emit_trace_hud()
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return self._make_result(response)
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def _make_progress_wrapper(self, original_hud, language: str):
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"""Wrap an expert's send_hud to also stream progress deltas to the user."""
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sink = self.sink
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progress_map = {
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"tool_call": {"query_db": "Daten werden abgerufen..." if language == "de" else "Fetching data...",
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"emit_actions": "UI wird erstellt..." if language == "de" else "Building UI...",
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"create_machine": "Maschine wird erstellt..." if language == "de" else "Creating machine...",
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"_default": "Verarbeite..." if language == "de" else "Processing..."},
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"tool_result": {"_default": ""}, # silent on result
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"planned": {"_default": "Plan erstellt..." if language == "de" else "Plan ready..."},
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}
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async def wrapper(data: dict):
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await original_hud(data)
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event = data.get("event", "")
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if event in progress_map:
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tool = data.get("tool", "_default")
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msg = progress_map[event].get(tool, progress_map[event].get("_default", ""))
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if msg:
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await sink.send_delta(msg + "\n")
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return wrapper
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async def _run_director_pipeline(self, command: Command, mem_ctx: str,
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dashboard: list = None) -> dict:
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"""Director: Input(F1) → Director(F2) → Thinker(F3) → [Interpreter(F4)] → Output."""
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a = command.analysis
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# Frame 2: Director
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rec = self._begin_frame(2, "director",
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input_summary=f"intent={a.intent} topic={a.topic}")
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plan = await self.nodes["director"].decide(command, self.history, memory_context=mem_ctx)
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plan_summary = f"goal={plan.goal} tools={len(plan.tool_sequence)} hint={plan.response_hint[:50]}"
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self._end_frame(rec, output_summary=plan_summary, route="thinker")
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# Frame 3: Thinker
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rec = self._begin_frame(3, "thinker",
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input_summary=plan_summary[:100])
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thought = await self.nodes["thinker"].process(
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command, plan, self.history, memory_context=mem_ctx)
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thought_summary = (f"response[{len(thought.response)}] tool={thought.tool_used or 'none'} "
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f"actions={len(thought.actions)} machines={len(thought.machine_ops)}")
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has_tool = bool(thought.tool_used and thought.tool_output)
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# Check interpreter condition
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if self.has_interpreter and has_tool:
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self._end_frame(rec, output_summary=thought_summary,
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route="interpreter", condition="has_tool_output=True")
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# Frame 4: Interpreter
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rec = self._begin_frame(4, "interpreter",
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input_summary=f"tool={thought.tool_used} output[{len(thought.tool_output)}]")
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interpreted = await self.nodes["interpreter"].interpret(
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thought.tool_used, thought.tool_output, command.source_text)
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thought.response = interpreted.summary
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interp_summary = f"summary[{len(interpreted.summary)}] facts={interpreted.key_facts}"
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self._end_frame(rec, output_summary=interp_summary, route="output+ui")
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# Frame 5: Output
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rec = self._begin_frame(5, "output+ui",
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input_summary=f"interpreted: {interpreted.summary[:80]}")
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path = "director+interpreter"
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else:
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self._end_frame(rec, output_summary=thought_summary,
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route="output+ui",
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condition="has_tool_output=False" if not has_tool else "")
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# Frame 4: Output
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rec = self._begin_frame(4, "output+ui",
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input_summary=f"response: {thought.response[:80]}")
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path = "director"
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|
|
|
response = await self._run_output_and_ui(thought, mem_ctx)
|
|
self.history.append({"role": "assistant", "content": response})
|
|
await self.memorizer.update(self.history)
|
|
self._trim_history()
|
|
|
|
controls_count = len(self.ui_node.current_controls)
|
|
self._end_frame(rec, output_summary=f"response[{len(response)}] controls={controls_count}")
|
|
self._end_trace(path)
|
|
await self._emit_trace_hud()
|
|
return self._make_result(response)
|
|
|
|
async def _run_thinker_pipeline(self, command: Command, mem_ctx: str,
|
|
dashboard: list = None) -> dict:
|
|
"""v1: Input(F1) → Thinker(F2) → Output(F3)."""
|
|
a = command.analysis
|
|
|
|
# Frame 2: Thinker
|
|
rec = self._begin_frame(2, "thinker",
|
|
input_summary=f"intent={a.intent} topic={a.topic}")
|
|
|
|
director = self.nodes.get("director")
|
|
if director and hasattr(director, "plan"):
|
|
is_complex = command.analysis.complexity == "complex"
|
|
text = command.source_text
|
|
is_data_request = (command.analysis.intent in ("request", "action")
|
|
and any(k in text.lower()
|
|
for k in ["daten", "data", "database", "db", "tabelle", "table",
|
|
"query", "abfrage", "untersuche", "investigate",
|
|
"analyse", "analyze", "customer", "kunde"]))
|
|
if is_complex or (is_data_request and len(text.split()) > 8):
|
|
await director.plan(self.history, self.memorizer.state, text)
|
|
mem_ctx = self._build_context(dashboard)
|
|
|
|
thought = await self.nodes["thinker"].process(command, self.history, memory_context=mem_ctx)
|
|
if director and hasattr(director, "current_plan"):
|
|
director.current_plan = ""
|
|
|
|
thought_summary = f"response[{len(thought.response)}] tool={thought.tool_used or 'none'}"
|
|
self._end_frame(rec, output_summary=thought_summary, route="output+ui")
|
|
|
|
# Frame 3: Output
|
|
rec = self._begin_frame(3, "output+ui",
|
|
input_summary=f"response: {thought.response[:80]}")
|
|
response = await self._run_output_and_ui(thought, mem_ctx)
|
|
self.history.append({"role": "assistant", "content": response})
|
|
await self.memorizer.update(self.history)
|
|
if director and hasattr(director, "update"):
|
|
await director.update(self.history, self.memorizer.state)
|
|
self._trim_history()
|
|
|
|
self._end_frame(rec, output_summary=f"response[{len(response)}]")
|
|
self._end_trace("thinker")
|
|
await self._emit_trace_hud()
|
|
return self._make_result(response)
|
|
|
|
async def _handle_action(self, text: str, dashboard: list = None) -> dict:
|
|
"""Handle ACTION: messages (button clicks)."""
|
|
parts = text.split("|", 1)
|
|
action = parts[0].replace("ACTION:", "").strip()
|
|
data = None
|
|
if len(parts) > 1:
|
|
try:
|
|
data = json.loads(parts[1].replace("data:", "").strip())
|
|
except (json.JSONDecodeError, Exception):
|
|
pass
|
|
|
|
self.sensor.note_user_activity()
|
|
|
|
# Frame 1: Try machine transition (no LLM)
|
|
rec = self._begin_frame(1, "ui", input_summary=f"action={action}")
|
|
handled, transition_result = self.ui_node.try_machine_transition(action)
|
|
if handled:
|
|
await self._send_hud({"node": "ui", "event": "machine_transition",
|
|
"action": action, "detail": transition_result})
|
|
controls = self.ui_node.get_machine_controls()
|
|
for ctrl in self.ui_node.current_controls:
|
|
if not ctrl.get("machine_id"):
|
|
controls.append(ctrl)
|
|
self.ui_node.current_controls = controls
|
|
await self.sink.send_controls(controls)
|
|
await self._send_hud({"node": "ui", "event": "controls", "controls": controls})
|
|
self.sink.reset()
|
|
for i in range(0, len(transition_result), 12):
|
|
await self.sink.send_delta(transition_result[i:i + 12])
|
|
await self.sink.send_done()
|
|
self.history.append({"role": "user", "content": f"[clicked {action}]"})
|
|
self.history.append({"role": "assistant", "content": transition_result})
|
|
|
|
self._end_frame(rec, output_summary=f"machine_transition: {transition_result[:80]}")
|
|
self._end_trace("action_machine")
|
|
await self._emit_trace_hud()
|
|
return self._make_result(transition_result)
|
|
|
|
# Try local UI action
|
|
result, controls = await self.ui_node.process_local_action(action, data)
|
|
if result is not None:
|
|
if controls:
|
|
await self.sink.send_controls(controls)
|
|
self.sink.reset()
|
|
for i in range(0, len(result), 12):
|
|
await self.sink.send_delta(result[i:i + 12])
|
|
await self.sink.send_done()
|
|
self.history.append({"role": "user", "content": f"[clicked {action}]"})
|
|
self.history.append({"role": "assistant", "content": result})
|
|
|
|
self._end_frame(rec, output_summary=f"local_action: {result[:80]}")
|
|
self._end_trace("action_local")
|
|
await self._emit_trace_hud()
|
|
return self._make_result(result)
|
|
|
|
# Complex action — needs full pipeline
|
|
self._end_frame(rec, output_summary="no local handler", route="director/thinker")
|
|
|
|
action_desc = f"ACTION: {action}"
|
|
if data:
|
|
action_desc += f" | data: {json.dumps(data)}"
|
|
self.history.append({"role": "user", "content": action_desc})
|
|
|
|
mem_ctx = self._build_context(dashboard)
|
|
command = Command(
|
|
analysis=InputAnalysis(intent="action", topic=action, complexity="simple"),
|
|
source_text=action_desc)
|
|
|
|
if self.has_director:
|
|
return await self._run_director_pipeline(command, mem_ctx, dashboard)
|
|
else:
|
|
return await self._run_thinker_pipeline(command, mem_ctx, dashboard)
|
|
|
|
# --- Helpers ---
|
|
|
|
def _build_context(self, dashboard: list = None) -> str:
|
|
"""Build the full context string for nodes."""
|
|
sensor_lines = self.sensor.get_context_lines()
|
|
director = self.nodes.get("director")
|
|
director_line = director.get_context_line() if director else ""
|
|
mem_ctx = self.memorizer.get_context_block(
|
|
sensor_lines=sensor_lines, ui_state=self.ui_node.state)
|
|
if director_line:
|
|
mem_ctx += f"\n\n{director_line}"
|
|
machine_summary = self.ui_node.get_machine_summary()
|
|
if machine_summary:
|
|
mem_ctx += f"\n\n{machine_summary}"
|
|
if dashboard is not None:
|
|
mem_ctx += f"\n\n{self._format_dashboard(dashboard)}"
|
|
sensor_flags = self.sensor.consume_flags()
|
|
if sensor_flags:
|
|
flag_lines = ["Sensor flags:"]
|
|
for f in sensor_flags:
|
|
if f["type"] == "idle_return":
|
|
flag_lines.append(f" - User returned after {f['away_duration']} away.")
|
|
elif f["type"] == "workspace_mismatch":
|
|
flag_lines.append(f" - Workspace mismatch: {f['detail']}")
|
|
mem_ctx += "\n\n" + "\n".join(flag_lines)
|
|
return mem_ctx
|
|
|
|
def _format_dashboard(self, dashboard: list) -> str:
|
|
"""Format dashboard controls into context string."""
|
|
server_controls = self.ui_node.current_controls
|
|
server_buttons = [str(c.get("label", "")) for c in server_controls if isinstance(c, dict) and c.get("type") == "button"]
|
|
browser_buttons = [str(c.get("label", "")) for c in dashboard if isinstance(c, dict) and c.get("type") == "button"] if dashboard else []
|
|
lines = []
|
|
if server_buttons and not browser_buttons:
|
|
lines.append(f"WARNING: Server sent {len(server_buttons)} controls but dashboard shows NONE.")
|
|
lines.append(f" Expected: {', '.join(server_buttons)}")
|
|
lines.append(" Controls failed to render. You MUST re-emit them in ACTIONS.")
|
|
elif server_buttons and sorted(server_buttons) != sorted(browser_buttons):
|
|
lines.append("WARNING: Dashboard mismatch.")
|
|
lines.append(f" Server: {', '.join(server_buttons)}")
|
|
lines.append(f" Browser: {', '.join(browser_buttons) or 'nothing'}")
|
|
if not dashboard:
|
|
lines.append("Dashboard: empty")
|
|
else:
|
|
lines.append("Dashboard (user sees):")
|
|
for ctrl in dashboard:
|
|
ctype = ctrl.get("type", "unknown")
|
|
if ctype == "button":
|
|
lines.append(f" - Button: {ctrl.get('label', '?')}")
|
|
elif ctype == "table":
|
|
lines.append(f" - Table: {len(ctrl.get('data', []))} rows")
|
|
else:
|
|
lines.append(f" - {ctype}: {ctrl.get('label', ctrl.get('text', '?'))}")
|
|
return "\n".join(lines)
|
|
|
|
async def _run_output_and_ui(self, thought: ThoughtResult, mem_ctx: str) -> str:
|
|
"""Run Output and UI nodes in parallel. Returns response text."""
|
|
self.sink.reset()
|
|
output_task = asyncio.create_task(
|
|
self.nodes["output"].process(thought, self.history, self.sink, memory_context=mem_ctx))
|
|
ui_task = asyncio.create_task(
|
|
self.ui_node.process(thought, self.history, memory_context=mem_ctx))
|
|
response, controls = await asyncio.gather(output_task, ui_task)
|
|
if controls:
|
|
await self.sink.send_controls(controls)
|
|
return response
|
|
|
|
def _check_condition(self, name: str, command: Command = None,
|
|
thought: ThoughtResult = None) -> bool:
|
|
"""Evaluate a named condition."""
|
|
if name == "reflex" and command:
|
|
return (command.analysis.intent == "social"
|
|
and command.analysis.complexity == "trivial")
|
|
if name == "has_tool_output" and thought:
|
|
return bool(thought.tool_used and thought.tool_output)
|
|
return False
|
|
|
|
def _make_result(self, response: str) -> dict:
|
|
"""Build the result dict returned to callers."""
|
|
return {
|
|
"response": response,
|
|
"controls": self.ui_node.current_controls,
|
|
"memorizer": self.memorizer.state,
|
|
"frames": self.frame,
|
|
"trace": self.last_trace.to_dict(),
|
|
}
|
|
|
|
def _trim_history(self):
|
|
if len(self.history) > 40:
|
|
self.history[:] = self.history[-40:]
|