src/state/schema.py

"""

VentureForge State Schema

=========================

Single source of truth for all data passed between agents in the

LangGraph orchestration layer.



All agents read from and write to VentureForgeState instances.

Updates are immutable: agents never mutate state in place. Instead,

they return dict patches that the graph layer merges into a new

copy of the state via model_copy(update=...).



Pydantic v2 is required:

    pip install pydantic>=2.0

"""

from __future__ import annotations

from datetime import datetime, timezone
from enum import Enum
from typing import Any, Literal
from uuid import UUID, uuid4

from pydantic import BaseModel, Field, computed_field, field_validator, model_validator

# =============================================================================
# ENUMS
# =============================================================================


class DataSource(str, Enum):
    """Where a pain point or piece of evidence originated."""

    REDDIT = "reddit"
    HACKERNEWS = "hackernews"
    PRODUCTHUNT = "producthunt"
    WEB = "web"
    YOUTUBE = "youtube"


class Verdict(str, Enum):
    """Final recommendation from the Scorer."""

    PURSUE = "pursue"
    EXPLORE = "explore"
    PARK = "park"


class TargetAgent(str, Enum):
    """Which worker the reflection loop should send the revision to."""

    PAIN_POINT_MINER = "pain_point_miner"
    IDEA_GENERATOR = "idea_generator"
    PITCH_WRITER = "pitch_writer"


class PipelineStage(str, Enum):
    """Current stage of the pipeline."""

    IDLE = "idle"
    MINING = "mining"
    GENERATING = "generating"
    SCORING = "scoring"
    WRITING = "writing"
    CRITIQUING = "critiquing"
    REVISING = "revising"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELLED = "cancelled"


# =============================================================================
# RUBRIC MODELS (binary booleans — all checks are True/False)
# =============================================================================


class PainPointRubric(BaseModel):
    """Binary rubric applied by the Pain Point Miner to self-filter output."""

    is_genuine_current_frustration: bool
    has_verbatim_quote: bool
    user_segment_specific: bool

    @computed_field
    @property
    def all_pass(self) -> bool:
        return all(self.model_dump(exclude={'all_pass'}).values())


class FeasibilityRubric(BaseModel):
    """Binary checks for feasibility (Scorer).



    PG-style: manual-first and schlep are POSITIVE signals, not penalties.

    """

    can_be_solved_manually_first: bool
    has_schlep_or_unsexy_advantage: bool
    can_2_3_person_team_build_mvp_in_6_months: bool


class DemandRubric(BaseModel):
    """Binary checks for demand (Scorer)."""

    addresses_at_least_2_pain_points: bool
    is_painkiller_not_vitamin: bool
    has_clear_vein_of_early_adopters: bool


class NoveltyRubric(BaseModel):
    """Binary checks for novelty (Scorer)."""

    differentiated_from_current_behavior: bool
    has_path_out_of_niche: bool


class FatalFlaw(BaseModel):
    """A specific, falsifiable reason an idea might fail."""

    flaw: str
    severity: Literal["fatal", "major", "minor"]


class CritiqueRubric(BaseModel):
    """Binary checks applied by the Critic to pitch briefs.

    PG pressure-test criteria with concrete, non-vague definitions.
    Reduced from 7 to 5 checks - removed manual outreach requirements.
    Added minimum_evidence_sources check to ensure multiple sources.
    Added scorer_verdict_justified check to validate Scorer output.
    Phase 3: Added validation_plan_complete check for discovery questions.
    """

    all_claims_evidence_backed: bool
    no_hallucinated_source_urls: bool
    tagline_under_12_words: bool
    target_is_contained_fire: bool
    competition_embraced_with_thesis: bool
    minimum_evidence_sources: bool  # At least 2 distinct source URLs
    scorer_verdict_justified: bool  # Scorer's "pursue" verdict aligns with pitch quality
    validation_plan_complete: bool  # Exactly 5 open-ended discovery questions


# =============================================================================
# AGENT OUTPUT MODELS
# =============================================================================


class PainPointEvidence(BaseModel):
    """A single piece of evidence supporting a pain point."""
    
    source_url: str = Field(..., min_length=5)
    raw_quote: str = Field(..., min_length=5)
    source: DataSource


class PainPoint(BaseModel):
    """A structured user pain point with multiple evidence sources."""

    id: UUID = Field(default_factory=uuid4)
    title: str = Field(..., min_length=5, max_length=200)
    description: str = Field(..., min_length=10, max_length=500)
    rubric: PainPointRubric
    passes_rubric: bool
    
    # Multiple evidence sources (1-10 per pain point)
    evidence: list[PainPointEvidence] = Field(min_length=1, max_length=10)
    
    # Computed properties for backward compatibility
    @property
    def source_url(self) -> str:
        """Primary source URL (first evidence item)."""
        return self.evidence[0].source_url if self.evidence else ""
    
    @property
    def raw_quote(self) -> str:
        """Primary quote (first evidence item)."""
        return self.evidence[0].raw_quote if self.evidence else ""
    
    @property
    def source(self) -> DataSource:
        """Primary source (first evidence item)."""
        return self.evidence[0].source if self.evidence else DataSource.WEB
    
    @property
    def evidence_count(self) -> int:
        """Number of evidence sources."""
        return len(self.evidence)


class Idea(BaseModel):
    """A startup idea generated from clustered pain points."""

    id: UUID = Field(default_factory=uuid4)
    title: str = Field(..., min_length=3, max_length=100)
    one_liner: str = Field(..., max_length=120)
    problem: str = Field(..., min_length=20, max_length=800)
    solution: str = Field(..., min_length=20, max_length=800)
    target_user: str = Field(..., min_length=5, max_length=200)
    key_features: list[str] = Field(default_factory=list, min_length=3, max_length=5)
    addresses_pain_point_ids: list[UUID] = Field(default_factory=list, min_length=2)


class ScoredIdea(BaseModel):
    """An idea with binary rubric evaluation applied by the Scorer."""

    idea_id: UUID
    reasoning_trace: str
    feasibility_rubric: FeasibilityRubric
    demand_rubric: DemandRubric
    novelty_rubric: NoveltyRubric
    core_assumption: str
    fatal_flaws: list[FatalFlaw] = Field(default_factory=list)
    yes_count: int = Field(..., ge=0, le=8)
    total_checks: int = 8
    verdict: Literal["pursue", "explore", "park"]
    one_risk: str = Field(..., max_length=300)
    rank: int | None = None

    @model_validator(mode="after")
    def _derive_verdict(self) -> "ScoredIdea":
        """Derive verdict from yes_count AND fatal flaw severity.



        Per the Scorer prompt: a fatal severity flaw ALWAYS parks,

        regardless of yes_count.

        """
        has_fatal = any(f.severity == "fatal" for f in self.fatal_flaws)
        if self.yes_count <= 2 or has_fatal:
            self.verdict = "park"
        elif 3 <= self.yes_count <= 5 and not has_fatal:
            self.verdict = "explore"
        elif self.yes_count >= 6 and not has_fatal:
            self.verdict = "pursue"
        return self


class CompetitiveLandscape(BaseModel):
    """Competitive analysis for a startup idea.
    
    Per orchestration.json and PROMPTS.md, this captures:
    - What users currently do instead of using this product
    - Direct competitors solving the same problem
    - The specific habit/behavior this product must replace
    """

    current_behavior: str = Field(
        ...,
        min_length=20,
        description="What customers do today instead of using this product (the real competitor)"
    )
    direct_competitors: str = Field(
        ...,
        min_length=10,
        description="Companies solving the same problem, if any"
    )
    real_enemy: str = Field(
        ...,
        min_length=10,
        description="The specific habit or behavior this product must replace"
    )
    
    @field_validator('direct_competitors', mode='before')
    @classmethod
    def convert_list_to_string(cls, v):
        """Convert list of competitors to comma-separated string.
        
        The LLM often generates a list like ["Conan", "vcpkg", "CMake"],
        but the schema expects a string like "Conan, vcpkg, CMake".
        """
        if isinstance(v, list):
            return ", ".join(str(item) for item in v)
        return v


class ValidationPlan(BaseModel):
    """Customer discovery and validation strategy.
    
    Per orchestration.json and PROMPTS.md, this includes:
    - 5 open-ended discovery questions for customer interviews
    - Specific signals that prove the problem is real
    """

    discovery_questions: list[str] = Field(
        ...,
        min_length=5,
        max_length=5,
        description="5 open-ended questions for customer discovery (no yes/no questions)"
    )
    validation_criteria: str = Field(
        ...,
        min_length=20,
        description="Specific signals that prove the problem is real and worth solving"
    )


class PitchBrief(BaseModel):
    """A one-page investor pitch brief written for a single idea.
    
    Extended per orchestration.json spec to include competitive analysis,
    differentiation, and validation planning.
    """

    idea_id: UUID
    title: str = Field(..., min_length=3, max_length=120)
    tagline: str = Field(..., max_length=120)
    problem: str = Field(..., min_length=20)
    solution: str = Field(..., min_length=20)
    target_user: str = Field(..., min_length=5)
    market_opportunity: str = Field(..., min_length=20)
    competitive_landscape: CompetitiveLandscape
    differentiation: str = Field(
        ...,
        min_length=20,
        description="Why someone would switch from current behavior to this product"
    )
    validation_plan: ValidationPlan
    business_model: str = Field(..., min_length=20)
    go_to_market: str = Field(..., min_length=20)
    key_risk: str = Field(..., min_length=10)
    next_steps: str = Field(..., min_length=10)
    # FIX #4: Reduced min_length from 2 to 1 (LLM often generates only 1 link)
    evidence_links: list[str] = Field(..., min_length=1)
    markdown_content: str = Field(..., min_length=100)
    revision_count: int = Field(default=0, ge=0, le=2)
    
    # FIX #3: Validate tagline word count (prompt says "under 12 words")
    @field_validator('tagline')
    @classmethod
    def validate_tagline_word_count(cls, v: str) -> str:
        word_count = len(v.split())
        if word_count > 12:
            raise ValueError(f"Tagline must be under 12 words (got {word_count})")
        return v


class Critique(BaseModel):
    """Output of the Critic agent after reviewing a pitch brief.



    The model validator enforces that ``all_pass``, ``failing_checks``,

    ``approval_status`` and ``target_agent`` are consistent with the

    rubric and the documented target-agent priority rules.

    """

    idea_id: UUID
    reasoning_trace: str
    rubric: CritiqueRubric
    all_pass: bool
    approval_status: Literal["approved", "revise", "max_revisions_reached"]
    failing_checks: list[str] = Field(default_factory=list)
    target_agent: Literal["pain_point_miner", "idea_generator", "pitch_writer"]
    revision_feedback: str = Field(..., min_length=10)

    @model_validator(mode="after")
    def _sync_from_rubric(self) -> "Critique":
        """Ensure all_pass/failing_checks/approval_status/target_agent match rubric.

        Target-agent priority (revised for better revision targeting):
        1. pitch_writer — if no_hallucinated_source_urls fails (invented URLs)
           OR if only writing/tone checks fail (tagline_under_12_words)
           OR if minimum_evidence_sources fails (need to cite more sources).
        2. idea_generator — if positioning checks fail
           (target_is_contained_fire or competition_embraced_with_thesis)
           OR if scorer_verdict_justified fails (idea quality doesn't match "pursue" verdict)
           and evidence checks pass.
        3. pain_point_miner — ONLY if all_claims_evidence_backed fails AND
           no_hallucinated_source_urls passes (evidence is genuinely weak,
           not just misreferenced).
        
        Rationale: Hallucinated URLs are a pitch_writer problem (making up sources).
        Weak evidence is a pain_point_miner problem (need better sources).
        Insufficient evidence count is a pitch_writer problem (cite more existing sources).
        Unjustified scorer verdict is an idea_generator problem (idea needs rethinking).
        """
        rubric_dict = self.rubric.model_dump()
        self.failing_checks = [k for k, v in rubric_dict.items() if not v]
        self.all_pass = len(self.failing_checks) == 0

        # Approval status derived solely from rubric
        self.approval_status = "approved" if self.all_pass else "revise"

        # Enforce target_agent priority only when revision is required
        if not self.all_pass:
            r = self.rubric
            
            # Check which categories failed
            hallucinated_urls = not r.no_hallucinated_source_urls
            weak_claims = not r.all_claims_evidence_backed
            insufficient_sources = not r.minimum_evidence_sources
            scorer_issue = not r.scorer_verdict_justified
            positioning_failed = (not r.target_is_contained_fire) or (not r.competition_embraced_with_thesis)
            writing_failed = not r.tagline_under_12_words
            
            # Priority 1: Hallucinated URLs → pitch_writer (fix the URLs)
            if hallucinated_urls:
                self.target_agent = "pitch_writer"
            # Priority 2: Positioning or scorer issues → idea_generator
            elif positioning_failed or scorer_issue:
                self.target_agent = "idea_generator"
            # Priority 3: Insufficient evidence sources → pitch_writer (cite more)
            elif insufficient_sources:
                self.target_agent = "pitch_writer"
            # Priority 4: Weak claims but URLs are real → pitch_writer (tone down claims)
            elif weak_claims and not hallucinated_urls:
                self.target_agent = "pitch_writer"
            # Priority 5: Writing/tone only → pitch_writer
            else:
                self.target_agent = "pitch_writer"

        return self


class RunEvent(BaseModel):
    """A single high-level event emitted during a pipeline run.



    Used by the UI to render an agent execution log.

    """

    timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    agent: str
    stage: PipelineStage
    kind: Literal["info", "warning", "error"] = "info"
    message: str
    idea_id: UUID | None = None


# =============================================================================
# SHARED STATE
# =============================================================================


class VentureForgeState(BaseModel):
    """

    The single shared state object passed between all LangGraph nodes.



    Nodes should never mutate this object in place. Instead, each node

    returns a ``dict`` of field updates; LangGraph merges them into a

    new copy via ``model_copy(update=...)``.



    Immutable update example inside a node:

        return {

            "pain_points": new_pain_points,

            "current_stage": PipelineStage.GENERATING,

            "next_node": "idea_generator",

        }

    """

    # -----------------------------------------------------------------
    # Input / Run configuration
    # -----------------------------------------------------------------
    domain: str = Field(..., min_length=2, max_length=100)
    max_pain_points: int = Field(default=30, ge=5, le=100)
    ideas_per_run: int = Field(default=5, ge=1, le=20)
    top_n_pitches: int = Field(default=3, ge=1, le=10)
    max_revisions: int = Field(default=3, ge=0, le=5)

    # -----------------------------------------------------------------
    # Pipeline data (populated by worker agents)
    # -----------------------------------------------------------------
    pain_points: list[PainPoint] = Field(default_factory=list)
    ideas: list[Idea] = Field(default_factory=list)
    scored_ideas: list[ScoredIdea] = Field(default_factory=list)
    pitch_briefs: list[PitchBrief] = Field(default_factory=list)

    # -----------------------------------------------------------------
    # Reflection loop state (per-pitch revision tracking)
    # -----------------------------------------------------------------
    critique: Critique | None = None
    critiques: list[Critique] = Field(default_factory=list)
    revision_counts: dict[str, int] = Field(default_factory=dict)
    revision_feedback: str | None = None
    current_revision_idea_id: UUID | None = None  # Track which idea is being revised
    current_critique_index: int = Field(default=0, ge=0)  # Track which brief is being critiqued
    pain_point_miner_revision_count: int = Field(default=0, ge=0)  # Track pain_point_miner revisions separately
    
    # -----------------------------------------------------------------
    # Retry tracking (prevent infinite loops when validation fails)
    # -----------------------------------------------------------------
    idea_generation_attempts: int = Field(default=0, ge=0)
    """Total LLM invocations by idea_generator (validation retries + revisions)."""
    
    pitch_writer_attempts: int = Field(default=0, ge=0)
    """Total LLM invocations by pitch_writer (prevents infinite loops when JSON parsing fails)."""
    
    scorer_attempts: int = Field(default=0, ge=0)
    """Total LLM invocations by scorer (prevents infinite loops when JSON parsing fails)."""
    
    max_idea_generation_attempts: int = Field(default=10, ge=1)
    """Max validation retries per idea_generator run (when LLM produces invalid ideas)."""
    
    max_total_llm_calls_per_agent: int = Field(default=15, ge=1)
    """Global cap on total LLM calls per agent (validation + revisions combined).
    
    Prevents compounding retry mechanisms from causing excessive LLM usage.
    Example: max_idea_generation_attempts=10 + max_revisions=3 could theoretically
    cause 10 + 10 + 10 = 30 calls. This cap limits the total across all retries.
    """

    # -----------------------------------------------------------------
    # Orchestration control (set by orchestrator node)
    # -----------------------------------------------------------------
    next_node: str = Field(default="orchestrator")
    current_stage: PipelineStage = Field(default=PipelineStage.IDLE)
    previous_stage: PipelineStage | None = None

    # -----------------------------------------------------------------
    # Metadata & diagnostics
    # -----------------------------------------------------------------
    run_id: str = Field(default_factory=lambda: str(uuid4())[:8])
    started_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    completed_at: datetime | None = None
    error_log: list[str] = Field(default_factory=list)
    agent_timings: dict[str, float] = Field(
        default_factory=dict,
        description="agent_id -> elapsed seconds",
    )
    events: list[RunEvent] = Field(
        default_factory=list,
        description="High-level run events emitted by agents/orchestrator for UI logs.",
    )

    # -----------------------------------------------------------------
    # Derived properties (convenience for agent logic)
    # -----------------------------------------------------------------
    @computed_field
    @property
    def filtered_pain_points(self) -> list[PainPoint]:
        """Pain points that passed their own internal rubric."""
        return [pp for pp in self.pain_points if pp.passes_rubric]

    @computed_field
    @property
    def top_scored_ideas(self) -> list[ScoredIdea]:
        """Ideas sorted by yes_count desc, limited to top_n_pitches."""
        ranked = sorted(
            self.scored_ideas,
            key=lambda s: (s.yes_count, s.rank or 0),
            reverse=True,
        )
        return ranked[: self.top_n_pitches]

    @computed_field
    @property
    def can_revise(self) -> bool:
        """True if the most recently critiqued pitch can still be revised.
        
        For pain_point_miner revisions, check the global pain_point_miner_revision_count.
        For other agents, check the per-idea revision_counts.
        """
        if self.critique is None:
            return True
        
        if self.critique.target_agent == "pain_point_miner":
            return self.pain_point_miner_revision_count < self.max_revisions
        else:
            return self.get_revision_count(self.critique.idea_id) < self.max_revisions

    @computed_field
    @property
    def is_complete(self) -> bool:
        """All expected outputs are present."""
        return all(
            [
                self.pain_points,
                self.ideas,
                self.scored_ideas,
                self.pitch_briefs,
                self.critique is not None,
            ]
        )

    # -----------------------------------------------------------------
    # Per-pitch revision helpers
    # -----------------------------------------------------------------
    def get_revision_count(self, idea_id: UUID) -> int:
        """Return the number of revisions already done for a specific idea."""
        return self.revision_counts.get(str(idea_id), 0)

    def increment_revision_count(self, idea_id: UUID) -> "VentureForgeState":
        """Return a new state with the revision count bumped for this idea."""
        updated = dict(self.revision_counts)
        updated[str(idea_id)] = updated.get(str(idea_id), 0) + 1
        return self.model_copy(update={"revision_counts": updated})

    # -----------------------------------------------------------------
    # Immutable helpers
    # -----------------------------------------------------------------
    def log_error(self, agent_id: str, message: str) -> dict[str, Any]:
        """Return a state patch that appends an error and emits an error event."""
        entry = f"[{agent_id}] {message}"
        events = self.events + [
            RunEvent(
                agent=agent_id,
                stage=self.current_stage,
                kind="error",
                message=message,
            )
        ]
        return {"error_log": self.error_log + [entry], "events": events}

    def add_event(
        self,
        *,
        agent: str,
        stage: PipelineStage,
        kind: Literal["info", "warning", "error"] = "info",
        message: str,
        idea_id: UUID | None = None,
    ) -> dict[str, Any]:
        """Return a state patch that appends a RunEvent to ``events``.

        Agents and the orchestrator should call this to record high-level
        progress suitable for a UI log.
        
        NOTE: Use merge_patches() to combine multiple patches that include events,
        otherwise the last patch will overwrite previous events.
        """
        ev = RunEvent(agent=agent, stage=stage, kind=kind, message=message, idea_id=idea_id)
        return {"events": self.events + [ev]}
    
    @staticmethod
    def merge_patches(*patches: dict[str, Any]) -> dict[str, Any]:
        """Merge multiple state patches, properly handling list fields.
        
        For list fields (events, error_log, critiques), concatenates instead of overwriting.
        For dict fields (revision_counts, agent_timings), merges keys.
        For scalar fields, last patch wins.
        
        Example:
            patch1 = {"ideas": [...], "events": [ev1]}
            patch2 = state.add_event(...)  # {"events": [ev2]}
            merged = VentureForgeState.merge_patches(patch1, patch2)
            # Result: {"ideas": [...], "events": [ev1, ev2]}
        """
        result: dict[str, Any] = {}
        
        # List fields that should be concatenated
        list_fields = {"events", "error_log", "critiques", "pain_points", "ideas", "scored_ideas", "pitch_briefs"}
        # Dict fields that should be merged
        dict_fields = {"revision_counts", "agent_timings"}
        
        for patch in patches:
            for key, value in patch.items():
                if key in list_fields and isinstance(value, list):
                    # Concatenate lists
                    existing = result.get(key, [])
                    if isinstance(existing, list):
                        result[key] = existing + value
                    else:
                        result[key] = value
                elif key in dict_fields and isinstance(value, dict):
                    # Merge dicts
                    existing = result.get(key, {})
                    if isinstance(existing, dict):
                        result[key] = {**existing, **value}
                    else:
                        result[key] = value
                else:
                    # Scalar fields: last patch wins
                    result[key] = value
        
        return result

    def record_timing(self, agent_id: str, elapsed_s: float) -> dict[str, Any]:
        """Return a state patch recording agent timing."""
        timing = {**self.agent_timings, agent_id: elapsed_s}
        return {"agent_timings": timing}

    def bump_revision(self, critique: Critique) -> dict[str, Any]:
        """
        Return a state patch that increments the appropriate revision counter,
        archives the critique, stores the critique, and prepares state for
        the target agent.
        
        For pain_point_miner revisions, increment the global pain_point_miner_revision_count
        instead of the per-idea revision_counts (since all ideas will be regenerated).
        """
        patch = {
            "critiques": self.critiques + [critique],
            "critique": critique,
            "revision_feedback": critique.revision_feedback,
            "previous_stage": self.current_stage,
            "current_stage": PipelineStage.REVISING,
            "next_node": critique.target_agent,
        }
        
        if critique.target_agent == "pain_point_miner":
            # Pain point miner revisions affect all ideas, so track globally
            patch["pain_point_miner_revision_count"] = self.pain_point_miner_revision_count + 1
        else:
            # Per-idea revisions for idea_generator and pitch_writer
            idea_id = str(critique.idea_id)
            updated_counts = {**self.revision_counts, idea_id: self.revision_counts.get(idea_id, 0) + 1}
            patch["revision_counts"] = updated_counts
        
        return patch

    def mark_completed(self) -> dict[str, Any]:
        """Return a state patch marking the pipeline as complete."""
        return {
            "current_stage": PipelineStage.COMPLETED,
            "next_node": "__end__",
            "completed_at": datetime.now(timezone.utc),
        }

    def mark_failed(self, reason: str) -> dict[str, Any]:
        """Return a state patch marking the pipeline as failed."""
        patch = {
            "current_stage": PipelineStage.FAILED,
            "next_node": "__end__",
            "completed_at": datetime.now(timezone.utc),
        }
        patch.update(self.log_error("orchestrator", reason))
        return patch

    def mark_cancelled(self, reason: str = "Cancelled by user") -> dict[str, Any]:
        """Return a state patch marking the pipeline as cancelled."""
        patch = {
            "current_stage": PipelineStage.CANCELLED,
            "next_node": "__end__",
            "completed_at": datetime.now(timezone.utc),
        }
        patch.update(
            self.add_event(
                agent="orchestrator",
                stage=self.current_stage,
                kind="warning",
                message=reason,
            )
        )
        return patch

    def reset_for_revision(self, target_agent: TargetAgent | str, idea_id: UUID) -> dict[str, Any]:
        """
        Clear only the data for the specific idea being revised.
        
        For pitch_writer revisions: only remove the pitch brief for this idea.
        For idea_generator revisions: remove the idea, its score, and its pitch brief.
        For pain_point_miner revisions: this is more complex - we need to regenerate
        pain points, which affects all ideas. In this case, we still do a full reset
        but track the idea_id for context. We also clear revision_counts since all
        ideas will be regenerated with new UUIDs.
        """
        target = target_agent if isinstance(target_agent, str) else target_agent.value
        updates: dict[str, Any] = {
            "current_stage": PipelineStage.REVISING,
            "current_revision_idea_id": idea_id,
        }
        
        if target == "pain_point_miner":
            # Pain point revisions are expensive - they require full regeneration.
            # Instead of clearing everything, we'll keep existing pain points and
            # let pain_point_miner ADD new ones (append mode).
            # Ideas/scores/briefs stay intact unless they reference deleted pain points.
            # This preserves good work while addressing the critic's feedback.
            # 
            # NOTE: If you need full regeneration (e.g., domain pivot), use a new run.
            updates.update({
                "critique": None,
                # Don't clear pain_points - let pain_point_miner append
                # Don't clear ideas/scored_ideas/pitch_briefs - preserve good work
                # The critic's feedback will guide pain_point_miner to add better sources
            })
        elif target == "idea_generator":
            # Remove only the specific idea and its downstream artifacts
            filtered_ideas = [i for i in self.ideas if i.id != idea_id]
            filtered_scored = [s for s in self.scored_ideas if s.idea_id != idea_id]
            filtered_briefs = [b for b in self.pitch_briefs if b.idea_id != idea_id]
            updates.update({
                "ideas": filtered_ideas,
                "scored_ideas": filtered_scored,
                "pitch_briefs": filtered_briefs,
                "critique": None,
            })
        elif target == "pitch_writer":
            # Remove only the specific pitch brief
            filtered_briefs = [b for b in self.pitch_briefs if b.idea_id != idea_id]
            
            # Reset critique index to 0 so we re-review from the start
            # This ensures the revised brief is reviewed in the correct order
            updates.update({
                "pitch_briefs": filtered_briefs,
                "critique": None,
                "current_critique_index": 0,  # Reset to start
            })
        return updates


# =============================================================================
# QUICK VALIDATION
# =============================================================================

if __name__ == "__main__":
    # Smoke test: construct a minimal state end-to-end
    state = VentureForgeState(domain="developer tools")

    pp = PainPoint(
        title="No good local LLM tooling",
        description="Developers frustrated by lack of simple local LLM runners.",
        rubric=PainPointRubric(
            is_genuine_current_frustration=True,
            has_verbatim_quote=True,
            user_segment_specific=True,
        ),
        passes_rubric=True,
        source_url="https://reddit.com/r/programming/comments/abc123",
        raw_quote="I wish there was a simple local LLM runner that just works",
        source=DataSource.REDDIT,
    )

    pp2 = PainPoint(
        title="Managing multiple local models is painful",
        description="Developers struggle to keep track of which local models are installed and configured.",
        rubric=PainPointRubric(
            is_genuine_current_frustration=True,
            has_verbatim_quote=True,
            user_segment_specific=True,
        ),
        passes_rubric=True,
        source_url="https://news.ycombinator.com/item?id=123456",
        raw_quote="I have 5 different local LLM tools and I can never remember which one I configured for what.",
        source=DataSource.HACKERNEWS,
    )

    idea = Idea(
        title="DevFlow LLM",
        one_liner="One-click local LLM workspace for developers.",
        problem="Setting up local LLM environments is complicated.",
        solution="A CLI tool that downloads, configures, and runs any open-source LLM with sensible defaults.",
        target_user="solo developers wanting local AI without DevOps",
        key_features=["one-command setup", "auto GPU detection", "model registry"],
        addresses_pain_point_ids=[pp.id, pp2.id],
    )

    scored = ScoredIdea(
        idea_id=idea.id,
        reasoning_trace="The manual version is a bash script that downloads models. Demand is a well — deep but narrow. The schlep is ROCm compatibility. First 10 users are in r/LocalLLaMA.",
        feasibility_rubric=FeasibilityRubric(
            can_be_solved_manually_first=True,
            has_schlep_or_unsexy_advantage=True,
            can_2_3_person_team_build_mvp_in_6_months=True,
        ),
        demand_rubric=DemandRubric(
            addresses_at_least_2_pain_points=True,
            is_painkiller_not_vitamin=True,
            has_clear_vein_of_early_adopters=True,
        ),
        novelty_rubric=NoveltyRubric(
            differentiated_from_current_behavior=True,
            has_path_out_of_niche=True,
        ),
        core_assumption="Developers will switch if setup is < 1 minute.",
        fatal_flaws=[
            FatalFlaw(flaw="Ollama already solves this for most users", severity="major"),
            FatalFlaw(flaw="Cloud providers could release a simpler free tier", severity="minor"),
        ],
        yes_count=8,
        verdict="pursue",
        one_risk="Established competitors could copy the UX quickly.",
        rank=1,
    )

    brief = PitchBrief(
        idea_id=idea.id,
        title=idea.title,
        tagline="One-click local LLM workspace.",
        problem="Setting up local LLM environments is complicated.",
        solution="A CLI tool with sensible defaults.",
        target_user="Solo developers",
        market_opportunity="Growing demand for local AI among privacy-conscious solo developers who want LLM access without cloud dependencies.",
        business_model="Freemium CLI with paid enterprise features for teams.",
        go_to_market="ProductHunt launch followed by targeted outreach to solo devs.",
        key_risk="Low barrier to entry in the local tooling space.",
        next_steps="Interview 20 r/LocalLLaMA users about their setup pain, then run a concierge pilot with 5 early adopters.",
        evidence_links=[pp.source_url],
        markdown_content="# DevFlow LLM\n\nFull pitch brief here that is definitely more than one hundred characters long to satisfy the Pydantic validation rules. We need enough text to describe the product, the problem it solves, and why it is better than the competition.",
    )

    critique = Critique(
        idea_id=idea.id,
        reasoning_trace="Tagline is 5 words. URLs check out. Target user is a demographic, not a contained fire.",
        rubric=CritiqueRubric(
            all_claims_evidence_backed=True,
            no_hallucinated_source_urls=True,
            tagline_under_12_words=True,
            target_is_contained_fire=False,
            competition_embraced_with_thesis=True,
        ),
        all_pass=False,
        approval_status="revise",
        failing_checks=["target_is_contained_fire"],
        target_agent="idea_generator",
        revision_feedback="Target user is a demographic, not a contained community. Redefine as a specific reachable group.",
    )

    patch = {
        "pain_points": [pp, pp2],
        "ideas": [idea],
        "scored_ideas": [scored],
        "pitch_briefs": [brief],
        "critique": critique,
        "critiques": [critique],
        "current_stage": PipelineStage.COMPLETED,
    }
    state = state.model_copy(update=patch)

    print("[OK] Schema validation passed")
    print(f"   Run ID      : {state.run_id}")
    print(f"   Domain      : {state.domain}")
    print(f"   Pain points : {len(state.filtered_pain_points)} (passed rubric)")
    print(f"   Ideas       : {len(state.ideas)}")
    print(f"   Pursue      : {sum(1 for s in state.scored_ideas if s.verdict == 'pursue')}")
    print(f"   Is complete : {state.is_complete}")
    print(f"   Can revise  : {state.can_revise}")
    print(f"   Revision cnt: {state.get_revision_count(idea.id)}")