feat(agent-runner): add event-first context facts and pull APIs

Add EventLog and Transcript persistence entities for storing auditable
event facts and conversation history projection. Implement event-first
AgentRunContext builder that produces Protocol v1 compliant context
payloads with required fields: event, delivery, context (ContextAccess).

Key changes:
- EventLog ORM: auditable event records with indexes
- Transcript ORM: conversation history projection with composite indexes
- AgentRunContextBuilder: Protocol v1 payload with delivery, context, bootstrap
- EventLogStore/TranscriptStore: async stores for fact sources
- Host action handlers: HISTORY_PAGE, HISTORY_SEARCH, EVENT_GET, EVENT_PAGE
- Context validation: build_context output validates via SDK AgentRunContext
- Alembic migration for event_log and transcript tables
- Alembic env.py imports all ORM models for autogenerate discovery

Legacy compatibility: max-round messages go into bootstrap.messages and
compatibility.legacy_messages, not top-level messages field.

src/langbot/pkg/entity/persistence/event_log.py

@@ -0,0 +1,85 @@
+"""EventLog persistence entity for storing auditable event facts."""
+from __future__ import annotations
+
+import sqlalchemy
+import datetime
+
+from .base import Base
+
+
+class EventLog(Base):
+    """EventLog stores auditable event records for AgentRunner.
+
+    This is the fact source for events - messages, tool calls, system events, etc.
+    Large payloads are stored separately as artifacts; this table stores
+    references and summaries.
+    """
+
+    __tablename__ = 'event_log'
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
+    """Auto-increment ID for sequencing."""
+
+    event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
+    """Unique event identifier."""
+
+    event_type = sqlalchemy.Column(sqlalchemy.String(100), nullable=False, index=True)
+    """Event type (message.received, tool.call.started, etc.)."""
+
+    event_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    """When the event occurred."""
+
+    source = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
+    """Event source (platform, webui, api, scheduler, system, pipeline_compat)."""
+
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    """Bot UUID that handled this event."""
+
+    workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Workspace ID for multi-tenant deployments."""
+
+    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    """Conversation ID this event belongs to."""
+
+    thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Thread ID if platform supports threads."""
+
+    # Actor information
+    actor_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
+    """Actor type (user, system, runner)."""
+
+    actor_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Actor identifier."""
+
+    actor_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Actor display name."""
+
+    # Subject information
+    subject_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
+    """Subject type (message, tool_call, artifact)."""
+
+    subject_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Subject identifier."""
+
+    # Input information
+    input_summary = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Brief summary of input (truncated text, max 1000 chars)."""
+
+    input_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Full input JSON if reasonably sized (AgentInput as JSON string)."""
+
+    # Raw event reference
+    raw_ref = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Reference to raw event payload in ArtifactStore."""
+
+    run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    """Run ID that processed this event."""
+
+    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Runner ID that processed this event."""
+
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
+    """When this record was created."""
+
+    metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Additional metadata as JSON string."""

src/langbot/pkg/entity/persistence/transcript.py

@@ -0,0 +1,72 @@
+"""Transcript persistence entity for conversation history projection."""
+from __future__ import annotations
+
+import sqlalchemy
+import datetime
+
+from .base import Base
+
+
+class Transcript(Base):
+    """Transcript stores conversation-oriented message projection for history API.
+
+    This is a projection of EventLog, optimized for agent history retrieval.
+    It includes message content and artifact refs, but not raw platform payloads.
+    """
+
+    __tablename__ = 'transcript'
+
+    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
+    """Auto-increment ID for sequencing."""
+
+    transcript_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
+    """Unique transcript item identifier."""
+
+    event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    """Reference to the source event in EventLog."""
+
+    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    """Conversation this item belongs to."""
+
+    thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Thread ID if platform supports threads."""
+
+    role = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
+    """Message role: 'user', 'assistant', 'system', or 'tool'."""
+
+    item_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, default='message')
+    """Item type: 'message', 'tool_call', 'tool_result', 'system'."""
+
+    # Content
+    content = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Text content summary (may be truncated for large messages, max 4000 chars)."""
+
+    content_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Full structured content as JSON string (Message model dump)."""
+
+    # Artifact references
+    artifact_refs_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Artifact references as JSON string (list of ArtifactRef)."""
+
+    # Sequence for cursor-based pagination
+    seq = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, index=True)
+    """Sequence number within conversation (auto-increment per conversation)."""
+
+    # Context
+    run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    """Run ID that generated this item (for assistant messages)."""
+
+    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    """Runner ID that generated this item."""
+
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
+    """When this item was created."""
+
+    metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    """Additional metadata as JSON string (sender_id, platform, etc.)."""
+
+    # Indexes
+    __table_args__ = (
+        sqlalchemy.Index('ix_transcript_conversation_seq', 'conversation_id', 'seq'),
+        sqlalchemy.Index('ix_transcript_conversation_created', 'conversation_id', 'created_at'),
+    )