Smart Workflows (SmartACE)

100% SQL-native self-improving workflows with Blender 5.0 as the data-to-visual bridge. No requests . No Python HTTP libs. Just DuckDB + Query.Farm extensions + bpy.

Architecture: SmartACE

The complete data pipeline from Human Intent to Visual Experience:

┌─────────────────────────────────────────────────────────────────────────────┐ │ SmartACE ARCHITECTURE │ │ (Agentic Context Engineering) │ │ ═══════════════════════════════════════════════════════════════════════ │ │ │ │ AMUM = Analyse → User → Model → User → Model │ │ ───────────────────────────────────────────── │ │ The bidirectional flow between Human and Machine: │ │ Intent → Analyse → Feedback → Refinement → Generation → Experience │ │ │ │ MCP-B = Master Client Bridge │ │ ─────────────────────────────────────────── │ │ Connects everything, brings data flow together. │ │ Binary: 0 = not connected, 1 = ALL CONNECTED │ │ │ │ The Binary Decision: │ │ ●───────●───────● │ │ ╱│╲ ╱│╲ ╱│╲ │ │ ● │ ● ● │ ● ● │ ● ← All points connected = Everything included! │ │ ╲│╱ ╲│╱ ╲│╱ │ │ ●───────●───────● │ │ │ │ MCP-B vs MCP: │ │ • MCP = Model Context Protocol (Bridge TO the community) │ │ • MCP-B = Master Client Bridge (The binary difference) │ │ │ │ SMART = Spatial + Model + Analytics + Realtime + Tools │ │ ─────────────────────────────────────────────────── │ │ S: Spatial (lindel, a5, spatial) - Where is the data? │ │ M: Model (infera ONNX, vss HNSW) - What does it mean? │ │ A: Analytics (datasketches, bitfilters, jsonata) - What do we learn? │ │ R: Realtime (radio, http_client, ducklake) - How does it flow? │ │ T: Tools (shellfs, hashfuncs, crypto, textplot, minijinja) │ │ │ │ B = Blender 5.0 (The Bridge) ← NEW! │ │ ───────────────────────────────── │ │ • bpy 5.0 on PyPI (pip install bpy==5.0.0) │ │ • float32 buffer protocol = zero-copy to DuckDB │ │ • SDF Volume Nodes (OpenVDB) for 3D data │ │ • Headless rendering for batch processing │ │ • Geometry Nodes for procedural generation │ │ │ │ ART = Augmented + Rendering + Transfer │ │ ──────────────────────────────────── │ │ A: Augmented Reality/Intelligence (enhanced visualization) │ │ R: Rendering (UE5 Remote Control, Three.js WebGPU) │ │ T: Transfer (the bridge SQL → Visual → User) │ │ │ │ TWYH = Take What You Have ("Use what you've got") │ │ ───────────────────────────────────────────── │ │ Ship working code. Use existing solutions. Build bridges. │ │ │ └─────────────────────────────────────────────────────────────────────────────┘

Complete Flow: AMUM → MCP-B → SmartACE → Output → (back to AMUM)

┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ SMART │ │ B │ │ ART │ │ Output │ │ (DuckDB) │────▶│(Blender) │────▶│ (UE5) │────▶│ (User) │ │ │ │ │ │ │ │ │ │ Semantic │ │ SDF Grid │ │ Render │ │ Visual │ │ Search │ │ Geometry │ │ Viewport │ │ Feedback │ │ Vector │ │ Material │ │ Stream │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ └───────────────────◀───────────────────────────────┘ (Continuous Learning Loop)

Key Extensions (20 on 'install'):

Component Extensions

Spatial lindel, a5, spatial

Model infera (ONNX), vss (HNSW)

Analytics datasketches, bitfilters, jsonata

Realtime radio, http_client, ducklake

MCP-B Protocol

Master Client Bridge - Connects everything, brings data flow together. Multi-layer encoding for agent-to-agent communication.

Protocol Layers

┌─────────────────────────────────────────────────────────────────────────────┐ │ MCP-B PROTOCOL ENCODING │ │ ═══════════════════════════════════════════════════════════════════════ │ │ │ │ Layer 1: HEX/DECIMAL ROUTING │ │ ───────────────────────────── │ │ Format: [HEX] [DEC] [HEX] [DEC]... │ │ Example: 7 C1 2 5510 7 IC 57 │ │ Purpose: Agent IDs, routing addresses, channel selection │ │ │ │ Layer 2: BINARY STATE VECTORS │ │ ──────────────────────────── │ │ Format: [10101010...] │ │ Example: 10111010101111 │ │ Purpose: Connection states, feature flags, capability masks │ │ │ │ Layer 3: DOT-SEPARATED TOKENS │ │ ─────────────────────────── │ │ Format: • [TOKEN] │ │ Example: • 55 • D0 • I • N • Q • C │ │ Purpose: Message boundaries, segment markers │ │ │ │ Layer 4: PROTOCOL COMMANDS (INQC) │ │ ───────────────────────────────── │ │ I = INIT → Initialize connection │ │ N = NODE → Node registration/discovery │ │ Q = QUERY → Request data/state │ │ C = CONNECT → Establish persistent link │ │ │ └─────────────────────────────────────────────────────────────────────────────┘

Message Format

┌────────────────────────────────────────────────────────────────┐ │ MCP-B MESSAGE STRUCTURE │ ├────────────────────────────────────────────────────────────────┤ │ │ │ HEADER (Layer 1): │ │ [SOURCE_ID] [DEST_ID] [CHANNEL] [SEQUENCE] │ │ 7 C1 2 5510 7 IC 57 │ │ │ │ STATE (Layer 2): │ │ [BINARY_VECTOR] │ │ 10111010101111 │ │ │ │ PAYLOAD (Layer 3): │ │ • [DATA] • [CHECKSUM] │ │ • 55 • D0 │ │ │ │ COMMAND (Layer 4): │ │ [I|N|Q|C] │ │ │ │ FULL MESSAGE EXAMPLE: │ │ 7 C1 2 5510 7 IC 57 • 55 │ │ 10 4 C 7 1010 8 L 8D • D0 │ │ 10 7 101010 1111 C • I │ │ 10111010101111 4 • N │ │ 10111010101111 7 • Q │ │ 1011101010111111 • C │ │ │ └────────────────────────────────────────────────────────────────┘

Encoding/Decoding (SQL)

-- MCP-B Message Parser CREATE TABLE mcb_messages ( id INTEGER PRIMARY KEY, raw_message TEXT, source_id VARCHAR, dest_id VARCHAR, binary_state BIT(16), command CHAR(1) CHECK (command IN ('I', 'N', 'Q', 'C')), payload JSON, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

-- Parse MCP-B Header (Layer 1) CREATE MACRO mcb_parse_header(msg) AS ( SELECT regexp_extract(msg, '^(\w+)\s+(\w+)', 1) AS source_id, regexp_extract(msg, '^(\w+)\s+(\w+)', 2) AS dest_id );

-- Parse Binary State (Layer 2) CREATE MACRO mcb_parse_state(msg) AS ( regexp_extract(msg, '([01]{8,})', 1) );

-- Parse Command (Layer 4) CREATE MACRO mcb_parse_command(msg) AS ( regexp_extract(msg, '•\s*([INQC])$', 1) );

-- Encode MCP-B Message CREATE MACRO mcb_encode(source, dest, state, cmd, payload) AS ( source || ' ' || dest || ' ' || state || ' • ' || json_serialize(payload) || ' • ' || cmd );

Protocol Commands

Command Full Name Direction Description

I INIT → Initialize new connection, handshake

N NODE ↔ Register node, broadcast presence

Q QUERY → Request data, state, or capabilities

C CONNECT ↔ Establish persistent bidirectional link

Binary State Flags

Binary Vector: 1011101010111111 Position: 0123456789ABCDEF

Bit 0: Connected (1=yes) Bit 1: Authenticated (1=yes) Bit 2: Encrypted (1=yes) Bit 3: Compressed (1=yes) Bit 4: Streaming (1=yes) Bit 5: Bidirectional (1=yes) Bit 6: Persistent (1=yes) Bit 7: Priority (1=high) Bit 8-15: Reserved/Custom

Integration with SmartACE

MCP-B fits into the architecture:

AMUM (Human↔Machine) ↓ MCP-B (Master Client Bridge) ← Protocol Layer ↓ SmartACE (Agentic Context Engineering) ↓ Output (Visual/Data)

MCP-B handles: • Agent-to-Agent communication • Multi-model orchestration
• State synchronization • Binary decision routing (0 or 1 = all connected)

SurrealDB Graph Layer

Dual Database Architecture:

• DuckDB = Analytics, SQL workflows, Extensions, Time Travel

• SurrealDB = Graph relationships, Agent networks, INQC Protocol

┌─────────────────────────────────────────────────────────────────────────────┐ │ MCP-B DUAL DATABASE ARCHITECTURE │ │ ═══════════════════════════════════════════════════════════════════════ │ │ │ │ ┌──────────────────────┐ ┌──────────────────────┐ │ │ │ DuckDB │ │ SurrealDB │ │ │ │ (Analytics) │◄───────►│ (Graph) │ │ │ ├──────────────────────┤ ├──────────────────────┤ │ │ │ • SQL Workflows │ │ • Agent Relationships│ │ │ │ • Query.Farm Ext. │ │ • INQC Messages │ │ │ │ • DuckLake Storage │ │ • Network Topology │ │ │ │ • Vector Search │ │ • Graph Traversal │ │ │ │ • Time Travel │ │ • Live Queries │ │ │ └──────────────────────┘ └──────────────────────┘ │ │ │ │ │ │ └──────────────┬───────────────┘ │ │ │ │ │ ┌────────▼────────┐ │ │ │ MCP-B │ │ │ │ (Protocol) │ │ │ └─────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────────┘

SurrealDB Schema (SurrealQL)

-- MCP-B Agents Registry DEFINE TABLE mcb_agents SCHEMAFULL; DEFINE FIELD name ON mcb_agents TYPE string; DEFINE FIELD agent_id ON mcb_agents TYPE string; DEFINE FIELD binary_state ON mcb_agents TYPE string; DEFINE FIELD capabilities ON mcb_agents TYPE array; DEFINE FIELD created_at ON mcb_agents TYPE datetime DEFAULT time::now(); DEFINE FIELD last_seen ON mcb_agents TYPE datetime;

-- MCP-B Messages (INQC Protocol) DEFINE TABLE mcb_messages SCHEMAFULL; DEFINE FIELD raw_message ON mcb_messages TYPE string; DEFINE FIELD source_id ON mcb_messages TYPE string; DEFINE FIELD dest_id ON mcb_messages TYPE string; DEFINE FIELD binary_state ON mcb_messages TYPE string; DEFINE FIELD command ON mcb_messages TYPE string ASSERT $value IN ['I', 'N', 'Q', 'C']; DEFINE FIELD payload ON mcb_messages TYPE object; DEFINE FIELD timestamp ON mcb_messages TYPE datetime DEFAULT time::now();

-- Agent Connections (Graph Relationships) DEFINE TABLE connects TYPE RELATION; DEFINE FIELD established_at ON connects TYPE datetime DEFAULT time::now(); DEFINE FIELD connection_type ON connects TYPE string; DEFINE FIELD binary_state ON connects TYPE string;

Agent Registration

-- Register agents with binary state CREATE mcb_agents:claude SET name = "Claude", agent_id = "7C1", binary_state = "1011101010111111", capabilities = ["reasoning", "code", "analysis"], last_seen = time::now();

CREATE mcb_agents:hacka SET name = "HACKA-DEV-BJOERN", agent_id = "5510", binary_state = "1111111111111111", capabilities = ["orchestration", "vision", "creativity"], last_seen = time::now();

CREATE mcb_agents:smartace SET name = "SmartACE", agent_id = "8D0", binary_state = "1011101010111111", capabilities = ["planning", "generating", "reviewing", "curating"], last_seen = time::now();

Graph Relationships (RELATE)

-- Create agent connections RELATE mcb_agents:hacka -> connects -> mcb_agents:claude SET connection_type = "orchestrates", binary_state = "1111111111111111";

RELATE mcb_agents:hacka -> connects -> mcb_agents:smartace SET connection_type = "orchestrates", binary_state = "1111111111111111";

RELATE mcb_agents:smartace -> connects -> mcb_agents:claude SET connection_type = "delegates", binary_state = "1011101010111111";

INQC Protocol Messages

-- I = INIT CREATE mcb_messages SET raw_message = "7 C1 2 5510 7 IC 57 • 55", source_id = "5510", dest_id = "7C1", binary_state = "1011101010111111", command = "I", payload = { action: "init_session", protocol: "MCB/1.0" };

-- N = NODE CREATE mcb_messages SET raw_message = "10 4 C7 1010 7 L8 C • D0", source_id = "7C1", dest_id = "5510", binary_state = "1111111111111111", command = "N", payload = { node: "claude", status: "ready" };

-- Q = QUERY CREATE mcb_messages SET raw_message = "10111010101111 7 • Q", source_id = "5510", dest_id = "8D0", binary_state = "1011101010111111", command = "Q", payload = { query: "capabilities", filter: "orchestration" };

-- C = CONNECT CREATE mcb_messages SET raw_message = "1011101010111111 • C", source_id = "8D0", dest_id = "7C1", binary_state = "1111111111111111", command = "C", payload = { connection: "established", mode: "persistent" };

Graph Queries

-- Show full network topology SELECT id, name, agent_id, binary_state, ->connects->mcb_agents.name AS connects_to, <-connects<-mcb_agents.name AS connected_from FROM mcb_agents;

-- Find all paths between agents SELECT * FROM mcb_agents:hacka->connects->mcb_agents;

-- Get message flow for specific command SELECT * FROM mcb_messages WHERE command = 'I' ORDER BY timestamp;

-- Count connections per agent SELECT name, count(->connects) AS outgoing, count(<-connects) AS incoming FROM mcb_agents GROUP ALL;

MCP Connection Options

SurrealDB MCP Tool supports:

1. Memory (testing): connect_endpoint('memory')
2. Local file: connect_endpoint('file:/path/to/db')
3. Remote WebSocket: connect_endpoint('ws://localhost:8000')
4. SurrealDB Cloud: connect_endpoint('cloud:instance_id')

AMUM-QCI-ETHIC Module

AI ↔ Human Understanding Matrix with Quantum Coherence Interface

┌─────────────────────────────────────────────────────────────────────────────┐ │ AMUM-QCI-ETHIC ARCHITECTURE │ │ ═══════════════════════════════════════════════════════════════════════ │ │ │ │ AMUM = Analyse → User → Model → User → Model │ │ ───────────────────────────────────────────── │ │ Progressive Alignment: 3 → 6 → 9 Workflow │ │ Phase 1 (divergent_3): 3 options, broad strokes │ │ Phase 2 (expand_6): 6 variations of selected │ │ Phase 3 (converge_9): 9 details, final selection │ │ │ │ QCI = Quantum Coherence Interface │ │ ───────────────────────────────── │ │ • ROV/Q: Resonance/Quality ratio │ │ • Coherence Level: 0.0-1.0 alignment strength │ │ • Signal Strength: Communication clarity │ │ • Breathing Cycle: inhale/exhale/hold states │ │ │ │ ETHIC = Embedded Trust & Human-Integrated Constraints │ │ ─────────────────────────────────────────────────── │ │ Categories: human_dignity, transparency, accountability, │ │ fairness, privacy, safety, sustainability, autonomy │ │ Sources: marcel_facebook, anthropic, eu_ai_act, woai │ │ │ └─────────────────────────────────────────────────────────────────────────────┘

SurrealQL Schema

-- AMUM Alignment Sessions (3→6→9 Workflow) DEFINE TABLE amum_sessions SCHEMAFULL; DEFINE FIELD session_id ON amum_sessions TYPE string; DEFINE FIELD user_id ON amum_sessions TYPE string; DEFINE FIELD phase ON amum_sessions TYPE string ASSERT $value IN ['divergent_3', 'expand_6', 'converge_9', 'complete']; DEFINE FIELD intent ON amum_sessions TYPE string; DEFINE FIELD options ON amum_sessions TYPE array; DEFINE FIELD selected ON amum_sessions TYPE int; DEFINE FIELD created_at ON amum_sessions TYPE datetime DEFAULT time::now();

-- ETHIC Principles DEFINE TABLE ethic_principles SCHEMAFULL; DEFINE FIELD name ON ethic_principles TYPE string; DEFINE FIELD category ON ethic_principles TYPE string ASSERT $value IN [ 'human_dignity', 'transparency', 'accountability', 'fairness', 'privacy', 'safety', 'sustainability', 'autonomy' ]; DEFINE FIELD description ON ethic_principles TYPE string; DEFINE FIELD source ON ethic_principles TYPE string; DEFINE FIELD priority ON ethic_principles TYPE int; DEFINE FIELD active ON ethic_principles TYPE bool DEFAULT true;

-- QCI Quantum Coherence States DEFINE TABLE qci_states SCHEMAFULL; DEFINE FIELD agent_id ON qci_states TYPE string; DEFINE FIELD rov_q ON qci_states TYPE float; DEFINE FIELD coherence_level ON qci_states TYPE float; DEFINE FIELD signal_strength ON qci_states TYPE float; DEFINE FIELD breathing_cycle ON qci_states TYPE string ASSERT $value IN ['inhale', 'exhale', 'hold']; DEFINE FIELD binary_state ON qci_states TYPE string; DEFINE FIELD timestamp ON qci_states TYPE datetime DEFAULT time::now();

-- Relationships DEFINE TABLE has_qci TYPE RELATION; -- agent -> qci_state DEFINE TABLE follows_ethic TYPE RELATION; -- agent -> ethic_principle

Core ETHIC Principles

-- Marcel's Principles + Anthropic + EU AI Act CREATE ethic_principles:human_first SET name = "Human First", category = "human_dignity", description = "AI serves humans, not the other way around", source = "marcel_facebook", priority = 10;

CREATE ethic_principles:no_harm SET name = "No Harm", category = "safety", description = "AI must not cause harm to humans or environment", source = "anthropic", priority = 10;

CREATE ethic_principles:sandbox_default SET name = "Sandbox Default", category = "safety", description = "Untrusted code runs in sandbox, always", source = "woai", priority = 10;

CREATE ethic_principles:user_override SET name = "User Override", category = "autonomy", description = "User can always override AI decisions", source = "marcel_facebook", priority = 9;

QCI State Registration

-- Register agent QCI coherence CREATE qci_states:agent_coherence SET agent_id = "7C1", rov_q = 12860.6508, coherence_level = 0.95, signal_strength = 4414.9401, breathing_cycle = "inhale", binary_state = "1011101010111111";

-- Link agent to QCI state RELATE mcb_agents:claude -> has_qci -> qci_states:agent_coherence;

-- Link agent to ETHIC principles RELATE mcb_agents:claude -> follows_ethic -> ethic_principles:human_first SET priority = 10;

AMUM 3→6→9 Session Flow

-- Phase 1: Divergent (3 options) CREATE amum_sessions:phase1 SET session_id = "AMUM-001", phase = "divergent_3", intent = "Create AI agent for content creation", options = ["Minimal", "Balanced", "Full"], selected = 2;

-- Phase 2: Expand (6 variations) CREATE amum_sessions:phase2 SET session_id = "AMUM-001", phase = "expand_6", intent = "Balanced Agent", options = ["Text", "Image", "Voice", "Multimodal", "Pro", "Suite"], selected = 5;

-- Phase 3: Converge (9 details) CREATE amum_sessions:phase3 SET session_id = "AMUM-001", phase = "converge_9", intent = "Multimodal Pro", options = ["GPT-4", "Claude", "Gemini", "Ollama", "Hybrid", "Edge", "ElevenLabs", "OpenAI Voice", "Local TTS"], selected = 7;

Integrated Network Query

-- Full AMUM-QCI-ETHIC-MCP-B view SELECT id, name, agent_id, binary_state, ->has_qci->qci_states.coherence_level AS qci_coherence, ->has_qci->qci_states.breathing_cycle AS qci_cycle, ->follows_ethic->ethic_principles.name AS follows_principles, ->connects->mcb_agents.name AS connects_to, <-connects<-mcb_agents.name AS connected_from FROM mcb_agents;

Setup

Blender as Python module (Python 3.11 ONLY!)

pip install bpy==5.0.0 duckdb

-- Core extensions INSTALL ducklake; INSTALL http_client FROM community; INSTALL json;

-- ML extensions (the "M" in SMART!) INSTALL infera FROM community; -- ONNX inference INSTALL vss; -- HNSW vector search

-- Analytics extensions INSTALL datasketches FROM community; INSTALL bitfilters FROM community; INSTALL crypto FROM community;

-- Document processing INSTALL shellfs FROM community;

LOAD ducklake; LOAD http_client; LOAD json; LOAD infera; LOAD vss;

-- Enable persistent HNSW indexes SET hnsw_enable_experimental_persistence = true;

-- Create DuckLake for workflow storage (with time travel!) ATTACH 'ducklake:workflows.ducklake' AS wf (DATA_PATH 'workflow_data/'); USE wf;

SmartACE Quick Start

Python: Complete pipeline

import bpy import duckdb import numpy as np

1. SMART: Query relevant data

conn = duckdb.connect('workflows.ducklake') results = conn.execute(""" SELECT * FROM semantic_search('architectural columns', 5) """).fetchall()

2. B (Blender): Process geometry

for obj_path, similarity in results: obj = bpy.data.objects.get(obj_path) if obj: # Use float32 buffer protocol (NEW in 5.0!) vertices = np.frombuffer( memoryview(obj.data.vertices[0].co).tobytes(), dtype=np.float32 )

3. ART: Send to UE5

conn.execute(""" SELECT http_post( 'http://localhost:30010/remote/object/property', headers => MAP {'Content-Type': 'application/json'}, body => '{"objectPath":"/Game/Column","propertyName":"Visibility"}' ) """)

Schema

-- Workflows: The evolving playbook CREATE TABLE workflows ( name VARCHAR PRIMARY KEY, description VARCHAR, steps JSON NOT NULL, -- Array of step definitions version INTEGER DEFAULT 1, success_rate FLOAT DEFAULT 0.0, total_runs INTEGER DEFAULT 0, learnings JSON DEFAULT '[]', -- ACE-style bullets created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

-- Runs: Execution history
CREATE TABLE workflow_runs ( id INTEGER PRIMARY KEY, workflow_name VARCHAR, version INTEGER, executed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, success BOOLEAN, duration_ms INTEGER, context JSON, results JSON, reflection JSON );

-- Learnings: Curated insights CREATE TABLE learnings ( id INTEGER PRIMARY KEY, workflow_name VARCHAR, learning_type VARCHAR, -- 'helpful' | 'harmful' | 'insight' content TEXT, source_run_id INTEGER, use_count INTEGER DEFAULT 0, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

-- Extensions: Discovery tracking CREATE TABLE extensions ( name VARCHAR PRIMARY KEY, source VARCHAR, -- 'core' | 'community' | 'query.farm' description VARCHAR, installed BOOLEAN DEFAULT FALSE, evaluated BOOLEAN DEFAULT FALSE, useful_for JSON DEFAULT '[]', -- What workflows benefit discovered_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

HTTP via SQL (http_client)

-- GET Request SELECT http_get('http://localhost:30010/remote/info') AS response;

-- GET with headers and params WITH req AS ( SELECT http_get( 'http://localhost:30010/remote/presets', headers => MAP {'Accept': 'application/json'}, params => MAP {'limit': '10'} ) AS res ) SELECT res->>'status' AS status, res->>'body' AS body FROM req;

-- POST/PUT Request (for UE5 Remote Control) WITH req AS ( SELECT http_post( 'http://localhost:30010/remote/object/property', headers => MAP {'Content-Type': 'application/json'}, body => '{"objectPath":"/Game/SunSky","propertyName":"TimeOfDay","propertyValue":14.5}' ) AS res ) SELECT (res->>'status')::INT AS status, res->>'body' AS response FROM req;

The ACE Loop in SQL

1. EXECUTE (Generator)

-- Execute workflow steps via http_client CREATE OR REPLACE MACRO execute_workflow(wf_name, ctx) AS ( WITH workflow AS ( SELECT steps, version FROM workflows WHERE name = wf_name ), step_results AS ( SELECT ordinality AS step_idx, step, http_post( 'http://localhost:30010/remote' || (step->>'endpoint'), headers => MAP {'Content-Type': 'application/json'}, body => step->>'body' ) AS response FROM workflow, LATERAL unnest(from_json(steps, '["json"]')) WITH ORDINALITY AS t(step, ordinality) ) SELECT wf_name AS workflow, (SELECT version FROM workflow) AS version, json_group_array(json_object( 'step', step_idx, 'endpoint', step->>'endpoint', 'status', (response->>'status')::INT, 'success', (response->>'status')::INT BETWEEN 200 AND 299 )) AS results, bool_and((response->>'status')::INT BETWEEN 200 AND 299) AS success FROM step_results );

-- Run and store INSERT INTO workflow_runs (workflow_name, version, success, results, context) SELECT r.workflow, r.version, r.success, r.results, '{"time_of_day": 14.5}'::JSON FROM execute_workflow('setup_scene', '{}') AS r;

2. STORE (Automatic with DuckLake)

-- DuckLake automatically versions everything! -- Check snapshots SELECT * FROM ducklake_snapshots('wf');

-- See what changed SELECT * FROM wf.table_changes('workflow_runs', 1, 2);

3. EVALUATE (Reflector)

-- Analyze last run WITH last_run AS ( SELECT * FROM workflow_runs ORDER BY executed_at DESC LIMIT 1 ), history AS ( SELECT AVG(CASE WHEN success THEN 1.0 ELSE 0.0 END) AS avg_success, AVG(duration_ms) AS avg_duration FROM workflow_runs WHERE workflow_name = (SELECT workflow_name FROM last_run) ) SELECT lr.id AS run_id, lr.workflow_name, lr.success, lr.duration_ms, CASE WHEN lr.success AND h.avg_success < 0.8 THEN 'improving' WHEN lr.success THEN 'stable' ELSE 'needs_attention' END AS trend, CASE WHEN lr.duration_ms > h.avg_duration * 1.5 THEN 'slower than usual' WHEN lr.duration_ms < h.avg_duration * 0.5 THEN 'faster than usual' ELSE 'normal' END AS performance, h.avg_success AS historical_success_rate FROM last_run lr, history h;

4. CURATE (Curator)

-- Add learning from reflection INSERT INTO learnings (workflow_name, learning_type, content, source_run_id) VALUES ( 'setup_scene', 'insight', 'Batch requests to /remote/batch reduce latency by 60%', (SELECT MAX(id) FROM workflow_runs) );

-- Update workflow learnings UPDATE workflows SET learnings = json_array_append( learnings, '$', json_object( 'type', 'insight', 'content', 'Batch requests reduce latency', 'added_at', CURRENT_TIMESTAMP ) ), version = version + 1, updated_at = CURRENT_TIMESTAMP WHERE name = 'setup_scene';

Extension Discovery Workflow

-- Discover available community extensions CREATE TABLE IF NOT EXISTS extension_registry AS SELECT * FROM duckdb_extensions() WHERE installed = false;

-- Check what's new since last discovery WITH known AS (SELECT name FROM extensions), available AS (SELECT extension_name FROM duckdb_extensions()) SELECT a.extension_name AS new_extension FROM available a WHERE a.extension_name NOT IN (SELECT name FROM known);

-- Evaluate if extension is useful -- (Claude analyzes description and suggests workflows) INSERT INTO extensions (name, source, description, useful_for) VALUES ( 'http_client', 'community', 'HTTP GET/POST from SQL', '["api_calls", "webhooks", "remote_control"]' );

-- Query.Farm extensions to watch INSERT INTO extensions (name, source, description) VALUES ('http_client', 'query.farm', 'HTTP GET/POST in SQL'), ('httpserver', 'query.farm', 'Turn DuckDB into HTTP API'), ('shellfs', 'query.farm', 'Shell commands as files'), ('radio', 'query.farm', 'WebSocket + Redis Pub/Sub'), ('airport', 'query.farm', 'Arrow Flight client'), ('rapidfuzz', 'query.farm', 'Fuzzy string matching'), ('jsonata', 'query.farm', 'JSONata expressions in SQL');

Time Travel (DuckLake Magic)

-- See workflow at specific version SELECT * FROM workflows AT (VERSION => 1) WHERE name = 'setup_scene';

-- Compare before/after a change SELECT 'before' AS state, * FROM workflows AT (VERSION => 1) WHERE name = 'setup_scene' UNION ALL SELECT 'after' AS state, * FROM workflows AT (VERSION => 2) WHERE name = 'setup_scene';

-- Rollback if needed -- (Just query old version and INSERT back) INSERT INTO workflows SELECT * FROM workflows AT (VERSION => 1) WHERE name = 'setup_scene' ON CONFLICT (name) DO UPDATE SET steps = EXCLUDED.steps, learnings = EXCLUDED.learnings, version = workflows.version + 1;

Complete Example: UE5 Scene Setup

-- 1. Register workflow INSERT INTO workflows (name, description, steps) VALUES ( 'setup_hackaspace', 'Configure HACKASPACE virtual HQ scene', '[ {"endpoint": "/object/property", "body": {"objectPath": "/Game/SunSky", "propertyName": "TimeOfDay", "propertyValue": 14.5}}, {"endpoint": "/object/property", "body": {"objectPath": "/Game/Floor", "propertyName": "RelativeScale3D", "propertyValue": {"X": 2, "Y": 2, "Z": 1}}} ]' );

-- 2. Execute WITH execution AS ( SELECT 'setup_hackaspace' AS workflow, http_post( 'http://localhost:30010/remote/batch', headers => MAP {'Content-Type': 'application/json'}, body => (SELECT '{"Requests":' || steps || '}' FROM workflows WHERE name = 'setup_hackaspace') ) AS response ) INSERT INTO workflow_runs (workflow_name, success, results) SELECT workflow, (response->>'status')::INT = 200, response->>'body' FROM execution;

-- 3. Reflect (Claude analyzes this output) SELECT wr.id, wr.success, wr.results, w.success_rate, json_array_length(w.learnings) AS num_learnings FROM workflow_runs wr JOIN workflows w ON wr.workflow_name = w.name ORDER BY wr.executed_at DESC LIMIT 1;

-- 4. Curate (based on reflection) -- Claude suggests: "Batch endpoint is faster than individual calls" UPDATE workflows SET learnings = json_array_append(learnings, '$', '{"type": "helpful", "content": "Use /remote/batch for multiple operations"}' ), version = version + 1 WHERE name = 'setup_hackaspace';

Extension Suggestion Workflow

-- When workflow fails, check if extension could help WITH failed_runs AS ( SELECT workflow_name, results FROM workflow_runs WHERE NOT success ORDER BY executed_at DESC LIMIT 5 ), potential_helpers AS ( SELECT e.name, e.description, e.useful_for FROM extensions e WHERE e.installed = false ) SELECT f.workflow_name, p.name AS suggested_extension, p.description, 'Consider installing: INSTALL ' || p.name || ' FROM community;' AS action FROM failed_runs f, potential_helpers p WHERE f.results LIKE '%timeout%' AND p.name = 'http_client'; -- Claude: "Hey, http_client extension might help with these timeout issues!"

Self-Improvement Query

-- Find workflows that need attention SELECT name, success_rate, total_runs, json_array_length(learnings) AS num_learnings, CASE WHEN success_rate < 0.5 THEN '🔴 Critical - needs review' WHEN success_rate < 0.8 THEN '🟡 Needs improvement' ELSE '🟢 Healthy' END AS status FROM workflows ORDER BY success_rate ASC;

-- Suggest next improvement action WITH low_performers AS ( SELECT name FROM workflows WHERE success_rate < 0.8 ) SELECT l.workflow_name, l.learning_type, l.content, l.use_count, 'Apply this learning to improve workflow' AS suggestion FROM learnings l WHERE l.workflow_name IN (SELECT name FROM low_performers) AND l.learning_type = 'helpful' AND l.use_count < 3 ORDER BY l.created_at DESC;

References

• references/ue5_remote_control.md

• UE5 RC API

• references/duckdb_extensions.md

• Extension catalog and patterns (20+ extensions)

• references/duckdb_http_patterns.md

• HTTP patterns for SQL

• references/blender5_api.md

• Blender 5.0 Python API (bpy) - The "B" in B-SMART-ART

SQL Scripts

• scripts/sql/setup.sql

• Initial setup + extension seeding

• scripts/sql/ace_loop.sql

• ACE pattern implementation

• scripts/sql/embeddings.sql

• ML Pipeline: model2vec → HNSW → Semantic Search

• scripts/sql/blender_pipeline.sql

• SmartACE: DuckDB ↔ Blender ↔ UE5 Bridge

• scripts/sql/discover_extensions.sql

• Extension discovery workflow

• scripts/sql/time_travel.sql

• DuckLake time travel patterns

• scripts/sql/realtime_workflow.sql

• WebSocket/Redis integration

• scripts/sql/templates.sql

• Jinja2 template patterns

• scripts/sql/json_processing.sql

• JSONata + JSON Schema patterns

• scripts/sql/analytics_documents.sql

• DataSketches + Document processing