# Creative Agents

Agents for creative tasks: writing, design, image generation, video, music, and content creation.

---

## Creative Agent Capabilities

| Capability | Description | Tools |
|---|---|---|
| **Writing** | Articles, stories, copy, scripts | LLMs, grammarly, Hemingway |
| **Image Generation** | Art, illustrations, concepts | Midjourney, DALL-E, Stable Diffusion, Flux |
| **Video Generation** | Clips, animations, editing | Sora, Runway, Pika, Luma, Kling |
| **Music Generation** | Composition, production, mastering | Suno, Udio, Stable Audio, MusicLM |
| **Design** | UI/UX, branding, marketing | Figma, Canva, Adobe, generative |
| **3D/AR/VR** | Models, scenes, immersive | Spline, Bezi, Meshy, Tripo |
| **Content Strategy** | Calendar, SEO, distribution | Notion, Airtable, Buffer, Later |
| **Localization** | Translation, cultural adaptation | DeepL, Lokalise, Crowdin |
| **Brand Voice** | Consistency, guidelines, compliance | Writer, Typeface, Jasper |

---

## Creative Agent Architectures

### 1. Writing Agent
```python
class WritingAgent:
    def __init__(self):
        self.styles = StyleLibrary()
        self.templates = TemplateLibrary()
        self.editor = EditorTools()
    
    async def write(self, spec: WritingSpec) -> WrittenPiece:
        # 1. Select style/voice
        style = self.styles.get(spec.style) if spec.style else self.analyze_voice(spec.samples)
        
        # 2. Research if needed
        research = await self.research(spec.topic) if spec.needs_research else None
        
        # 3. Outline
        outline = await self.outline(spec, research)
        
        # 4. Draft
        draft = await self.draft(outline, style, spec)
        
        # 5. Edit & polish
        polished = await self.edit(draft, style, spec.constraints)
        
        # 6. Format
        formatted = await self.format(polished, spec.format)
        
        return WrittenPiece(
            content=formatted,
            word_count=len(formatted.split()),
            readability=self.analyze_readability(formatted),
            seo_score=self.analyze_seo(formatted, spec.keywords)
        )
    
    async def edit(self, text: str, style: Style, constraints: Constraints) -> str:
        # Grammar & spelling
        corrected = await self.grammar_check(text)
        
        # Style consistency
        styled = await self.apply_style(corrected, style)
        
        # Constraints
        constrained = self.apply_constraints(styled, constraints)
        
        # Flow & clarity
        polished = await self.improve_flow(constrained)
        
        return polished
```

### 2. Copywriting Agent
```python
class CopywritingAgent:
    def __init__(self):
        self.frameworks = CopyFrameworks()
        self.ab_tester = ABTester()
    
    async def write_copy(self, brief: CopyBrief) -> CopyVariants:
        # Generate multiple variants using different frameworks
        variants = await asyncio.gather(*[
            self.write_with_framework(framework, brief)
            for framework in self.frameworks.get_for_goal(brief.goal)
        ])
        
        # Score variants
        scored = await self.score_variants(variants, brief)
        
        return CopyVariants(variants=scored)
    
    async def write_with_framework(self, framework: CopyFramework, brief: CopyBrief) -> CopyVariant:
        if framework == "AIDA":
            return await self.write_aida(brief)
        elif framework == "PAS":
            return await self.write_pas(brief)
        elif framework == "BAB":
            return await self.write_bab(brief)
        elif framework == "FAB":
            return await self.write_fab(brief)
        elif framework == "QUEST":
            return await self.write_quest(brief)
```

### 3. Content Strategy Agent
```python
class ContentStrategyAgent:
    def __init__(self):
        self.calendar = ContentCalendar()
        self.seo = SEOAnalyzer()
        self.analytics = ContentAnalytics()
    
    async def plan_content(self, strategy: ContentStrategy) -> ContentPlan:
        # 1. Audit existing content
        audit = await self.audit_existing(strategy.channels)
        
        # 2. Keyword research
        keywords = await self.seo.research(strategy.topics, strategy.competitors)
        
        # 3. Gap analysis
        gaps = self.identify_gaps(audit, keywords)
        
        # 4. Generate ideas
        ideas = await self.generate_ideas(gaps, strategy.pillars)
        
        # 5. Prioritize
        prioritized = self.prioritize(ideas, strategy.goals)
        
        # 6. Schedule
        calendar = self.calendar.schedule(prioritized, strategy.frequency)
        
        return ContentPlan(
            calendar=calendar,
            keywords=keywords,
            pillars=strategy.pillars,
            kpis=strategy.kpis
        )
```

---

## Visual Creative Agents

### 1. Image Generation Agent
```python
class ImageGenerationAgent:
    def __init__(self):
        self.models = {
            "midjourney": MidjourneyClient(),
            "dalle3": DALL_E3Client(),
            "stable_diffusion": SDClient(),
            "flux": FluxClient(),
            "ideogram": IdeogramClient()
        }
        self.upscaler = Upscaler()
        self.editor = ImageEditor()
    
    async def generate(self, spec: ImageSpec) -> ImageResult:
        # Select best model for task
        model = self.select_model(spec)
        
        # Build prompt
        prompt = self.build_prompt(spec)
        
        # Generate
        images = await self.models[model].generate(
            prompt=prompt,
            aspect_ratio=spec.aspect_ratio,
            style=spec.style,
            quality=spec.quality,
            seed=spec.seed
        )
        
        # Post-process
        if spec.upscale:
            images = await asyncio.gather(*[
                self.upscaler.upscale(img, factor=spec.upscale_factor)
                for img in images
            ])
        
        if spec.edit_instructions:
            images = await asyncio.gather(*[
                self.editor.edit(img, spec.edit_instructions)
                for img in images
            ])
        
        return ImageResult(images=images, prompt=prompt, model=model)
    
    def select_model(self, spec: ImageSpec) -> str:
        if spec.style == "photorealistic":
            return "midjourney" if spec.quality == "high" else "flux"
        elif spec.style == "illustration":
            return "dalle3"
        elif spec.style == "logo":
            return "ideogram"
        elif spec.style == "text_in_image":
            return "ideogram"
        return "stable_diffusion"  # Most flexible
```

### 2. Video Generation Agent
```python
class VideoGenerationAgent:
    def __init__(self):
        self.models = {
            "sora": SoraClient(),
            "runway": RunwayGen3Client(),
            "pika": PikaClient(),
            "luma": LumaDreamMachineClient(),
            "kling": KlingClient(),
            "veo": VeoClient()
        }
        self.editor = VideoEditor()
    
    async def generate(self, spec: VideoSpec) -> VideoResult:
        model = self.select_model(spec)
        
        # Generate
        if spec.type == "text_to_video":
            video = await self.models[model].text_to_video(
                prompt=spec.prompt,
                duration=spec.duration,
                aspect_ratio=spec.aspect_ratio,
                fps=spec.fps
            )
        elif spec.type == "image_to_video":
            video = await self.models[model].image_to_video(
                image=spec.image,
                prompt=spec.prompt,
                duration=spec.duration
            )
        elif spec.type == "video_to_video":
            video = await self.models[model].video_to_video(
                video=spec.source_video,
                prompt=spec.prompt
            )
        
        # Post-process
        if spec.edit_instructions:
            video = await self.editor.edit(video, spec.edit_instructions)
        
        if spec.add_audio:
            video = await self.add_audio(video, spec.audio_spec)
        
        return VideoResult(video=video, metadata=self.extract_metadata(video))
```

### 3. Design Agent
```python
class DesignAgent:
    def __init__(self):
        self.figma = FigmaClient()
        self.canva = CanvaClient()
        self.adobe = AdobeExpressClient()
    
    async def create_design(self, spec: DesignSpec) -> DesignResult:
        # 1. Research & moodboard
        moodboard = await self.create_moodboard(spec)
        
        # 2. Generate concepts
        concepts = await self.generate_concepts(spec, moodboard)
        
        # 3. Refine selected
        refined = await self.refine_concept(concepts[0], spec)
        
        # 4. Create deliverables
        deliverables = await self.create_deliverables(refined, spec.deliverables)
        
        # 5. Design system
        if spec.design_system:
            system = await self.create_design_system(refined)
        
        return DesignResult(
            concept=refined,
            deliverables=deliverables,
            design_system=system
        )
```

### 4. 3D/AR/VR Agent
```python
class ThreeDAgent:
    def __init__(self):
        self.models = {
            "meshy": MeshyClient(),
            "tripo": TripoClient(),
            "spline": SplineClient(),
            "bezi": BeziClient()
        }
        self.optimizer = ModelOptimizer()
    
    async def generate_3d(self, spec: ThreeDSpec) -> ThreeDResult:
        model = self.select_model(spec)
        
        if spec.type == "text_to_3d":
            model_output = await self.models[model].text_to_3d(
                prompt=spec.prompt,
                style=spec.style,
                polygon_count=spec.polygon_count
            )
        elif spec.type == "image_to_3d":
            model_output = await self.models[model].image_to_3d(
                image=spec.image,
                views=spec.views
            )
        elif spec.type == "video_to_3d":
            model_output = await self.models[model].video_to_3d(
                video=spec.video
            )
        
        # Optimize
        optimized = await self.optimizer.optimize(
            model_output,
            target_polygons=spec.target_polygons,
            format=spec.format
        )
        
        # Convert formats
        formats = await self.convert_formats(optimized, spec.formats)
        
        return ThreeDResult(models=formats, metadata=model_output.metadata)
```

### 5. Music/Audio Agent
```python
class MusicAgent:
    def __init__(self):
        self.models = {
            "suno": SunoClient(),
            "udio": UdioClient(),
            "stable_audio": StableAudioClient(),
            "musiclm": MusicLMClient(),
            "audioldm": AudioLDMClient()
        }
        self.editor = AudioEditor()
        self.mastering = MasteringEngine()
    
    async def generate_music(self, spec: MusicSpec) -> MusicResult:
        model = self.select_model(spec)
        
        if spec.type == "text_to_music":
            audio = await self.models[model].text_to_music(
                prompt=spec.prompt,
                duration=spec.duration,
                style=spec.style,
                tempo=spec.tempo,
                key=spec.key
            )
        elif spec.type == "continuation":
            audio = await self.models[model].continue_audio(
                audio=spec.seed_audio,
                duration=spec.duration
            )
        elif spec.type == "stem_separation":
            stems = await self.models[model].separate_stems(spec.audio)
            return MusicResult(stems=stems)
        
        # Mastering
        if spec.master:
            audio = await self.mastering.master(audio, spec.target_loudness)
        
        return MusicResult(audio=audio, metadata=self.extract_metadata(audio))
    
    async def generate_sound_effect(self, spec: SFXSpec) -> AudioResult:
        return await self.models["audioldm"].text_to_audio(
            prompt=spec.prompt,
            duration=spec.duration
        )
```

---

## Creative Workflows

### 1. Campaign Creation Workflow
```mermaid
graph TD
    A[Brief] --> B[Research & Strategy]
    B --> C[Concept Development]
    C --> D[Copywriting]
    C --> E[Visual Design]
    D --> F[Copy Review]
    E --> G[Design Review]
    F --> H[Integration]
    G --> H
    H --> I[Stakeholder Review]
    I --> J[Revisions]
    J --> K[Final Approval]
    K --> L[Production]
    L --> M[Distribution]
```

### 2. Brand Identity Workflow
```mermaid
graph TD
    A[Brand Brief] --> B[Research & Audit]
    B --> C[Strategy & Positioning]
    C --> D[Visual Identity]
    D --> E[Logo Design]
    D --> F[Color Palette]
    D --> G[Typography]
    D --> H[Iconography]
    E --> I[Design System]
    F --> I
    G --> I
    H --> I
    I --> J[Guidelines Document]
    J --> K[Asset Production]
    K --> L[Implementation]
```

### 3. Social Media Content Pipeline
```mermaid
graph LR
    A[Content Calendar] --> B[Idea Generation]
    B --> C[Copywriting]
    C --> D[Visual Creation]
    D --> E[Video Production]
    C --> F[Copy Review]
    D --> G[Design Review]
    E --> G
    F --> H[Scheduling]
    G --> H
    H --> I[Publishing]
    I --> J[Engagement Monitoring]
    J --> K[Performance Analysis]
    K --> A
```

---

## Creative Tools & Platforms

### Image Generation
| Tool | Strengths | API |
|---|---|---|
| **Midjourney v6** | Artistic, photorealistic | Discord, API (unofficial) |
| **DALL-E 3** | Prompt adherence, text | OpenAI API |
| **Stable Diffusion XL** | Open, customizable, ControlNet | Local, Replicate, API |
| **Flux** | Fast, high quality | Replicate, API |
| **Ideogram** | Text rendering, logos | API |
| **Recraft** | Vector, icons, illustrations | API |
| **Playground** | Mixed media, editing | Web, API |

### Video Generation
| Tool | Strengths | Duration |
|---|---|---|
| **Sora** | High quality, physics | 60s |
| **Runway Gen-3** | Control, consistency | 10s |
| **Pika 1.5** | Effects, modification | 5s |
| **Luma Dream Machine** | Fast, free tier | 5s |
| **Kling** | Chinese market, long | 120s |
| **Veo** | Google, 1080p | TBD |

### Music/Audio
| Tool | Strengths |
|---|---|
| **Suno v3** | Full songs, vocals |
| **Udio** | High quality, control |
| **Stable Audio 2.0** | Open, long-form |
| **MusicLM** | Google, quality |
| **AudioLDM** | Open, sound effects |
| **Mubert** | Royalty-free, API |

### Design
| Tool | Type | API |
|---|---|---|
| **Figma** | UI/UX, design systems | REST, Plugin API |
| **Canva** | Templates, marketing | API |
| **Adobe Express** | Templates, brand kits | API |
| **Spline** | 3D web, interactive | API |
| **Bezi** | 3D design, AR | API |
| **Recraft** | Vector, illustrations | API |

### 3D Generation
| Tool | Type | Output |
|---|---|---|
| **Meshy** | Text/Image → 3D | GLB, OBJ, FBX |
| **Tripo** | Image → 3D (fast) | GLB |
| **CSM** | Image/Video → 3D | GLB |
| **Luma Genie** | Text → 3D | GLB |
| **Spline** | 3D web editor | Web, GLB |
| **Bezi** | 3D design tool | GLB, USDZ |

---

## Evaluation & Quality

### Creative Quality Metrics
| Dimension | Metrics |
|---|---|
| **Visual Quality** | FID, CLIP Score, Aesthetic Score |
| **Text Quality** | Perplexity, BLEU, ROUGE, Human Eval |
| **Audio Quality** | FAD, MOS, Pitch Accuracy |
| **Video Quality** | FVD, FVMD, Consistency |
| **Design** | Usability, Accessibility, Brand Compliance |
| **Originality** | Uniqueness, Novelty, Diversity |

### Quality Control
```python
class CreativeQualityControl:
    def __init__(self):
        self.aesthetic = AestheticScorer()
        self.brand = BrandComplianceChecker()
        self.accessibility = AccessibilityChecker()
        self.plagiarism = PlagiarismDetector()
    
    async def validate(self, creative: CreativeOutput) -> QCResult:
        checks = await asyncio.gather(
            self.aesthetic.score(creative),
            self.brand.check(creative),
            self.accessibility.check(creative),
            self.plagiarism.check(creative)
        )
        
        return QCResult(
            passed=all(c.passed for c in checks),
            scores={c.name: c.score for c in checks},
            issues=[i for c in checks for i in c.issues]
        )
```

---

## Legal & Ethics

### Copyright & IP
```python
class IPCompliance:
    def __init__(self):
        self.copyright = CopyrightChecker()
        self.trademark = TrademarkChecker()
        self.licensing = LicenseValidator()
    
    async def check(self, creative: CreativeOutput) -> IPReport:
        return IPReport(
            copyright=self.copyright.check(creative),
            trademark=self.trademark.check(creative),
            licensing=self.licensing.validate(creative.assets)
        )
```

### Bias & Fairness
```python
class BiasAuditor:
    def __init__(self):
        self.demographic = DemographicAnalyzer()
        self.stereotype = StereotypeDetector()
    
    async def audit(self, creative: CreativeOutput) -> BiasReport:
        return BiasReport(
            demographic=self.demographic.analyze(creative),
            stereotypes=self.stereotype.detect(creative),
            recommendations=self.generate_recommendations(creative)
        )
```

---

## Related Notes

- [[ToolUse/Function-Calling.md]] — Creative tools as functions
- [[ToolUse/MCP-Protocol.md]] — Creative tools via MCP
- [[ToolUse/Browser-Automation.md]] — Web research for creative
- [[ToolUse/Code-Execution.md]] — Creative coding
- [[ToolUse/API-Integration.md]] — Creative platform APIs
- [[ToolUse/Custom-Tools.md]] — Building creative tools
- [[Frameworks/OpenAI-Agents-SDK.md]] — Creative tools in SDK
- [[Frameworks/LangGraph.md]] — Creative workflows
- [[Frameworks/PydanticAI.md]] — Structured creative output
- [[ModelArchitectures/Multimodal-Foundations.md]] — Multimodal models
- [[ModelArchitectures/Small-Language-Models.md]] — SLMs for creative
- [[Domains/Coding-Agents.md]] — Creative coding
- [[Domains/Customer-Facing-Agents.md]] — Marketing agents
- [[Evaluation/Agent-Benchmarks.md]] — Creative benchmarks
- [[Production/Observability.md]] — Creative observability
- [[Production/Cost-Optimization.md]] — Creative cost optimization
- [[Security/Secure-Coding.md]] — Secure creative code
- [[Frontiers/Creative-Agents.md]] — Frontier creative AI
- [[Frontiers/Research-Directions.md]] — Creative AI research

---

*Sources: Midjourney, DALL-E, Stable Diffusion, Flux, Ideogram, Recraft, Sora, Runway, Pika, Luma, Kling, Veo, Suno, Udio, Stable Audio, MusicLM, AudioLDM, Figma, Canva, Adobe Express, Spline, Bezi, Meshy, Tripo, CSM, Luma Genie, Spline, Runway, Pika, Luma, Kling, Sora, Midjourney, DALL-E, Stable Diffusion, Flux, ControlNet, LoRA, DreamBooth, Textual Inversion, CLIP, BLIP, LAION, Aesthetic Predictor, LAION-Aesthetics, PickScore, ImageReward, HPS, FID, CLIP Score, IS, FVD, FVMD, Inception Score, Precision/Recall, SSIM, PSNR, LPIPS, Brand Guidelines, WCAG, Section 508, Copyright Law, Fair Use, DMCA, Creative Commons, AI Art Copyright, Training Data Ethics, Bias in Generative AI, Stereotype Detection, Demographic Parity, Equalized Odds, Counterfactual Fairness.*