Multi-modal Support (Vision, Audio, Image Gen)
Relevant source files
The following files were used as context for generating this wiki page:
Multi-modal support in the Hermes Agent ecosystem enables the processing and generation of non-textual data, including visual analysis, image generation, and speech synthesis. This system integrates specialized tools like vision_analyze, image_generate, and text_to_speech to bridge the gap between abstract textual concepts and physical or digital media.
The architecture relies on a tool-based framing where the agent orchestrates external capabilities to analyze source images, generate visual content (such as comics, infographics, and article illustrations), and handle audio outputs. These capabilities are primarily utilized within "creative" skills to transform raw data or narratives into structured multi-modal artifacts.
Image Analysis (Vision)
The agent performs visual analysis using the vision_analyze tool. This tool accepts either a local file path or a URL as input to extract descriptive metadata from images.
Reference Extraction Logic
When you provide reference images, the agent uses vision capabilities to extract specific traits for downstream generation. This process converts binary visual data into textual descriptions because generation tools often operate on text prompts rather than direct image-to-image inputs.
- Trait Extraction: The agent asks for specific details such as art style, color palettes (including hex code approximations), composition, and subjects.
- Storage: Extracted descriptions are saved as sidecar
.mdfiles in areferences/directory. - Usage Modes:
direct: Uses the full description (composition, subject, style) in the final prompt.style: Appends only style characteristics (e.g., "line treatment", "texture").palette: Appends only the color scheme traits.
Sources: optional-skills/creative/baoyu-article-illustrator/references/workflow.md:3-37, optional-skills/creative/baoyu-comic/SKILL.md:21-39
Vision Analysis Flow
The following diagram illustrates how the agent processes an input image to prepare for multi-modal generation.
Explanation: The agent detects the image source, analyzes it to produce a text description, and saves that description for use in generation prompts. Sources: optional-skills/creative/baoyu-article-illustrator/references/workflow.md:46-59
Image Generation
Image generation is handled by the image_generate tool. This tool is "prompt-only," meaning it requires a textual description and an aspect ratio to produce an image URL.
Tool Capabilities and Constraints
The image_generate tool operates under specific constraints defined by the agent's environment:
- Inputs: A text prompt and an
aspect_ratioenum (landscape,portrait, orsquare). - Outputs: A JSON object containing a temporary image URL.
- Storage: The agent must explicitly download the URL (using
terminaltools likecurl) to save the image to a local directory.
| Aspect Preset | Ratio Mapping | Supported Tool Enum |
|---|---|---|
| landscape | 16:9, 4:3, 3:2 | landscape |
| portrait | 9:16, 3:4, 2:3 | portrait |
| square | 1:1 | square |
Sources: optional-skills/creative/baoyu-article-illustrator/SKILL.md:123-134, optional-skills/creative/baoyu-comic/SKILL.md:154-171
Prompt Construction Workflow
Before generating an image, the agent creates a "reproducibility record" in the form of a prompt file.
Explanation: The agent ensures the prompt is saved before generation, calls the generation tool, and then manually downloads the result to ensure persistence. Sources: optional-skills/creative/baoyu-article-illustrator/references/workflow.md:143-165, optional-skills/creative/baoyu-comic/SKILL.md:146-152
Multi-modal Skill Applications
The project includes several specialized skills that leverage these multi-modal tools for complex creative tasks.
Creative Skill Catalog
| Skill Name | Modality Mix | Primary Output |
|---|---|---|
baoyu-infographic | Text + Image Gen | High-density information graphics |
baoyu-article-illustrator | Text + Vision + Image Gen | Context-aware article images |
baoyu-comic | Text + Image Gen | Knowledge-based educational comics |
text-to-speech | Text + Audio | Synthesized speech from prose |
Sources: skills/creative/baoyu-infographic/SKILL.md, optional-skills/creative/baoyu-article-illustrator/SKILL.md, optional-skills/creative/baoyu-comic/SKILL.md, agent/learn_prompt.py:65
Character and Style Consistency
For multi-page or multi-image generations, the agent maintains consistency through text-based definitions:
- Characters: A
characters.mdfile defines physical traits in text. These descriptions are embedded into every subsequent generation prompt. - Styles: Style definitions (e.g.,
ligne-claire,cyberpunk-neon) are loaded from reference files and combined with the user's specific content to ensure a uniform visual language across an entire set of images.
Sources: optional-skills/creative/baoyu-comic/SKILL.md:183-195, skills/creative/baoyu-infographic/SKILL.md:143-152
Technical Implementation Details
The multi-modal system is built on the terminal tool and specific agent primitives.
Tool Framing Rules
Skills must reference multi-modal capabilities using their Hermes tool names in backticks:
vision_analyze: For image to text.image_generate: For text to image.text_to_speech: For text to audio.terminal: For downloading binaries and managing local files.
Sources: agent/learn_prompt.py:61-68, optional-skills/creative/baoyu-article-illustrator/references/workflow.md:6-14
Data Integrity and Security
- Data Preservation: During multi-modal generation, the agent is strictly prohibited from summarizing or altering source statistics (e.g., "73% increase" must remain verbatim).
- Secret Stripping: The agent must scan all source content for API keys, tokens, or credentials before including them in visual outputs or generation prompts.
Sources: skills/creative/baoyu-infographic/SKILL.md:204-208, optional-skills/creative/baoyu-article-illustrator/SKILL.md:158-160
Multi-modal support in Hermes Agent centralizes non-textual processing through a structured workflow of analysis, prompt-based generation, and local binary management. This approach ensures that creative outputs remain consistent, secure, and reproducible across different media formats.