Integration / Document & File Management
VLM Run integration
VLM Run provides multimodal agents, structured extraction, predictions, files, skills, feedback, and evaluation APIs.
About VLM Run
VLM Run is a multimodal AI platform for structured extraction, predictions, files, skills, feedback, and evaluations. It helps teams build reliable vision-language workflows without stitching together model, file, and eval infrastructure by hand.
All integrations- Category
- Document & File Management, AI & Machine Learning
- Connection
- API key
- Type
- Tool
- Actions
- 11
- Tags
- library
Example prompts
What you can ask Growf to do with VLM Run
- “Extract invoice fields from uploaded PDFs”
- “Classify product images by defect type”
- “Evaluate extraction quality against gold labels”
Available actions
11 actions your agents can run in VLM Run
Create Skill
Create a reusable skill from exactly one uploaded zip, prompt, or chat session.
Discover Extraction Schemas
List supported structured-extraction domains, or return the full JSON schema for one domain when domain is provided.
Execute Agent
Start a VLM Run agent execution from an existing agent name or inline configuration over multimodal inputs. Execution may consume credits and is asynchronous by default; poll the returned ID with VLM_RUN_GET_RUN.
Extract Structured JSON
Start structured JSON extraction from images, a document, a video, or audio using a domain, custom schema, or skill. Extraction may consume credits; document, video, and audio runs are asynchronous by default, so poll the returned prediction ID with VLM_RUN_GET_RUN.
Find Files
List uploaded files or find one by file ID or MD5 hash. In list mode, use offset and limit until has_more is false.
Find Skills
List VLM Run skills or find one exact skill by ID, name, and optional version. In list mode, continue from next_offset while has_more is true.
Get Run
Get the current status and result of one structured-extraction prediction or agent execution; call repeatedly to poll asynchronous work.
List Agents
Return agents available to the connected account for selection before execution.
List Artifacts
List artifact metadata belonging to exactly one chat session or agent execution. Use offset and limit to traverse pages until has_more is false.
List Runs
List structured-extraction predictions or agent executions for the connected account. Use offset and limit to traverse pages until has_more is false.
Upload File
Upload a local file to VLM Run for extraction, agent input, or skill creation. Retain the returned file ID for tools that consume uploaded files.
Put VLM Run to work inside Growf.
Connect it once and every workspace, agent and workflow can use it — securely, with your team in the loop.