VLM Run
VLM Run provides multimodal agents, structured extraction, predictions, files, skills, feedback, and evaluation APIs.
Integration
- Vendor
- VLM Run
- Category
- docs
- Auth
- API_KEY
- Tools
- 11
- Composio slug
vlm_run
Tools
- 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 predictio
- 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.
Setup
- Create or open a Switchy Project.
- Open Integrations and connect VLM Run.
- Authorise through api_key. Switchy stores the connection against your org.
- Everyone in the Project can now @-mention the VLM Run tools in chat.
What teammates see: by default, memories from VLM Run are scoped to the Project (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Projects have used this MCP together with a given model.