Workflow · saas · workflow
Jockey: Twelve Labs' open-source conversational video agent built on LangGraph
Jockey v1.0, built on the legacy LangChain AgentExecutor, lacked the granular control and scalability needed for complex multi-step video processing workflows.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User query submitted
The workflow begins with an initial query input from the user.
Tools used
LangGraphLangGraph CloudLangGraph StudioTwelve Labs APIsLLMs
Outcome
Jockey v1.1 with LangGraph provides granular control over each workflow step, optimizes token usage, enables more accurate node response guidance, and supports scalable production deployment via LangGraph Cloud.
What failed first
Jockey v1.0 on the legacy LangChain AgentExecutor lacked flexible API support for custom cognitive architectures and could not provide the low-level node-level control required for complex video workflows.
Grounding & classification
Source type: technical build writeup
19 fields verified against source quotes.
agentic workflowconversational aimulti agent workflowsummarizationnamed customersource backedtools describedworkflow describedmediasoftwaretechnical build writeupagentic task executionextract classify route