Definition
What is LLM orchestration?
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Definition
LLM orchestration is the coordination layer that sequences multiple LLM calls, tool invocations, retries, and conditional branches into a coherent agent or workflow — typically backed by a state machine or graph framework.
A single LLM call answers a question. An LLM orchestration framework runs many calls together — planning, executing tools, observing results, retrying on failure, branching based on output, persisting state to memory, and handing off to humans for approval. Popular frameworks include LangGraph (graph-based, explicit state), CrewAI (role-based agents), AutoGen (conversational), and custom state machines. Orchestration is what turns LLM capabilities into reliable agentic systems.
Why frameworks matter
You can build an agent with a while loop, an if/else, and a list of tool definitions. People do; it works at small scale. The reason production agents reach for orchestration frameworks is that the simple loop has no story for: persistent state across restarts, parallel branches that converge, retries with backoff, observability of each node’s input/output, hot-swappable nodes for A/B testing, and human-in-the-loop interruption.
Glitch Grow’s Ads Operator, Sales Agent, and Social Media Agent all use LangGraph — a graph-based orchestration framework where nodes are functions and edges are conditional transitions. The framework handles the persistence, retries, and interrupts; you write the nodes.