You are building your agentic workflow and are in the middle of picking your star LLM task for the brain of the operation. But which one should you pick? LLM Text Complete or LLM Chat Complete? Decisions, decisions.
Let's make this easy.
Text completion is a raw “continue this text” interface. You send a single prompt string; the model predicts the next tokens.
Chat completion wraps the same next-token predictor in a message schema (system, user, assistant turns + optional tool calls). It preserves conversation context and role instructions.
You just want the model to generate a single output from a prompt. Think of this as a utility function. So no need for ongoing conversation, memory, or back-and-forth. This is a simple task where you just want to integrate an LLM into your workflow and get going.
It's perfect for:
Example in Orkes Conductor:
You might have a task in your workflow that takes a customer complaint and generates a concise summary. Drop in an LLM Text Complete task, connect it to an AI prompt, and there you go, you have your answer.
It's simple, predictable, and fast.
You’re building something more agent-like, where the model needs to reason, respond, and interact over time.
You can think of Chat Complete as building a mini-assistant that can ask clarifying questions, use external tools, hold multi-step conversations, remember context, and adapt its behavior based on how you interact with it.
It's great for:
One of the key differences between Text Complete and Chat Complete in Orkes Conductor is the concept of messages.
In Text Complete, you send the model a single prompt and it gives you back a single completion. Simple.
But in Chat Complete, you build a conversationhistory using messages. Each message has two fields: roles (tells the model who is speaking) and message (the actual content of what's being said).
Supported Roles
This structure lets you simulate an ongoing conversation instead of just throwing isolated prompts at the model.
Each new message you add gives the model more context to work with, so it can respond in a way that feels conversational and stateful.
In Text Complete, the AI prompt goes in the prompt field. In LLM Chat Complete, it goes in the instructions field. The difference is just how much extra context you can layer in.
In LLM Text Complete, you rely only on the prompt (with optional promptVariables and tunings like temperature and topP).
While in LLM Chat Complete, you still put the prompt in instructions, but you can also add messages to simulate a conversation history, so that you can actually build an agent that "remembers" previous results.
Example in Orkes Conductor:
Let’s say you're building a vacation planner AI agent that interacts with a weather API, asks for your travel dates, then recommends destinations.
Your workflow might include a Chat Complete task like this:
So now you're thinking in agents and not just one-off responses.
It’s tempting to just default to Chat Complete because it feels more powerful, but if you need something simpler, Text Complete can do the job.
You can totally combine both in the same Orkes Conductor workflow. That is part of a well-designed agentic workflow, and this modularity is what makes agentic workflows in Conductor so awesome.
The best way to understand the difference between LLM Text Complete and LLM Chat Complete is to build with them. As with anything else really.
You can head over to Orkes Conductor Developer Edition to explore prebuilt examples or create your own workflow using both task types. You can experiment, tweak, and see how each one behaves in real time.
It's a great way to get a feel for which one fits your use case best.
There are also other AI tasks you can explore, like LLM Store Embeddings for vector storing, LLM Index Document for converting documents into embeddings, and LLM Search Index for retrieving relevant chunks in RAG-style workflows. These tasks layer naturally with Text Complete and Chat Complete to form full agentic pipelines.
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