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.
TL;DR
Use LLM Text Complete for one-shot helpers (summaries, rewrites, structured outputs).
Use LLM Chat Complete for agent-like tasks (multi-turn, tool use, reasoning, context retention).
The difference between text and chat completion simplified
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.
Use LLM Text Complete when...
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:
Summarizing text
Sentiment from reviews
Email subject lines
Blog meta descriptions
Translations
Rewriting code comments
And more...
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.
summarize-complaint.json
1{
2"taskType":"LLM_TEXT_COMPLETE",
3"inputParameters":{
4"llmProvider":"MariaOpenAI",
5"model":"gpt-4o-mini",
6"prompt":"summarizer-prompt"
7}
8}
It's simple, predictable, and fast.
Use LLM Chat Complete when…
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:
AI travel agents that ask for your preferences before planning a trip
Customer service bots that troubleshoot based on your answers
Agents that take actions based on your inputs (e.g., book, order, update)
Debugging assistants that help fix code with follow-up questions
Educational tutors that teach based on your answers
Messages and Roles in LLM Chat Complete
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
System: Sets the rules, tone, and context of the entire conversation. This is how the AI should behave. For example, "You are a helpful travel agent who always asks clarifying questions."
User (or human): Represents the end-user's input. For example, "Plan me a trip to Europe in October."
Assistant: Represents the model's responses back to the user. For example, "Sure! Do you prefer warm weather or cooler destinations?"
This structure lets you simulate an ongoing conversation instead of just throwing isolated prompts at the model.
travel-agent-llm-chat-complete.json
1"messages":[
2{
3"role":"system",
4"message":"You are a travel planning assistant."
5},
6{
7"role":"user",
8"message":"Plan me a trip to Europe in October."
9},
10{
11"role":"assistant",
12"message":"Sure! Do you prefer warm weather or cooler destinations?"
13}
14]
15
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.
Where the AI prompt fits in
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:
travel-agent-workflow.json
1{
2"taskType":"LLM_CHAT_COMPLETE",
3"inputParameters":{
4"llmProvider":"MariaOpenAI",
5"model":"gpt-4o-mini",
6"instructions":"travel-agent-prompt",
7"messages":[
8{"role":"system","content":"You are a helpful travel assistant."},
9{"role":"user","content":"Where should I go in December?"}
10],
11}
12}
13
So now you're thinking in agents and not just one-off responses.
In short: Pick the right tool for the job
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.
Use Text Complete when you're building a helper that gives you a result from a prompt.
Use Chat Complete when you’re designing an agent that thinks, talks, and acts.
Good to Note:
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.
Best way is to try it yourself
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.
