# Flowise with Ollama: build and test a local Chatflow

Create a first Flowise chatflow using an existing Ollama model, the ChatOllama node, and a minimal conversational path before adding agents.

Author: Retrivium team
Updated: 2026-11-03
Canonical URL: https://retrivium.ai/journal/flowise-local-chatflow-first-run/
Language: en

A first local Flowise chatflow should connect an existing model through ChatOllama and complete a plain conversation. Choose the chatflow surface deliberately; Assistant, Chatflow, and Agentflow are different authoring paths rather than interchangeable template names.

## The concrete application boundary

Flowise documents several visual building surfaces. Chatflow exposes model and chain composition for a conversation, while Agentflow targets more explicit agent workflows. Current documentation points agent work toward Agentflow V2, so old V1 screenshots should not dictate a new design. For the first text task, a chat model and a compatible conversational chain are sufficient. The model runtime already needs the chosen model available. Flowise’s node does not download every model name typed into its field. Likewise, optional memory and tool components add behavior that should be evaluated after a direct answer works. This keeps visual authoring distinct from installing or operating inference infrastructure ([Flowise product documentation](https://docs.flowiseai.com/)) ([Flowise Agentflow V2](https://docs.flowiseai.com/using-flowise/agentflowv2.md)).

## A practical route

In the existing local Flowise interface create a Chatflow. From Chat Models drag ChatOllama onto the canvas. Enter the exact model running in the existing Ollama service and use the Base URL appropriate to that local arrangement. Connect the model to a compatible conversation or chain component required by the chosen chatflow design. Save the flow, open its chat preview, and send a simple prompt. Do not copy infrastructure commands from an example when the required runtime is already available ([Flowise ChatOllama](https://docs.flowiseai.com/integrations/langchain/chat-models/chatollama.md)).

## A worked example

Design a helper that turns a fictional product note into a concise question-and-answer draft. Start with the source text pasted into the chat preview. Change the requested output wording while leaving the model node unchanged. If the response changes as expected, you have evidence about the instruction path. Add memory only if a follow-up must depend on earlier turns, then test that new requirement separately.

| Check | Record |
| --- | --- |
| Canvas | The ChatOllama node and compatible chain connections saved in the chatflow. |
| Runtime | The actual model identity and reachable local Ollama service. |
| Preview | The supplied product note and the resulting draft response. |

## Reading the failure correctly

If a node is unconnected or required chain input is absent, changing the model cannot repair the graph. If the model name is illustrative rather than installed, correct the node selection. Distinguish these structural problems from an answer that is grammatically plausible but invents facts beyond the supplied note.

## What completes the check

Keep a minimal saved graph that produces the intended text response. A later agent or tool workflow should be introduced as a deliberate change in authoring surface and capability, with its own visible input and output. Keep the saved graph separate from its generated conversation history.

Related background: [A local RAG server: placing components around the GPU](/journal/rag-server-local-architecture/), [llama.cpp server: a local API from a GGUF model](/journal/llama-cpp-server-launch/).