Using the AI Chat Agent
The AI chat agent can build workflows, query databases, and analyze data — all from plain English. This tutorial shows you how.
Starting a Chat
Open the chat panel on the right side of the screen. You’ll see the default agent is ready.
Example 1: Build a Workflow
Type:
@canvas build a Python pipeline that loads a CSV file, filters rows where age > 30, and shows a histogram of the remaining ages
The Canvas agent will:
- Create a new flow
- Add a Python node to load the CSV
- Add a Python node to filter the data
- Add a Python node to plot the histogram
- Connect all nodes with edges
Example 2: Query a Database
Type:
Search PubMed for papers about CRISPR gene editing published in 2024
The agent will use the query_entrez_database tool to search PubMed and return results.
Example 3: Analyze Data
Type:
I have a counts matrix at /app/data/counts.csv. Calculate the top 10 most variable genes and create a heatmap.
The agent will write and execute Python code to:
- Load the CSV
- Calculate variance per gene
- Select top 10
- Generate a heatmap with matplotlib/seaborn
Using the Canvas Agent
Prefix your message with @canvas to use the specialized Canvas agent:
@canvas add a Python node called Preprocess@canvas connect the Preprocess node to the Analysis node@canvas verify the flow structure
Tips
- Be specific about file paths and parameters
- The agent can see your current canvas state
- Ask for explanations: “Why did you choose that method?”
- Request modifications: “Change the threshold to 0.01 instead”
Next: Custom Nodes