Aug 13: Context layer webinar
Chalk home page
  1. Developer Interface
  2. Chalk Assistant

Chalk Assistant is an agentic assistant built into the Chalk dashboard. It runs on an AI provider you connect: Chalk supplies the harness and the tools, and you supply the model. It acts on your environment through the same tool set as the Chalk MCP Server, letting you drive tasks from the UI that you can also drive from your own coding agents.

​
Enabling Chalk Assistant in your environment

Chalk Assistant is disabled by default. An Owner or Team Manager must enable it for an environment and configure the AI provider and model to use. Chalk Assistant supports OpenAI, Anthropic, and any OpenAI-compatible endpoint, which takes a base URL you supply. Every conversation in that environment runs on the provider and model configured there.

Notes:

  1. Your model provider can see any information about your environment carried in the conversation and in tool results.
  2. Messages, tool arguments, and tool results are stored in your environment’s data plane.
  3. An Owner or Team Manager can view every conversation in the environment.
  4. Every tool call runs the same permission check as the MCP Server against your account, so the assistant can change anything you can change (e.g., notebooks, monitors, Chalk Functions, sandboxes, and deployment source).
  5. Chalk never shows the API key again after you save it.
  6. Rotating the key takes effect immediately.

​
Using Chalk Assistant

You can reach the assistant two ways:

  • Click the brain icon in the top-right toolbar to toggle the sidebar.
  • Press ⌘K (Ctrl+K on Windows and Linux), type a question, then press Shift+Enter to hand it to the assistant.

You can attach objects from your environment instead of pasting their names. Click + Context to search and attach any of the following:

  • features, resolvers, and named queries from the active deployment,
  • datasets, registry models, Chalk Functions, and scaling groups,
  • SQL catalog tables.

The assistant’s tools cover queries and ChalkSQL, features and resolvers, sandboxes and notebooks, deployments, infrastructure, and observability. See the MCP Server page for more information.

Each time you open the sidebar it starts a fresh conversation. Your conversation list shows your own conversations.

​
What you can do with Chalk Assistant

Chalk Assistant is designed to help you with the work that surrounds a model:

  • agent-driven machine learning investigations,
  • data exploration and analysis,
  • online and offline query development,
  • reading what a feature or resolver actually computes,
  • debugging a slow or failing query,
  • environment administration.

The examples below show how it can help.

​
Explore features and data

Ask about the shape of your environment before you write code against it.

  • “Which features exist on the user namespace, and which of them are computed by SQL resolvers?”
  • “Show me the definition of user.fraud_score and every resolver that contributes to it.”
  • “Run an online query for user.id=1234 and return the fraud features with their timestamps.”
  • “Use ChalkSQL to count transactions per merchant over the last 30 days.”

​
Build and iterate in Chalk Notebooks

The assistant can create a Chalk Notebook, add cells, install dependencies, run cells, and read the results back. Changes it makes apply immediately, so review them in the notebook afterward.

  • “Create a notebook that loads the last 90 days of transactions and plots daily volume.”
  • “Add a cell that trains an XGBoost classifier on this dataframe and prints the AUC.”
  • “This cell fails with a KeyError. Read the error and fix the cell.”

​
Debug queries and deployments

The assistant reads the same query errors, plans, logs, and traces you would open by hand.

  • “The last online query for user.fraud_score returned an error. Find it and explain the cause.”
  • “Get the query plan and performance summary for the slowest resolver in this environment.”
  • “Search the logs for OOMKilled in the last hour and tell me which workload restarted.”

​
Operate the environment

Ask it to inspect infrastructure and set up monitoring, then check its work before you rely on it.

  • “List the deployments in this environment and tell me which one is active.”
  • “Search the deployed source for every resolver that reads from Snowflake.”
  • “Create a monitor that alerts when online query p99 latency exceeds 200ms.”

​
Call your own functions

Chalk Functions you have deployed are callable from a conversation, so a function already running in your environment becomes a step the assistant can take inside a longer task.

  • “Call the score_applicant function with this payload and summarize what it returned.”

​
See also