DeepTutor Documentation Outlines Setup, Configuration, and Troubleshooting Guidelines for External Agents

A recently published developer guide within the DeepTutor repository provides comprehensive instructions for external agents tasked with installing, configuring, and verifying the software or troubleshooting existing setups. The documentation outlines prerequisites, modular package layouts, and validation steps designed to ensure correct deployment without requiring unnecessary plugins.

Installation and Package Structure

The guide details requirements for setting up current development versions, specifying Python 3.11–3.14 alongside Node.js 22 LTS for full Web application deployments. For command-line interface (CLI) usage from source, the instructions note that Node and npm can be bypassed while maintaining the Python dependency environment.

Key structural details highlighted in the documentation include:

• Version 1.7.0 Layout: The updated package layout introduces a modular approach where bare `deeptutor` packages contain only core chat, CLI, and SDK capabilities, requiring the `[full]` extra for complete Web application features.
• Migration Steps: Upgrades from version 1.6.15 involve backing up the runtime home, executing package upgrades, and removing legacy compatibility packages such as `deeptutor-cli` to prevent file conflicts.
• Runtime Isolation: The framework relies on a designated runtime home (`DEEPTUTOR_HOME`) to manage data and configuration separately from learning content workspaces.

Configuration and Credential Management

Configuration procedures emphasize security by avoiding plain-text credentials in chat interfaces. Instead, operators are instructed to reference environment variables or private local files via JSON configuration files.

• Validation Commands: Commands like `--check` allow operators to validate inputs, endpoints, and port configurations prior to writing settings or contacting remote providers.
• Provider Support: The system supports various LLM, embedding, and search providers, requiring specific base URLs and API format parameters depending on the selected service.

Verification and Troubleshooting Workflow

The documentation outlines systematic checks to confirm system readiness, including dependency verification via `pip check` and diagnostic routines using `doctor` commands. To resolve issues, the guide recommends gathering minimal evidence, isolating symptoms across system boundaries—from browser interfaces down to storage layers—and testing single hypotheses before implementing focused fixes.