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🤖 AI Agents Overview

DAIV uses SWE AI agents to automate various development workflows in your GitLab and GitHub repositories. Each agent is designed for specific tasks and can work independently or collaborate with other agents to provide comprehensive development assistance.

How DAIV Agents Work

Core Architecture

DAIV agents are built on a modular architecture that combines several key components:

LangChain Framework: uses LangChain as a foundation for the agents.

LangGraph Framework: uses LangGraph to create sophisticated workflows with state management, decision-making capabilities, and error handling.

Language Models: Agents support multiple LLM providers including:

  • Anthropic Claude (Sonnet, Opus variants with thinking capabilities)
  • OpenAI GPT (including reasoning models like GPT-5, o4, etc.)
  • Google Gemini (including Gemini 2.5 Pro, etc.)
  • OpenRouter (access to various models from multiple providers)

Repository Integration: Direct integration with GitLab and GitHub through webhooks and APIs for real-time repository monitoring and interaction.

Context-Aware Processing: Agents have access to the entire repository content, allowing them to understand your codebase structure, dependencies, and coding patterns.

Core Available AI Agents

🎯 Plan and Execute Agent

Purpose: This is the core agent that is used by other agents. It is responsible for planning and executing the tasks.

Key Capabilities:

  • Breaks down complex tasks into self contained actionable steps
  • Handles error recovery and replanning
  • Coordinates between different tools and systems
  • Analyzes attached images from issues and comments (Markdown and HTML formats, including GitHub attachments)
  • Uses MCP tools to extend its capabilities (e.g. Fetch, Sentry, etc.)
  • Uses repository tools to manipulate the repository (e.g. code search, file operations, snippet replacement, etc.)
  • Uses sandbox environment to execute commands (e.g. code formatting, custom commands, etc.)
  • Support to AGENTS.md file to understand the repository context and conventions

🔍 Code Review Addressor Agent

Purpose: Responds to code review feedback by implementing requested changes or answering questions.

Key Capabilities:

  • Interprets reviewer comments and suggestions
  • Implements code changes based on feedback
  • Repairs failed CI/CD pipelines by analyzing job logs
  • Answers questions about the codebase

Workflow:

  1. Triggered by review comments on merge/pull requests that mention the bot
  2. Evaluates if comment requests code changes
  3. Plans and implements requested modifications using the Plan and Execute agent
  4. Updates the merge/pull request with changes
  5. Responds to reviewer with explanation, if no changes requested

📝 PR Describer Agent

Purpose: Generates comprehensive pull request metadata (title, description, summary, commit message, etc.).

Key Capabilities:

  • Analyzes code changes and their impact
  • Generates clear, detailed PR metadata

Agent Capabilities

🔧 MCP Tools Integration

Agents can use Model Context Protocol (MCP) tools to extend their capabilities:

Fetch Tools: Web scraping and HTTP requests for researching solutions Sentry Integration: Access to error monitoring and debugging information Custom Tools: Extensible framework for adding specialized functionality

🗂️ Repository Tools

All agents have access to powerful repository manipulation tools:

  • File Navigation: List, grep (using ripgrep), glob and read files and directories.
  • File Editing: Read, write, edit, rename and delete files.
  • Merge Request: Get the latest pipeline/workflow status and job logs for a merge/pull request.

🌐 Web Search Tools

Agents can use web search tools to gather information from the web:

  • Web Search: Search the web for information using DuckDuckGo or Tavily.

🏗️ Sandbox Environment

Agents can execute commands in isolated sandbox environments using daiv-sandbox:

  • Code Formatting: Apply repository-specific formatting rules (e.g. ruff, black, isort, etc.)
  • Custom Commands: Execute repository-specific commands (e.g. install dependencies, etc.)

Configuration and Customization

Repository Configuration

Control agent behavior using a .daiv.yml file in your repository root.

Learn more about configuration →

Model Selection

Configure which AI models agents use through environment variables:

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# Use Claude Sonnet for most tasks
PLAN_AND_EXECUTE_PLANNING_MODEL_NAME=openrouter:openai/gpt-4.1

# Use reasoning models for complex planning
PLAN_AND_EXECUTE_EXECUTION_MODEL_NAME=openrouter:openai/gpt-4.1

Learn more about model configuration →

Best Practices

Maximizing Agent Effectiveness

Write Clear Issues: Provide detailed descriptions with examples and acceptance criteria

Use Labels: Apply the daiv label to issues you want automated

Review Plans: Always review agent-generated plans before approval

Repository Setup

Comprehensive Documentation: Well-documented code helps agents understand context

Clear Patterns: Consistent code patterns make agent-generated code more accurate

Test Coverage: Good tests help agents validate their changes

CI/CD Integration: Proper pipeline configuration enables automatic fixing

Security Considerations

Review Changes: Always review agent-generated code before merging

Access Controls: Configure appropriate repository permissions

Sensitive Data: Ensure no secrets are exposed in repository configurations

Audit Trails: Monitor agent activities through LangSmith

Troubleshooting

Common Issues

Poor Quality Responses:

  • Improve issue descriptions with more context
  • Update repository description in .daiv.yml
  • Consider adjusting model selection

Getting Help

Logs and Monitoring: Check application logs for detailed error information

Configuration Validation: Use management commands to verify setup

Community Support: Join discussions and share experiences with other users

⏭️ Next Steps

Now that you understand how DAIV's agents work: