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Agentic Chat

Overview

MatterAI is an intelligent assistant embedded directly in your code review workflow that enables developers to interact with PRs through natural language. The agent lives inside your GitHub PR interface, eliminating context switching while providing deep insights into code changes. It parses PR context, understands code structure, and delivers targeted answers to technical questions without requiring you to leave your development environment.

Features

Code-Aware Conversations

  • Contextual Code Understanding: Parses PR diffs, file context, and repo structure to provide relevant answers
  • Implementation Explanations: Analyzes code changes and explains implementation decisions with technical precision
  • Change Impact Analysis: Identifies potential side effects and integration points affected by code modifications

Advanced Query Processing

  • PR-Specific Context Window: All responses consider the full PR context including diffs, comments, and CI results
  • Code-Specific Commands: Use /matter to initiate queries with automatic PR context inclusion
  • Technical Response Calibration: Responses tailored to engineering terminology and conceptual frameworks

Tool Calling Capabilities

  • Web Search Integration: Execute targeted web searches for libraries, APIs, or technical documentation
  • Documentation Retrieval: Search and retrieve content from:
    • Repository markdown docs
    • JIRA tickets with automatic cross-referencing
    • Linear items and project workflows
  • Knowledge Graph Connections: Link code changes to related documentation and previous implementations

Engineering Workflow Integration

  • GitHub Native Experience: Functions within GitHub’s PR interface with minimal UI disruption
  • CI/CD Visibility: Understands build processes and deployment configurations
  • Branch Strategy Awareness: Recognizes branching patterns and development workflows

Benefits

For Engineers

  • Reduced Context Acquisition Time: Quickly understand unfamiliar code without jumping through multiple files
  • Technical Depth On-Demand: Get implementation details and architectural context when needed
  • Learning Acceleration: Discover patterns and techniques used by other team members
  • Dependency Insight: Understand how changes impact dependent systems and services

For Engineering Teams

  • Knowledge Distribution: Critical implementation details are explained and preserved in PR conversations
  • Consistent Review Quality: All reviewers have access to the same level of context and explanation
  • Code Standards Enforcement: Consistent guidance on team coding standards and patterns
  • Cross-Team Code Understanding: Bridge knowledge gaps between frontend, backend, and infrastructure teams

For Engineering Organizations

  • Technical Debt Visibility: Better understand the implications of implementation decisions
  • Architecture Coherence: Maintain consistent patterns across multiple teams and services
  • Deployment Risk Reduction: More thorough reviews mean fewer production incidents
  • Developer Productivity Optimization: Faster review cycles and reduced time spent explaining changes

Usage Examples

Example 1: Technical Change Explanation

Example 2: Implementation Technical Deep-Dive

Example 3: Architectural Decision Analysis

Example 4: Technical Risk Assessment

Example 5: Tool-Assisted Investigation