potpie

Unlike AI coding tools that search files for relevant snippets, Potpie maps the relationships between every function, class, module, and dependency in a repository, enabling its agents to reason about code architecture holistically rather than in isolated fragments.

The knowledge graph approach gives Potpie agents genuine codebase comprehension.

When a developer asks a debugging question, the Debugging Agent doesn't just search for similar codeit traces the actual call path from a stack trace through the codebase, understands the data flow, and provides step-by-step debugging guidance specific to that application's actual architecture.

This produces genuinely useful, contextually grounded answers rather than generic suggestions that happen to match surface-level keywords.

Potpie offers specialized agents for different engineering tasks: the Q&A Agent explains functions, features, and architecture from first principles; the Code Generation Agent writes new features and refactors existing code grounded in the actual codebase patterns; the Testing Agent generates unit and integration tests that cover edge cases specific to the code's behavior; and the Documentation Agent produces software specifications, PRDs, and architecture documents that accurately reflect what the code actually does.

Multi-LLM support allows Potpie to use OpenAI, Claude, or Gemini as the underlying language model for agent reasoning, giving engineering teams flexibility to use their preferred AI provider or optimize for cost and performance on different task types.

Integration options include a VS Code extension for in-editor use, a Slack app for team-accessible code assistance, and a REST API for embedding Potpie's capabilities into custom development workflows and tooling.