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Basic Information

DevChat is an open-source project that enables developers to generate and run AI-driven workflows using natural language, with a focus on improving developer productivity inside the IDE. The repository provides a core library and CLI for creating prompt-centric software development workflows, and it is supported by system workflow collections and editor integrations for Visual Studio Code and the IntelliJ Platform. DevChat promotes knowledge engineering to let AI systems deeply understand private project knowledge, and it emphasizes simplified personalization so users can define customized automation in plain sentences rather than complex drag-and-drop pipelines. The project positions itself as a community-driven effort to bridge the last mile of LLM productivity for developer teams, offering plugins, autonomous agents, and curated workflows to handle tasks like repository changes, API test generation, and progress notifications.

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App Details

Features
Natural-language workflow generation that converts a few sentences into actionable AI workflows and assistants. Knowledge engineering and integrated knowledge graph support that balance static pre-construction and dynamic construction to improve semantic queries and private knowledge understanding. A collection-oriented approach with system default workflows and community contributed plugins and agents for common developer tasks. Editor integrations including a Visual Studio Code extension and an IntelliJ Platform plugin to operate without leaving the IDE. Core components include a library and CLI for building and running workflows. Example use cases described include submitting GitLab merge requests, generating automated API test cases across multiple APIs, and providing progress updates via voice notifications.
Use Cases
DevChat helps developers automate repetitive and context-heavy tasks by letting them instruct AI in natural language and get tailored workflows that run inside familiar tools. It reduces the friction of customizing AI assistants for team-specific processes, enabling faster generation of code artifacts, test scenarios, and operational steps without deep workflow engineering. The knowledge engineering features make private documentation and API surfaces more actionable for LLMs, which can improve the quality of generated tests and reduce exploratory effort. Editor plugins keep interactions close to development workflows, while the core library, CLI, and community workflow repository make it easier to extend, share, and reproduce effective automations across teams.

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