Flare

Product Introduction

1. Definition: Flare is a graph-first, agentic Integrated Development Environment (IDE) built as a desktop application or a local web server. It is an open-source (MIT licensed) tool that visualizes a codebase as a real-time, interactive dependency graph where files are nodes and imports are edges, while providing an integrated terminal for AI coding agents like Claude, Codex, or OpenCode.
2. Core Value Proposition: Flare exists to solve the context and collaboration gap in AI-assisted software development. Unlike traditional chat-based AI coding tools that output linear logs, Flare provides a live, visual map of the entire codebase. This allows developers to maintain architectural oversight, understand the impact of AI-generated changes instantly, and collaborate with multiple AI agents through a shared task board, all while running completely locally with no cloud account required.

Main Features

1. Live Dependency Graph Visualization: Flare parses the project's source code to construct and maintain a real-time dependency graph. It offers three distinct, lens-aware views: a left-to-right dependency-ordered Canvas, a radial Wheel view for cross-repo dependency analysis, and a treemap Districts view for codebase mass analysis. The graph updates live as files are edited, with visual cues (like amber highlighting) showing a file's "blast radius" of dependents.
2. Agentic Workflow & Collaboration Panel: Central to Flare is its control panel for human-agent collaboration. It features a Task Board (Kanban) where work is defined for AI agents, complete with contextual codebase data. A Design Decisions log captures architectural proposals from agents for human approval. A Questions panel lets agents park queries without blocking. All panels are accessible via the Model Context Protocol (MCP), enabling agents to autonomously pick up tasks, log progress, and file follow-up work.
3. Intelligent Change Review & History: Flare groups file edits into author-attributed "bursts" and snapshots them into a local shadow history. The Review Cockpit analyzes these changes not just line-by-line, but with context: it shows which tests ran (or didn't) after the edits, the agent's recorded intent, and flags "agent smells" like tests modified with their source. It tiers files by review priority based on blast radius, test coverage, and complexity, and allows one-click revert per file or per entire burst.
4. Contextual Awareness & Alerting: The IDE monitors terminal processes to attribute code changes to specific AI agents (Claude, Codex, etc.). It classifies all shell commands (read, write, verify, destructive) in a persistent log. Crucially, it proactively alerts the developer via a notification queue when a high-risk file (e.g., one with many dependents in a cycle) is modified, pulling the developer into the review process instead of waiting for them to find the change.

Problems Solved

1. Pain Point: Loss of architectural context and control when using AI coding assistants. Developers are handed a chat log of changes without a clear, immediate understanding of how those changes affect the overall system structure, leading to technical debt and bugs.
2. Target Audience: Software engineers and technical leads using AI agents for coding, refactoring, or feature development; teams running multiple AI agents concurrently; developers working on large, complex codebases where dependency management is critical.
3. Use Cases: Onboarding to a new codebase using the graph for comprehension; supervising a major refactor by tracking dependency impact live; managing a squad of AI agents working on different project tasks via the shared board; conducting efficient code reviews of AI-generated changes using the risk-prioritized review cockpit.

Unique Advantages

1. Differentiation: Unlike VS Code with Copilot (chat-based) or traditional IDE dependency graphs (static), Flare is a live collaboration environment built from the ground up for multi-agent, graph-centric workflows. It moves beyond suggesting code to managing the entire agentic workflow with context, oversight, and history.
2. Key Innovation: The integration of a real-time, interactive dependency graph as the primary interface with a stateful collaboration panel over MCP. This creates a shared context between human and machine, where the agent acts on work defined in the visual map and its changes are immediately reflected back into it, creating a closed-loop, observable system.

Frequently Asked Questions (FAQ)

1. How does Flare compare to using Claude or Cursor AI? Flare is not a direct alternative to AI models like Claude; it is an orchestration and oversight environment that runs those models. While Cursor integrates AI into a traditional editor, Flare is a graph-first IDE designed for managing and visualizing the output of AI agents, providing architectural oversight and multi-agent workflow management that chat interfaces lack.
2. Is Flare a cloud service or does it run locally? Flare runs 100% locally on your machine. It is a desktop Electron app (or can be served from your localhost) and requires no account, subscription, or cloud service. Your code and AI interactions never leave your computer, aligning with its MIT open-source license.
3. What AI agents are compatible with Flare? Flare is agent-agnostic through terminal integration and MCP. It has built-in process detection for popular agents like claude, codex, and opencode run in its terminal. More broadly, any CLI-based AI coding tool or MCP-compatible server can interact with its task board and collaboration features.
4. Can Flare handle large, complex codebases? Yes, Flare is designed for scale. Features like progressive folder expansion (opening one directory level at a time), semantic zoom (switching to a "plate" view when zoomed out), and aggregate meta-nodes for collapsed directories prevent visual overload. The underlying graph uses SCC condensation and crossing reduction algorithms for readability.
5. How does Flare's "Routine" feature work with AI agents? The Routine configures an agent's autonomous behavior. It can instruct an agent to check the board for new work before stopping, park questions instead of halting, and decide how to handle unapproved design decisions. This "working agreement" is stored per-project and served to agents via MCP, enabling persistent, multi-session collaboration aligned with project rules.