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Non-Linear: Product Vision

One-liner

A graph-native issue tracker for small IT teams where both humans and AI agents navigate a shared decomposition tree of project structure.

Core Thesis

Software projects are easier to conceptualize top-down using graphs. Traditional issue trackers (Jira, Linear, GitHub Issues) are flat or loosely hierarchical — they force teams to reverse-engineer structure from ticket lists and labels. Non-Linear makes the graph the first-class data model, so both humans and AI agents can reason about project topology directly.

Why Now

  1. AI agents need topology. Current tools bolt on AI after the fact. An agent that wants to understand "what should I work on next?" or "what's blocked?" has to reverse-engineer structure from flat ticket lists. A graph-native model gives agents rails to traverse. This is a structural advantage, not a feature.

  2. Small teams are underserved. There's a gap between "too simple" (Trello, GitHub Issues) and "too heavy" (Jira, Azure DevOps). Small teams need more structure than a Kanban board but can't justify Jira administration overhead.

  3. Agent ecosystems are emerging. Building for AI-agent workflows is a bet on timing. Teams are beginning to use agents for code review, task decomposition, triage, and status reporting. An agent-native tracker is positioned for this shift.

Target Users

  • Startup dev teams (3-10 people) building software products
  • Freelancers managing multiple client projects with cross-project dependencies
  • One-person teams with AI agents where the agent acts as a focused collaborator

Competitive Landscape

Tool Strength Gap
Linear Fast, opinionated, clean Flat structure, AI retrofitted
Jira Powerful, extensible Heavy, complex, AI bolted on
GitHub Issues/Projects Integrated with code Minimal structure
Plane.so Open-source Linear alternative Same flat model
Trello Simple, visual No hierarchy, no agent support
Kumu / Obsidian Canvas Graph modeling Not issue trackers

The gap: No tool combines graph-native project modeling with AI-agent-first API design.

Design Principles

  1. Graph is the spine. The decomposition tree defines project structure. Everything else — views, permissions, agent navigation — derives from graph position.
  2. Depth is type. Issues are untyped. A node's position in the tree implies its abstraction level. Root = project, children = components, leaves = tasks. Labels handle orthogonal concerns.
  3. Two graphs, separated. The decomposition tree (strict parent→child hierarchy) and the association graph (lateral links like "blocks", "relates-to") are distinct relationship types. The tree is structural; links are annotation.
  4. Agents are first-class actors. Not assistants bolted on — agents have accounts, roles, permissions, and can traverse the graph independently.
  5. Granular trust. The permission system is policy-based from day one. Roles are convenience bundles over a granular engine, not hardcoded ceilings.