Introduction
Welcome to the NeatContext documentation.
NeatContext is a local-first desktop application for building the context you feed to LLM-based tools. Your domain profiles, knowledge folders, model configuration, and tool connections live on your own machine. NeatContext does not host a model of its own — it orchestrates the model you bring (an OpenAI-compatible or Anthropic-compatible endpoint, or a local model), grounding it in the knowledge and tools you give it.
Why NeatContext
A general-purpose LLM answers from general knowledge. That is rarely enough for real operational work, where the right answer depends on your team's context: what you own, which runbooks apply, and which actions are dangerous in your environment.
NeatContext lets you assemble that context deliberately, from four building blocks:
- Domain profiles — a Markdown description of a team or domain: what it owns, how it investigates, and its guardrails.
- Knowledge bases — local folders of Markdown (runbooks, TSGs, postmortems) the model searches for grounded, citable answers.
- Extensions — connectors that give the model read/write tools for your real systems, over the Model Context Protocol (MCP).
- Your model — any tool-calling-capable LLM you configure.
The payoff is concrete: give two different teams their own profile and knowledge for the same incident, and each correctly arrives at its own right action. The Incident Analysis walkthrough demonstrates exactly this.
Where to go next
- Getting Started — install NeatContext, connect your model, and build your first workspace.
- Core Concepts — how profiles, knowledge, extensions, and models fit together.
- Incident Analysis walkthrough — a full, hands-on demonstration of the advantage.
- Building Extensions — write your own connector.
This documentation is actively growing. If something is missing or unclear, corrections are welcome.