> ## Documentation Index
> Fetch the complete documentation index at: https://cxity.kairaxis.com/llms.txt
> Use this file to discover all available pages before exploring further.

# What Is Contexity? Local-First AI Agent Context Continuity

> Contexity is a local-first context layer for AI coding agents. Structured memory, freshness control, and bounded retrieval — no cloud required.

Contexity is a local-first context continuity layer for AI coding agents — not a chatbot, IDE, vector database, or generic notes app. It is a local engine that gives agents structured, provenance-labeled project context and the bounded retrieval tools they need to use that context correctly.

## The Problem Contexity Solves

AI coding agents routinely lose or misuse context in ways that cost real time and produce unreliable results:

* They repeat full repo discovery at the start of every session.
* They forget prior investigations the moment a session ends.
* They reuse stale notes after files move or behavior changes.
* They cannot explain where a remembered fact originally came from.
* They cannot demonstrate whether stored context actually saved any work.

Contexity addresses each of these failure modes by storing structured, source-backed project context and exposing bounded retrieval to agents at exactly the moment they need it.

## What Contexity Gives Agents

When you attach Contexity to a project, agents gain access to a set of capabilities they cannot build for themselves mid-task:

* **Project identity and state** — a stable, persistent identity for the project that survives session boundaries and team handoffs.
* **Task-aware context packs** — bounded bundles of relevant context assembled for a specific task, not an unbounded dump of everything.
* **Source-backed project intelligence** — project structure, dependency relationships, and behavioral patterns derived directly from source, not from agent memory alone.
* **Stale context suppression** — entries that no longer reflect reality are filtered out before the agent ever sees them, so agents do not act on outdated assumptions.
* **Candidate-first memory writes** — agent-proposed memory additions enter a candidate state and require confirmation before they are treated as trusted context.
* **Run ledgers and closeout checkpoints** — every agent run is tracked from start to finish, giving you an auditable record of what the agent read and what it changed.
* **Visible metrics when enabled** — opt-in heuristic metrics surface estimated context reuse and session savings, useful for evaluating how much work Contexity is doing across your projects.
* **External source capture** — Contexity can ingest context from Slack threads, issue comments, documentation, external repositories, and product direction documents, so agents are not limited to what they can read from source files.

## What Contexity Does Not Do

Understanding the boundaries of Contexity is just as important as understanding what it provides.

Contexity does **not** automatically trust everything an agent writes. Agent-proposed memory entries go through a candidate stage before they are promoted to trusted context.

Contexity does **not** treat external text as model instructions. Content ingested from external sources is stored as data for retrieval, not fed into the model's instruction context.

Contexity does **not** require you to manually paste context every time you start a session. Once you attach a project and connect an agent host, context retrieval happens automatically through the MCP tool interface.

<Warning>
  Heuristic metrics are useful for product feedback, but public proof claims require paired A/B benchmark evidence.
</Warning>