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— entries that no longer reflect reality are filtered out before the agent ever sees them, so agents do not act on outdated assumptions.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.
Heuristic metrics are useful for product feedback, but public proof claims require paired A/B benchmark evidence.
