Open Context Format
.context is an open file format for capturing organisational, project, or domain-specific knowledge in a portable, machine-readable form — ready to load into AI assistants, knowledge tools, or your own applications.
What it is
Teams build up deep, hard-won knowledge about their organisations, processes, and decisions. That knowledge lives in notes, transcripts, and the heads of the people who were in the room. .context is a standard for bundling it — so it travels, ages well, and can be reasoned over by AI tools that didn't attend the meetings.
A .context file is a ZIP archive with a defined internal structure. It bundles structured knowledge — interview notes, process documentation, stakeholder profiles, decisions, learnings — into a single artefact. Any tool that implements the spec can open it.
Readable by any tool that implements the spec. Not tied to a vendor, model, or platform.
Every claim traceable to its source. Provenance metadata is a first-class citizen of the format.
Diffable, revisable, and historically navigable. Just a ZIP — works with any version control system.
Self-describing for AI consumers. The AGENTS.md file tells an AI agent exactly how to reason with the contents.
File structure
A .context file is a standard ZIP archive. Rename any .context file to .zip and open it with any archive tool. The structure inside is what the spec defines.
Folder numbering is conventional, not enforced — readers must not rely on order. The folder names (after the numeric prefix) are part of the spec and must be used as shown.
Required files
A valid .context file must contain exactly three files at its root. These three together give you machine-readable identity, human orientation, and AI orientation.
The only mandatory machine-readable file. Identifies the context, declares its format version, and carries metadata. A reader uses this to determine whether it can open the file and how to present it.
// Minimum viable manifest.json { "context_format_version": "0.1", "id": "acme-project-2026", "title": "Acme — Project Alpha Context", "created": "2026-05-01T10:00:00Z", "updated": "2026-05-01T10:00:00Z", "version": "1.0.0", "authors": [ { "name": "Your Team" } ], "licence": "proprietary", "summary": "One sentence. What this context is." }
Optional fields: tags, domain, language, successor_of, dependencies, provenance_policy.
A human-readable overview for someone browsing the file in a file manager, IDE, or text editor. It should answer four questions:
An orientation document for AI agents — and for humans reasoning with the context rather than about it. This is what makes .context more than a generic ZIP of documents: it is self-describing for AI consumers.
An agent loading a .context file should read AGENTS.md first and use it to calibrate every subsequent retrieval and response.
It should include:
Provenance
Knowledge without provenance is noise. Every document in a .context file should declare where its claims came from, who said them, and how confident the author was.
Claims include source attribution in the document body.
According to Maya (2026-04-15
interview), the approval process
takes 6–8 weeks in most regions…
A YAML block at the top of the file declares sources, date, and confidence.
--- sources: - type: interview person: Maya date: 2026-04-15 confidence: high last_verified: 2026-05-01 ---
Documents without provenance are valid but should be flagged by readers. A well-formed .context file tells an AI agent not just what the answer is, but how confident to be in it.
Content conventions
All content follows a small set of conventions that make .context files consistent and interoperable across tools.
.yaml files.maya-interview-2026-04-15.md..txt, .pdf, .html) when fidelity to the source matters.Tooling
A native macOS application for collecting research fragments from anywhere, letting AI find what matters, and exporting clean .context files ready for any AI tool.
Import Markdown, PDFs, transcripts, Apple Notes, and web links. The AI sorts and organises every import, spots significance, finds patterns, and checks progress against your knowledge base — you review and approve. When you're ready, export a portable, LLM-ready .context file in one click.
Drag in files or paste URLs. Supports Markdown, PDF, plain text, Apple Notes, and web links.
The AI sorts imports, spots significance, finds repeated themes, and matches against your existing knowledge base.
Sources, notes, people, and open questions — all in one place, with tags to navigate across projects.
Keyboard shortcuts for adding notes, links, and transcripts without breaking your flow.
Live indicators for metadata completeness, source linkage, provenance clarity, and export readiness.
One click produces a portable, minimal, LLM-ready .context file from your workspace.
Status & roadmap
The spec is stable enough to build against. The reference implementation (Context Reader v1) validates the format in production use. The format is in active use; the spec may receive backward-compatible clarifications before a 1.0 release.
.context files — composition, inheritance, cross-references?AGENTS.md have a structured schema, or remain free-form prose?