Local-first knowledge graph

Shared context for your team and your agents.

Ingest a document once. Everyone on the team, and every AI agent they use, can traverse it, with the sources attached.

Open workspaceTry the demo

Free during beta · no invitation needed · your knowledge, connected

Local checks for sensitive data. Your choice of AI provider. Privacy, explained

Knowledge graph35 entities · 68 connections · demo vault35 entities · 68 links
Search the graph…
Drag nodes · scroll to zoom · click for details
Person 7 Organization 3 Project 3 Meeting 4 Decision 4 Concept 4 Repository 2 Conversation 1 Source 4 Agent 1 Skill 1

Swipe to scroll the page. Use two fingers to zoom or move the graph. Tap an entity to explore.

How it works

Your knowledge, connected in three steps.

01

Drop in what you already have

PDFs, decks, spreadsheets, email, Drive, Gmail, GitHub repos, exported chats. Screened for sensitive data before any model reads it.

PDF DOC XLS PPT MD
Plus any PDF, Office, or text file, a pasted link, or a local git repo. All sources ↓
02

An agent builds the graph, once

People, projects, decisions, meetings, and concepts each become one markdown file. Relationships are plain links. Every fact keeps its source.

wrote people/ada-patel.md
linked → projects/relay.md
updated people/README.md
03

Answers for people. Context for agents.

Teammates chat with the graph and get cited answers. Coding agents and assistants read the same folder over CLI or MCP. Nobody ingests the file twice.

✓ 3 claims · 3 verified
Connect a source, then start a sync when you want to bring in new changes. 7 connected · 3 on the roadmap · 13 file types

Shared context

Build the context once. Share it with the team and every agent.

One vault for you, your team, and your agents. Add knowledge once. Share it everywhere.

Every answer links back to its sources.

Hover the highlighted names →

What changed across Relay, and who owns the next steps?
Read projects/relay.md Read people/ada-patel.md Read meetings/relay-release-readiness-review.md

Relay is ready for a focused partner review. Relay keeps the first rollout bounded. Ada Patel coordinates the outcome and Julian Morgan owns delivery.

The standing decision is Keep account context traceable; the next checkpoint is the release readiness review.

✓ 2 claims · 2 verified · $0.014
Context graph 14 of 35 files

Early results

OpenContext reduces token costs, API calls, and tool calls.

20.3% fewer model API responses. 16.4% fewer tool calls.

Preliminary results. Results may vary.

Based on preliminary testing on one coding task, codebase, and model. Cost and token figures average three paired comparisons; API response and tool-call figures average the two pairs with recorded counts. Costs combine recorded and transcript-estimated usage and exclude graph creation and maintenance. Quality was mixed across these preliminary comparisons.

Read the full analysis →
−20.6%
mean agent-run cost
$2.34 vs $2.94 across these three pairs
−33%
cache-read tokens
Mean reduction across the same three comparisons
Explore a hypothetical 20% reduction
$100$2,000
API token spend in USD per person, per month.
Team token budget / month
$500,000
Potential savings / month
$100,000
Read the full analysisIllustrates a 20% reduction in your team’s token budget, not a measured team-wide result. Excludes graph creation, maintenance, and human time.

Try it on your own questions

Stop asking around. Ask the graph.

The question you would have posted in Slack today. Type it, open the workspace, and it is the first thing you ask.

Answers cite the files behind them Browser workspace, no installation Free during beta Your teammates and agents read the same graph

OpenContext 0.2.0, published by Alec Merle, St. Louis, Missouri, United States. © 2026 Dovah LLC. All rights reserved.

This site loads its assets locally, sets no cookies, and runs no analytics. The graph is an interactive example.