Context beats parameters. Graph beats prompt.
Oxagen turns your business data and your code into one connected map. Your read it to get the right answer the first time — and you run them on cheaper models because the context did the heavy lifting.
npx @oxagen/mcp-install init --agent cursor-native · -backed · -scoped · SOC 2 Type II in progress
Connect once
Plug it in. Keep reading forever.
Connectors for code, business data, and -compatible tools. Plug a source in once and your graph stays current as data changes — so every agent you ship reads the same up-to-date map.
- Connector classes for code repos, CRM, finance, docs, and messaging — plus MCP for the rest
- OAuth and encrypted-at-rest token vault — credentials never sit on our servers
- Continuous embeddings, entity extraction, and edge discovery — the graph keeps sharpening
- Expose the graph as an MCP server in Claude, ChatGPT, Cursor, or any compatible AI client
Context layer
The context your agents should have been reading
Business ontology and code graph in one typed, traversable layer. Agents stop hallucinating. Models get cheaper. Evals prove the delta.
Business map + code map
Two maps. One context layer.
Your business has rules. Your code has structure. Oxagen turns both into one connected map your agents can walk — so they stop guessing what an Account is or which service owns it.
- Business map — customers, orders, contracts, picked up automatically from the systems you already use
- Code map — every class, function, and call path in your repos, so agents know what your code actually does
- Fast lookups across both — under 50ms, even when the answer takes three or four hops to find
Model selection
Run Haiku. Get Opus-level results.
Better context lets you swap big expensive models for small cheap ones. Cut 95% of inference cost. The eval suite proves the accuracy held — same dashboard, same week.
- 95% drop in inference cost — better context replaces model size as the quality lever
- Every agent refers to the same node across runs and sessions — no duplicates, no drift
- Built-in evals: accuracy + cost across model sizes, re-run on every graph update
Security
Walled off by default.
Hard walls between customers, enforced by the database itself. AES-256 at rest, TLS 1.3 in transit. Your data never trains anyone's model. Ship agents without leaking context across the line.
- Row-level security — the database blocks queries that cross customer lines
- OAuth tokens encrypted AES-256-GCM — never plaintext
- Plug-in auth (JWT, Auth0, custom) — no lock-in to one identity vendor
95%
Inference cost reduction
<50ms
p95 3-hop graph retrieval
2 graphs
Business ontology + codebase
Evals
Built in, not bolted on
Ingest
Universal Connectors + MCP Ingest Surface
Plug your data sources in once. Or let other MCP servers feed the graph. The ontology stays in sync either way.
Plaid
Connect bank and financial accounts securely. Oxagen uses Plaid to map your spending, income, and cash flow.
Google Calendar
Surface what matters, automate scheduling, and connect how you spend your time with the rest of your ontology.
Gmail
Manage your email, draft replies, and connect communications with the rest of your ontology.
Google Photos
Integrate your image memories, albums, and photo libraries across devices.
Google Drive
Docs, Sheets, Slides, and PDFs become `document` nodes; the extracted text feeds the LLM extractor so the people, organizations, and concepts in your files become typed entities.
Google Meet
Conference records + participants merge into the same `meeting` node Calendar wrote — invite and attendance share one neighborhood.
Google Contacts
Seeds the canonical `person` corpus that Gmail, Calendar, Meet, and Zoom dedupe against on every subsequent sync.
GitHub
Sync a GitHub repository into a typed code graph — files, functions, classes, imports, calls, commits — refreshed on every push.
Google BigQuery
Point the workspace at any query and ingest the result as typed nodes, with incremental delta loads + cross-domain edges into the rest of your graph.
Run faster models. Get better results.
Graph your context. Let agents traverse it. Ship evals that prove the delta — in accuracy, in cost, in agent performance.
No sales call required. Self-serve from install to first query.