Your data, always
in sync.
Pull from Notion, Google Drive, Gmail, S3, and more. Configure once — your AI agent's context stays fresh automatically.
Your agent's knowledge is always stale.
Manual File Parsing
Extracting clean text from multi-format PDFs, docx, and markdowns manually is tedious and fails to scale.
Scattered Signals
Knowledge is trapped across static local documents, API documentation pages, and team chats with no unified ingest.
Stale Context
If information changes on a page or a document is modified, your AI agents have no way to deduplicate and update their memory.
Set once. Always fresh.
Ingest via API or Upload
Upload PDFs/docx files or call our url-ingestion endpoint directly to fetch content via httpx (HTTP/2 enabled).
Celery Background Queue
Background workers process the raw content, stripping navs, scripts, and null bytes to avoid database errors.
Realtime WebSocket Broadcasts
Once processed, our server broadcasts an inbox_update message to instantly refresh all active client interfaces.
{
"url": "https://example.com/api-docs",
"tags": [
"kubernetes",
"deployment"
],
"auto_approve": true
}Connect everything your team uses.
Technical specifications of our sync engine.
Memwyre’s data connectors are built to address the stale context problem by integrating directly into your team's existing workflow. The sync engine operates by monitoring third-party APIs (such as Notion, Google Drive, Slack, and GitHub) and ingesting new updates into your memory vault. For databases like Notion, the connector polls the workspace API and extracts text blocks, database relations, and properties, transforming them into clean, structured Markdown.
When processing file attachments from Google Drive or local uploads (PDF, Docx, or raw text), the system extracts raw text and passes it to our parsing queue. This Celery-backed ingestion pipeline strips navigation elements, scripts, and null bytes, preventing database errors and eliminating useless token bloat before indexing.
For engineering environments, the GitHub connector maps commits, pull request changes, and issues into relational entities. This allows your developer agents to understand the context behind a code change or issue resolution. Our API endpoint supports automated URL scraping where you can POST a web address with custom tags; the server scrapes the content, parses it to Markdown, and broadcasts a WebSocket notification to update all active MCP clients in under 300 milliseconds.
To ensure secure connectivity, all third-party integrations utilize OAuth 2.0 authorization flows. The tokens are encrypted at rest using AES-256-GCM and are stored within an isolated vault environment. The connector orchestrator supports customizable sync schedules (ranging from real-time webhook updates to hourly or daily cron-style polling cycles) depending on your team's API limits and data rate constraints. This guarantees that your background workers do not exceed platform thresholds.
Once data is successfully ingested and normalized into clean Markdown, it is parsed by our backend semantic engine to build detailed context profiles. These profiles map entity relationships, ensuring that when an AI assistant queries your memory vault, it receives a cohesive knowledge graph rather than disjointed chunks of text. You can monitor all active data syncs, execution logs, and pending document approvals directly in the Memwyre Web Dashboard interface. This administrative dashboard provides granular sync state oversight, active data bandwidth monitoring, and debug execution logs for every data connector.
Context infrastructure for AI agents.
Stop importing. Start connecting. Set up your first data stream in minutes.
Connect Your Vault