Yes, DeepResearch can be integrated with external tools like note-taking apps or knowledge bases. The platform provides APIs, webhooks, and standardized data formats that allow developers to connect it with third-party systems. For example, you can use RESTful APIs to sync research data to tools like Notion, Evernote, or Obsidian, or push results to knowledge bases like Confluence. Authentication is typically handled via OAuth or API keys, ensuring secure access to data.
A common integration approach involves using DeepResearch’s API to automate data flow. Suppose you want to save research summaries to a Notion database. You could write a script that fetches the latest data from DeepResearch’s /results endpoint, formats it into markdown, and sends it to Notion’s API. Webhooks add another layer of automation—for instance, triggering a Zapier workflow when a new project is tagged “complete” in DeepResearch, which then creates a task in Trello or updates a row in Airtable. These workflows reduce manual steps and keep external tools in sync with the latest research.
DeepResearch also supports extensibility through custom plugins or middleware. Developers can build connectors using Python, Node.js, or other languages, leveraging SDKs provided by target platforms. For example, a Jupyter Notebook integration could pull datasets from DeepResearch for analysis, then push visualizations back as attachments. Open-source community plugins for tools like Roam Research or Logseq demonstrate how structured data from DeepResearch can enhance note-taking with bidirectional links. The key is designing clear data mappings (e.g., JSON to Markdown) and handling errors like rate limits or schema mismatches. With these tools, developers can tailor integrations to specific team workflows without heavy overhead.