Skip to main content
PipeRun is a Brazilian CRM platform for managing sales funnels, deals, contacts, companies, activities, and commercial proposals. This connector extracts the whole sales operation: deals and their stage history, contacts and companies (including custom fields), activities, calls, notes, emails, proposals with installments and payments, goals, and all the supporting configuration entities.

​
Configuring PipeRun as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the PipeRun option from the list of connectors. Click Next and you’ll be prompted to add your access.

​
1. Add account access

You’ll need to authorize Nekt to access your PipeRun data. The following configurations are available:
  • API Token: The personal API token of a PipeRun user. In PipeRun, open the user profile screen; the token is shown just below the user photo. Use a manager (G1) user, so the token can read all the account data; the connector only sees what that user is allowed to see. For more information, check the official documentation.
  • Start Date (advanced): The earliest record date to sync for incremental streams, based on each entity’s update timestamp. Leave empty to sync all history.
Once you’re done, click Next.

​
2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.
Tip: The stream can be found more easily by typing its name.
Permissions are granted per module in PipeRun. If your API token’s user lacks permission to access a specific module (e.g., proposal payments), the connector will gracefully skip that stream and continue the extraction instead of failing. If you don’t see data you were expecting, verify your user permissions or use a manager (G1) user token to access everything.
Select the streams and click Next.

​
3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.
  • Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
  • Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
  • Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
  • Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
    • Incremental: every time the extraction happens, we’ll get only the new data, which is good if, for example, you want to keep every record ever fetched.
    • Full table: every time the extraction happens, we’ll get the current state of the data, which is good if, for example, you don’t want to have deleted data in your catalog.
Once you are done configuring, click Next.

​
4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:
  • Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
  • Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.
Once you are ready, click Next to finalize the setup.

​
5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.
For you to be able to see it on your Catalog, you need at least one successful source run.

​
Streams and Fields

​
Available streams

Most streams sync incrementally using each entity’s update timestamp (deal stage history uses the stage-entry date). Reference tables such as cities and CNAEs are always synced as full tables. About the columns you’ll see in the Catalog:
  • Every stream has id as its primary key.
  • Fields that hold nested objects or lists (e.g. a deal’s tags and involved_companies, a proposal’s items, a person’s contact_emails) arrive as JSON strings, ready to be parsed in a Query or Notebook.
  • Streams can be joined together by their id columns (e.g. deals.stage_id to pipeline_stages.id).

​
Fields by stream

Below you’ll find available data streams from PipeRun and their corresponding fields:
Deals (opportunities) of the PipeRun account.
Tasks, meetings, and other activities linked to deals.

​
Implementation Notes

​
API quirks

  • with= accepts undocumented relations. /activities?with=users,persons,companies embeds the involved users (“Envolvidos”), persons and companies. /deals?with=companies embeds the deal’s involved companies (“Empresas envolvidas”), each a full company object plus role (2 and 4 seen, meaning undocumented); the main company_id is not in that list. Both work on the paginated list at about the same latency as without them. The connector processes these related entities and extracts core identification data appropriately.
  • Incremental sync limitation: Not verified: whether linking a company to a deal bumps the deal’s updated_at. If it doesn’t, an incremental sync might miss links added to deals that were not otherwise edited.

​
Skills for agents

Download PipeRun skills file

PipeRun connector documentation as plain markdown, for use in AI agent contexts.