Anyone built an email or calendar assistant that syncs and indexes data?
10 comments
Founder of https://www.nango.dev here.
A lot of teams use us for their Gmail & Google calendar integrations.
If you want to run complex queries across large parts of the data, syncing + indexing on your side will be necessary. Limits on filters, pagination & rate limits make it infeasible to search across most of a user's inbox without tens of seconds to minutes of latency.
But before you sync all the data, I would test if your users actually need to run such queries.
Both Gmail & Google Calendar have a query endpoint that searches across many fields. I would start with a simple tool for your agent to run queries on that, and expand from there if necessary.
Both Nango and Composio could do this for you.
With Nango, you would also get syncs on the same platform, if it turns out you need them.
Hope this helps!
A lot of teams use us for their Gmail & Google calendar integrations.
If you want to run complex queries across large parts of the data, syncing + indexing on your side will be necessary. Limits on filters, pagination & rate limits make it infeasible to search across most of a user's inbox without tens of seconds to minutes of latency.
But before you sync all the data, I would test if your users actually need to run such queries.
Both Gmail & Google Calendar have a query endpoint that searches across many fields. I would start with a simple tool for your agent to run queries on that, and expand from there if necessary.
Both Nango and Composio could do this for you.
With Nango, you would also get syncs on the same platform, if it turns out you need them.
Hope this helps!
Thank you that is really helpful. Will check Nango out.
When teams integrate Gmail or other tools with Nango, what usually triggers them to start syncing data instead of just using the query endpoints? Is there a specific type of query or user behavior that makes them realize they need to index and sync data? Just curious
When teams integrate Gmail or other tools with Nango, what usually triggers them to start syncing data instead of just using the query endpoints? Is there a specific type of query or user behavior that makes them realize they need to index and sync data? Just curious
It varies a lot. Which is why we always recommend to start with the feature requirements/user problem and work backwards from there.
Examples: - Low latency to show X last emails a person had with a specific email address
- Enriching data from the emails/calendar with other data from your product (E.g. mapping email recipients to contacts)
- Knowing when a calendar event has changed (sometimes also possible with webhooks)
- Detecting deletes (maybe also possible with webhooks, not sure for gmail/calendar)
Examples: - Low latency to show X last emails a person had with a specific email address
- Enriching data from the emails/calendar with other data from your product (E.g. mapping email recipients to contacts)
- Knowing when a calendar event has changed (sometimes also possible with webhooks)
- Detecting deletes (maybe also possible with webhooks, not sure for gmail/calendar)
Convictional founder here. Our experience is different than others:
- We had to sync, pre process and index the data to make the resultant knowledge search outputs actually good. MCP totally fails at this by comparison.
- It is not hugely painful thanks to bulk APIs, in Gmail in particular, as well as webhooks. We implemented both of them and it works well (so far).
- We wired it all up ourselves. Given the conclusion we had about pre-processing and indexing being required to make it work well, this seems preferred.
I think that MCP and using an integration platform will ultimately not work for any kind of agentic or deep research task heavily depending on Gmail context.
- We had to sync, pre process and index the data to make the resultant knowledge search outputs actually good. MCP totally fails at this by comparison.
- It is not hugely painful thanks to bulk APIs, in Gmail in particular, as well as webhooks. We implemented both of them and it works well (so far).
- We wired it all up ourselves. Given the conclusion we had about pre-processing and indexing being required to make it work well, this seems preferred.
I think that MCP and using an integration platform will ultimately not work for any kind of agentic or deep research task heavily depending on Gmail context.
That’s really interesting, especially your point about preprocessing and indexing being required to make search outputs good.
What was the first sign that made you realize querying live APIs wasn’t enough?
Was it latency, missing data, or just that results weren’t relevant? And when you say preprocessing, what kind of transformations or normalization ended up being most important?
Was it latency, missing data, or just that results weren’t relevant? And when you say preprocessing, what kind of transformations or normalization ended up being most important?
Keywords or vector search on their own don't get good results for high entropy queries. MCP type approach is good for low entropy things like fact-based single source answers. [1]
[1]: https://arxiv.org/abs/2504.07106
[1]: https://arxiv.org/abs/2504.07106
what is your company doing exactly?
Collaborative email, meeting recording, knowledge search and goal tracking in one thing. The search applies across emails and meetings, but also other things. We had to figure out whether it would be sufficiently good to connect third party tools, and basically concluded no. We did some research to understand why (1).
[1]: https://arxiv.org/abs/2504.07106
[1]: https://arxiv.org/abs/2504.07106
I access gmail with the web client that comes with the iPhone and I've done the same with Android, performance is good enough in both cases. For that matter I've used the IMAP support in EM Client to access gmail.
Not quite sure what you're asking for. Are you asking for a GUI/cli, or something else?
I've built exactly what you're describing, but for the sake of a b2c product.
I've built exactly what you're describing, but for the sake of a b2c product.
The part I’m still trying to figure out is how much data actually needs to be synced and indexed. Some tools seem to just call APIs on demand, while others keep everything in a local or vector store for faster retrieval.
If you’ve built something like this:
- Did you bother syncing and indexing the data, or just query live APIs?
- How painful is it to keep that data fresh without hitting rate limits?
- Did you use something like Merge.dev or Composio, or just wire it all up yourself?
I’m mostly trying to understand what the practical tradeoffs are before going too deep.