Connai centralizes multi-app context into one vector DB for agents
jthorare built Connai, a centralized vector database that ingests data from 10+ apps so AI agents can query cross-application context through a single MCP server instead of juggling independent per-app MCP configs.
Score breakdown
Connai replaces the per-project rebuild of context retrieval and OAuth integrations with a single shared vector DB, letting agents reason across application boundaries through one MCP endpoint rather than stitching together independent per-app configs.
- 01Connai ingests data from 10+ apps into a single queryable vector DB accessible via one MCP server.
- 02The stated motivation is eliminating repeated rebuilds of context retrieval, memory management, and OAuth app integrations per project.
- 03Per-app MCP servers worked individually but agents failed at cross-application context — e.g., meeting transcript → Jira ticket → PR workflows.
jthorare built Connai after repeatedly rebuilding context retrieval, memory management, and app integrations from scratch for each new project. The core problem was that while dedicated per-app MCP servers worked fine individually, agents struggled to reason across application boundaries — for example, tracing a meeting transcript through product and engineering decisions, to a Jira ticket, to a pull request, in order to debug or enhance an end-to-end epic.
The author has tested the shared-intelligence aspect by connecting "Paperclip" agent teams to the DB, effectively running a swarm of agents against one centralized knowledge store.
The solution is a centralized vector DB that ingests data from all connected apps, exposing a single MCP server endpoint that agents can query for grounded, cited responses without returning every piece of stored data on every query. A key friction point the author highlights is the OAuth setup burden: getting a client and secret configured for each app integration consumed significant time, especially when multiple teammates needed access to the same shared DB.
The author has tested the shared-intelligence aspect by connecting "Paperclip" agent teams to the DB, effectively running a swarm of agents against one centralized knowledge store. Open-sourcing or making the tool self-hostable is under consideration, given that companies are unlikely to want their entire company data stored in a third-party SaaS — though the SaaS form remains the easiest way to try it out. The post invites community feedback.
Key facts
- 01Connai ingests data from 10+ apps into a single queryable vector DB accessible via one MCP server.
- 02The stated motivation is eliminating repeated rebuilds of context retrieval, memory management, and OAuth app integrations per project.
- 03Per-app MCP servers worked individually but agents failed at cross-application context — e.g., meeting transcript → Jira ticket → PR workflows.
- 04The system is designed to return grounded, cited query responses rather than dumping all stored data on every query.
- 05The author connected 'Paperclip' agent teams to the shared DB to test swarm-style shared intelligence.
- 06Open-sourcing or self-hosting is being considered because companies may not want all company data in a third-party SaaS.
- 07The project is currently in a SaaS form described as the easiest way to try it out.
Topics
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