How Knowledge is Retrieved
Team members access the shared knowledge base through memoryd's MCP server. When an AI tool calls memory_search, memoryd finds the most relevant knowledge and returns it directly to the tool.
The flow
AI tool calls memory_search → memoryd finds relevant knowledge → results returned to the tool
1. Understand the query
When an AI tool calls memory_search with a query, memoryd converts it into a mathematical representation (an embedding) that captures its meaning — not just keywords, but concepts.
This happens locally on every team member's machine using a lightweight model. No data leaves the machine for this step.
2. Search the shared store
memoryd searches the team's shared knowledge base for relevant items. The search strategy depends on your setup:
| Setup | How search works |
|---|---|
| Atlas cluster (recommended) | Hybrid search — combines meaning-based similarity with keyword matching, then diversifies results. Best for teams. |
| Local MongoDB (solo dev) | Vector search — meaning-based similarity only. Good for individual use. |
With Atlas, the search also filters out low-quality items automatically — noise that was captured but never proved useful doesn't clutter the results.
See Hybrid Search for how the Atlas search pipeline works.
3. Return results
The most relevant knowledge items are returned to the AI tool via MCP. The tool receives the actual content — past debugging sessions, architecture decisions, deployment procedures — and can use it as context for its current task.
A result limit (default: 5 items, configurable via retrieval_top_k) controls how many items are returned per search.
4. Quality feedback
When knowledge items are retrieved, memoryd records that they were useful. Over time, items that are retrieved frequently earn higher quality scores. Items that are never retrieved eventually decay and are cleaned up by the quality maintenance process.
This creates a virtuous cycle: the more your team uses memoryd, the better the knowledge quality becomes.