Memory-Augmented RAG

Combining document retrieval with structured agent knowledge.

Beyond Standard RAG

Standard RAG retrieves documents. Memory-augmented RAG also retrieves agent experience. The result: context that includes not just what the documents say, but what the agent has learned from working with them.

The Pattern

Retrieve from two sources, combine in the prompt:

1. Document retriever — fetch relevant docs from your vector DB.
2. Knowledge retriever — fetch relevant structured agent knowledge from LocusGraph.
3. Combine — merge both into the agent's context window.

// 1. Retrieve documents (your existing RAG pipeline)
const docs = await vectorDB.search({
  query: userQuery,
  limit: 5,
});
 
// 2. Retrieve structured agent knowledge from LocusGraph
const knowledge = await client.retrieveMemories({
  query: userQuery,
  limit: 5,
});
 
// 3. Combine in prompt
const prompt = `
## Relevant Documents
${docs.map(d => d.content).join('\n\n')}
 
## Agent Knowledge
${knowledge.map(m => m.payload.value).join('\n\n')}
 
## User Question
${userQuery}
`;

What Structured Agent Knowledge Adds

Documents tell you what exists. Structured agent knowledge tells you what works.

A document might say "use retry logic for network calls." LocusGraph adds "exponential backoff with a 3-second base works best for this API — linear retry caused rate limiting last week."

Storing RAG Outcomes

Close the loop by storing what the agent learns from each RAG interaction:

// After answering, store what worked
await client.storeEvent({
  graph_id: 'support-bot',
  event_kind: 'observation',
  source: 'agent',
  context_id: 'rag:effectiveness',
  payload: { topic: 'query_pattern', value: 'Users asking about auth need both the setup guide and the troubleshooting doc' },
});

Over time, LocusGraph learns which document combinations answer which question types. RAG gets smarter with every interaction.

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