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Documentation Index

Fetch the complete documentation index at: https://docs.m4trix.dev/llms.txt

Use this file to discover all available pages before exploring further.

Build agents that use retrieval-augmented generation (RAG) or tool-calling patterns.

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Approach

  1. Define events — e.g. rag-request (query + context) and rag-response (answer chunks)
  2. Agent logic — Fetch from your vector store, build context, call the LLM with the augmented prompt
  3. Streaming — Emit response chunks as they arrive from the LLM

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Tool-Calling Pattern

For agents that call tools (e.g. search, calculator):
  1. Emit a tool-request event with the tool name and params
  2. Another agent (or external system) handles the tool and emits tool-response
  3. The original agent receives tool-response and continues
This can be implemented with multiple agents on different channels, or with a single agent that manages tool state internally.

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Example Repos

  • core-example — Basic streaming agent; extend with RAG by adding retrieval before the LLM call
  • open-ai-speech-to-speech-example — Uses OpenAI Realtime API; shows integration patterns

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Common Recipes

See Common Recipes for copyable snippets (retrieval, tool loops, etc.).