LlamaIndex
run-llama/llama_index
Document agents + RAG platform for building context-augmented LLM apps. GitHub: run-llama/llama_index (~52k stars).
GitHub repository
Overview
LlamaIndex (run-llama/llama_index) is listed in LimeDock Directories as an AI agent resource. Document agents + RAG platform for building context-augmented LLM apps. Group: memory-infra. Community size: ~52k GitHub stars. Repo (copy/paste): https://github.com/run-llama/llama_index This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/run-llama/llama_index GitHub path: run-llama/llama_index
How to open it: 1. Copy: https://github.com/run-llama/llama_index 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** You need serious document RAG/agents over company knowledge.
1. Install LlamaIndex. 2. Ingest a doc set. 3. Build a query engine; then try an agent over tools+docs.
Example prompts
- “Build LlamaIndex RAG over our Notion export.”
- “LlamaIndex vs Haystack?”
- “Agent vs query-engine when to choose.”
Use cases and examples
- Evaluate run-llama/llama_index as an AI agent building block
- Pilot LlamaIndex on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- Python
- Documents
Tips
- Chunking and metadata quality dominate.
- Eval questions before agents.