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LlamaIndex Framework
Use Cases

Prompting

Prompting LLMs is a fundamental unit of any LLM application. You can build an entire application entirely around prompting, or orchestrate with other modules (e.g. retrieval) to build RAG, agents, and more.

LlamaIndex supports LLM abstractions and simple-to-advanced prompt abstractions to make complex prompt workflows possible.

LlamaIndex supports 40+ LLM integrations, from proprietary model providers like OpenAI, Anthropic to open-source models/model providers like Mistral, Ollama, Replicate. It provides all the tools to standardize interface around common LLM usage patterns, including but not limited to async, streaming, function calling.

Here’s the full module guide for LLMs.

LlamaIndex has robust prompt abstractions that capture all the common interaction patterns with LLMs.

Here’s the full module guide for prompts.

LlamaIndex has robust abstractions for creating sequential prompt chains, as well as general DAGs to orchestrate prompts with any other component. This allows you to build complex workflows, including RAG with multi-hop query understanding layers, as well as agents.

These pipelines are integrated with observability partners out of the box.

Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/