llamaindex-agent

LlamaIndex agent and query engine setup for RAG-powered agents

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

llamaindex-agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

LlamaIndex agent and query engine setup for RAG-powered agents

Teams using llamaindex-agent should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/llamaindex-agent/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/ai-agents-conversational/skills/llamaindex-agent/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/llamaindex-agent/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How llamaindex-agent Compares

Feature / Agent	llamaindex-agent	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

LlamaIndex agent and query engine setup for RAG-powered agents

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# LlamaIndex Agent Skill

## Capabilities

- Set up LlamaIndex query engines
- Configure ReAct agents with tools
- Implement OpenAI function calling agents
- Design sub-question query engines
- Set up multi-document agents
- Implement chat engines with memory

## Target Processes

- rag-pipeline-implementation
- knowledge-base-qa

## Implementation Details

### Agent Types

1. **ReActAgent**: Reasoning and acting agent
2. **OpenAIAgent**: Function calling agent
3. **StructuredPlannerAgent**: Plan-and-execute style
4. **SubQuestionQueryEngine**: Complex query decomposition

### Query Engine Types

- VectorStoreIndex query engine
- Summary index query engine
- Knowledge graph query engine
- SQL query engine

### Configuration Options

- LLM selection
- Tool definitions
- Memory configuration
- Verbose/debug settings
- Query transform modules

### Best Practices

- Appropriate index selection
- Clear tool descriptions
- Memory for multi-turn
- Monitor query performance

### Dependencies

- llama-index
- llama-index-agent-openai

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