> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Auto RAG Agent • AI Agent SDK
> AutoRagAgent: Agent wrapper with automatic RAG retrieval decision.
# AutoRagAgent
> Defined in the [**Auto RAG Agent**](../modules/auto_rag_agent) module.
AI Agent
Agent wrapper with automatic RAG retrieval decision.
Decides when to perform retrieval based on policy and heuristics,
then composes RAG context with Agent chat.
This follows the same pattern as AutoAgents but for RAG:
* AutoAgents: auto-generates agent configs from instructions
* AutoRagAgent: auto-decides when to retrieve context
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#8B0000', 'primaryTextColor': '#fff', 'primaryBorderColor': '#710101', 'lineColor': '#189AB4', 'secondaryColor': '#189AB4', 'tertiaryColor': '#fff' }}}%%
graph LR
input["Input Data"] --> agent["Agent: AutoRagAgent"]
agent --> output["Output Result"]
style agent fill:#8B0000,color:#fff
style input fill:#8B0000,color:#fff
style output fill:#8B0000,color:#fff
```
## Constructor
No description available.
No description available.
No description available.
## Methods
Lazy load RAG from agent if not provided.
Delegate name to wrapped agent.
Chat with automatic RAG retrieval decision.
Async version of chat.
## Usage
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, AutoRagAgent
agent = Agent(
name="Research Assistant",
knowledge=["docs/manual.pdf"],
)
auto_rag = AutoRagAgent(agent=agent)
# Auto mode - decides based on query
result = auto_rag.chat("What are the key findings?")
# Force retrieval
result = auto_rag.chat("Hello", force_retrieval=True)
# Skip retrieval
result = auto_rag.chat("What is 2+2?", skip_retrieval=True)
```
## Source
`praisonaiagents/agents/auto_rag_agent.py` at line 105
***
## Related Documentation