Documentation Index
Fetch the complete documentation index at: https://docs.aisearchapi.io/llms.txt
Use this file to discover all available pages before exploring further.
This is a CrewAI integration for the AI Search API.
It connects your CrewAI agents with context-aware search, multi-message prompts, and intelligent answers with citations.
👉 Get started now:
Features
- 🔍 Prompt + Context Search – Send a query with structured context
- 💬 Multi-Message Context – Handle several user messages in one query
- 📚 Source Citations – Responses include references when available
- ⚡ CrewAI Integration – Works with Agent, Task, Crew right away
- 🖥️ Local LLM Support – Use Ollama for reasoning + Search API for live info
- 🛡️ Error Handling – Clear exceptions for invalid models or roles
Installation
pip install crewai-aisearchapi
Quick Start (with Ollama + CrewAI)
from crewai import Agent, Task, Crew, Process, LLM
from crewai_aisearchapi import AISearchTool
llm = LLM(
model="ollama/llama3.2:3b",
base_url="http://localhost:11434",
temperature=0.2,
)
tool = AISearchTool(api_key="your-api-key")
agent = Agent(
role="Researcher",
goal="Answer questions with context and sources.",
backstory="Careful and concise.",
tools=[tool],
llm=llm,
verbose=True,
)
task = Task(
description="Answer: '{question}'. Keep it short.",
expected_output="2–4 sentences.",
agent=agent,
markdown=True,
)
crew = Crew(agents=[agent], tasks=[task], process=Process.sequential, verbose=True)
if __name__ == "__main__":
print(crew.kickoff(inputs={"question": "What is RLHF in AI?"}))
Contextual Prompts
Add multiple context messages for better answers:
result = tool.run({
"prompt": "Explain how RLHF improves AI safety.",
"context": [
{"role": "user", "content": "Keep it simple, I'm new to ML."},
{"role": "user", "content": "Add one practical example."}
],
"response_type": "markdown"
})
Configuration Options
from crewai_aisearchapi import AISearchTool, AISearchToolConfig
config = AISearchToolConfig(
default_response_type="markdown",
include_sources=True,
timeout=30,
verbose=True
)
tool = AISearchTool(api_key="your-api-key", config=config)
Handling Responses
The tool returns:
- Answer (AI response)
- Sources (when available)
- Response time
Example:
Reinforcement Learning with Human Feedback (RLHF) helps align AI models with human intent...
**Sources:**
- [1] https://example.com/rlhf-overview
- [2] https://research.example.org/rlhf
*Response time: 120ms*
Environment Variables
export AISEARCH_API_KEY="your-api-key"
import os
from crewai_aisearchapi import AISearchTool
tool = AISearchTool(api_key=os.getenv("AISEARCH_API_KEY"))
Troubleshooting
| Problem | Fix |
|---|
| model not found | Run ollama pull llama3.2:3b |
| context role error | Ensure all context messages use "role": "user" |
| API key error | Check AISEARCH_API_KEY is set correctly |
Resources
License
MIT License
