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:

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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

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Installation

pip install crewai-aisearchapi

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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?"}))

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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"
})

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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)

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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*

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Environment Variables

export AISEARCH_API_KEY="your-api-key"
In Python: 
import os
from crewai_aisearchapi import AISearchTool

tool = AISearchTool(api_key=os.getenv("AISEARCH_API_KEY"))

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Troubleshooting

ProblemFix
model not foundRun ollama pull llama3.2:3b
context role errorEnsure all context messages use "role": "user"
API key errorCheck AISEARCH_API_KEY is set correctly

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Resources

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License

MIT License