CrewAI — Multi-Agent Workflows

┌─────────────────────────────────────────────────┐
│                    CREW                          │
│  Process: Sequential / Hierarchical              │
│                                                  │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐       │
│  │ AGENT 1  │  │ AGENT 2  │  │ AGENT 3  │       │
│  │Researcher│─▸│ Analyst  │─▸│  Writer  │       │
│  │          │  │          │  │          │       │
│  │ Role     │  │ Role     │  │ Role     │       │
│  │ Goal     │  │ Goal     │  │ Goal     │       │
│  │Backstory │  │Backstory │  │Backstory │       │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘       │
│       │             │             │              │
│  ┌────▼─────┐  ┌────▼─────┐  ┌────▼─────┐       │
│  │  TASK 1  │  │  TASK 2  │  │  TASK 3  │       │
│  │ Research │  │ Analyze  │  │  Write   │       │
│  │ Topic    │  │ Findings │  │  Report  │       │
│  └──────────┘  └──────────┘  └──────────┘       │
│                     │                            │
│              ┌──────▼──────┐                     │
│              │   TOOLS     │                     │
│              │ Web Search  │                     │
│              │ File Access │                     │
│              │ RAG Query   │                     │
│              └─────────────┘                     │
└─────────────────────────────────────────────────┘
                      │
               ┌──────▼──────┐
               │   Ollama    │
               │  (Local)    │
               └─────────────┘

# Create project directory
mkdir -p ~/ai-stack/crewai && cd ~/ai-stack/crewai

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install CrewAI
pip install crewai crewai-tools

OPENAI_API_BASE=http://localhost:11434/v1
OPENAI_API_KEY=ollama
OPENAI_MODEL_NAME=mistral

#!/usr/bin/env python3
"""Content research pipeline using CrewAI + Ollama."""

from crewai import Agent, Task, Crew, Process
from langchain_community.llms import Ollama

# Configure local LLM
llm = Ollama(model="mistral", base_url="http://localhost:11434")

# Define Agents
researcher = Agent(
    role="Research Specialist",
    goal="Gather comprehensive information on the given topic",
    backstory="""You are an expert researcher with years of experience
    in finding and synthesizing information from multiple sources.
    You excel at identifying key trends and data points.""",
    llm=llm,
    verbose=True
)

analyst = Agent(
    role="Data Analyst",
    goal="Analyze research findings and extract actionable insights",
    backstory="""You are a skilled analyst who turns raw research
    into structured insights. You identify patterns, gaps, and
    opportunities that others miss.""",
    llm=llm,
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Create a clear, well-structured report from the analysis",
    backstory="""You are a professional writer who excels at
    making complex topics accessible. Your reports are concise,
    actionable, and well-organized.""",
    llm=llm,
    verbose=True
)

# Define Tasks
research_task = Task(
    description="""Research the topic: {topic}
    Gather key facts, statistics, trends, and expert opinions.
    Focus on recent developments and practical implications.""",
    expected_output="A comprehensive research brief with key findings",
    agent=researcher
)

analysis_task = Task(
    description="""Analyze the research findings:
    - Identify the top 3 key trends
    - Highlight opportunities and risks
    - Compare with industry benchmarks""",
    expected_output="A structured analysis with actionable insights",
    agent=analyst
)

writing_task = Task(
    description="""Write a professional report that includes:
    - Executive summary (3 sentences)
    - Key findings (bullet points)
    - Detailed analysis
    - Recommendations""",
    expected_output="A polished, publication-ready report",
    agent=writer
)

# Assemble Crew
crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    process=Process.sequential,
    verbose=True
)

if __name__ == "__main__":
    result = crew.kickoff(
        inputs={"topic": "self-hosted AI infrastructure trends in 2025"}
    )
    print("\n" + "="*60)
    print("FINAL REPORT:")
    print("="*60)
    print(result)

python3 research_crew.py

from crewai_tools import BaseTool
from qdrant_client import QdrantClient
from langchain_community.embeddings import OllamaEmbeddings

class RAGSearchTool(BaseTool):
    name: str = "Company Knowledge Search"
    description: str = "Search the company knowledge base for relevant information"
    
    def _run(self, query: str) -> str:
        embeddings = OllamaEmbeddings(
            base_url="http://localhost:11434",
            model="nomic-embed-text"
        )
        client = QdrantClient(
            url="http://localhost:6333",
            api_key="your-qdrant-api-key"
        )
        
        vector = embeddings.embed_query(query)
        results = client.search(
            collection_name="documents",
            query_vector=vector,
            limit=5
        )
        
        context = "\n\n".join([r.payload["text"] for r in results])
        return f"Relevant context:\n{context}"

# Add to your agent:
# researcher = Agent(..., tools=[RAGSearchTool()])

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn

app = FastAPI()

class CrewRequest(BaseModel):
    topic: str

@app.post("/run-research")
async def run_research(request: CrewRequest):
    # Import your crew from above
    result = crew.kickoff(inputs={"topic": request.topic})
    return {"result": str(result)}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8001)

pip install fastapi uvicorn
python3 crew_api.py