agentic-research-agent-deeplearning

name	agentic-research-agent-deeplearning
description	Build and deploy FastAPI-based research agents with planning, tool-using (Tavily, arXiv, Wikipedia), and Postgres state management in a single container.
triggers	["set up the agentic research agent service","create a research workflow with planning agents","build a FastAPI research agent with Postgres","implement multi-step research workflow with agents","deploy the reflective research agent container","use Tavily and arXiv tools in research agents","create task progress tracking for agent workflows","build agentic AI research service with state management"]

Agentic Research Agent (DeepLearning.AI)

Skill by ara.so — AI Agent Skills collection.

Overview

The Reflective Research Agent is a FastAPI-based service that orchestrates multi-step research workflows using AI agents. It features:

• Planning Agent: Breaks down research tasks into executable steps
• Tool-Using Agents: Research, writer, and editor agents with access to Tavily search, arXiv papers, and Wikipedia
• State Management: Postgres database tracks task state, progress, and results
• Single Container: All-in-one Docker image with Postgres + FastAPI
• Live Progress: Real-time status updates for long-running research tasks

The service exposes a web UI and REST API for kicking off research workflows, monitoring progress, and retrieving final reports.

Installation

Prerequisites

# Ensure Docker is installed
docker --version

# Create .env file with API keys
cat > .env << EOF
OPENAI_API_KEY=your-openai-key
TAVILY_API_KEY=your-tavily-key
EOF

Clone and Build

# Clone the repository
git clone https://github.com/deeplearning-ai/agentic-ai-public.git
cd agentic-ai-public

# Build Docker image
docker build -t fastapi-postgres-service .

Run the Service

# Run container with ports exposed
docker run --rm -it \
  -p 8000:8000 \
  -p 5432:5432 \
  --name fpsvc \
  --env-file .env \
  fastapi-postgres-service

Access the service:

• Web UI: http://localhost:8000
• API Docs: http://localhost:8000/docs
• Postgres: postgresql://app:local@localhost:5432/appdb

Project Structure

.
├── main.py                    # FastAPI application entry point
├── src/
│   ├── planning_agent.py     # Planner and executor logic
│   ├── agents.py             # Research, writer, editor agents
│   └── research_tools.py     # Tool implementations (Tavily, arXiv, Wikipedia)
├── templates/
│   └── index.html            # Web UI template
├── static/                   # CSS/JS assets
├── docker/
│   └── entrypoint.sh         # Container startup script
├── requirements.txt
└── Dockerfile

Key API Endpoints

Start Research Task

# Kick off a research workflow
curl -X POST http://localhost:8000/generate_report \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Large Language Models for scientific discovery",
    "model": "openai:gpt-4o"
  }'

# Response: {"task_id": "550e8400-e29b-41d4-a716-446655440000"}

Poll Progress

# Get live progress updates
curl http://localhost:8000/task_progress/550e8400-e29b-41d4-a716-446655440000

# Response shows current step, substeps, and status
{
  "task_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "in_progress",
  "current_step": "research",
  "steps_completed": 1,
  "total_steps": 4,
  "substeps": [
    {"name": "tavily_search", "status": "completed"},
    {"name": "arxiv_search", "status": "in_progress"}
  ]
}

Get Final Report

# Retrieve completed research report
curl http://localhost:8000/task_status/550e8400-e29b-41d4-a716-446655440000

# Response includes full report and metadata
{
  "task_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "completed",
  "report": "# Research Report\n\n## Introduction\n...",
  "created_at": "2024-01-15T10:30:00Z",
  "completed_at": "2024-01-15T10:35:00Z"
}

Core Code Patterns

Creating Custom Research Tools

# src/research_tools.py
import requests
from typing import Dict, Any

def tavily_search_tool(query: str, max_results: int = 5) -> Dict[str, Any]:
    """Search using Tavily API"""
    api_key = os.getenv("TAVILY_API_KEY")
    response = requests.post(
        "https://api.tavily.com/search",
        json={
            "query": query,
            "max_results": max_results,
            "search_depth": "advanced"
        },
        headers={"Authorization": f"Bearer {api_key}"}
    )
    return response.json()

def arxiv_search_tool(query: str, max_results: int = 5) -> list:
    """Search arXiv papers"""
    import arxiv
    search = arxiv.Search(
        query=query,
        max_results=max_results,
        sort_by=arxiv.SortCriterion.Relevance
    )
    return [
        {
            "title": result.title,
            "summary": result.summary,
            "authors": [author.name for author in result.authors],
            "pdf_url": result.pdf_url
        }
        for result in search.results()
    ]

Building Custom Agents

# src/agents.py
from aisuite.client import Client

def research_agent(prompt: str, tools: list, model: str = "openai:gpt-4o") -> str:
    """Agent that conducts research using available tools"""
    client = Client()
    
    # Prepare tool descriptions for the agent
    tool_descriptions = "\n".join([
        f"- {tool['name']}: {tool['description']}" 
        for tool in tools
    ])
    
    system_prompt = f"""You are a research agent. You have access to:
{tool_descriptions}

Use these tools to gather comprehensive information about the topic.
Synthesize findings into a cohesive research summary."""
    
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": prompt}
    ]
    
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=0.7
    )
    
    return response.choices[0].message.content

def writer_agent(research_content: str, model: str = "openai:gpt-4o") -> str:
    """Agent that transforms research into a formatted report"""
    client = Client()
    
    messages = [
        {"role": "system", "content": },
        {: , : research_content}
    ]
    
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=
    )
    
     response.choices[].message.content

 () -> :
    
    client = Client()
    
    messages = [
        {: , : },
        {: , : draft_report}
    ]
    
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=
    )
    
     response.choices[].message.content

Implementing Planning Agent

# src/planning_agent.py
import json
from aisuite.client import Client

def planner_agent(prompt: str, model: str = "openai:gpt-4o") -> dict:
    """Create a multi-step plan for research workflow"""
    client = Client()
    
    system_prompt = """You are a research planning agent. Create a step-by-step plan to complete the research task.
Output JSON with structure:
{
  "steps": [
    {"name": "research", "description": "...", "tools": ["tavily", "arxiv"]},
    {"name": "write", "description": "..."},
    {"name": "edit", "description": "..."}
  ]
}"""
    
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"Plan research for: {prompt}"}
    ]
    
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=0.7
    )
    
    return json.loads(response.choices[0].message.content)

def executor_agent_step(step: dict, context: dict, model: str) -> str:
    """Execute a single step from the plan"""
    step_name = step["name"]
    
    if step_name == "research":
        # Execute research with tools
        results = []
        if "tavily" in step.get("tools", []):
            results.append(tavily_search_tool(context[]))
           step.get(, []):
            results.append(arxiv_search_tool(context[]))
         research_agent(context[], results, model)
    
     step_name == :
         writer_agent(context[], model)
    
     step_name == :
         editor_agent(context[], model)
    
     

Database Models and State Management

# main.py or models.py
from sqlalchemy import create_engine, Column, String, Text, DateTime, Enum
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
import enum
from datetime import datetime
import os

Base = declarative_base()

class TaskStatus(enum.Enum):
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"

class ResearchTask(Base):
    __tablename__ = "research_tasks"
    
    task_id = Column(String, primary_key=True)
    prompt = Column(Text, nullable=False)
    model = Column(String, nullable=False)
    status = Column(Enum(TaskStatus), default=TaskStatus.PENDING)
    current_step = Column(String)
    report = Column(Text)
    created_at = Column(DateTime, default=datetime.utcnow)
    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
    completed_at = Column(DateTime)

# Initialize database
DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://app:local@127.0.0.1:5432/appdb")
engine = create_engine(DATABASE_URL)
SessionLocal = sessionmaker(bind=engine)

# Create tables
Base.metadata.create_all(bind=engine)

FastAPI Application Setup

# main.py
from fastapi import FastAPI, BackgroundTasks, HTTPException
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
import uuid
import threading

app = FastAPI(title="Reflective Research Agent")

# Mount templates and static files
templates = Jinja2Templates(directory="templates")
app.mount("/static", StaticFiles(directory="static"), name="static")

class ResearchRequest(BaseModel):
    prompt: str
    model: str = "openai:gpt-4o"

@app.get("/")
async def home(request: Request):
    """Render web UI"""
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/generate_report")
async def generate_report(req: ResearchRequest, background_tasks: BackgroundTasks):
    """Kick off research workflow in background"""
    task_id = str(uuid.uuid4())
    
    # Create task in database
    db = SessionLocal()
    task = ResearchTask(
        task_id=task_id,
        prompt=req.prompt,
        model=req.model,
        status=TaskStatus.PENDING
    )
    db.add(task)
    db.commit()
    db.close()
    
    
    thread = threading.Thread(target=run_workflow, args=(task_id, req.prompt, req.model))
    thread.start()
    
     {: task_id}

 ():
    
    db = SessionLocal()
    task = db.query(ResearchTask).filter_by(task_id=task_id).first()
    
    :
        
        task.status = TaskStatus.IN_PROGRESS
        db.commit()
        
        
        plan = planner_agent(prompt, model)
        
        
        context = {: prompt}
         step  plan[]:
            task.current_step = step[]
            db.commit()
            
            result = executor_agent_step(step, context, model)
            context[] = result
        
        
        task.report = context.get(, )
        task.status = TaskStatus.COMPLETED
        task.completed_at = datetime.utcnow()
        db.commit()
        
     Exception  e:
        task.status = TaskStatus.FAILED
        task.report = 
        db.commit()
    :
        db.close()


  ():
    
    db = SessionLocal()
    task = db.query(ResearchTask).filter_by(task_id=task_id).first()
    db.close()
    
      task:
         HTTPException(status_code=, detail=)
    
     {
        : task.task_id,
        : task.status.value,
        : task.current_step,
        : task.created_at.isoformat()
    }


  ():
    
    db = SessionLocal()
    task = db.query(ResearchTask).filter_by(task_id=task_id).first()
    db.close()
    
      task:
         HTTPException(status_code=, detail=)
    
     {
        : task.task_id,
        : task.status.value,
        : task.report,
        : task.created_at.isoformat(),
        : task.completed_at.isoformat()  task.completed_at  
    }

Configuration

Environment Variables

# Required
OPENAI_API_KEY=sk-...                    # OpenAI API key
TAVILY_API_KEY=tvly-...                  # Tavily search API key

# Optional - Database (defaults set by entrypoint)
DATABASE_URL=postgresql://app:local@127.0.0.1:5432/appdb
POSTGRES_USER=app
POSTGRES_PASSWORD=local
POSTGRES_DB=appdb

# Optional - Behavior
RESET_DB_ON_STARTUP=0                    # Set to 1 to drop tables on startup

Requirements

fastapi>=0.104.0
uvicorn[standard]>=0.24.0
sqlalchemy>=2.0.0
psycopg2-binary>=2.9.0
python-dotenv>=1.0.0
jinja2>=3.1.0
requests>=2.31.0
wikipedia>=1.4.0
arxiv>=2.0.0
aisuite>=0.1.0

Troubleshooting

Container Won't Start

# Check logs
docker logs fpsvc

# Verify Postgres initialization
docker exec -it fpsvc bash -lc "pg_lsclusters"

# Test database connection
docker exec -it fpsvc bash -lc "psql $DATABASE_URL -c 'SELECT 1'"

Tables Missing After Restart

The default main.py may drop tables on startup. Disable this:

# In main.py, comment out or guard:
# Base.metadata.drop_all(bind=engine)

# Or use environment flag:
if os.getenv("RESET_DB_ON_STARTUP") != "1":
    Base.metadata.create_all(bind=engine)

API Key Errors

# Verify .env is loaded
docker exec -it fpsvc bash -lc "echo \$OPENAI_API_KEY"

# If empty, restart with explicit env vars
docker run --rm -it \
  -p 8000:8000 \
  -e OPENAI_API_KEY=sk-... \
  -e TAVILY_API_KEY=tvly-... \
  --name fpsvc \
  fastapi-postgres-service

Tool Failures (Tavily, arXiv, Wikipedia)

# Add error handling to tools
def tavily_search_tool(query: str, max_results: int = 5) -> Dict[str, Any]:
    try:
        api_key = os.getenv("TAVILY_API_KEY")
        if not api_key:
            return {"error": "TAVILY_API_KEY not set"}
        # ... rest of implementation
    except Exception as e:
        return {"error": f"Tavily search failed: {str(e)}"}

# Wikipedia rate limiting - add retry logic
import time
def wikipedia_search_tool(query: str, retries: int = 3) -> list:
    for attempt in range(retries):
        try:
            # ... search logic
            return results
        except Exception as e:
            if attempt < retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise

Development with Hot Reload

# Mount source code for live updates
docker run --rm -it \
  -p 8000:8000 -p 5432:5432 \
  -v "$PWD":/app \
  --env-file .env \
  --name fpsvc \
  fastapi-postgres-service \
  bash -lc "pg_ctlcluster 17 main start && uvicorn main:app --host 0.0.0.0 --port 8000 --reload"

Debugging Long-Running Tasks

# Add detailed logging
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def run_workflow(task_id: str, prompt: str, model: str):
    logger.info(f"Starting workflow for task {task_id}")
    
    for step in plan["steps"]:
        logger.info(f"Executing step: {step['name']}")
        result = executor_agent_step(step, context, model)
        logger.info(f"Step {step['name']} completed: {len(result)} chars")

Best Practices

1. Always use environment variables for API keys, never hardcode
2. Implement retries for external API calls (Tavily, arXiv)
3. Add timeouts to prevent stuck tasks
4. Log progress to database for observability
5. Handle partial failures gracefully in multi-step workflows
6. Use background tasks or message queues for production deployments
7. Add rate limiting to prevent API quota exhaustion
8. Cache results for repeated queries to reduce costs