> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Recommendation Agent

> Learn how to create AI agents for personalized recommendations across various domains.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Preferences] --> Agent[Recommendation Agent]
    Agent --> Out[Recommendations]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

Recommendation agent with web search for personalized suggestions.

***

## Simple

**Agents: 1** — Single agent analyzes preferences and generates recommendations.

### Workflow

1. Receive user preferences
2. Search for current options
3. Generate personalized recommendations

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai duckduckgo-search
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents import duckduckgo

agent = Agent(
    name="Recommender",
    instructions="Provide personalized suggestions based on preferences.",
    tools=[duckduckgo]
)

result = agent.start("Recommend 5 sci-fi movies from 2024")
print(result)
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Recommend good books about AI" --web-search
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Recommendations
roles:
  recommender:
    role: Recommendation Specialist
    goal: Generate personalized recommendations
    backstory: You are an expert at finding great content
    tools:
      - duckduckgo
    tasks:
      recommend:
        description: Recommend 5 sci-fi movies from 2024
        expected_output: A list of recommendations
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents import duckduckgo

agent = Agent(
    name="Recommender",
    instructions="You are a recommendation agent.",
    tools=[duckduckgo]
)

agent.launch(port=8080)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Recommend podcasts about technology"}'
```

***

## Advanced Workflow (All Features)

**Agents: 1** — Single agent with memory, persistence, structured output, and session resumability.

### Workflow

1. Initialize session for preference tracking
2. Configure SQLite persistence for recommendation history
3. Search and recommend with structured output
4. Store preferences in memory for personalization
5. Resume session for refined recommendations

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai duckduckgo-search pydantic
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents import duckduckgo
from pydantic import BaseModel

class Recommendation(BaseModel):
    category: str
    items: list[str]
    descriptions: list[str]
    ratings: list[str]

session = Session(session_id="rec-001", user_id="user-1")

agent = Agent(
    name="Recommender",
    instructions="Generate structured recommendations.",
    tools=[duckduckgo],
    memory=True
)

task = Task(
    description="Recommend 5 sci-fi movies from 2024 with ratings",
    expected_output="Structured recommendations",
    agent=agent,
    output_pydantic=Recommendation
)

agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Recommend sci-fi movies" --web-search --memory --verbose
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Recommendations
memory: true
memory_config:
  provider: sqlite
  db_path: recommendations.db
roles:
  recommender:
    role: Recommendation Specialist
    goal: Generate structured recommendations
    backstory: You are an expert at finding great content
    tools:
      - duckduckgo
    memory: true
    tasks:
      recommend:
        description: Recommend 5 sci-fi movies from 2024
        expected_output: Structured recommendations
        output_json:
          category: string
          items: array
          descriptions: array
          ratings: array
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml --verbose
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents import duckduckgo

agent = Agent(
    name="Recommender",
    instructions="Generate structured recommendations.",
    tools=[duckduckgo],
    memory=True
)

agent.launch(port=8080)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Recommend books", "session_id": "rec-001"}'
```

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test recommendations" --web-search --verbose
```

## Cleanup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
rm -f recommendations.db
```

## Features Demonstrated

| Feature           | Implementation                         |
| ----------------- | -------------------------------------- |
| Workflow          | Personalized recommendation generation |
| DB Persistence    | SQLite via `memory_config`             |
| Observability     | `--verbose` flag                       |
| Tools             | DuckDuckGo search                      |
| Resumability      | `Session` with `session_id`            |
| Structured Output | Pydantic `Recommendation` model        |

## Next Steps

* [Shopping Agent](/agents/shopping) for price comparisons
* [Research Agent](/agents/research) for detailed research
* [Memory](/features/advanced-memory) for persistent context