> ## 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.

# Single Agent

> Learn how to create a basic single-purpose AI agent for simple tasks.

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

Single-purpose agent for content generation. Minimal setup, no external tools.

***

## Simple

**Agents: 1** — Single task requires only one agent.

### Workflow

1. Receive input prompt
2. Process with LLM
3. Return generated content

### Setup

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

### Run — Python

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

agent = Agent(
    name="ContentWriter",
    instructions="You are a content writer. Output in markdown format."
)

result = agent.start("Write a short blog post about AI assistants")
print(result)
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Write a short blog post about AI assistants"
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Content Generation
roles:
  content_writer:
    role: Content Writer
    goal: Generate engaging content
    backstory: You are an expert content writer
    tasks:
      write_content:
        description: Write a short blog post about AI assistants
        expected_output: A markdown formatted blog post
```

```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

agent = Agent(
    name="ContentWriter",
    instructions="You are a content writer. Output in markdown format."
)

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": "Write a haiku about coding"}'
```

***

## Advanced Workflow (All Features)

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

### Workflow

1. Initialize session with unique ID for resumability
2. Configure SQLite persistence for conversation history
3. Process input with structured Pydantic output
4. Store results in memory for future context
5. Resume session later with same session\_id

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai 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 pydantic import BaseModel

# Structured output schema
class BlogPost(BaseModel):
    title: str
    content: str
    tags: list[str]

# Create session for resumability
session = Session(session_id="blog-session-001", user_id="user-1")

# Agent with memory enabled
agent = Agent(
    name="ContentWriter",
    instructions="You are a content writer. Output structured JSON.",
    memory=True
)

# Task with structured output
task = Task(
    description="Write a short blog post about AI assistants",
    expected_output="Structured blog post",
    agent=agent,
    output_pydantic=BlogPost
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later with same session_id
session2 = Session(session_id="blog-session-001", user_id="user-1")
context = session2.get_context()
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# With memory and verbose
praisonai "Write a blog post about AI" --memory --verbose

# With session persistence
praisonai "Continue the blog post" --memory --session blog-session-001
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Content Generation
memory: true
memory_config:
  provider: sqlite
  db_path: content.db
roles:
  content_writer:
    role: Content Writer
    goal: Generate engaging content with structured output
    backstory: You are an expert content writer
    memory: true
    tasks:
      write_content:
        description: Write a short blog post about AI assistants
        expected_output: Structured blog post with title, content, and tags
        output_json:
          title: string
          content: string
          tags: 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

agent = Agent(
    name="ContentWriter",
    instructions="You are a content writer.",
    memory=True
)

# Launch with persistence
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": "Write a blog post", "session_id": "blog-001"}'
```

***

## Save Output to File

Save agent responses to files using different methods:

<Tabs>
  <Tab title="write_file Tool">
    Agent decides when to save:

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

    agent = Agent(
        name="Writer",
        instructions="Write content and save to files",
        tools=[write_file]
    )
    agent.start("Write a poem and save it to poem.txt")
    ```
  </Tab>

  <Tab title="Task output_file">
    Auto-save task result:

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

    agent = Agent(name="Writer")
    task = Task(
        description="Write a poem",
        agent=agent,
        output_file="poem.txt",
        create_directory=True
    )
    agents = AgentTeam(agents=[agent], tasks=[task])
    agents.start()
    ```
  </Tab>

  <Tab title="Manual">
    Full control:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    agent = Agent(name="Writer")
    response = agent.start("Write a poem")
    with open("poem.txt", "w") as f:
        f.write(response)
    ```
  </Tab>
</Tabs>

<Tip>
  See [Save Agent Output](/features/save-output) for complete guide.
</Tip>

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Verbose output
praisonai "test prompt" --verbose

# Check telemetry
praisonai "test prompt" --telemetry
```

## Cleanup

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

## Features Demonstrated

| Feature           | Implementation                 |
| ----------------- | ------------------------------ |
| Workflow          | Single-step content generation |
| DB Persistence    | SQLite via `memory_config`     |
| Observability     | `--verbose` flag               |
| Resumability      | `Session` with `session_id`    |
| Structured Output | Pydantic `BlogPost` model      |

## Next Steps

* [Prompt Chaining](/features/promptchaining) for multi-step workflows
* [Web Search Agent](/agents/websearch) for tool-enabled agents
* [Memory](/features/advanced-memory) for persistent context