Praison AI

Markdown Agent

Learn how to create AI agents for generating and formatting content in Markdown.

Generate READMEs, changelogs, and docs as clean, properly formatted Markdown with a single Agent.

python
from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="You are a Markdown agent. Output in proper Markdown format.",
)

agent.start("Write a README for a Python web scraping project")
Diagram

Markdown Agent

📋 Request

🤖 Agent

🧠 Markdown Formatting

✅ Markdown Output

Content generation agent that outputs properly formatted Markdown.

Quick Start

  1. Simple Usage

    Ask for content and get Markdown back.

    python
    from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="You are a Markdown agent. Output in proper Markdown format.",
)

agent.start("Write a README for a Python web scraping project")
  2. With Configuration

    Enable memory so the agent revises documents across turns.

    python
    from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="Generate and iteratively refine Markdown documents.",
    memory=True,
)

agent.start("Draft a README, then add an Installation section.")

How It Works

Diagram
LLMAgentUserLLMAgentUser“Write a README for my project”Generate contentDraft textFormatted Markdown

Simple

Agents: 1 — Single agent for content generation with Markdown formatting.

Workflow

  1. Receive content request
  2. Generate content with LLM
  3. Format output as Markdown

Setup

bash
pip install praisonaiagents praisonai
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="You are a Markdown agent. Output in proper Markdown format."
)

result = agent.start("Write a README for a Python web scraping project")
print(result)

Run — CLI

bash
praisonai "Write a README for a Python project"

Run — agents.yaml

yaml
framework: praisonai
topic: Documentation Generation
roles:
  markdown_writer:
    role: Markdown Content Specialist
    goal: Generate well-formatted Markdown content
    backstory: You are an expert technical writer
    tasks:
      write_docs:
        description: Write a README for a Python web scraping project
        expected_output: A complete README.md
bash
praisonai agents.yaml

Serve API

python
from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="You are a Markdown agent."
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Write a changelog for version 2.0"}'

Advanced Workflow (All Features)

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

Workflow

  1. Initialize session for document tracking
  2. Configure SQLite persistence for content history
  3. Generate content with structured output
  4. Store in memory for iterative editing
  5. Resume session for document updates

Setup

bash
pip install praisonaiagents praisonai pydantic
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent, Task, AgentTeam, Session
from pydantic import BaseModel

# Structured output schema
class Document(BaseModel):
    title: str
    sections: list[str]
    content: str

# Create session for document tracking
session = Session(session_id="docs-001", user_id="user-1")

# Agent with memory
agent = Agent(
    name="MarkdownWriter",
    instructions="Generate structured Markdown documents.",
    memory=True
)

# Task with structured output
task = Task(
    description="Write a README for a Python web scraping project",
    expected_output="Structured document",
    agent=agent,
    output_pydantic=Document
)

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

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="docs-001", user_id="user-1")
history = session2.search_memory("README")

Run — CLI

bash
praisonai "Write a README" --memory --verbose

Run — agents.yaml

yaml
framework: praisonai
topic: Documentation Generation
memory: true
memory_config:
  provider: sqlite
  db_path: docs.db
roles:
  markdown_writer:
    role: Markdown Content Specialist
    goal: Generate structured Markdown content
    backstory: You are an expert technical writer
    memory: true
    tasks:
      write_docs:
        description: Write a README for a Python web scraping project
        expected_output: Structured document
        output_json:
          title: string
          sections: array
          content: string
bash
praisonai agents.yaml --verbose

Serve API

python
from praisonaiagents import Agent

agent = Agent(
    name="MarkdownWriter",
    instructions="Generate structured Markdown documents.",
    memory=True
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Write a changelog", "session_id": "docs-001"}'

Monitor / Verify

bash
praisonai "test markdown" --verbose

Cleanup

bash
rm -f docs.db

Features Demonstrated

FeatureImplementation
WorkflowSingle-step content generation
DB PersistenceSQLite via memory_config
Observability--verbose flag
ResumabilitySession with session_id
Structured OutputPydantic Document model

Best Practices

State the target format in the instructions

Tell the agent whether you want GitHub-flavoured Markdown, a changelog, or a README. A precise instruction produces consistent heading levels and code fences.

Use structured output for multi-section docs

When a document has fixed sections, define a Pydantic schema so title, sections, and body arrive as separate fields you can render however you like.

Enable memory for iterative editing

Set memory=True so the agent amends an existing draft on follow-up turns instead of regenerating the whole document.

Save output to a file when generating docs at scale

Attach a file-writing tool or set output_file on a Task so generated Markdown lands on disk without manual copy-paste.

Related

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