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Prompts are the initial instructions given to agents at the start of an episode. They provide the context and goal for the task, forming the agent’s initial observation.

​
What is a Prompt?

A prompt is a sequence of blocks (text and/or images) that describes the task to the agent: 
type Prompt = Blocks  // Array of TextBlock | ImageBlock
Purpose:
  • Tell the agent what problem to solve
  • Provide initial context
  • Set expectations for the episode

​
Accessing Prompts

Prompts are retrieved via the API after creating a session: 
GET /gsm8k/prompt
X-Session-ID: abc-123
Response: 
[
  {
    "text": "What is 2+2?",
    "detail": null,
    "type": "text"
  }
]
Typical flow:
  1. Create session with task
  2. Get prompt for that task
  3. Agent reads prompt to understand what to do
  4. Agent begins calling tools to solve task

​
Prompt Structure

​
Text Prompts

Simple text instructions: 
[
  {
    "text": "Solve this math problem: If x + 5 = 12, what is x?",
    "detail": null,
    "type": "text"
  }
]

​
Multi-Line Prompts

Complex instructions: 
[
  {
    "text": "You are given a Python programming challenge.\n\nWrite a function that reverses a string.\n\nRequirements:\n-Function name: reverse_string\n-Input: string\n-Output: reversed string\n\nYou have access to a Python REPL. Submit your function when ready.",
    "detail": null,
    "type": "text"
  }
]

​
Multi-Modal Prompts

Text + images: 
[
  {
    "text": "What object is shown in this image?",
    "detail": null,
    "type": "text"
  },
  {
    "data": "iVBORw0KGgoAAAANSUhEUgA...",
    "mimeType": "image/jpeg",
    "detail": null,
    "type": "image"
  }
]

​
Generating Prompts

Prompts are generated by an environment’s get_prompt() method. For example, in the Python SDK: 
from ors import Environment, tool, Blocks, TextBlock, ToolOutput

class MathEnvironment(Environment):
    def get_prompt(self) -> Blocks:
        """Generate prompt from task"""
        question = self.task_spec["question"]

        return [
            TextBlock(
                text=f"Solve this math problem: {question}",
                type="text"
            )
        ]
Key points:
  • Prompts are task-specific
  • Use self.task_spec to access task data
  • Return list of blocks (even for single text)

​
Prompt Design Patterns

​
Pattern 1: Simple Question

Direct question from task: 
def get_prompt(self) -> Blocks:
    return [TextBlock(text=self.task_spec["question"])]
Example:
“What is the capital of France?”

​
Pattern 2: Contextual Instructions

Add context and instructions: 
def get_prompt(self) -> Blocks:
    problem = self.task_spec["problem"]

    prompt_text = f"""You are a coding assistant. Solve the following problem:

{problem}

You have access to a Python REPL. Use the 'python' tool to write and test code.
When you have a solution, use the 'submit' tool.
"""

    return [TextBlock(text=prompt_text)]
Note the tool instructions may be redundant if the available are already passed into the agent’s context.

​
Pattern 3: Role-Playing

Set agent persona: 
def get_prompt(self) -> Blocks:
    scenario = self.task_spec["scenario"]

    prompt_text = f"""You are a customer service agent.

A customer has the following issue:
{scenario}

Your goal is to resolve their issue professionally and efficiently.
Use the 'respond' tool to communicate with the customer.
"""

    return [TextBlock(text=prompt_text)]

​
Pattern 4: Multi-Modal

Include images: 
import base64

def get_prompt(self) -> Blocks:
    question = self.task_spec["question"]
    image_path = self.task_spec["image_path"]

    # Load and encode image
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode()

    return [
        TextBlock(text=question),
        ImageBlock(
            data=image_data,
            mimeType="image/png",
            type="image"
        )
    ]

​
Pattern 5: Few-Shot Examples

Provide examples in prompt: 
def get_prompt(self) -> Blocks:
    problem = self.task_spec["problem"]

    prompt_text = f"""Solve the following math problem.

Examples:
-Problem: "2 + 2" → Answer: 4
-Problem: "10 - 3" → Answer: 7
-Problem: "5 * 6" → Answer: 30

Now solve:
Problem: "{problem}"

Use the 'submit' tool with your answer.
"""

    return [TextBlock(text=prompt_text)]

​
Dynamic Prompts

Prompts can be customized based on task properties: 
def get_prompt(self) -> Blocks:
    difficulty = self.task_spec.get("difficulty", "medium")

    if difficulty == "easy":
        instructions = "This is a simple problem. Take your time."
    elif difficulty == "hard":
        instructions = "This is a challenging problem. Think carefully and use all available tools."
    else:
        instructions = "Solve this problem step by step."

    problem = self.task_spec["problem"]

    return [TextBlock(text=f"{instructions}\n\nProblem: {problem}")]

​
Next Steps

Tools

Design tools agents use after reading prompts

Tasks & Splits

Organize tasks that prompts are generated from

Implementing a Server

Implement get_prompt() in your environment

Data Types

See prompt data structure (Blocks)
Key Takeaway: Prompts are the agent’s starting point for each episode. Design them to be clear, specific, and informative. Good prompts guide agents toward successful task completion by setting context.