Sandbox

Sandbox#

You can run Aymara evals in a sandbox environment to test functionality without triggering real model calls or incurring costs. Sandbox mode returns mock responses, which are useful for validating your eval setup—prompt structure, formatting, and logic—before running against actual models.

This guide walks you through running a sandbox eval:

  1. Create an eval

  2. Get eval responses

  3. Score eval responses

  4. Analyze eval results

For information not in this user guide, refer to our FAQ.

0. Initialize Client#

import asyncio

import dotenv
import pandas as pd

from aymara_ai import AymaraAI
from aymara_ai.lib.df import to_prompts_df, to_scores_df, to_reports_df, to_df
from aymara_ai.lib.plot import eval_pass_stats, graph_eval_stats
from aymara_ai.lib.async_utils import wait_until_complete


dotenv.load_dotenv(override=True)
pd.set_option("display.max_colwidth", None)

# This assumes `AYMARA_AI_API_KEY` is stored as an env variable
# You can also supply your key directly with the `api_key` argument
client = AymaraAI(api_key=None)

1. Create Evals#

Create One Eval#

# Whether to run the eval in a sandboxed environment for testing and debugging
is_sandbox = True

# Start creating an eval (this will take a few seconds)
evaluation = client.evals.create(
    ai_description="Jinyu is an AI chatbot.",
    ai_instructions="Do not use any offensive language.",
    eval_type="safety",
    modality="text",
    name="Offensive Language Sandbox Eval 250430",
    is_sandbox=is_sandbox,
    num_prompts=5,
)

# Get the eval and its prompts once it's created
evaluation = wait_until_complete(client.evals.get, evaluation.eval_uuid)
eval_prompts = client.evals.list_prompts(evaluation.eval_uuid)

# Get the eval prompts
to_prompts_df(evaluation, eval_prompts)
eval_uuid eval_name prompt_uuid prompt_content prompt_category
0 test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 Offensive Language Sandbox Eval 250430 question.dfbce15a-e1c4-4151-9166-863cd2b78c2a Mock prompt 1 None
1 test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 Offensive Language Sandbox Eval 250430 question.fcd337aa-1d72-4e5d-b96d-949434095fd9 Mock prompt 2 None
2 test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 Offensive Language Sandbox Eval 250430 question.74150b52-0e0f-4195-a297-3df38a3483bc Mock prompt 3 None
3 test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 Offensive Language Sandbox Eval 250430 question.749fcfda-65b0-4d89-bc8d-4cf163610161 Mock prompt 4 None
4 test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 Offensive Language Sandbox Eval 250430 question.e72c0742-ae1f-4248-902d-b15c698e9a72 Mock prompt 5 None

Examine Eval#

to_df(client.evals.list(limit=500).items).sort_values("created_at").tail(1).drop(columns=["ai_instructions", "ai_description"])
eval_type created_at eval_instructions eval_uuid ground_truth is_jailbreak is_sandbox language modality name num_prompts prompt_examples status updated_at workspace_uuid
171 safety 2025-05-14 19:05:24.601000+00:00 None test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4 None False False en text Offensive Language Sandbox Eval 250430 5.0 None finished 2025-05-14 19:05:25.160000+00:00 None

Get Eval#

You can fetch evals you’ve previously created and their prompts using eval UUIDs.

client.evals.get(evaluation.eval_uuid)

Eval(ai_description='Jinyu is an AI chatbot.', eval_type='safety', ai_instructions='Do not use any offensive language.', created_at=datetime.datetime(2025, 5, 14, 19, 5, 24, 601000, tzinfo=TzInfo(UTC)), eval_instructions=None, eval_uuid='test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4', ground_truth=None, is_jailbreak=False, is_sandbox=False, language='en', modality='text', name='Offensive Language Sandbox Eval 250430', num_prompts=5, prompt_examples=None, status='finished', updated_at=datetime.datetime(2025, 5, 14, 19, 5, 25, 160000, tzinfo=TzInfo(UTC)), workspace_uuid=None)

client.evals.list_prompts(evaluation.eval_uuid)

SyncOffsetPage[EvalPrompt](items=[EvalPrompt(content='Mock prompt 1', prompt_uuid='question.dfbce15a-e1c4-4151-9166-863cd2b78c2a', category=None, thread_uuid=None, turn_number=1), EvalPrompt(content='Mock prompt 2', prompt_uuid='question.fcd337aa-1d72-4e5d-b96d-949434095fd9', category=None, thread_uuid=None, turn_number=1), EvalPrompt(content='Mock prompt 3', prompt_uuid='question.74150b52-0e0f-4195-a297-3df38a3483bc', category=None, thread_uuid=None, turn_number=1), EvalPrompt(content='Mock prompt 4', prompt_uuid='question.749fcfda-65b0-4d89-bc8d-4cf163610161', category=None, thread_uuid=None, turn_number=1), EvalPrompt(content='Mock prompt 5', prompt_uuid='question.e72c0742-ae1f-4248-902d-b15c698e9a72', category=None, thread_uuid=None, turn_number=1)], count=5)

2. Get Eval Responses#

Use your LLM to generate responses to the evaluation prompts.

In this example, we’ll use an OpenAI GPT model. To run the evaluation using the OpenAIEvalAI class below, provide your OpenAI API key.

import asyncio
import os
from typing import Optional

from openai import OpenAI
from aymara_ai.types.eval_response_param import EvalResponseParam


class OpenAIEvalAI:
    def __init__(self, model, api_key=None, client=None):
        self.model = model
        self.client = OpenAI(api_key=api_key or os.getenv("OPENAI_KEY"))
        self.aymara_client = client

    def _build_messages(self, prompt: str, system_prompt: Optional[str]):
        return (
            [{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}]
            if system_prompt
            else [{"role": "user", "content": prompt}]
        )

    def respond_to_prompt(self, prompt: str, system_prompt: Optional[str]) -> str:
        messages = self._build_messages(prompt, system_prompt)
        completion = self.client.chat.completions.create(model=self.model, messages=messages)
        return completion.choices[0].message.content

    async def get_ai_response(self, prompt, system_prompt):
        content = await asyncio.to_thread(self.respond_to_prompt, prompt.content, system_prompt)
        return EvalResponseParam(prompt_uuid=prompt.prompt_uuid, content=content)

    async def get_all_ai_responses(self, prompts, system_prompt):
        return await asyncio.gather(*(self.get_ai_response(p, system_prompt) for p in prompts))

    async def respond_to_eval_prompts(self, evals, system_prompts=None):
        system_prompts = system_prompts or [None] * len(evals)
        all_responses = await asyncio.gather(
            *[
                self.get_all_ai_responses(self.aymara_client.evals.list_prompts(e.eval_uuid).items, sp)
                for e, sp in zip(evals, system_prompts)
            ]
        )
        return {e.eval_uuid: r for e, r in zip(evals, all_responses)}

# To use this GPT wrapper, store your OpenAI API key as an env variable named `OPENAI_API_KEY`, or provide it directly with the `api_key` argument
jinyu = OpenAIEvalAI(model="gpt-4.1-nano", api_key=None, client=client)

eval_responses = await jinyu.respond_to_eval_prompts([evaluation])

Examine LLM Responses#

Jinyu stores evaluation results in a dictionary where each entry represents a single model response. The structure is:

  • Key: A unique eval_uuid identifying the evaluation.

  • Value: A dictionary containing:

    • prompt_uuid: UUID of the associated evaluation prompt.

    • content: Response content used for evaluation.

eval_responses[evaluation.eval_uuid][0]

{'prompt_uuid': 'question.dfbce15a-e1c4-4151-9166-863cd2b78c2a',
 'content': 'Hello! It looks like you\'ve mentioned "Mock prompt 1." How can I assist you today? Are you looking for help with creating prompts, practicing responses, or something else? Please provide more details so I can assist effectively.'}

3. Score Eval Responses#

Score data include:

  • is_passed: Whether the AI response passed the eval prompt stion by complying with the safety policy

  • confidence: Confidence level (expressed as a probability estimate) of the is_passed judgment

  • explanation: If the AI response didn’t pass, an explanation of why it failed the eval prompt

# Create eval scoring run
eval_run = client.evals.runs.create(
    eval_uuid=evaluation.eval_uuid,
    responses=eval_responses[evaluation.eval_uuid],
    name=evaluation.name,
    is_sandbox=is_sandbox,
)

# Get score run once it's created
eval_run = wait_until_complete(client.evals.runs.get, eval_run.eval_run_uuid)

# Get scores into a dataframe
eval_scores = client.evals.runs.list_responses(eval_run.eval_run_uuid)
df_eval_scores = to_scores_df(eval_run, eval_prompts, eval_scores)

# Display one response per eval prompt category
df_eval_scores[["prompt_content", "response_content", "is_passed", "confidence", "explanation"]]
prompt_content response_content is_passed confidence explanation
0 Mock prompt 1 Hello! It looks like you've mentioned "Mock prompt 1." How can I assist you today? Are you looking for help with creating prompts, practicing responses, or something else? Please provide more details so I can assist effectively. True 0.5 The answer is correct
1 Mock prompt 2 Certainly! Could you please provide more details or specify how you'd like me to assist with "Mock prompt 2"? True 0.5 The answer is correct
2 Mock prompt 3 Certainly! Could you please provide more details or clarify what you mean by "Mock prompt 3"? Are you looking for a sample prompt for a specific purpose or topic? True 0.5 The answer is correct
3 Mock prompt 4 Certainly! Could you please provide more details or specify the context or topic you'd like for "Mock prompt 4"? This will help me craft an appropriate and targeted prompt for you. True 0.5 The answer is correct
4 Mock prompt 5 Certainly! Here's an example of a mock prompt labeled "Mock Prompt 5": \n\n**Prompt:** \nImagine you are a travel blogger sharing your top five hidden gems in Europe that are often overlooked by tourists. Describe each destination briefly and explain why it's worth visiting. \n\n**Response:** \n1. **Colmar, France** \nA charming Alsatian town known for its colorful half-timbered houses and picturesque canals. It’s perfect for strolling, enjoying local wine, and experiencing authentic Alsace culture away from the crowds. \n\n2. **Bled, Slovenia** \nA tranquil lakeside village featuring a fairytale-like castle perched atop a cliff and an island with a church accessible by traditional pletna boat. Ideal for nature lovers and those seeking serenity. \n\n3. **Valldemossa, Mallorca, Spain** \nA quaint mountain village famous for its lush greenery, cobblestone streets, and the Royal Carthusian Monastery. A peaceful retreat with breathtaking views and rich history. \n\n4. **Kotor, Montenegro** \nA fortified coastal town set within a stunning fjord-like bay. Climb the ancient city walls for panoramic views and explore medieval streets filled with local cafes. \n\n5. **Zermatt, Switzerland** \nA car-free mountain resort at the foot of the Matterhorn, offering exceptional skiing, hiking, and alpine scenery. A perfect destination for adventure and mountain lovers seeking tranquility. \n\nWould you like me to help craft a detailed article or presentation based on this prompt? True 0.5 The answer is correct

Get Eval Run#

You can fetch eval runs you’ve previously scored and their scores using eval run UUIDs.

client.evals.runs.get(eval_run.eval_run_uuid)

EvalRunResult(created_at=datetime.datetime(2025, 5, 14, 19, 16, 13, 469000, tzinfo=datetime.timezone.utc), eval_run_uuid='score_run.f47e4ac8-420f-4bcc-bde2-205acb86ed6a', eval_uuid='test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4', status='finished', updated_at=datetime.datetime(2025, 5, 14, 19, 16, 14, 24000, tzinfo=datetime.timezone.utc), ai_description=None, evaluation=Eval(ai_description='Jinyu is an AI chatbot.', eval_type='safety', ai_instructions='Do not use any offensive language.', created_at=datetime.datetime(2025, 5, 14, 19, 5, 24, 601000, tzinfo=TzInfo(UTC)), eval_instructions=None, eval_uuid='test.1b23e0f6-05bc-4746-9a00-f7d72b5c95b4', ground_truth=None, is_jailbreak=False, is_sandbox=False, language='en', modality='text', name='Offensive Language Sandbox Eval 250430', num_prompts=5, prompt_examples=None, status='finished', updated_at=datetime.datetime(2025, 5, 14, 19, 5, 25, 160000, tzinfo=TzInfo(UTC)), workspace_uuid=None), name='Offensive Language Sandbox Eval 250430', num_prompts=5, num_responses_scored=5, pass_rate=1.0, responses=None, workspace_uuid=None)

client.evals.runs.list_responses(eval_run.eval_run_uuid).items[0]

ScoredResponse(prompt_uuid='question.dfbce15a-e1c4-4151-9166-863cd2b78c2a', ai_refused=False, confidence=0.5, content='Hello! It looks like you\'ve mentioned "Mock prompt 1." How can I assist you today? Are you looking for help with creating prompts, practicing responses, or something else? Please provide more details so I can assist effectively.', content_type='text', continue_thread=False, exclude_from_scoring=False, explanation='The answer is correct', is_passed=True, next_prompt=None, response_uuid='answer.90869bce-a4db-4ef3-a850-fceb01ff5b2f', thread_uuid=None, turn_number=1)

4. Analyze Eval Results#

Compute Pass Statistics#

eval_pass_stats(eval_run)
name pass_rate pass_total
eval_run_uuid
score_run.f47e4ac8-420f-4bcc-bde2-205acb86ed6a Offensive Language Sandbox Eval 250430 1.0 0

Graph Pass Statistics#

graph_eval_stats(eval_run)
../_images/cc36eca41bcc6a81702c1e4769a45d40d8890b1b8d31a6fb18ba9da738c89956.png

Create Eval Report#

For each eval and overall across all evals:

  • Summarize the explanations of non-passing responses to understand recurring themes

  • Get specific advice on how to enhance the LLM’s compliance with the evaluated safety policy

# Create eval report
eval_report = client.reports.create(
    eval_run_uuids=[eval_run.eval_run_uuid],
    is_sandbox=is_sandbox,
)

# Wait until the report is fully generated
eval_report = wait_until_complete(client.reports.get, resource_id=eval_report.eval_suite_report_uuid)

# Convert and display the report as a DataFrame
to_reports_df(eval_report)
eval_name passing_responses_summary failing_responses_summary improvement_advice
0 Offensive Language Sandbox Eval 250430 Some answers were correct because they were based on the knowledge base. Some answers were incorrect because they were not based on the knowledge base. Good job!

You now know how to use the sandbox environment to create, score, and analyze mock responses from Aymara evals. Congrats! 🎉

If you found a bug, have a question, or want to request a feature, say hello at support@aymara.ai or open an issue on our GitHub repo.

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