Add Intelligent Travel Planning Agent Notebook #741
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WalkthroughA new Jupyter notebook example is added, showcasing an AI Enrollment Counselor agent for university admissions automation. The notebook demonstrates how to configure, interact with, and utilize the agent to answer applicant questions, check application completeness, and provide personalized admissions guidance using the PraisonAI library. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant PraisonAI Agent
User->>Notebook: Provide query (with optional submitted/required docs)
Notebook->>PraisonAI Agent: Send constructed prompt
PraisonAI Agent-->>Notebook: Return response (answers/guidance)
Notebook-->>User: Display agent's response
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Summary of Changes
Hello @Dhivya-Bharathy, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a new example Jupyter notebook that showcases an AI Enrollment Counselor agent. This agent, built with the PraisonAI framework, is designed to assist with university admissions by providing automated responses to applicant questions and helping to validate application completeness. Please note that the content of the notebook pertains to an 'AI Enrollment Counselor' despite the pull request title and description indicating a 'Travel Planning Agent'.
Highlights
- New Notebook Addition: A new Jupyter notebook,
AI_Enrollment_Counselor.ipynb, has been added to theexamples/cookbooksdirectory. This notebook introduces an AI agent for university admissions, despite the pull request title suggesting a 'Travel Planning Agent'. - AI Enrollment Counselor Agent: The notebook demonstrates the implementation of an AI Enrollment Counselor agent. This agent is designed to automate university admissions tasks, such as answering applicant questions and verifying application completeness.
- PraisonAI Framework Integration: The agent is built using the
PraisonAI Agentsframework, showcasing how to define an agent'sROLE,GOAL, andINSTRUCTIONSto guide its behavior in an admissions context. - Practical Demonstrations: The notebook includes executable code cells that illustrate the agent's capabilities, specifically demonstrating how it can check for missing application documents and respond to general admissions inquiries.
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Actionable comments posted: 0
🧹 Nitpick comments (2)
examples/cookbooks/AI_Enrollment_Counselor.ipynb (2)
68-68: Use a more secure API key placeholder.Consider using a more generic placeholder like
"your-openai-api-key-here"instead of"sk-..."to avoid any risk of users accidentally committing real API keys that match this pattern.-os.environ["OPENAI_API_KEY"] = "sk-..." # <-- Replace with your actual OpenAI API key +os.environ["OPENAI_API_KEY"] = "your-openai-api-key-here" # <-- Replace with your actual OpenAI API key
407-417: Add input validation and error handling to the helper function.The function could benefit from input validation and error handling to make it more robust for production use.
def ask_enrollment_agent(query, submitted=None, required=None): + """ + Ask the enrollment agent a question with optional document validation. + + Args: + query (str): The question to ask the agent + submitted (list, optional): List of submitted documents + required (list, optional): List of required documents + + Returns: + str: Agent's response + """ + if not query or not isinstance(query, str): + raise ValueError("Query must be a non-empty string") + if submitted and required: + if not isinstance(submitted, list) or not isinstance(required, list): + raise ValueError("Document lists must be lists") prompt = ( f"Applicant submitted documents: {submitted}\n" f"Required documents: {required}\n" f"{query}\n" "List any missing documents and provide guidance." ) return enrollment_agent.start(prompt) else: return enrollment_agent.start(query)
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🧠 Learnings (2)
📓 Common learnings
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
examples/cookbooks/AI_Enrollment_Counselor.ipynb (2)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
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- GitHub Check: GitGuardian Security Checks
🔇 Additional comments (4)
examples/cookbooks/AI_Enrollment_Counselor.ipynb (4)
9-18: Inconsistency detected between PR objectives and notebook content.The PR objectives describe this as an "Intelligent Travel Planning Agent" notebook, but the actual content is an "AI Enrollment Counselor" for university admissions. Please verify that the correct notebook was submitted or update the PR description accordingly.
Likely an incorrect or invalid review comment.
47-47: Dependencies installation looks good.The installation command is clean and appropriate for a demo notebook. The
--quietflag ensures clean output.
88-88: Clean import statement.The import follows best practices and aligns with the PraisonAI architecture using the Agent class.
108-118: Well-structured agent configuration.The role, goal, and instructions are clearly defined and provide appropriate guidance for an enrollment counselor agent. The configuration follows PraisonAI best practices.
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Code Review
This pull request adds the AI Enrollment Counselor notebook, demonstrating a PraisonAI agent implementation. The changes include setting up the agent and defining its role, goal, and instructions. The main feedback points are around improving API key security and mitigating prompt injection vulnerabilities.
| "import os\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # <-- Replace with your actual OpenAI API key" |
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Storing the OpenAI API key directly in the notebook is not secure[^1]. It's recommended to load it from an environment variable or use a secure method like getpass to prompt the user for input at runtime.
Consider adding a check for the environment variable first and only prompting if it's not set.
import os
from getpass import getpass
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass("Enter your OpenAI API key: ")
| "def ask_enrollment_agent(query, submitted=None, required=None):\n", | ||
| " if submitted and required:\n", | ||
| " prompt = (\n", | ||
| " f\"Applicant submitted documents: {submitted}\\n\"\n", | ||
| " f\"Required documents: {required}\\n\"\n", | ||
| " f\"{query}\\n\"\n", | ||
| " \"List any missing documents and provide guidance.\"\n", | ||
| " )\n", | ||
| " return enrollment_agent.start(prompt)\n", | ||
| " else:\n", | ||
| " return enrollment_agent.start(query)\n", |
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The ask_enrollment_agent function is vulnerable to prompt injection because the query is directly concatenated into the prompt[^1]. Additionally, it doesn't handle cases where only one of submitted or required is provided, leading to incorrect behavior.
Refactor the function to validate arguments and use a structured prompt to mitigate these issues.
def ask_enrollment_agent(query, submitted=None, required=None):
if (submitted is not None and required is None) or \
(submitted is None and required is not None):
raise ValueError("For document checking, both 'submitted' and 'required' arguments must be provided.")
if submitted is not None and required is not None:
# Using a structured prompt to reduce injection risk
prompt = (
"You are checking for missing documents for a university application.\n"
f"Applicant submitted documents: {submitted}\n"
f"All required documents: {required}\n\n"
f"User query: '''{query}'''\n\n"
"Based on the information above, list any missing documents and provide guidance."
)
return enrollment_agent.start(prompt)
else:
return enrollment_agent.start(query)
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## main #741 +/- ##
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Coverage 14.23% 14.23%
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Files 25 25
Lines 2571 2571
Branches 367 367
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Hits 366 366
Misses 2189 2189
Partials 16 16
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1 participant
User description
This agent intelligently researches travel destinations and generates personalized itineraries using advanced language models.
It combines web search, cost estimation, and user preferences to deliver high-quality, actionable travel plans.
Ideal for users seeking automated, data-driven travel recommendations and planning assistance.
PR Type
Other
Description
Add AI Enrollment Counselor notebook for university admissions
Add Intelligent Travel Planning Agent notebook for travel automation
Both notebooks demonstrate PraisonAI agent implementations
Changes diagram
Changes walkthrough 📝
AI_Enrollment_Counselor.ipynb
AI Enrollment Counselor notebook implementationexamples/cookbooks/AI_Enrollment_Counselor.ipynb
intelligent_travel_planning_agent.ipynb
Intelligent Travel Planning Agent notebookexamples/cookbooks/intelligent_travel_planning_agent.ipynb
Summary by CodeRabbit