Add 12 Folders to Cookbooks, remove duplicates, and fix invalid notebooks #756
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…gent_PraisonAI.ipynb
…ed invalid notebook metadata.
…ensure valid JSON
…gets, duplicates, ensure valid JSON)
… no duplicates, clean structure
WalkthroughThis update removes two Jupyter notebooks—one for code analysis and one for an e-commerce shopping assistant—while introducing new or revised notebooks for code analysis, conversational chat agents, a technical assistant using Mistral-7B-Instruct, a multimodal vision/audio agent, and an AI court simulation. Minor visual changes were also made to a progress bar in another notebook. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant Agent
participant Model
User->>Notebook: Provide input/query or code source
Notebook->>Agent: Prepare context and invoke agent/task
Agent->>Model: Run model inference (e.g., code analysis, chat, Q&A)
Model-->>Agent: Return generated response
Agent-->>Notebook: Structured report or answer
Notebook-->>User: Display result/output
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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 significantly refactors and expands the examples/cookbooks directory by introducing a new, organized folder structure for AI agents. It also adds a substantial number of new agent notebooks while simultaneously cleaning up the repository by removing duplicates and fixing existing invalid notebooks, resulting in a more structured and robust collection of examples.
Highlights
- Enhanced Organization: Introduced 12 new categorized folders within
examples/cookbooks/to improve the structure and navigation of agent notebooks. This includes new categories like 'Conversational Chat Agents', 'Programming Code Analysis Agents', 'Research Knowledge QA Agents', and 'Vision Multimodal Agents'. - Content Expansion: Added numerous new agent notebooks across various categories, increasing the total unique agents to 62. Examples include new agents for Qwen Colab, Code Analysis, MistralTech, AI Court Simulation, and BERT + Whisper models.
- Repository Cleanup: Addressed and fixed issues with invalid notebooks and removed 2 duplicate agents, contributing to a cleaner and more reliable repository of examples.
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Code Review
This pull request adds new cookbook examples, which is a great addition to the repository. My review identified several critical and high-severity issues in the new notebooks that would prevent them from running correctly, including loading incorrect models, using undefined variables, and using hardcoded outputs instead of executing the demonstrated code. I've provided specific code suggestions to fix these issues and make the notebooks functional and easy to follow. Additionally, I've pointed out some medium-severity issues like broken links and the use of deprecated arguments to improve the overall quality of the examples.
| "from transformers import AutoTokenizer, AutoModel\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "bert_model = AutoModel.from_pretrained(\"bert-large-cased\")\n", | ||
| "bert_tokenizer = AutoTokenizer.from_pretrained(\"bert-large-cased\")\n", | ||
| "\n", | ||
| "def classify_text(text):\n", | ||
| " inputs = bert_tokenizer(text, return_tensors=\"pt\")\n", | ||
| " with torch.no_grad():\n", | ||
| " outputs = bert_model(**inputs)\n", | ||
| " return outputs.last_hidden_state.mean(dim=1)\n" |
There was a problem hiding this comment.
This notebook demonstrates a Qwen Model Agent, but it incorrectly loads the bert-large-cased model and defines an unused classify_text function. This is misleading and will cause a NameError later on, since the chat_with_qwen function uses undefined model and tokenizer variables. Load the Qwen model and tokenizer instead of BERT.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Use AutoModelForCausalLM for chat models
model_name = "Qwen/Qwen1.5-1.8B-Chat"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
| "import json\n", | ||
| "from IPython.display import display, Markdown\n", | ||
| "\n", | ||
| "# Optional: Define agent info\n", | ||
| "agent_info = \"\"\"\n", | ||
| "### 👤 Agent: Code Analysis Expert\n", | ||
| "\n", | ||
| "**Role**: Provides comprehensive code evaluation and recommendations\n", | ||
| "**Backstory**: Expert in architecture, best practices, and technical assessment\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "# Analysis Result Data\n", | ||
| "analysis_result = {\n", | ||
| " \"overall_quality\": 85,\n", | ||
| " \"code_metrics\": [\n", | ||
| " {\n", | ||
| " \"category\": \"Architecture and Design\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"Modular structure with clear separation of concerns.\",\n", | ||
| " \"Use of type annotations improves code readability and maintainability.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Code Maintainability\",\n", | ||
| " \"score\": 85,\n", | ||
| " \"findings\": [\n", | ||
| " \"Consistent use of type hints and NamedTuple for structured data.\",\n", | ||
| " \"Logical organization of functions and classes.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Performance Optimization\",\n", | ||
| " \"score\": 75,\n", | ||
| " \"findings\": [\n", | ||
| " \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n", | ||
| " \"Efficient use of optional parameters to control execution flow.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Security Practices\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"No obvious security vulnerabilities in the code.\",\n", | ||
| " \"Proper encapsulation of functionality.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Test Coverage\",\n", | ||
| " \"score\": 70,\n", | ||
| " \"findings\": [\n", | ||
| " \"Lack of explicit test cases in the provided code.\",\n", | ||
| " \"Use of type checking suggests some level of validation.\"\n", | ||
| " ]\n", | ||
| " }\n", | ||
| " ],\n", | ||
| " \"architecture_score\": 80,\n", | ||
| " \"maintainability_score\": 85,\n", | ||
| " \"performance_score\": 75,\n", | ||
| " \"security_score\": 80,\n", | ||
| " \"test_coverage\": 70,\n", | ||
| " \"key_strengths\": [\n", | ||
| " \"Strong use of type annotations and typing extensions.\",\n", | ||
| " \"Clear separation of CLI argument parsing and business logic.\"\n", | ||
| " ],\n", | ||
| " \"improvement_areas\": [\n", | ||
| " \"Increase test coverage to ensure robustness.\",\n", | ||
| " \"Optimize I/O operations to improve performance.\"\n", | ||
| " ],\n", | ||
| " \"tech_stack\": [\"Python\", \"argparse\", \"typing_extensions\"],\n", | ||
| " \"recommendations\": [\n", | ||
| " \"Add unit tests to improve reliability.\",\n", | ||
| " \"Consider async I/O for improved performance in CLI tools.\"\n", | ||
| " ]\n", | ||
| "}\n", | ||
| "\n", | ||
| "# Display Agent Info and Analysis Report\n", | ||
| "display(Markdown(agent_info))\n", | ||
| "print(\"─── 📊 AGENT CODE ANALYSIS REPORT ───\")\n", | ||
| "print(json.dumps(analysis_result, indent=4))\n" |
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This notebook defines analyze_code but never calls it. The output is generated from a hardcoded analysis_result dictionary. Call the analyze_code function with a sample repository and display its actual output to make this a functional example.
import json
from IPython.display import display, Markdown
# Example usage to run the analysis and view results.
# Replace with a real GitHub URL or local path to a repository.
code_source = "https://github.com/Dhivya-Bharathy/PraisonAI"
# Run the analysis
analysis_result = analyze_code(code_source)
# Display Agent Info and Analysis Report
agent_info = f"""### 👤 Agent: {code_analyzer.role}\n\n**Goal**: {code_analyzer.goal}"""
display(Markdown(agent_info))
print("─── 📊 AGENT CODE ANALYSIS REPORT ───")
# The result is a Pydantic model, so use .model_dump() for clean JSON output
print(json.dumps(analysis_result.model_dump(), indent=4))
| "if not api_key:\n", | ||
| " print(\"🔑 Enter your OpenAI API key:\")\n", | ||
| " api_key = input(\"API Key: \").strip()\n", | ||
| " os.environ['OPENAI_API_KEY'] = \"Enter your api key\"\n", |
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There's a bug in the API key setup. If the user provides an API key via input, it's not correctly set as an environment variable. The os.environ assignment should use the api_key variable from the input.
if not api_key:
print("🔑 Enter your OpenAI API key:")
api_key = input("API Key: ").strip()
os.environ['OPENAI_API_KEY'] = api_key
| "def chat_with_qwen(prompt: str, max_length: int = 256):\n", | ||
| " inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", | ||
| " with torch.no_grad():\n", | ||
| " outputs = model.generate(**inputs, max_new_tokens=max_length)\n", | ||
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" |
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This function has issues:
- It doesn't use the model's chat template, which is important for chat-finetuned models like Qwen-1.5-Chat to get optimal responses.
- The
tokenizer.decodecall includes the prompt in the output becausemodel.generatereturns the full sequence (prompt + generation). The output should be processed to only return the generated response.
def chat_with_qwen(prompt: str, max_length: int = 256):
messages = [{"role": "user", "content": prompt}]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(input_ids, max_new_tokens=max_length)
response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
return response
| "id": "PvTxUutEce5s" | ||
| }, | ||
| "source": [ | ||
| "# ✅ Use the official BERT Large model" |
There was a problem hiding this comment.
This markdown is misleading. The notebook demonstrates a Qwen model agent, not BERT. Update it to reflect the notebook's content.
# ✅ Use the Qwen 1.5 1.8B Chat model
| " tokenizer = AutoTokenizer.from_pretrained(\n", | ||
| " model_name, use_auth_token=os.environ[\"HF_TOKEN\"], trust_remote_code=True\n", | ||
| " )\n", | ||
| " model = AutoModelForCausalLM.from_pretrained(\n", | ||
| " model_name,\n", | ||
| " use_auth_token=os.environ[\"HF_TOKEN\"],\n", | ||
| " trust_remote_code=True,\n", | ||
| " device_map=\"auto\",\n", | ||
| " torch_dtype=torch.float16\n", | ||
| " )\n", |
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The use_auth_token argument is deprecated in the transformers library and will be removed in a future version. Use the token argument instead for authentication.
tokenizer = AutoTokenizer.from_pretrained(
model_name, token=os.environ.get("HF_TOKEN"), trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
token=os.environ.get("HF_TOKEN"),
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.float16
)
| "agent = MistralTechAgent(model, tokenizer)\n", | ||
| "\n", | ||
| "prompt = \"You are an AI agent helping with technical queries. Explain what a language model is.\"\n", | ||
| "response = agent.chat(prompt)\n", |
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The prompt_template defined earlier is not used here, which makes the example inconsistent. For instruction-tuned models like Mistral, it's best to follow their specific prompt format for optimal results (using [INST] and [/INST] tags).
agent = MistralTechAgent(model, tokenizer)
user_input = "Explain what a language model is."
# Format the prompt using the template and Mistral's instruction format
prompt = f"[INST] {prompt_template['content']}\n{user_input} [/INST]"
response = agent.chat(prompt)
| "id": "k0GmORjCRMGL" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/LegaliaAI_MiniCourt.ipynb)\n" |
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The 'Open in Colab' badge points to a non-existent file LegaliaAI_MiniCourt.ipynb. It should point to the correct file name, AI_CourtSimulation.ipynb, to work correctly.
[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Research_Knowledge_QA_Agents/AI_CourtSimulation.ipynb)
| "# Example usage (requires uploaded audio file like sample.wav)\n", | ||
| "# from google.colab import files\n", | ||
| "# uploaded = files.upload()\n", | ||
| "# print(transcribe_audio(\"sample.wav\"))\n", |
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The audio transcription part of the demo is commented out, which makes the notebook incomplete. Include a command to download a sample audio file and then call the transcribe_audio function to make it a fully runnable example.
# Download a sample audio file for demonstration
!wget -q https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0010_8k.wav -O sample.wav
# Transcribe the downloaded audio file
transcription = transcribe_audio("sample.wav")
print("Whisper transcription:", transcription)
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 17
🔭 Outside diff range comments (1)
examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb (1)
1-332: Notebook has fundamental functionality issues that need to be addressed.The notebook has several critical issues that make it non-functional:
- Claims to use Qwen model but never loads it
- Loads an unrelated BERT model instead
- References undefined variables in the main chat function
- Has incorrect Colab badge URL
This notebook needs a complete restructure to match its stated purpose.
Here's a corrected version of the key sections:
# Install dependencies (keep existing) !pip install -q transformers accelerate torch # Load Qwen model and tokenizer from transformers import AutoTokenizer, AutoModelForCausalLM import torch model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B") # Agent function (corrected) def chat_with_qwen(prompt: str, max_length: int = 256): inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=max_length) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage response = chat_with_qwen("What are the benefits of using transformers library?") print(response)
🧹 Nitpick comments (10)
examples/cookbooks/Conversational_Chat_Agents/TinyLlama_1_1B_model_SimpleAIAgent.ipynb (3)
28-30: Fix broken “Open in Colab” badge linkThe notebook was moved into
Conversational_Chat_Agents/, but the badge URL still points to the old path. This will 404.-[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb) +[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Conversational_Chat_Agents/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)
278-284: Consider loading the model undertorch.no_grad()and withtrust_remote_code=FalseWhile the code works, wrapping the load in
torch.no_grad()prevents unnecessary graph construction and shaves memory.
Also, explicitly settingtrust_remote_code=False(unless absolutely required) reduces supply-chain risk.-from transformers import AutoTokenizer, AutoModelForCausalLM -import torch - -model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" -tokenizer = AutoTokenizer.from_pretrained(model_name) -model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" +with torch.no_grad(): + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=False) + model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.float16, + device_map="auto", + trust_remote_code=False, + )
305-309: Wrap generation intorch.no_grad()and rename argumentDisables gradient tracking during inference and clarifies that the argument limits new tokens—not total length.
-def generate_response(prompt, max_length=256): - inputs = tokenizer(prompt, return_tensors="pt").to(model.device) - outputs = model.generate(**inputs, max_new_tokens=max_length) - return tokenizer.decode(outputs[0], skip_special_tokens=True) +def generate_response(prompt: str, max_new_tokens: int = 256) -> str: + inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + with torch.no_grad(): + outputs = model.generate(**inputs, max_new_tokens=max_new_tokens) + return tokenizer.decode(outputs[0], skip_special_tokens=True)examples/cookbooks/Vision_Multimodal_Agents/Bert_Whisper_Agent.ipynb (2)
233-237: Consider renaming the function for clarity.The function name
classify_textis misleading since it returns sentence embeddings rather than performing classification. Consider renaming toget_text_embeddingorencode_textfor better clarity.-def classify_text(text): +def get_text_embedding(text): inputs = bert_tokenizer(text, return_tensors="pt") with torch.no_grad(): outputs = bert_model(**inputs) return outputs.last_hidden_state.mean(dim=1) # Sentence embedding
597-597: Update function call if renaming is applied.If the
classify_textfunction is renamed toget_text_embedding, remember to update this example usage as well.-embedding = classify_text("Google Colab is a great place to run small-scale NLP models.") +embedding = get_text_embedding("Google Colab is a great place to run small-scale NLP models.")examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb (3)
534-551: Improve the MistralTechAgent class implementation.The class implementation is functional but could be enhanced for better usability and robustness.
Consider these improvements:
class MistralTechAgent: def __init__(self, model, tokenizer): self.model = model self.tokenizer = tokenizer + # Set pad_token if not already set + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token - def chat(self, prompt: str, max_new_tokens=256) -> str: + def chat(self, prompt: str, max_new_tokens: int = 256, temperature: float = 0.7, top_p: float = 0.9) -> str: + """ + Generate a response to the given prompt. + + Args: + prompt: Input text prompt + max_new_tokens: Maximum number of tokens to generate + temperature: Sampling temperature (0.0 = deterministic) + top_p: Top-p sampling parameter + + Returns: + Generated response text + """ inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device) with torch.no_grad(): outputs = self.model.generate( **inputs, max_new_tokens=max_new_tokens, - do_sample=False, # DETERMINISTIC output - temperature=1.0, - top_p=1.0, + do_sample=temperature > 0, + temperature=temperature if temperature > 0 else 1.0, + top_p=top_p, pad_token_id=self.tokenizer.eos_token_id ) full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True) return full_output[len(prompt):].strip()
508-510: Enhance fallback model handling.The fallback to
distilgpt2may not provide the same quality of technical responses as an instruction-tuned model. Consider a more appropriate fallback.except Exception as e: print(f"❌ Failed to load {model_name}\nError: {e}") - model_name = "distilgpt2" - tokenizer = AutoTokenizer.from_pretrained(model_name) - model = AutoModelForCausalLM.from_pretrained(model_name) + print("🔄 Falling back to microsoft/DialoGPT-medium...") + model_name = "microsoft/DialoGPT-medium" + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModelForCausalLM.from_pretrained(model_name) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token model.eval()
40-40: Update Colab badge URL to point to main repository.The Colab badge currently points to a personal fork (
Dhivya-Bharathy/PraisonAI) which may not be accessible to all users.-[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/MistralTechAgent.ipynb) +[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb)examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb (2)
67-69: Add a security warning about API key handling.Consider adding a comment to remind users about API key security:
import os +# WARNING: Never commit your actual API key to version control +# Consider using environment variables or secret management tools in production os.environ['OPENAI_API_KEY'] = 'your_api_key_here'
101-117: Consider removing redundant score fields to avoid data inconsistency.The
CodeAnalysisReportmodel has individual score fields (lines 104-108) that duplicate information already captured in thecode_metricslist. This redundancy could lead to inconsistencies.Consider either:
- Remove the individual score fields and derive them from
code_metrics, or- Remove score from
CodeMetricsand keep only the individual fieldsOption 1 (recommended):
class CodeAnalysisReport(BaseModel): overall_quality: int code_metrics: List[CodeMetrics] - architecture_score: int - maintainability_score: int - performance_score: int - security_score: int - test_coverage: int key_strengths: List[str] improvement_areas: List[str] tech_stack: List[str] recommendations: List[str] complexity_metrics: Dict[str, int] best_practices: List[Dict[str, str]] potential_risks: List[str] documentation_quality: int
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📒 Files selected for processing (9)
examples/cookbooks/Code_Analysis_Agent.ipynb(0 hunks)examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb(1 hunks)examples/cookbooks/Conversational_Chat_Agents/TinyLlama_1_1B_model_SimpleAIAgent.ipynb(2 hunks)examples/cookbooks/E_commerce_Shopping_Assistant.ipynb(0 hunks)examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb(1 hunks)examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb(1 hunks)examples/cookbooks/Research_Knowledge_QA_Agents/AI_CourtSimulation.ipynb(1 hunks)examples/cookbooks/Vision_Multimodal_Agents/Bert_Whisper_Agent.ipynb(1 hunks)examples/cookbooks/qwen_colab_agent.ipynb(0 hunks)
💤 Files with no reviewable changes (3)
- examples/cookbooks/E_commerce_Shopping_Assistant.ipynb
- examples/cookbooks/qwen_colab_agent.ipynb
- examples/cookbooks/Code_Analysis_Agent.ipynb
🧰 Additional context used
🧠 Learnings (3)
examples/cookbooks/Conversational_Chat_Agents/TinyLlama_1_1B_model_SimpleAIAgent.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/llm/llm.ts : Replace all references to 'LLM' or 'litellm' with 'aisdk' usage for large language model calls in Node.js/TypeScript code.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.windsurfrules:0-0
Timestamp: 2025-06-30T10:06:44.129Z
Learning: Applies to src/praisonai-ts/src/{llm,agent,agents,task}/**/*.ts : Replace all references to 'LLM' or 'litellm' with 'aisdk' usage in TypeScript code.
examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb (7)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
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/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
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/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'.
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/index.ts : The main entry point 'src/index.ts' should re-export key classes and functions (such as 'Agent', 'Agents', 'Task', etc.) for easy import by consumers.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/tests/**/*.py : Test files should be placed in the `tests/` directory and demonstrate specific usage patterns, serving as both test and documentation.
examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb (3)
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'.
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.
🔇 Additional comments (7)
examples/cookbooks/Conversational_Chat_Agents/TinyLlama_1_1B_model_SimpleAIAgent.ipynb (1)
58-67: Output-only diff — safe to ignoreThe modified rows are ANSI progress-bar artifacts, not source. Nothing to review here.
examples/cookbooks/Research_Knowledge_QA_Agents/AI_CourtSimulation.ipynb (3)
126-180: Well-structured agent creation with clear role definitions.The agent implementation demonstrates good practices with:
- Clear role separation and appropriate instructions for each agent type
- Consistent use of lightweight GPT-4o-mini model
- Reasonable word limits to maintain concise responses
- Proper markdown formatting configuration
249-273: Excellent helper function design with proper error handling.The helper functions demonstrate solid software engineering practices:
- Clean separation of display logic from execution logic
- Robust error handling with try-catch blocks and user-friendly error display
- Well-structured HTML formatting for enhanced user experience
- Consistent interface for agent execution across different roles
1086-1147: Well-structured simulation logic with realistic court proceedings flow.The main simulation demonstrates excellent organization:
- Clear progression through realistic trial phases (opening statements, witness testimony, final verdict)
- Consistent and contextual prompt engineering for each agent role
- Effective use of visual separators and timing to enhance the simulation experience
- Proper integration with error handling and display helper functions
This creates an engaging and educational demonstration of multi-agent interaction.
examples/cookbooks/Vision_Multimodal_Agents/Bert_Whisper_Agent.ipynb (1)
71-71: Package installation looks good.The package installation command includes all necessary dependencies for both BERT and Whisper models with appropriate versions.
examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb (1)
1-617: Overall assessment: Good structure with room for improvement.The notebook provides a clear demonstration of using Mistral-7B-Instruct for technical assistance. The code is well-organized with proper sections and includes helpful progress outputs. The fallback mechanism for model loading is a thoughtful addition for environments with limited resources.
The notebook successfully demonstrates:
- Proper model loading with authentication
- GPU optimization and device mapping
- Clean class implementation
- Practical testing example
- Good documentation and structure
examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb (1)
139-169: Well-structured agent and task configuration!The agent role, goal, and backstory are clearly defined, and the task description comprehensively covers all aspects of code analysis with proper output schema validation.
| "def chat_with_qwen(prompt: str, max_length: int = 256):\n", | ||
| " inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", | ||
| " with torch.no_grad():\n", | ||
| " outputs = model.generate(**inputs, max_new_tokens=max_length)\n", | ||
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" |
There was a problem hiding this comment.
Fix undefined variables in chat_with_qwen function.
The function references tokenizer and model variables that are never defined in the notebook, which will cause a NameError when executed.
This function will fail because tokenizer and model are not defined. The variables need to be defined in the previous cell where models are loaded.
🤖 Prompt for AI Agents
In examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb around
lines 258 to 262, the function chat_with_qwen uses the variables tokenizer and
model which are not defined in the notebook, causing a NameError. To fix this,
ensure that both tokenizer and model are properly initialized and assigned in a
previous cell before this function is called, typically by loading the
pre-trained model and tokenizer from the appropriate library or checkpoint.
| "response = chat_with_qwen(\"What are the benefits of using transformers library?\")\n", | ||
| "print(response)" |
There was a problem hiding this comment.
This example will fail due to undefined variables.
The example tries to use chat_with_qwen function which references undefined tokenizer and model variables, causing the notebook to fail when executed.
This code will raise a NameError because the required variables are not defined. Ensure the Qwen model and tokenizer are properly loaded before using this function.
🤖 Prompt for AI Agents
In examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb around
lines 310 to 311, the function chat_with_qwen is called but the required
variables tokenizer and model are not defined, causing a NameError. To fix this,
ensure that the Qwen model and tokenizer are properly loaded and assigned to
these variables before calling chat_with_qwen. Add the necessary import and
initialization code for the tokenizer and model earlier in the notebook.
| "id": "k37CX_LeeY_1" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/qwen_colab_agent.ipynb)\n" |
There was a problem hiding this comment.
Fix the Colab badge URL to match the current filename.
The Colab badge links to qwen_colab_agent.ipynb but the current file is Qwen_Colab_Agent.ipynb (different capitalization).
- "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/qwen_colab_agent.ipynb)\n"
+ "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb)\n"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/qwen_colab_agent.ipynb)\n" | |
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb)\n" |
🤖 Prompt for AI Agents
In examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb at line
25, the Colab badge URL references the file with incorrect capitalization as
qwen_colab_agent.ipynb. Update the URL to match the exact filename
capitalization Qwen_Colab_Agent.ipynb to ensure the link works correctly.
| "id": "PvTxUutEce5s" | ||
| }, | ||
| "source": [ | ||
| "# ✅ Use the official BERT Large model" |
There was a problem hiding this comment.
Misleading section header - this notebook is about Qwen, not BERT.
The section header mentions "official BERT Large model" but the notebook is supposed to demonstrate a Qwen chatbot agent.
- "# ✅ Use the official BERT Large model"
+ "# Load Qwen Model and Tokenizer"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "# ✅ Use the official BERT Large model" | |
| "# Load Qwen Model and Tokenizer" |
🤖 Prompt for AI Agents
In examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb at line
80, the section header incorrectly references "official BERT Large model" which
is misleading since the notebook is about the Qwen chatbot agent. Update the
section header to accurately reflect that it is about the Qwen model instead of
BERT.
| "from transformers import AutoTokenizer, AutoModel\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "bert_model = AutoModel.from_pretrained(\"bert-large-cased\")\n", | ||
| "bert_tokenizer = AutoTokenizer.from_pretrained(\"bert-large-cased\")\n", | ||
| "\n", | ||
| "def classify_text(text):\n", | ||
| " inputs = bert_tokenizer(text, return_tensors=\"pt\")\n", | ||
| " with torch.no_grad():\n", | ||
| " outputs = bert_model(**inputs)\n", | ||
| " return outputs.last_hidden_state.mean(dim=1)\n" | ||
| ] |
There was a problem hiding this comment.
Remove unrelated BERT model loading or clarify its purpose.
The notebook loads a BERT model and defines a classify_text function, but this appears unrelated to the Qwen chatbot functionality described in the introduction. The classify_text function is also never used in the notebook.
If BERT is needed for a specific purpose, please add documentation explaining why. Otherwise, replace this with the actual Qwen model loading:
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
-bert_model = AutoModel.from_pretrained("bert-large-cased")
-bert_tokenizer = AutoTokenizer.from_pretrained("bert-large-cased")
-
-def classify_text(text):
- inputs = bert_tokenizer(text, return_tensors="pt")
- with torch.no_grad():
- outputs = bert_model(**inputs)
- return outputs.last_hidden_state.mean(dim=1)
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "from transformers import AutoTokenizer, AutoModel\n", | |
| "import torch\n", | |
| "\n", | |
| "bert_model = AutoModel.from_pretrained(\"bert-large-cased\")\n", | |
| "bert_tokenizer = AutoTokenizer.from_pretrained(\"bert-large-cased\")\n", | |
| "\n", | |
| "def classify_text(text):\n", | |
| " inputs = bert_tokenizer(text, return_tensors=\"pt\")\n", | |
| " with torch.no_grad():\n", | |
| " outputs = bert_model(**inputs)\n", | |
| " return outputs.last_hidden_state.mean(dim=1)\n" | |
| ] | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B") | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B") |
🤖 Prompt for AI Agents
In examples/cookbooks/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb around
lines 225 to 236, the code loads a BERT model and defines a classify_text
function that is unrelated to the Qwen chatbot functionality and is unused.
Remove the BERT model loading and the classify_text function entirely unless
there is a clear documented reason to keep them. If BERT is required, add
comments explaining its purpose; otherwise, replace this section with the
correct Qwen model loading code to align with the notebook's main functionality.
| " tokenizer = AutoTokenizer.from_pretrained(\n", | ||
| " model_name, use_auth_token=os.environ[\"HF_TOKEN\"], trust_remote_code=True\n", | ||
| " )\n", | ||
| " model = AutoModelForCausalLM.from_pretrained(\n", | ||
| " model_name,\n", | ||
| " use_auth_token=os.environ[\"HF_TOKEN\"],\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace deprecated use_auth_token parameter.
The use_auth_token parameter is deprecated and will be removed in v5 of Transformers. The warnings in the output confirm this deprecation.
Apply this fix to use the current parameter name:
tokenizer = AutoTokenizer.from_pretrained(
- model_name, use_auth_token=os.environ["HF_TOKEN"], trust_remote_code=True
+ model_name, token=os.environ["HF_TOKEN"], trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
- use_auth_token=os.environ["HF_TOKEN"],
+ token=os.environ["HF_TOKEN"],
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.float16
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| " tokenizer = AutoTokenizer.from_pretrained(\n", | |
| " model_name, use_auth_token=os.environ[\"HF_TOKEN\"], trust_remote_code=True\n", | |
| " )\n", | |
| " model = AutoModelForCausalLM.from_pretrained(\n", | |
| " model_name,\n", | |
| " use_auth_token=os.environ[\"HF_TOKEN\"],\n", | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_name, token=os.environ["HF_TOKEN"], trust_remote_code=True | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| token=os.environ["HF_TOKEN"], | |
| trust_remote_code=True, | |
| device_map="auto", | |
| torch_dtype=torch.float16 | |
| ) |
🤖 Prompt for AI Agents
In examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb
around lines 493 to 498, the use_auth_token parameter is deprecated. Replace
use_auth_token=os.environ["HF_TOKEN"] with token=os.environ["HF_TOKEN"] in both
AutoTokenizer.from_pretrained and AutoModelForCausalLM.from_pretrained calls to
use the current parameter name and avoid deprecation warnings.
| "import os\n", | ||
| "os.environ[\"HF_TOKEN\"] = \"Enter your hugging api key\"\n", | ||
| "os.environ[\"TRANSFORMERS_CACHE\"] = \"/content/hf_cache\" # Optional: Faster repeat runs\n" |
There was a problem hiding this comment.
Address security concern with hardcoded API key placeholder.
The hardcoded placeholder "Enter your hugging api key" poses a security risk and will cause authentication failures. Users might accidentally commit actual API keys if they replace this placeholder directly in the notebook.
Consider using a more secure approach:
-import os
-os.environ["HF_TOKEN"] = "Enter your hugging api key"
-os.environ["TRANSFORMERS_CACHE"] = "/content/hf_cache" # Optional: Faster repeat runs
+import os
+from getpass import getpass
+
+# Secure API key input
+if "HF_TOKEN" not in os.environ:
+ os.environ["HF_TOKEN"] = getpass("Enter your Hugging Face API token: ")
+
+os.environ["TRANSFORMERS_CACHE"] = "/content/hf_cache" # Optional: Faster repeat runs📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "import os\n", | |
| "os.environ[\"HF_TOKEN\"] = \"Enter your hugging api key\"\n", | |
| "os.environ[\"TRANSFORMERS_CACHE\"] = \"/content/hf_cache\" # Optional: Faster repeat runs\n" | |
| import os | |
| from getpass import getpass | |
| # Secure API key input | |
| if "HF_TOKEN" not in os.environ: | |
| os.environ["HF_TOKEN"] = getpass("Enter your Hugging Face API token: ") | |
| os.environ["TRANSFORMERS_CACHE"] = "/content/hf_cache" # Optional: Faster repeat runs |
🤖 Prompt for AI Agents
In examples/cookbooks/Programming_Code_Analysis_Agents/MistralTechAgent.ipynb
around lines 107 to 109, avoid hardcoding the API key placeholder directly in
the code to prevent security risks and accidental commits of sensitive keys.
Instead, modify the code to read the HF_TOKEN from a secure environment variable
or prompt the user to input the key at runtime without storing it in the
notebook. This ensures the key is not exposed in the source code or version
control.
| "id": "P3X8PXPyojnL" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/code_analysis_agent.ipynb)\n" |
There was a problem hiding this comment.
Fix the Colab badge URL to point to the main repository.
The Colab badge link currently points to your fork (Dhivya-Bharathy/PraisonAI) instead of the main repository (MervinPraison/PraisonAI).
Apply this fix:
- "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/code_analysis_agent.ipynb)\n"
+ "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb)\n"Also note that the path in the URL should match the new location of the notebook after reorganization.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "[](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/code_analysis_agent.ipynb)\n" | |
| "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb)\n" |
🤖 Prompt for AI Agents
In examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb
at line 21, update the Colab badge URL to point to the main repository by
replacing "Dhivya-Bharathy/PraisonAI" with "MervinPraison/PraisonAI". Also,
ensure the notebook path in the URL matches its current location after
reorganization.
| "source": [ | ||
| "import json\n", | ||
| "from IPython.display import display, Markdown\n", | ||
| "\n", | ||
| "# Optional: Define agent info\n", | ||
| "agent_info = \"\"\"\n", | ||
| "### 👤 Agent: Code Analysis Expert\n", | ||
| "\n", | ||
| "**Role**: Provides comprehensive code evaluation and recommendations\n", | ||
| "**Backstory**: Expert in architecture, best practices, and technical assessment\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "# Analysis Result Data\n", | ||
| "analysis_result = {\n", | ||
| " \"overall_quality\": 85,\n", | ||
| " \"code_metrics\": [\n", | ||
| " {\n", | ||
| " \"category\": \"Architecture and Design\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"Modular structure with clear separation of concerns.\",\n", | ||
| " \"Use of type annotations improves code readability and maintainability.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Code Maintainability\",\n", | ||
| " \"score\": 85,\n", | ||
| " \"findings\": [\n", | ||
| " \"Consistent use of type hints and NamedTuple for structured data.\",\n", | ||
| " \"Logical organization of functions and classes.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Performance Optimization\",\n", | ||
| " \"score\": 75,\n", | ||
| " \"findings\": [\n", | ||
| " \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n", | ||
| " \"Efficient use of optional parameters to control execution flow.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Security Practices\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"No obvious security vulnerabilities in the code.\",\n", | ||
| " \"Proper encapsulation of functionality.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Test Coverage\",\n", | ||
| " \"score\": 70,\n", | ||
| " \"findings\": [\n", | ||
| " \"Lack of explicit test cases in the provided code.\",\n", | ||
| " \"Use of type checking suggests some level of validation.\"\n", | ||
| " ]\n", | ||
| " }\n", | ||
| " ],\n", | ||
| " \"architecture_score\": 80,\n", | ||
| " \"maintainability_score\": 85,\n", | ||
| " \"performance_score\": 75,\n", | ||
| " \"security_score\": 80,\n", | ||
| " \"test_coverage\": 70,\n", | ||
| " \"key_strengths\": [\n", | ||
| " \"Strong use of type annotations and typing extensions.\",\n", | ||
| " \"Clear separation of CLI argument parsing and business logic.\"\n", | ||
| " ],\n", | ||
| " \"improvement_areas\": [\n", | ||
| " \"Increase test coverage to ensure robustness.\",\n", | ||
| " \"Optimize I/O operations to improve performance.\"\n", | ||
| " ],\n", | ||
| " \"tech_stack\": [\"Python\", \"argparse\", \"typing_extensions\"],\n", | ||
| " \"recommendations\": [\n", | ||
| " \"Add unit tests to improve reliability.\",\n", | ||
| " \"Consider async I/O for improved performance in CLI tools.\"\n", | ||
| " ]\n", | ||
| "}\n", | ||
| "\n", | ||
| "# Display Agent Info and Analysis Report\n", | ||
| "display(Markdown(agent_info))\n", | ||
| "print(\"─── 📊 AGENT CODE ANALYSIS REPORT ───\")\n", | ||
| "print(json.dumps(analysis_result, indent=4))\n" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Show actual usage and complete the example data.
The example section uses hardcoded data instead of demonstrating actual usage of the analyze_code function. Additionally, the example data is missing required fields from the CodeAnalysisReport model.
Replace the hardcoded example with actual usage:
-import json
-from IPython.display import display, Markdown
-
-# Optional: Define agent info
-agent_info = """
-### 👤 Agent: Code Analysis Expert
-
-**Role**: Provides comprehensive code evaluation and recommendations
-**Backstory**: Expert in architecture, best practices, and technical assessment
-"""
-
-# Analysis Result Data
-analysis_result = {
- # ... hardcoded data ...
-}
-
-# Display Agent Info and Analysis Report
-display(Markdown(agent_info))
-print("─── 📊 AGENT CODE ANALYSIS REPORT ───")
-print(json.dumps(analysis_result, indent=4))
+# Example usage
+try:
+ # Analyze a GitHub repository
+ repo_url = "https://github.com/example/sample-project" # Replace with actual repo
+
+ print("🔍 Analyzing code repository...")
+ result = analyze_code(repo_url)
+
+ print("\n─── 📊 CODE ANALYSIS REPORT ───")
+ print(result.model_dump_json(indent=2))
+
+except Exception as e:
+ print(f"❌ Analysis failed: {e}")
+
+ # Show example output structure
+ print("\n📋 Example output structure:")
+ example = {
+ "overall_quality": 85,
+ "code_metrics": [...],
+ "architecture_score": 80,
+ "maintainability_score": 85,
+ "performance_score": 75,
+ "security_score": 80,
+ "test_coverage": 70,
+ "complexity_metrics": {"cyclomatic": 15, "cognitive": 20},
+ "best_practices": [{"practice": "DRY", "adherence": "Good"}],
+ "documentation_quality": 75,
+ # ... rest of fields
+ }
+ print(json.dumps(example, indent=2))Also, ensure the example includes all required fields: complexity_metrics, best_practices, and documentation_quality.
Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb
around lines 341 to 422, the example uses hardcoded analysis data instead of
demonstrating actual usage of the analyze_code function, and it lacks required
fields like complexity_metrics, best_practices, and documentation_quality.
Replace the hardcoded data with a call to analyze_code on a sample code snippet
to generate a real report, and ensure the resulting example output includes all
required fields from the CodeAnalysisReport model, including complexity_metrics,
best_practices, and documentation_quality.
| "def analyze_code(code_source: str) -> CodeAnalysisReport:\n", | ||
| " \"\"\"\n", | ||
| " Analyze code from directory path or GitHub URL\n", | ||
| " \"\"\"\n", | ||
| " # Ingest code content\n", | ||
| " summary, tree, content = ingest(code_source)\n", | ||
| "\n", | ||
| " # Concatenate context into structured format\n", | ||
| " context_text = f\"\"\"\n", | ||
| " CODE REPOSITORY ANALYSIS\n", | ||
| " =======================\n", | ||
| "\n", | ||
| " SUMMARY\n", | ||
| " -------\n", | ||
| " {summary}\n", | ||
| "\n", | ||
| " REPOSITORY STRUCTURE\n", | ||
| " -------------------\n", | ||
| " {tree}\n", | ||
| "\n", | ||
| " SOURCE CODE\n", | ||
| " -----------\n", | ||
| " {content}\n", | ||
| " \"\"\"\n", | ||
| "\n", | ||
| " # Initialize and run analysis\n", | ||
| " agents = PraisonAIAgents(\n", | ||
| " agents=[code_analyzer],\n", | ||
| " tasks=[code_analysis_task]\n", | ||
| " )\n", | ||
| "\n", | ||
| " return agents.start(context_text)" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for robustness.
The function should handle potential failures in code ingestion and agent execution.
def analyze_code(code_source: str) -> CodeAnalysisReport:
"""
Analyze code from directory path or GitHub URL
"""
- # Ingest code content
- summary, tree, content = ingest(code_source)
+ try:
+ # Ingest code content
+ summary, tree, content = ingest(code_source)
+ except Exception as e:
+ raise ValueError(f"Failed to ingest code from {code_source}: {str(e)}")
# Concatenate context into structured format
context_text = f"""
CODE REPOSITORY ANALYSIS
=======================
SUMMARY
-------
{summary}
REPOSITORY STRUCTURE
-------------------
{tree}
SOURCE CODE
-----------
{content}
"""
- # Initialize and run analysis
- agents = PraisonAIAgents(
- agents=[code_analyzer],
- tasks=[code_analysis_task]
- )
-
- return agents.start(context_text)
+ try:
+ # Initialize and run analysis
+ agents = PraisonAIAgents(
+ agents=[code_analyzer],
+ tasks=[code_analysis_task]
+ )
+ return agents.start(context_text)
+ except Exception as e:
+ raise RuntimeError(f"Failed to analyze code: {str(e)}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def analyze_code(code_source: str) -> CodeAnalysisReport:\n", | |
| " \"\"\"\n", | |
| " Analyze code from directory path or GitHub URL\n", | |
| " \"\"\"\n", | |
| " # Ingest code content\n", | |
| " summary, tree, content = ingest(code_source)\n", | |
| "\n", | |
| " # Concatenate context into structured format\n", | |
| " context_text = f\"\"\"\n", | |
| " CODE REPOSITORY ANALYSIS\n", | |
| " =======================\n", | |
| "\n", | |
| " SUMMARY\n", | |
| " -------\n", | |
| " {summary}\n", | |
| "\n", | |
| " REPOSITORY STRUCTURE\n", | |
| " -------------------\n", | |
| " {tree}\n", | |
| "\n", | |
| " SOURCE CODE\n", | |
| " -----------\n", | |
| " {content}\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " # Initialize and run analysis\n", | |
| " agents = PraisonAIAgents(\n", | |
| " agents=[code_analyzer],\n", | |
| " tasks=[code_analysis_task]\n", | |
| " )\n", | |
| "\n", | |
| " return agents.start(context_text)" | |
| def analyze_code(code_source: str) -> CodeAnalysisReport: | |
| """ | |
| Analyze code from directory path or GitHub URL | |
| """ | |
| try: | |
| # Ingest code content | |
| summary, tree, content = ingest(code_source) | |
| except Exception as e: | |
| raise ValueError(f"Failed to ingest code from {code_source}: {str(e)}") | |
| # Concatenate context into structured format | |
| context_text = f""" | |
| CODE REPOSITORY ANALYSIS | |
| ======================= | |
| SUMMARY | |
| ------- | |
| {summary} | |
| REPOSITORY STRUCTURE | |
| ------------------- | |
| {tree} | |
| SOURCE CODE | |
| ----------- | |
| {content} | |
| """ | |
| try: | |
| # Initialize and run analysis | |
| agents = PraisonAIAgents( | |
| agents=[code_analyzer], | |
| tasks=[code_analysis_task] | |
| ) | |
| return agents.start(context_text) | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to analyze code: {str(e)}") |
🤖 Prompt for AI Agents
In examples/cookbooks/Programming_Code_Analysis_Agents/Code_Analysis_Agent.ipynb
between lines 192 and 223, the analyze_code function lacks error handling for
potential failures during code ingestion and agent execution. Add try-except
blocks around the ingest call and the agents.start call to catch exceptions, log
or handle errors appropriately, and ensure the function fails gracefully or
returns meaningful error information.
2 participants
User description
PR Type
Enhancement
Description
• Reorganized 62 unique agent notebooks into 12 categorized folders under
examples/cookbooks/for better structure and navigation• Added comprehensive agent implementations across multiple domains:
• Removed 8 duplicate agent notebooks to eliminate redundancy
• Fixed invalid notebook issues and ensured all agents are properly structured
• Enhanced repository organization with clear categorization for easier discovery and maintenance
Changes walkthrough 📝
6 files
AI_Data_Analysis_Agent.ipynb
Complete AI Data Analysis Agent notebook implementationexamples/cookbooks/Research_Knowledge_QA_Agents/AI_Data_Analysis_Agent.ipynb
• Added complete Jupyter notebook for AI Data Analysis Agent with 1032
lines of code
• Implemented data visualization, preprocessing, and
statistical analysis tools
• Created comprehensive data analysis
interface with file upload, insights generation, and custom
visualizations
• Included dependencies installation, API key setup,
and sample data generation functionality
Ai_Market_Startup_Trend_Agent.ipynb
Complete AI Market & Startup Trend Agent notebook implementationexamples/cookbooks/Finance_Market_Job_Agents/Ai_Market_Startup_Trend_Agent.ipynb
• Added complete Jupyter notebook for AI Market & Startup Trend Agent
with 402 lines of code
• Implemented news search, article
summarization, and trend analysis tools using DuckDuckGo and
Newspaper3k
• Created market trend analysis system with real-time news
gathering and AI-powered insights
• Included Anthropic API integration
and sample topic suggestions for testing
Intelligent_Programming_Agent.ipynb
Add intelligent programming agent notebook with multi-agent codegenerationexamples/cookbooks/Programming_Code_Analysis_Agents/Intelligent_Programming_Agent.ipynb
• Added complete Jupyter notebook for AI code generation system using
PraisonAI framework
• Implemented multi-agent programming assistant
with code generation, debugging, and validation capabilities
•
Includes custom Manim code tools for syntax validation and code
analysis
• Features demo execution with Qwen2.5 model integration for
natural language to code conversion
Qwen2_5_InstructionAgent.ipynb
Add Qwen2.5 instruction agent notebook for conversational chatexamples/cookbooks/Conversational_Chat_Agents/Qwen2_5_InstructionAgent.ipynb
• Added complete Jupyter notebook demonstrating Qwen2.5-0.5B-Instruct
model usage
• Implemented simple chat-based text generation with
Hugging Face Transformers
• Includes authentication setup, model
loading, and response generation
• Features photosynthesis explanation
example with proper tokenization and output handling
Universal_Desktop_Agents.ipynb
Complete Universal Desktop Agents Notebook Implementationexamples/cookbooks/Productivity_Workflow_Agents/Universal_Desktop_Agents.ipynb
• Added complete Jupyter notebook for Universal Desktop Utility Agents
using PraisonAI
• Implemented three specialized agents: Terminal
Agent, File Summarize Agent, and General Purpose Agent
• Included
setup instructions, dependencies installation, and example usage
demonstrations
• Added Colab badge and comprehensive documentation
with role-based agent configurations
Gemma2B_Instruction_Agent.ipynb
Complete Gemma 2B Instruction Agent Notebook Implementationexamples/cookbooks/Programming_Code_Analysis_Agents/Gemma2B_Instruction_Agent.ipynb
• Added complete Jupyter notebook for Gemma 2B instruction-tuned model
implementation
• Included model setup, tokenization, training
configuration, and inference examples
• Added data preparation steps
with sample dataset and tokenization functions
• Implemented model
saving functionality and comprehensive documentation
62 files
Summary by CodeRabbit
New Features
Bug Fixes
Removals