Merge pull request #52 from MervinPraison/develop

v0.0.30

.env.example

@@ -1,3 +1,6 @@
-OPENAI_MODEL_NAME="gpt-4-turbo-preview"
+OPENAI_MODEL_NAME="gpt-4o"
 OPENAI_API_KEY="Enter your API key"
-OPENAI_API_BASE="https://api.openai.com/v1"
+OPENAI_API_BASE="https://api.openai.com/v1"
+CHAINLIT_USERNAME=admin
+CHAINLIT_USERNAME=admin
+CHAINLIT_AUTH_SECRET="chainlit create-secret to create"

.github/workflows/python-package.yml

@@ -29,12 +29,12 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    # - name: Lint with flake8
+    #   run: |
+    #     # stop the build if there are Python syntax errors or undefined names
+    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     # - name: Test with pytest
     #   run: |
     #     pytest

.gitignore

@@ -13,4 +13,27 @@ assets/*
 praisonAI.egg-info
 flagged
 test.yaml
-db
+db
+praisonai_prompt.txt
+watch.sh
+docs.sh
+other
+
+.chainlit
+.files
+
+site
+# Ignore Sphinx build directory
+docs/_build/
+docs/source/
+docs/build/
+
+# Ignore Sphinx cache
+docs/_build/doctrees/
+docs/_build/doctrees/*
+docs/_build/doctrees/.doctrees
+
+# Ignore Sphinx logs and local build files
+docs/_build/.buildinfo
+docs/_build/.buildinfo/*
+docs/_build/.buildinfo/.buildinfo

.readthedocs.yml

@@ -0,0 +1,23 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+  jobs:
+    post_install:
+    - bash docs/generate_docs.sh
+
+mkdocs:
+  configuration: mkdocs.yml
+  fail_on_warning: true
+
+python:
+  install:
+    - requirements: docs/requirements.txt

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==0.0.18 gunicorn markdown
+RUN pip install flask praisonai==0.0.30 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

LICENSE

@@ -0,0 +1,20 @@
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -2,6 +2,14 @@
 
 Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
 
+## TL;DR
+```bash
+pip install praisonai
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init create a movie script about dog in moon
+praisonai
+```
+
 ## Installation
 
 ```bash
@@ -9,13 +17,21 @@ pip install praisonai
 ```
 
 ## Initialise
+
+```bash
+export OPENAI_API_KEY="Enter your API key"
+```
+
+Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
+
+Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
     
 ```bash
 praisonai --init create a movie script about dog in moon
 ```
 This will automatically create agents.yaml file in the current directory.
 
-To initialse with a specific agent framework:
+### To initialse with a specific agent framework (Optional):
 
 ```bash
 praisonai --framework autogen --init create movie script about cat in mars
@@ -33,7 +49,7 @@ or
 python -m praisonai
 ```
 
-### Specify the agent framework
+### Specify the agent framework (Optional):
 
 ```bash
 praisonai --framework autogen
@@ -45,6 +61,68 @@ praisonai --framework autogen
 praisonai --auto create a movie script about Dog in Moon
 ```
 
+## Create Custom Tools
+
+### TL;DR to Create a Custom Tool
+
+```bash
+pip install praisonai duckduckgo-search
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init research about the latest AI News and prepare a detailed report
+```
+
+- Add `- InternetSearchTool` in the agents.yaml file in the tools section. 
+- Create a file called tools.py and add this code [tools.py](./tools.py)
+
+```bash
+praisonai
+```
+
+### Pre-requisite to Create a Custom Tool
+`agents.yaml` file should be present in the current directory. 
+
+If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
+
+### Step 1 to Create a Custom Tool
+
+Create a file called tools.py in the same directory as the agents.yaml file.
+
+```python
+# example tools.py
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+```
+
+### Step 2 to Create a Custom Tool
+
+Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
+
+```yaml
+framework: crewai
+topic: research about the latest AI News and prepare a detailed report
+roles:
+  research_analyst:
+    backstory: Experienced in gathering and analyzing data related to AI news trends.
+    goal: Analyze AI News trends
+    role: Research Analyst
+    tasks:
+      gather_data:
+        description: Conduct in-depth research on the latest AI News trends from reputable
+          sources.
+        expected_output: Comprehensive report on current AI News trends.
+    tools:
+    - InternetSearchTool
+```
+
 ## Test
     
 ```bash
@@ -53,7 +131,7 @@ python -m unittest tests.test
 
 ## Agents Playbook 
 
-### Simple Playbook
+### Simple Playbook Example
 
 ```yaml
 framework: crewai
@@ -69,7 +147,7 @@ roles:
         expected_output: 'Complete script ready for production.'
 ```
 
-### Detailed Playbook
+### Detailed Playbook Example
 
 ```yaml
 framework: crewai
@@ -142,6 +220,12 @@ if __name__ == "__main__":
     auto()
 ```
 
+## Include CrewAI Tools
+
+```
+pip install "praisonai[crewai-tools]"
+```
+
 ## Deploy 
     
 ```bash
@@ -150,7 +234,7 @@ gcloud services enable run.googleapis.com
 gcloud services enable containerregistry.googleapis.com
 gcloud services enable cloudbuild.googleapis.com
 
-export OPENAI_MODEL_NAME="gpt-4-turbo-preview"
+export OPENAI_MODEL_NAME="gpt-4o"
 export OPENAI_API_KEY="Enter your API key"
 export OPENAI_API_BASE="https://api.openai.com/v1"
 
@@ -169,9 +253,11 @@ gcloud run deploy praisonai-service \
     --region us-central1 \
     --allow-unauthenticated \
     --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
+```
 
 ## Other Models
 
+```bash
 Ollama
 OPENAI_API_BASE='http://localhost:11434/v1'
 OPENAI_MODEL_NAME='mistral'
@@ -191,6 +277,7 @@ Mistral API¶
 OPENAI_API_BASE=https://api.mistral.ai/v1
 OPENAI_MODEL_NAME="mistral-small"
 OPENAI_API_KEY=your-mistral-api-key
+```
 
 ## Contributing
 
@@ -201,3 +288,4 @@ OPENAI_API_KEY=your-mistral-api-key
 - Push to your fork: `git push origin new-feature`
 - Submit a pull request via GitHub's web interface.
 - Await feedback from project maintainers.
+

agents-scrapewebitetool.yaml

@@ -0,0 +1,42 @@
+framework: crewai
+topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
+roles:
+  story_developer:
+    backstory: Experienced in crafting compelling narratives for a diverse audience.
+    goal: Develop an engaging storyline for a movie about cats in Mars
+    role: Story Developer
+    tasks:
+      storyline_creation:
+        description: Create a captivating storyline for a movie about cats in Mars
+          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
+        expected_output: Detailed storyline with key plot points, character motivations,
+          and setting descriptions.
+    tools:
+    - 'ScrapeWebsiteTool'
+  screenwriter:
+    backstory: Proficient in translating complex narratives into engaging screenplay
+      format.
+    goal: Transform the storyline into a script
+    role: Screenwriter
+    tasks:
+      script_creation:
+        description: Transform the developed storyline into a script for the movie
+          about cats in Mars.
+        expected_output: Complete script with dialogues, scenes, and stage directions
+          ready for production.
+    tools:
+    - ''
+  director:
+    backstory: Experienced in visual storytelling and bringing scripts to life on
+      screen.
+    goal: Oversee the production of the movie
+    role: Director
+    tasks:
+      movie_production:
+        description: Take the script and lead the team to produce the movie 'Cats
+          in Mars' based on the provided content and script.
+        expected_output: Completed movie ready for distribution with compelling visuals
+          and engaging storytelling.
+    tools:
+    - ''
+dependencies: []

agents.yaml

@@ -1,42 +1,39 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
+framework: autogen
+topic: research about the causes of lung disease
 roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
     tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung
+          diseases.
+        expected_output: Report detailing key findings on lung disease causes.
     tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
+    - 'InternetSearchTool'
+  medical_writer:
+    backstory: Skilled in translating complex medical information into accessible
+      content.
+    goal: Compile comprehensive content on lung disease causes
+    role: Medical Writer
     tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
+      content_creation:
+        description: Create detailed content summarizing the research findings on
+          lung disease causes.
+        expected_output: Document outlining various causes and risk factors of lung
+          diseases.
     tools:
     - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
+  editor:
+    backstory: Proficient in editing medical content for accuracy and clarity.
+    goal: Review and refine content on lung disease causes
+    role: Editor
     tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
+      content_review:
+        description: Edit and refine the compiled content on lung disease causes for
+          accuracy and coherence.
+        expected_output: Finalized document on lung disease causes ready for dissemination.
     tools:
     - ''
 dependencies: []

agents_playbook/create-movie-agents.yaml

@@ -0,0 +1,42 @@
+framework: crewai
+topic: create a movie about cat in mars based on https://www.grammarly.com/blog/how-to-write-a-story/
+roles:
+  story_developer:
+    backstory: Experienced in crafting compelling narratives for a diverse audience.
+    goal: Develop an engaging storyline for a movie about cats in Mars
+    role: Story Developer
+    tasks:
+      storyline_creation:
+        description: Create a captivating storyline for a movie about cats in Mars
+          inspired by the blog post at https://www.grammarly.com/blog/how-to-write-a-story/
+        expected_output: Detailed storyline with key plot points, character motivations,
+          and setting descriptions.
+    tools:
+    - 'ScrapeWebsiteTool'
+  screenwriter:
+    backstory: Proficient in translating complex narratives into engaging screenplay
+      format.
+    goal: Transform the storyline into a script
+    role: Screenwriter
+    tasks:
+      script_creation:
+        description: Transform the developed storyline into a script for the movie
+          about cats in Mars.
+        expected_output: Complete script with dialogues, scenes, and stage directions
+          ready for production.
+    tools:
+    - ''
+  director:
+    backstory: Experienced in visual storytelling and bringing scripts to life on
+      screen.
+    goal: Oversee the production of the movie
+    role: Director
+    tasks:
+      movie_production:
+        description: Take the script and lead the team to produce the movie 'Cats
+          in Mars' based on the provided content and script.
+        expected_output: Completed movie ready for distribution with compelling visuals
+          and engaging storytelling.
+    tools:
+    - ''
+dependencies: []

agents_playbook/finance-research.yaml

@@ -0,0 +1,43 @@
+framework: crewai
+topic: research about nvidia growth
+roles:
+  data_collector:
+    backstory: An experienced researcher with the ability to efficiently collect and
+      organize vast amounts of data.
+    goal: Gather information on Nvidia's growth
+    role: Data Collector
+    tasks:
+      data_collection_task:
+        description: Collect data on Nvidia's growth from various sources such as
+          financial reports, news articles, and company announcements.
+        expected_output: A comprehensive document detailing data points on Nvidia's
+          growth over the years.
+    tools:
+    - 'YouSearchTool'
+  data_analyst:
+    backstory: Specializes in extracting insights from large datasets, proficient
+      in quantitative and qualitative analysis.
+    goal: Analyze the collected data to identify trends and patterns
+    role: Data Analyst
+    tasks:
+      data_analysis_task:
+        description: Analyze the collected data to identify key trends and patterns
+          in Nvidia's growth.
+        expected_output: An analytical report summarizing trends, patterns, and key
+          growth metrics of Nvidia.
+    tools:
+    - ''
+  report_preparer:
+    backstory: Experienced in creating detailed reports and presentations, turning
+      analytical data into actionable insights.
+    goal: Generate a final report on Nvidia's growth
+    role: Report Preparer
+    tasks:
+      report_preparation_task:
+        description: Create a detailed report based on the analysis, highlighting
+          Nvidia's growth trajectory.
+        expected_output: A polished, comprehensive report summarizing Nvidia's growth
+          with visual aids and key insights.
+    tools:
+    - ''
+dependencies: []

agents_playbook/internet-research-agents.yaml

@@ -0,0 +1,26 @@
+framework: crewai
+topic: research about the latest AI News and prepare a detailed report
+roles:
+  research_analyst:
+    backstory: Experienced in gathering and analyzing data related to AI news trends.
+    goal: Analyze AI News trends
+    role: Research Analyst
+    tasks:
+      gather_data:
+        description: Conduct in-depth research on the latest AI News trends from reputable
+          sources.
+        expected_output: Comprehensive report on current AI News trends.
+    tools:
+    - 'InternetSearchTool'
+  content_creator:
+    backstory: Skilled in writing informative and engaging content about AI News.
+    goal: Create detailed reports on AI News
+    role: Content Creator
+    tasks:
+      compile_report:
+        description: Compile all gathered data into a detailed report with analysis
+          and insights.
+        expected_output: Well-structured report on the latest AI News with key insights.
+    tools:
+    - ''
+dependencies: []

agents_playbook/medical-research-pubmed-agents.yaml

@@ -0,0 +1,39 @@
+framework: crewai
+topic: research about the causes of lung disease
+roles:
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
+    tasks:
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung
+          diseases.
+        expected_output: Report detailing key findings on lung disease causes.
+    tools:
+    - 'PubmedQueryRun'
+  medical_writer:
+    backstory: Skilled in translating complex medical information into accessible
+      content.
+    goal: Compile comprehensive content on lung disease causes
+    role: Medical Writer
+    tasks:
+      content_creation:
+        description: Create detailed content summarizing the research findings on
+          lung disease causes.
+        expected_output: Document outlining various causes and risk factors of lung
+          diseases.
+    tools:
+    - ''
+  editor:
+    backstory: Proficient in editing medical content for accuracy and clarity.
+    goal: Review and refine content on lung disease causes
+    role: Editor
+    tasks:
+      content_review:
+        description: Edit and refine the compiled content on lung disease causes for
+          accuracy and coherence.
+        expected_output: Finalized document on lung disease causes ready for dissemination.
+    tools:
+    - ''
+dependencies: []

chainlit.md

@@ -0,0 +1 @@
+# Welcome to Praison AI! 🚀🤖

docs/CNAME

@@ -0,0 +1 @@
+docs.praison.ai

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/agents_playbook.md

@@ -0,0 +1,60 @@
+# Agents Playbook 
+
+## Simple Playbook Example
+
+```yaml
+framework: crewai
+topic: Artificial Intelligence
+roles:
+  screenwriter:
+    backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
+    goal: Create scripts from concepts.
+    role: Screenwriter
+    tasks:
+      scriptwriting_task:
+        description: 'Develop scripts with compelling characters and dialogue about {topic}.'
+        expected_output: 'Complete script ready for production.'
+```
+
+## Detailed Playbook Example
+
+```yaml
+framework: crewai
+topic: Artificial Intelligence
+roles:
+  movie_concept_creator:
+    backstory: 'Creative thinker with a deep understanding of cinematic storytelling,
+      capable of using AI-generated storylines to create unique and compelling movie
+      ideas.'
+    goal: Generate engaging movie concepts using AI storylines
+    role: Movie Concept Creator
+    tasks:
+      movie_concept_development:
+        description: 'Develop movie concepts from AI-generated storylines, ensuring
+          they are engaging and have strong narrative arcs.'
+        expected_output: 'Well-structured movie concept document with character
+          bios, settings, and plot outlines.'
+  screenwriter:
+    backstory: 'Expert in writing engaging dialogue and script structure, able to
+      turn movie concepts into production-ready scripts.'
+    goal: Write compelling scripts based on movie concepts
+    role: Screenwriter
+    tasks:
+      scriptwriting_task:
+        description: 'Turn movie concepts into polished scripts with well-developed
+          characters, strong dialogue, and effective scene transitions.'
+        expected_output: 'Production-ready script with a beginning, middle, and
+          end, along with character development and engaging dialogues.'
+  editor:
+    backstory: 'Adept at identifying inconsistencies, improving language usage,
+      and maintaining the overall flow of the script.'
+    goal: Refine the scripts and ensure continuity of the movie storyline
+    role: Editor
+    tasks:
+      editing_task:
+        description: 'Review, edit, and refine the scripts to ensure they are cohesive
+          and follow a well-structured narrative.'
+        expected_output: 'A polished final draft of the script with no inconsistencies,
+          strong character development, and effective dialogue.'
+dependencies: []
+```

docs/api.md

@@ -0,0 +1,6 @@
+# API Reference
+::: praisonai
+::: praisonai.auto
+::: praisonai.agents_generator
+::: praisonai.cli
+::: praisonai.deploy

docs/api/praisonai/agents_generator.html

@@ -0,0 +1,966 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+<meta name="generator" content="pdoc 0.10.0" />
+<title>praisonai.agents_generator API documentation</title>
+<meta name="description" content="" />
+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
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+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.agents_generator</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python"># praisonai/agents_generator.py
+
+import sys
+from .version import __version__
+import yaml, os
+from rich import print
+from dotenv import load_dotenv
+from crewai import Agent, Task, Crew
+from crewai.telemetry import Telemetry
+load_dotenv()
+import autogen
+import argparse
+from .auto import AutoGenerator
+from crewai_tools import (
+    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
+    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
+    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
+    YoutubeVideoSearchTool
+)
+from .inbuilt_tools import *
+import inspect
+from pathlib import Path
+import importlib
+import importlib.util
+from praisonai_tools import BaseTool
+import os
+
+os.environ[&#34;OTEL_SDK_DISABLED&#34;] = &#34;true&#34;
+
+def noop(*args, **kwargs):
+    pass
+
+def disable_crewai_telemetry():
+    for attr in dir(Telemetry):
+        if callable(getattr(Telemetry, attr)) and not attr.startswith(&#34;__&#34;):
+            setattr(Telemetry, attr, noop)
+            
+disable_crewai_telemetry()
+
+class AgentsGenerator:
+    def __init__(self, agent_file, framework, config_list):
+        &#34;&#34;&#34;
+        Initialize the AgentsGenerator object.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Attributes:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+        &#34;&#34;&#34;
+        self.agent_file = agent_file
+        self.framework = framework
+        self.config_list = config_list
+        
+    def is_function_or_decorated(self, obj):
+        &#34;&#34;&#34;
+        Checks if the given object is a function or has a __call__ method.
+
+        Parameters:
+            obj (object): The object to be checked.
+
+        Returns:
+            bool: True if the object is a function or has a __call__ method, False otherwise.
+        &#34;&#34;&#34;
+        return inspect.isfunction(obj) or hasattr(obj, &#39;__call__&#39;)
+
+    def load_tools_from_module(self, module_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified module path.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        &#34;&#34;&#34;
+        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
+    
+    def load_tools_from_module_class(self, module_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        &#34;&#34;&#34;
+        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith(&#39;langchain_community.tools&#39;) or issubclass(x, BaseTool)) and x is not BaseTool)}
+
+    def load_tools_from_package(self, package_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified package path containing modules with functions or classes.
+
+        Parameters:
+            package_path (str): The path to the package containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified package path does not exist.
+
+        This function iterates through all the .py files in the specified package path, excluding those that start with &#34;__&#34;. For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+        &#34;&#34;&#34;
+        tools_dict = {}
+        for module_file in os.listdir(package_path):
+            if module_file.endswith(&#39;.py&#39;) and not module_file.startswith(&#39;__&#39;):
+                module_name = f&#34;{package_path.name}.{module_file[:-3]}&#34;  # Remove .py for import
+                module = importlib.import_module(module_name)
+                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
+                    tools_dict[name] = obj
+        return tools_dict
+
+    def generate_crew_and_kickoff(self):
+        &#34;&#34;&#34;
+        Generates a crew of agents and initiates tasks based on the provided configuration.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Returns:
+            str: The output of the tasks performed by the crew of agents.
+
+        Raises:
+            FileNotFoundError: If the specified agent file does not exist.
+
+        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is &#34;autogen&#34;, it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not &#34;autogen&#34;, it creates a crew of agents and initiates tasks based on the configuration.
+        &#34;&#34;&#34;
+        if self.agent_file == &#39;/app/api:app&#39; or self.agent_file == &#39;api:app&#39;:
+            self.agent_file = &#39;agents.yaml&#39;
+        try:
+            with open(self.agent_file, &#39;r&#39;) as f:
+                config = yaml.safe_load(f)
+        except FileNotFoundError:
+            print(f&#34;File not found: {self.agent_file}&#34;)
+            return
+
+        topic = config[&#39;topic&#39;]
+        tools_dict = {
+            &#39;CodeDocsSearchTool&#39;: CodeDocsSearchTool(),
+            &#39;CSVSearchTool&#39;: CSVSearchTool(),
+            &#39;DirectorySearchTool&#39;: DirectorySearchTool(),
+            &#39;DOCXSearchTool&#39;: DOCXSearchTool(),
+            &#39;DirectoryReadTool&#39;: DirectoryReadTool(),
+            &#39;FileReadTool&#39;: FileReadTool(),
+            # &#39;GithubSearchTool&#39;: GithubSearchTool(),
+            # &#39;SeperDevTool&#39;: SeperDevTool(),
+            &#39;TXTSearchTool&#39;: TXTSearchTool(),
+            &#39;JSONSearchTool&#39;: JSONSearchTool(),
+            &#39;MDXSearchTool&#39;: MDXSearchTool(),
+            &#39;PDFSearchTool&#39;: PDFSearchTool(),
+            # &#39;PGSearchTool&#39;: PGSearchTool(),
+            &#39;RagTool&#39;: RagTool(),
+            &#39;ScrapeElementFromWebsiteTool&#39;: ScrapeElementFromWebsiteTool(),
+            &#39;ScrapeWebsiteTool&#39;: ScrapeWebsiteTool(),
+            &#39;WebsiteSearchTool&#39;: WebsiteSearchTool(),
+            &#39;XMLSearchTool&#39;: XMLSearchTool(),
+            &#39;YoutubeChannelSearchTool&#39;: YoutubeChannelSearchTool(),
+            &#39;YoutubeVideoSearchTool&#39;: YoutubeVideoSearchTool(),
+        }
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+        tools_dir_path = Path(root_directory) / &#39;tools&#39;
+        
+        if os.path.isfile(tools_py_path):
+            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
+            # print(&#34;tools.py exists in the root directory. Loading tools.py and skipping tools folder.&#34;)
+        elif tools_dir_path.is_dir():
+            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
+            # print(&#34;tools folder exists in the root directory&#34;)
+        
+        framework = self.framework or config.get(&#39;framework&#39;)
+
+        agents = {}
+        tasks = []
+        if framework == &#34;autogen&#34;:
+            # Load the LLM configuration dynamically
+            # print(self.config_list)
+            llm_config = {&#34;config_list&#34;: self.config_list}
+            
+            # Assuming the user proxy agent is set up as per your requirements
+            user_proxy = autogen.UserProxyAgent(
+                name=&#34;User&#34;,
+                human_input_mode=&#34;NEVER&#34;,
+                is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().rstrip(&#34;.&#34;).lower().endswith(&#34;terminate&#34;) or &#34;TERMINATE&#34; in (x.get(&#34;content&#34;) or &#34;&#34;),
+                code_execution_config={
+                    &#34;work_dir&#34;: &#34;coding&#34;,
+                    &#34;use_docker&#34;: False,
+                },
+                # additional setup for the user proxy agent
+            )
+            
+            for role, details in config[&#39;roles&#39;].items():
+                agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
+                agent_goal = details[&#39;goal&#39;].format(topic=topic)
+                # Creating an AssistantAgent for each role dynamically
+                agents[role] = autogen.AssistantAgent(
+                    name=agent_name,
+                    llm_config=llm_config,
+                    system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Must Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
+                )
+                for tool in details.get(&#39;tools&#39;, []):
+                    if tool in tools_dict:
+                        try:
+                            tool_class = globals()[f&#39;autogen_{type(tools_dict[tool]).__name__}&#39;]
+                            print(f&#34;Found {tool_class.__name__} for {tool}&#34;)
+                        except KeyError:
+                            print(f&#34;Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.&#34;)
+                            continue
+                        tool_class(agents[role], user_proxy)
+
+                # Preparing tasks for initiate_chats
+                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+                    
+                    chat_task = {
+                        &#34;recipient&#34;: agents[role],
+                        &#34;message&#34;: description_filled,
+                        &#34;summary_method&#34;: &#34;last_msg&#34;, 
+                        # Additional fields like carryover can be added based on dependencies
+                    }
+                    tasks.append(chat_task)
+
+            response = user_proxy.initiate_chats(tasks)
+            result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
+        else:
+            for role, details in config[&#39;roles&#39;].items():
+                role_filled = details[&#39;role&#39;].format(topic=topic)
+                goal_filled = details[&#39;goal&#39;].format(topic=topic)
+                backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
+                
+                # Adding tools to the agent if exists
+                agent_tools = [tools_dict[tool] for tool in details.get(&#39;tools&#39;, []) if tool in tools_dict]
+                agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
+                agents[role] = agent
+
+                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+
+                    task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
+                    tasks.append(task)
+            crew = Crew(
+                agents=list(agents.values()),
+                tasks=tasks,
+                verbose=2
+            )
+
+            response = crew.kickoff()
+            result = f&#34;### Task Output ###\n{response}&#34;
+        return result</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="praisonai.agents_generator.disable_crewai_telemetry"><code class="name flex">
+<span>def <span class="ident">disable_crewai_telemetry</span></span>(<span>)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def disable_crewai_telemetry():
+    for attr in dir(Telemetry):
+        if callable(getattr(Telemetry, attr)) and not attr.startswith(&#34;__&#34;):
+            setattr(Telemetry, attr, noop)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.agents_generator.noop"><code class="name flex">
+<span>def <span class="ident">noop</span></span>(<span>*args, **kwargs)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def noop(*args, **kwargs):
+    pass</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="praisonai.agents_generator.AgentsGenerator"><code class="flex name class">
+<span>class <span class="ident">AgentsGenerator</span></span>
+<span>(</span><span>agent_file, framework, config_list)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Initialize the AgentsGenerator object.</p>
+<h2 id="parameters">Parameters</h2>
+<p>agent_file (str): The path to the agent file.
+framework (str): The framework to be used for the agents.
+config_list (list): A list of configurations for the agents.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
+<dd>The path to the agent file.</dd>
+<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
+<dd>The framework to be used for the agents.</dd>
+<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
+<dd>A list of configurations for the agents.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class AgentsGenerator:
+    def __init__(self, agent_file, framework, config_list):
+        &#34;&#34;&#34;
+        Initialize the AgentsGenerator object.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Attributes:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+        &#34;&#34;&#34;
+        self.agent_file = agent_file
+        self.framework = framework
+        self.config_list = config_list
+        
+    def is_function_or_decorated(self, obj):
+        &#34;&#34;&#34;
+        Checks if the given object is a function or has a __call__ method.
+
+        Parameters:
+            obj (object): The object to be checked.
+
+        Returns:
+            bool: True if the object is a function or has a __call__ method, False otherwise.
+        &#34;&#34;&#34;
+        return inspect.isfunction(obj) or hasattr(obj, &#39;__call__&#39;)
+
+    def load_tools_from_module(self, module_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified module path.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        &#34;&#34;&#34;
+        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
+    
+    def load_tools_from_module_class(self, module_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        &#34;&#34;&#34;
+        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith(&#39;langchain_community.tools&#39;) or issubclass(x, BaseTool)) and x is not BaseTool)}
+
+    def load_tools_from_package(self, package_path):
+        &#34;&#34;&#34;
+        Loads tools from a specified package path containing modules with functions or classes.
+
+        Parameters:
+            package_path (str): The path to the package containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified package path does not exist.
+
+        This function iterates through all the .py files in the specified package path, excluding those that start with &#34;__&#34;. For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+        &#34;&#34;&#34;
+        tools_dict = {}
+        for module_file in os.listdir(package_path):
+            if module_file.endswith(&#39;.py&#39;) and not module_file.startswith(&#39;__&#39;):
+                module_name = f&#34;{package_path.name}.{module_file[:-3]}&#34;  # Remove .py for import
+                module = importlib.import_module(module_name)
+                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
+                    tools_dict[name] = obj
+        return tools_dict
+
+    def generate_crew_and_kickoff(self):
+        &#34;&#34;&#34;
+        Generates a crew of agents and initiates tasks based on the provided configuration.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Returns:
+            str: The output of the tasks performed by the crew of agents.
+
+        Raises:
+            FileNotFoundError: If the specified agent file does not exist.
+
+        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is &#34;autogen&#34;, it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not &#34;autogen&#34;, it creates a crew of agents and initiates tasks based on the configuration.
+        &#34;&#34;&#34;
+        if self.agent_file == &#39;/app/api:app&#39; or self.agent_file == &#39;api:app&#39;:
+            self.agent_file = &#39;agents.yaml&#39;
+        try:
+            with open(self.agent_file, &#39;r&#39;) as f:
+                config = yaml.safe_load(f)
+        except FileNotFoundError:
+            print(f&#34;File not found: {self.agent_file}&#34;)
+            return
+
+        topic = config[&#39;topic&#39;]
+        tools_dict = {
+            &#39;CodeDocsSearchTool&#39;: CodeDocsSearchTool(),
+            &#39;CSVSearchTool&#39;: CSVSearchTool(),
+            &#39;DirectorySearchTool&#39;: DirectorySearchTool(),
+            &#39;DOCXSearchTool&#39;: DOCXSearchTool(),
+            &#39;DirectoryReadTool&#39;: DirectoryReadTool(),
+            &#39;FileReadTool&#39;: FileReadTool(),
+            # &#39;GithubSearchTool&#39;: GithubSearchTool(),
+            # &#39;SeperDevTool&#39;: SeperDevTool(),
+            &#39;TXTSearchTool&#39;: TXTSearchTool(),
+            &#39;JSONSearchTool&#39;: JSONSearchTool(),
+            &#39;MDXSearchTool&#39;: MDXSearchTool(),
+            &#39;PDFSearchTool&#39;: PDFSearchTool(),
+            # &#39;PGSearchTool&#39;: PGSearchTool(),
+            &#39;RagTool&#39;: RagTool(),
+            &#39;ScrapeElementFromWebsiteTool&#39;: ScrapeElementFromWebsiteTool(),
+            &#39;ScrapeWebsiteTool&#39;: ScrapeWebsiteTool(),
+            &#39;WebsiteSearchTool&#39;: WebsiteSearchTool(),
+            &#39;XMLSearchTool&#39;: XMLSearchTool(),
+            &#39;YoutubeChannelSearchTool&#39;: YoutubeChannelSearchTool(),
+            &#39;YoutubeVideoSearchTool&#39;: YoutubeVideoSearchTool(),
+        }
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+        tools_dir_path = Path(root_directory) / &#39;tools&#39;
+        
+        if os.path.isfile(tools_py_path):
+            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
+            # print(&#34;tools.py exists in the root directory. Loading tools.py and skipping tools folder.&#34;)
+        elif tools_dir_path.is_dir():
+            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
+            # print(&#34;tools folder exists in the root directory&#34;)
+        
+        framework = self.framework or config.get(&#39;framework&#39;)
+
+        agents = {}
+        tasks = []
+        if framework == &#34;autogen&#34;:
+            # Load the LLM configuration dynamically
+            # print(self.config_list)
+            llm_config = {&#34;config_list&#34;: self.config_list}
+            
+            # Assuming the user proxy agent is set up as per your requirements
+            user_proxy = autogen.UserProxyAgent(
+                name=&#34;User&#34;,
+                human_input_mode=&#34;NEVER&#34;,
+                is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().rstrip(&#34;.&#34;).lower().endswith(&#34;terminate&#34;) or &#34;TERMINATE&#34; in (x.get(&#34;content&#34;) or &#34;&#34;),
+                code_execution_config={
+                    &#34;work_dir&#34;: &#34;coding&#34;,
+                    &#34;use_docker&#34;: False,
+                },
+                # additional setup for the user proxy agent
+            )
+            
+            for role, details in config[&#39;roles&#39;].items():
+                agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
+                agent_goal = details[&#39;goal&#39;].format(topic=topic)
+                # Creating an AssistantAgent for each role dynamically
+                agents[role] = autogen.AssistantAgent(
+                    name=agent_name,
+                    llm_config=llm_config,
+                    system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Must Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
+                )
+                for tool in details.get(&#39;tools&#39;, []):
+                    if tool in tools_dict:
+                        try:
+                            tool_class = globals()[f&#39;autogen_{type(tools_dict[tool]).__name__}&#39;]
+                            print(f&#34;Found {tool_class.__name__} for {tool}&#34;)
+                        except KeyError:
+                            print(f&#34;Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.&#34;)
+                            continue
+                        tool_class(agents[role], user_proxy)
+
+                # Preparing tasks for initiate_chats
+                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+                    
+                    chat_task = {
+                        &#34;recipient&#34;: agents[role],
+                        &#34;message&#34;: description_filled,
+                        &#34;summary_method&#34;: &#34;last_msg&#34;, 
+                        # Additional fields like carryover can be added based on dependencies
+                    }
+                    tasks.append(chat_task)
+
+            response = user_proxy.initiate_chats(tasks)
+            result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
+        else:
+            for role, details in config[&#39;roles&#39;].items():
+                role_filled = details[&#39;role&#39;].format(topic=topic)
+                goal_filled = details[&#39;goal&#39;].format(topic=topic)
+                backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
+                
+                # Adding tools to the agent if exists
+                agent_tools = [tools_dict[tool] for tool in details.get(&#39;tools&#39;, []) if tool in tools_dict]
+                agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
+                agents[role] = agent
+
+                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+
+                    task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
+                    tasks.append(task)
+            crew = Crew(
+                agents=list(agents.values()),
+                tasks=tasks,
+                verbose=2
+            )
+
+            response = crew.kickoff()
+            result = f&#34;### Task Output ###\n{response}&#34;
+        return result</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff"><code class="name flex">
+<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates a crew of agents and initiates tasks based on the provided configuration.</p>
+<h2 id="parameters">Parameters</h2>
+<p>agent_file (str): The path to the agent file.
+framework (str): The framework to be used for the agents.
+config_list (list): A list of configurations for the agents.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>str</code></dt>
+<dd>The output of the tasks performed by the crew of agents.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>FileNotFoundError</code></dt>
+<dd>If the specified agent file does not exist.</dd>
+</dl>
+<p>This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate_crew_and_kickoff(self):
+    &#34;&#34;&#34;
+    Generates a crew of agents and initiates tasks based on the provided configuration.
+
+    Parameters:
+        agent_file (str): The path to the agent file.
+        framework (str): The framework to be used for the agents.
+        config_list (list): A list of configurations for the agents.
+
+    Returns:
+        str: The output of the tasks performed by the crew of agents.
+
+    Raises:
+        FileNotFoundError: If the specified agent file does not exist.
+
+    This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is &#34;autogen&#34;, it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not &#34;autogen&#34;, it creates a crew of agents and initiates tasks based on the configuration.
+    &#34;&#34;&#34;
+    if self.agent_file == &#39;/app/api:app&#39; or self.agent_file == &#39;api:app&#39;:
+        self.agent_file = &#39;agents.yaml&#39;
+    try:
+        with open(self.agent_file, &#39;r&#39;) as f:
+            config = yaml.safe_load(f)
+    except FileNotFoundError:
+        print(f&#34;File not found: {self.agent_file}&#34;)
+        return
+
+    topic = config[&#39;topic&#39;]
+    tools_dict = {
+        &#39;CodeDocsSearchTool&#39;: CodeDocsSearchTool(),
+        &#39;CSVSearchTool&#39;: CSVSearchTool(),
+        &#39;DirectorySearchTool&#39;: DirectorySearchTool(),
+        &#39;DOCXSearchTool&#39;: DOCXSearchTool(),
+        &#39;DirectoryReadTool&#39;: DirectoryReadTool(),
+        &#39;FileReadTool&#39;: FileReadTool(),
+        # &#39;GithubSearchTool&#39;: GithubSearchTool(),
+        # &#39;SeperDevTool&#39;: SeperDevTool(),
+        &#39;TXTSearchTool&#39;: TXTSearchTool(),
+        &#39;JSONSearchTool&#39;: JSONSearchTool(),
+        &#39;MDXSearchTool&#39;: MDXSearchTool(),
+        &#39;PDFSearchTool&#39;: PDFSearchTool(),
+        # &#39;PGSearchTool&#39;: PGSearchTool(),
+        &#39;RagTool&#39;: RagTool(),
+        &#39;ScrapeElementFromWebsiteTool&#39;: ScrapeElementFromWebsiteTool(),
+        &#39;ScrapeWebsiteTool&#39;: ScrapeWebsiteTool(),
+        &#39;WebsiteSearchTool&#39;: WebsiteSearchTool(),
+        &#39;XMLSearchTool&#39;: XMLSearchTool(),
+        &#39;YoutubeChannelSearchTool&#39;: YoutubeChannelSearchTool(),
+        &#39;YoutubeVideoSearchTool&#39;: YoutubeVideoSearchTool(),
+    }
+    root_directory = os.getcwd()
+    tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+    tools_dir_path = Path(root_directory) / &#39;tools&#39;
+    
+    if os.path.isfile(tools_py_path):
+        tools_dict.update(self.load_tools_from_module_class(tools_py_path))
+        # print(&#34;tools.py exists in the root directory. Loading tools.py and skipping tools folder.&#34;)
+    elif tools_dir_path.is_dir():
+        tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
+        # print(&#34;tools folder exists in the root directory&#34;)
+    
+    framework = self.framework or config.get(&#39;framework&#39;)
+
+    agents = {}
+    tasks = []
+    if framework == &#34;autogen&#34;:
+        # Load the LLM configuration dynamically
+        # print(self.config_list)
+        llm_config = {&#34;config_list&#34;: self.config_list}
+        
+        # Assuming the user proxy agent is set up as per your requirements
+        user_proxy = autogen.UserProxyAgent(
+            name=&#34;User&#34;,
+            human_input_mode=&#34;NEVER&#34;,
+            is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().rstrip(&#34;.&#34;).lower().endswith(&#34;terminate&#34;) or &#34;TERMINATE&#34; in (x.get(&#34;content&#34;) or &#34;&#34;),
+            code_execution_config={
+                &#34;work_dir&#34;: &#34;coding&#34;,
+                &#34;use_docker&#34;: False,
+            },
+            # additional setup for the user proxy agent
+        )
+        
+        for role, details in config[&#39;roles&#39;].items():
+            agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
+            agent_goal = details[&#39;goal&#39;].format(topic=topic)
+            # Creating an AssistantAgent for each role dynamically
+            agents[role] = autogen.AssistantAgent(
+                name=agent_name,
+                llm_config=llm_config,
+                system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Must Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
+            )
+            for tool in details.get(&#39;tools&#39;, []):
+                if tool in tools_dict:
+                    try:
+                        tool_class = globals()[f&#39;autogen_{type(tools_dict[tool]).__name__}&#39;]
+                        print(f&#34;Found {tool_class.__name__} for {tool}&#34;)
+                    except KeyError:
+                        print(f&#34;Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.&#34;)
+                        continue
+                    tool_class(agents[role], user_proxy)
+
+            # Preparing tasks for initiate_chats
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+                
+                chat_task = {
+                    &#34;recipient&#34;: agents[role],
+                    &#34;message&#34;: description_filled,
+                    &#34;summary_method&#34;: &#34;last_msg&#34;, 
+                    # Additional fields like carryover can be added based on dependencies
+                }
+                tasks.append(chat_task)
+
+        response = user_proxy.initiate_chats(tasks)
+        result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
+    else:
+        for role, details in config[&#39;roles&#39;].items():
+            role_filled = details[&#39;role&#39;].format(topic=topic)
+            goal_filled = details[&#39;goal&#39;].format(topic=topic)
+            backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
+            
+            # Adding tools to the agent if exists
+            agent_tools = [tools_dict[tool] for tool in details.get(&#39;tools&#39;, []) if tool in tools_dict]
+            agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
+            agents[role] = agent
+
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+
+                task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
+                tasks.append(task)
+        crew = Crew(
+            agents=list(agents.values()),
+            tasks=tasks,
+            verbose=2
+        )
+
+        response = crew.kickoff()
+        result = f&#34;### Task Output ###\n{response}&#34;
+    return result</code></pre>
+</details>
+</dd>
+<dt id="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated"><code class="name flex">
+<span>def <span class="ident">is_function_or_decorated</span></span>(<span>self, obj)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Checks if the given object is a function or has a <strong>call</strong> method.</p>
+<h2 id="parameters">Parameters</h2>
+<p>obj (object): The object to be checked.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>bool</code></dt>
+<dd>True if the object is a function or has a <strong>call</strong> method, False otherwise.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def is_function_or_decorated(self, obj):
+    &#34;&#34;&#34;
+    Checks if the given object is a function or has a __call__ method.
+
+    Parameters:
+        obj (object): The object to be checked.
+
+    Returns:
+        bool: True if the object is a function or has a __call__ method, False otherwise.
+    &#34;&#34;&#34;
+    return inspect.isfunction(obj) or hasattr(obj, &#39;__call__&#39;)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module"><code class="name flex">
+<span>def <span class="ident">load_tools_from_module</span></span>(<span>self, module_path)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Loads tools from a specified module path.</p>
+<h2 id="parameters">Parameters</h2>
+<p>module_path (str): The path to the module containing the tools.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>dict</code></dt>
+<dd>A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>FileNotFoundError</code></dt>
+<dd>If the specified module path does not exist.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def load_tools_from_module(self, module_path):
+    &#34;&#34;&#34;
+    Loads tools from a specified module path.
+
+    Parameters:
+        module_path (str): The path to the module containing the tools.
+
+    Returns:
+        dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
+
+    Raises:
+        FileNotFoundError: If the specified module path does not exist.
+    &#34;&#34;&#34;
+    spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}</code></pre>
+</details>
+</dd>
+<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class"><code class="name flex">
+<span>def <span class="ident">load_tools_from_module_class</span></span>(<span>self, module_path)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.</p>
+<h2 id="parameters">Parameters</h2>
+<p>module_path (str): The path to the module containing the tools.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>dict</code></dt>
+<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>FileNotFoundError</code></dt>
+<dd>If the specified module path does not exist.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def load_tools_from_module_class(self, module_path):
+    &#34;&#34;&#34;
+    Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
+
+    Parameters:
+        module_path (str): The path to the module containing the tools.
+
+    Returns:
+        dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+    Raises:
+        FileNotFoundError: If the specified module path does not exist.
+    &#34;&#34;&#34;
+    spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith(&#39;langchain_community.tools&#39;) or issubclass(x, BaseTool)) and x is not BaseTool)}</code></pre>
+</details>
+</dd>
+<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_package"><code class="name flex">
+<span>def <span class="ident">load_tools_from_package</span></span>(<span>self, package_path)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Loads tools from a specified package path containing modules with functions or classes.</p>
+<h2 id="parameters">Parameters</h2>
+<p>package_path (str): The path to the package containing the tools.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>dict</code></dt>
+<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>FileNotFoundError</code></dt>
+<dd>If the specified package path does not exist.</dd>
+</dl>
+<p>This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def load_tools_from_package(self, package_path):
+    &#34;&#34;&#34;
+    Loads tools from a specified package path containing modules with functions or classes.
+
+    Parameters:
+        package_path (str): The path to the package containing the tools.
+
+    Returns:
+        dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+    Raises:
+        FileNotFoundError: If the specified package path does not exist.
+
+    This function iterates through all the .py files in the specified package path, excluding those that start with &#34;__&#34;. For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+    &#34;&#34;&#34;
+    tools_dict = {}
+    for module_file in os.listdir(package_path):
+        if module_file.endswith(&#39;.py&#39;) and not module_file.startswith(&#39;__&#39;):
+            module_name = f&#34;{package_path.name}.{module_file[:-3]}&#34;  # Remove .py for import
+            module = importlib.import_module(module_name)
+            for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
+                tools_dict[name] = obj
+    return tools_dict</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="praisonai.agents_generator.disable_crewai_telemetry" href="#praisonai.agents_generator.disable_crewai_telemetry">disable_crewai_telemetry</a></code></li>
+<li><code><a title="praisonai.agents_generator.noop" href="#praisonai.agents_generator.noop">noop</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="praisonai.agents_generator.AgentsGenerator" href="#praisonai.agents_generator.AgentsGenerator">AgentsGenerator</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff" href="#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
+<li><code><a title="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated" href="#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">is_function_or_decorated</a></code></li>
+<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module">load_tools_from_module</a></code></li>
+<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class">load_tools_from_module_class</a></code></li>
+<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_package" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_package">load_tools_from_package</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/auto.html

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+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.auto</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">from openai import OpenAI
+from pydantic import BaseModel
+from typing import Dict, List, Optional
+import instructor
+import os
+import json
+import yaml
+from rich import print
+
+# Define Pydantic models outside of the generate method
+class TaskDetails(BaseModel):
+    description: str
+    expected_output: str
+
+class RoleDetails(BaseModel):
+    role: str
+    goal: str
+    backstory: str
+    tasks: Dict[str, TaskDetails]
+    tools: List[str]
+
+class TeamStructure(BaseModel):
+    roles: Dict[str, RoleDetails]
+
+class AutoGenerator:
+    def __init__(self, topic=&#34;Movie Story writing about AI&#34;, agent_file=&#34;test.yaml&#34;, framework=&#34;crewai&#34;, config_list: Optional[List[Dict]] = None):
+        &#34;&#34;&#34;
+        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
+        Note: autogen framework is different from this AutoGenerator class.
+
+        Args:
+            topic (str, optional): The topic for the generated team structure. Defaults to &#34;Movie Story writing about AI&#34;.
+            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to &#34;test.yaml&#34;.
+            framework (str, optional): The framework for the generated team structure. Defaults to &#34;crewai&#34;.
+            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
+                                                          If None, it defaults to using environment variables or hardcoded values.
+        Attributes:
+            config_list (list): A list containing the configuration details for the OpenAI API.
+            topic (str): The specified topic for the generated team structure.
+            agent_file (str): The specified name of the YAML file to save the generated team structure.
+            framework (str): The specified framework for the generated team structure.
+            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
+        &#34;&#34;&#34;
+        self.config_list = config_list or [
+            {
+                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
+                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
+                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
+            }
+        ]
+        self.topic = topic
+        self.agent_file = agent_file
+        self.framework = framework or &#34;crewai&#34;
+        self.client = instructor.patch(
+            OpenAI(
+                base_url=self.config_list[0][&#39;base_url&#39;],
+                api_key=os.getenv(&#34;OPENAI_API_KEY&#34;),
+            ),
+            mode=instructor.Mode.JSON,
+        )
+
+    def generate(self):
+        &#34;&#34;&#34;
+        Generates a team structure for the specified topic.
+
+        Args:
+            None
+
+        Returns:
+            str: The full path of the YAML file containing the generated team structure.
+
+        Raises:
+            Exception: If the generation process fails.
+
+        Usage:
+            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+            path = generator.generate()
+            print(path)
+        &#34;&#34;&#34;
+        response = self.client.chat.completions.create(
+            model=self.config_list[0][&#39;model&#39;],
+            response_model=TeamStructure,
+            max_retries=10,
+            messages=[
+                {&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;You are a helpful assistant designed to output complex team structures.&#34;},
+                {&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: self.get_user_content()}
+            ]
+        )
+        json_data = json.loads(response.model_dump_json())
+        self.convert_and_save(json_data)
+        full_path = os.path.abspath(self.agent_file)
+        return full_path
+
+    def convert_and_save(self, json_data):
+        &#34;&#34;&#34;Converts the provided JSON data into the desired YAML format and saves it to a file.
+
+        Args:
+            json_data (dict): The JSON data representing the team structure.
+            topic (str, optional): The topic to be inserted into the YAML. Defaults to &#34;Artificial Intelligence&#34;.
+            agent_file (str, optional): The name of the YAML file to save. Defaults to &#34;test.yaml&#34;.
+        &#34;&#34;&#34;
+
+        yaml_data = {
+            &#34;framework&#34;: self.framework,
+            &#34;topic&#34;: self.topic,
+            &#34;roles&#34;: {},
+            &#34;dependencies&#34;: []
+        }
+
+        for role_id, role_details in json_data[&#39;roles&#39;].items():
+            yaml_data[&#39;roles&#39;][role_id] = {
+                &#34;backstory&#34;: &#34;&#34; + role_details[&#39;backstory&#39;],
+                &#34;goal&#34;: role_details[&#39;goal&#39;],
+                &#34;role&#34;: role_details[&#39;role&#39;],
+                &#34;tasks&#34;: {},
+                # &#34;tools&#34;: role_details.get(&#39;tools&#39;, []),
+                &#34;tools&#34;: [&#39;&#39;]
+            }
+
+            for task_id, task_details in role_details[&#39;tasks&#39;].items():
+                yaml_data[&#39;roles&#39;][role_id][&#39;tasks&#39;][task_id] = {
+                    &#34;description&#34;: &#34;&#34; + task_details[&#39;description&#39;],
+                    &#34;expected_output&#34;: &#34;&#34; + task_details[&#39;expected_output&#39;]
+                }
+
+        # Save to YAML file, maintaining the order
+        with open(self.agent_file, &#39;w&#39;) as f:
+            yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
+
+    def get_user_content(self):
+        &#34;&#34;&#34;
+        Generates a prompt for the OpenAI API to generate a team structure.
+
+        Args:
+            None
+
+        Returns:
+            str: The prompt for the OpenAI API.
+
+        Usage:
+            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+            prompt = generator.get_user_content()
+            print(prompt)
+        &#34;&#34;&#34;
+        user_content = &#34;&#34;&#34;Generate a team structure for  \&#34;&#34;&#34;&#34; + self.topic + &#34;&#34;&#34;\&#34; task. 
+No Input data will be provided to the team.
+The team will work in sequence. First role will pass the output to the next role, and so on.
+The last role will generate the final output.
+Think step by step.
+With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
+List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
+Only use Available Tools. Do Not use any other tools. 
+Example Below: 
+Use below example to understand the structure of the output. 
+The final role you create should satisfy the provided task: &#34;&#34;&#34; + self.topic + &#34;&#34;&#34;.
+{
+&#34;roles&#34;: {
+&#34;narrative_designer&#34;: {
+&#34;role&#34;: &#34;Narrative Designer&#34;,
+&#34;goal&#34;: &#34;Create AI storylines&#34;,
+&#34;backstory&#34;: &#34;Skilled in narrative development for AI, with a focus on story resonance.&#34;,
+&#34;tools&#34;: [&#34;ScrapeWebsiteTool&#34;],
+&#34;tasks&#34;: {
+&#34;story_concept_development&#34;: {
+&#34;description&#34;: &#34;Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .&#34;,
+&#34;expected_output&#34;: &#34;Document with narrative arcs, character bios, and settings.&#34;
+}
+}
+},
+&#34;scriptwriter&#34;: {
+&#34;role&#34;: &#34;Scriptwriter&#34;,
+&#34;goal&#34;: &#34;Write scripts from AI concepts&#34;,
+&#34;backstory&#34;: &#34;Expert in dialogue and script structure, translating concepts into scripts.&#34;,
+&#34;tasks&#34;: {
+&#34;scriptwriting_task&#34;: {
+&#34;description&#34;: &#34;Turn narrative concepts into scripts, including dialogue and scenes.&#34;,
+&#34;expected_output&#34;: &#34;Production-ready script with dialogue and scene details.&#34;
+}
+}
+}
+}
+}
+        &#34;&#34;&#34;
+        return user_content
+
+    
+# generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+# print(generator.generate())</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="praisonai.auto.AutoGenerator"><code class="flex name class">
+<span>class <span class="ident">AutoGenerator</span></span>
+<span>(</span><span>topic='Movie Story writing about AI', agent_file='test.yaml', framework='crewai', config_list: Optional[List[Dict]] = None)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Initialize the AutoGenerator class with the specified topic, agent file, and framework.
+Note: autogen framework is different from this AutoGenerator class.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The topic for the generated team structure. Defaults to "Movie Story writing about AI".</dd>
+<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The name of the YAML file to save the generated team structure. Defaults to "test.yaml".</dd>
+<dt><strong><code>framework</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The framework for the generated team structure. Defaults to "crewai".</dd>
+<dt><strong><code>config_list</code></strong> :&ensp;<code>Optional[List[Dict]]</code>, optional</dt>
+<dd>A list containing the configuration details for the OpenAI API.
+If None, it defaults to using environment variables or hardcoded values.</dd>
+</dl>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
+<dd>A list containing the configuration details for the OpenAI API.</dd>
+<dt><strong><code>topic</code></strong> :&ensp;<code>str</code></dt>
+<dd>The specified topic for the generated team structure.</dd>
+<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
+<dd>The specified name of the YAML file to save the generated team structure.</dd>
+<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
+<dd>The specified framework for the generated team structure.</dd>
+<dt><strong><code>client</code></strong> :&ensp;<code>instructor.Client</code></dt>
+<dd>An instance of the instructor.Client class initialized with the specified OpenAI API configuration.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class AutoGenerator:
+    def __init__(self, topic=&#34;Movie Story writing about AI&#34;, agent_file=&#34;test.yaml&#34;, framework=&#34;crewai&#34;, config_list: Optional[List[Dict]] = None):
+        &#34;&#34;&#34;
+        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
+        Note: autogen framework is different from this AutoGenerator class.
+
+        Args:
+            topic (str, optional): The topic for the generated team structure. Defaults to &#34;Movie Story writing about AI&#34;.
+            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to &#34;test.yaml&#34;.
+            framework (str, optional): The framework for the generated team structure. Defaults to &#34;crewai&#34;.
+            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
+                                                          If None, it defaults to using environment variables or hardcoded values.
+        Attributes:
+            config_list (list): A list containing the configuration details for the OpenAI API.
+            topic (str): The specified topic for the generated team structure.
+            agent_file (str): The specified name of the YAML file to save the generated team structure.
+            framework (str): The specified framework for the generated team structure.
+            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
+        &#34;&#34;&#34;
+        self.config_list = config_list or [
+            {
+                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
+                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
+                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
+            }
+        ]
+        self.topic = topic
+        self.agent_file = agent_file
+        self.framework = framework or &#34;crewai&#34;
+        self.client = instructor.patch(
+            OpenAI(
+                base_url=self.config_list[0][&#39;base_url&#39;],
+                api_key=os.getenv(&#34;OPENAI_API_KEY&#34;),
+            ),
+            mode=instructor.Mode.JSON,
+        )
+
+    def generate(self):
+        &#34;&#34;&#34;
+        Generates a team structure for the specified topic.
+
+        Args:
+            None
+
+        Returns:
+            str: The full path of the YAML file containing the generated team structure.
+
+        Raises:
+            Exception: If the generation process fails.
+
+        Usage:
+            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+            path = generator.generate()
+            print(path)
+        &#34;&#34;&#34;
+        response = self.client.chat.completions.create(
+            model=self.config_list[0][&#39;model&#39;],
+            response_model=TeamStructure,
+            max_retries=10,
+            messages=[
+                {&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;You are a helpful assistant designed to output complex team structures.&#34;},
+                {&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: self.get_user_content()}
+            ]
+        )
+        json_data = json.loads(response.model_dump_json())
+        self.convert_and_save(json_data)
+        full_path = os.path.abspath(self.agent_file)
+        return full_path
+
+    def convert_and_save(self, json_data):
+        &#34;&#34;&#34;Converts the provided JSON data into the desired YAML format and saves it to a file.
+
+        Args:
+            json_data (dict): The JSON data representing the team structure.
+            topic (str, optional): The topic to be inserted into the YAML. Defaults to &#34;Artificial Intelligence&#34;.
+            agent_file (str, optional): The name of the YAML file to save. Defaults to &#34;test.yaml&#34;.
+        &#34;&#34;&#34;
+
+        yaml_data = {
+            &#34;framework&#34;: self.framework,
+            &#34;topic&#34;: self.topic,
+            &#34;roles&#34;: {},
+            &#34;dependencies&#34;: []
+        }
+
+        for role_id, role_details in json_data[&#39;roles&#39;].items():
+            yaml_data[&#39;roles&#39;][role_id] = {
+                &#34;backstory&#34;: &#34;&#34; + role_details[&#39;backstory&#39;],
+                &#34;goal&#34;: role_details[&#39;goal&#39;],
+                &#34;role&#34;: role_details[&#39;role&#39;],
+                &#34;tasks&#34;: {},
+                # &#34;tools&#34;: role_details.get(&#39;tools&#39;, []),
+                &#34;tools&#34;: [&#39;&#39;]
+            }
+
+            for task_id, task_details in role_details[&#39;tasks&#39;].items():
+                yaml_data[&#39;roles&#39;][role_id][&#39;tasks&#39;][task_id] = {
+                    &#34;description&#34;: &#34;&#34; + task_details[&#39;description&#39;],
+                    &#34;expected_output&#34;: &#34;&#34; + task_details[&#39;expected_output&#39;]
+                }
+
+        # Save to YAML file, maintaining the order
+        with open(self.agent_file, &#39;w&#39;) as f:
+            yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
+
+    def get_user_content(self):
+        &#34;&#34;&#34;
+        Generates a prompt for the OpenAI API to generate a team structure.
+
+        Args:
+            None
+
+        Returns:
+            str: The prompt for the OpenAI API.
+
+        Usage:
+            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+            prompt = generator.get_user_content()
+            print(prompt)
+        &#34;&#34;&#34;
+        user_content = &#34;&#34;&#34;Generate a team structure for  \&#34;&#34;&#34;&#34; + self.topic + &#34;&#34;&#34;\&#34; task. 
+No Input data will be provided to the team.
+The team will work in sequence. First role will pass the output to the next role, and so on.
+The last role will generate the final output.
+Think step by step.
+With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
+List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
+Only use Available Tools. Do Not use any other tools. 
+Example Below: 
+Use below example to understand the structure of the output. 
+The final role you create should satisfy the provided task: &#34;&#34;&#34; + self.topic + &#34;&#34;&#34;.
+{
+&#34;roles&#34;: {
+&#34;narrative_designer&#34;: {
+&#34;role&#34;: &#34;Narrative Designer&#34;,
+&#34;goal&#34;: &#34;Create AI storylines&#34;,
+&#34;backstory&#34;: &#34;Skilled in narrative development for AI, with a focus on story resonance.&#34;,
+&#34;tools&#34;: [&#34;ScrapeWebsiteTool&#34;],
+&#34;tasks&#34;: {
+&#34;story_concept_development&#34;: {
+&#34;description&#34;: &#34;Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .&#34;,
+&#34;expected_output&#34;: &#34;Document with narrative arcs, character bios, and settings.&#34;
+}
+}
+},
+&#34;scriptwriter&#34;: {
+&#34;role&#34;: &#34;Scriptwriter&#34;,
+&#34;goal&#34;: &#34;Write scripts from AI concepts&#34;,
+&#34;backstory&#34;: &#34;Expert in dialogue and script structure, translating concepts into scripts.&#34;,
+&#34;tasks&#34;: {
+&#34;scriptwriting_task&#34;: {
+&#34;description&#34;: &#34;Turn narrative concepts into scripts, including dialogue and scenes.&#34;,
+&#34;expected_output&#34;: &#34;Production-ready script with dialogue and scene details.&#34;
+}
+}
+}
+}
+}
+        &#34;&#34;&#34;
+        return user_content</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="praisonai.auto.AutoGenerator.convert_and_save"><code class="name flex">
+<span>def <span class="ident">convert_and_save</span></span>(<span>self, json_data)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Converts the provided JSON data into the desired YAML format and saves it to a file.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>json_data</code></strong> :&ensp;<code>dict</code></dt>
+<dd>The JSON data representing the team structure.</dd>
+<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".</dd>
+<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The name of the YAML file to save. Defaults to "test.yaml".</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def convert_and_save(self, json_data):
+    &#34;&#34;&#34;Converts the provided JSON data into the desired YAML format and saves it to a file.
+
+    Args:
+        json_data (dict): The JSON data representing the team structure.
+        topic (str, optional): The topic to be inserted into the YAML. Defaults to &#34;Artificial Intelligence&#34;.
+        agent_file (str, optional): The name of the YAML file to save. Defaults to &#34;test.yaml&#34;.
+    &#34;&#34;&#34;
+
+    yaml_data = {
+        &#34;framework&#34;: self.framework,
+        &#34;topic&#34;: self.topic,
+        &#34;roles&#34;: {},
+        &#34;dependencies&#34;: []
+    }
+
+    for role_id, role_details in json_data[&#39;roles&#39;].items():
+        yaml_data[&#39;roles&#39;][role_id] = {
+            &#34;backstory&#34;: &#34;&#34; + role_details[&#39;backstory&#39;],
+            &#34;goal&#34;: role_details[&#39;goal&#39;],
+            &#34;role&#34;: role_details[&#39;role&#39;],
+            &#34;tasks&#34;: {},
+            # &#34;tools&#34;: role_details.get(&#39;tools&#39;, []),
+            &#34;tools&#34;: [&#39;&#39;]
+        }
+
+        for task_id, task_details in role_details[&#39;tasks&#39;].items():
+            yaml_data[&#39;roles&#39;][role_id][&#39;tasks&#39;][task_id] = {
+                &#34;description&#34;: &#34;&#34; + task_details[&#39;description&#39;],
+                &#34;expected_output&#34;: &#34;&#34; + task_details[&#39;expected_output&#39;]
+            }
+
+    # Save to YAML file, maintaining the order
+    with open(self.agent_file, &#39;w&#39;) as f:
+        yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.auto.AutoGenerator.generate"><code class="name flex">
+<span>def <span class="ident">generate</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates a team structure for the specified topic.</p>
+<h2 id="args">Args</h2>
+<p>None</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>str</code></dt>
+<dd>The full path of the YAML file containing the generated team structure.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>Exception</code></dt>
+<dd>If the generation process fails.</dd>
+</dl>
+<h2 id="usage">Usage</h2>
+<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
+path = generator.generate()
+print(path)</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate(self):
+    &#34;&#34;&#34;
+    Generates a team structure for the specified topic.
+
+    Args:
+        None
+
+    Returns:
+        str: The full path of the YAML file containing the generated team structure.
+
+    Raises:
+        Exception: If the generation process fails.
+
+    Usage:
+        generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+        path = generator.generate()
+        print(path)
+    &#34;&#34;&#34;
+    response = self.client.chat.completions.create(
+        model=self.config_list[0][&#39;model&#39;],
+        response_model=TeamStructure,
+        max_retries=10,
+        messages=[
+            {&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;You are a helpful assistant designed to output complex team structures.&#34;},
+            {&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: self.get_user_content()}
+        ]
+    )
+    json_data = json.loads(response.model_dump_json())
+    self.convert_and_save(json_data)
+    full_path = os.path.abspath(self.agent_file)
+    return full_path</code></pre>
+</details>
+</dd>
+<dt id="praisonai.auto.AutoGenerator.get_user_content"><code class="name flex">
+<span>def <span class="ident">get_user_content</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Generates a prompt for the OpenAI API to generate a team structure.</p>
+<h2 id="args">Args</h2>
+<p>None</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>str</code></dt>
+<dd>The prompt for the OpenAI API.</dd>
+</dl>
+<h2 id="usage">Usage</h2>
+<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
+prompt = generator.get_user_content()
+print(prompt)</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">    def get_user_content(self):
+        &#34;&#34;&#34;
+        Generates a prompt for the OpenAI API to generate a team structure.
+
+        Args:
+            None
+
+        Returns:
+            str: The prompt for the OpenAI API.
+
+        Usage:
+            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
+            prompt = generator.get_user_content()
+            print(prompt)
+        &#34;&#34;&#34;
+        user_content = &#34;&#34;&#34;Generate a team structure for  \&#34;&#34;&#34;&#34; + self.topic + &#34;&#34;&#34;\&#34; task. 
+No Input data will be provided to the team.
+The team will work in sequence. First role will pass the output to the next role, and so on.
+The last role will generate the final output.
+Think step by step.
+With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
+List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
+Only use Available Tools. Do Not use any other tools. 
+Example Below: 
+Use below example to understand the structure of the output. 
+The final role you create should satisfy the provided task: &#34;&#34;&#34; + self.topic + &#34;&#34;&#34;.
+{
+&#34;roles&#34;: {
+&#34;narrative_designer&#34;: {
+&#34;role&#34;: &#34;Narrative Designer&#34;,
+&#34;goal&#34;: &#34;Create AI storylines&#34;,
+&#34;backstory&#34;: &#34;Skilled in narrative development for AI, with a focus on story resonance.&#34;,
+&#34;tools&#34;: [&#34;ScrapeWebsiteTool&#34;],
+&#34;tasks&#34;: {
+&#34;story_concept_development&#34;: {
+&#34;description&#34;: &#34;Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .&#34;,
+&#34;expected_output&#34;: &#34;Document with narrative arcs, character bios, and settings.&#34;
+}
+}
+},
+&#34;scriptwriter&#34;: {
+&#34;role&#34;: &#34;Scriptwriter&#34;,
+&#34;goal&#34;: &#34;Write scripts from AI concepts&#34;,
+&#34;backstory&#34;: &#34;Expert in dialogue and script structure, translating concepts into scripts.&#34;,
+&#34;tasks&#34;: {
+&#34;scriptwriting_task&#34;: {
+&#34;description&#34;: &#34;Turn narrative concepts into scripts, including dialogue and scenes.&#34;,
+&#34;expected_output&#34;: &#34;Production-ready script with dialogue and scene details.&#34;
+}
+}
+}
+}
+}
+        &#34;&#34;&#34;
+        return user_content</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+<dt id="praisonai.auto.RoleDetails"><code class="flex name class">
+<span>class <span class="ident">RoleDetails</span></span>
+<span>(</span><span>**data: Any)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
+<p>A base class for creating Pydantic models.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>__class_vars__</code></strong></dt>
+<dd>The names of classvars defined on the model.</dd>
+<dt><strong><code>__private_attributes__</code></strong></dt>
+<dd>Metadata about the private attributes of the model.</dd>
+<dt><strong><code>__signature__</code></strong></dt>
+<dd>The signature for instantiating the model.</dd>
+<dt><strong><code>__pydantic_complete__</code></strong></dt>
+<dd>Whether model building is completed, or if there are still undefined fields.</dd>
+<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
+<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
+<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
+<dd>Whether the model has a custom <code>__init__</code> function.</dd>
+<dt><strong><code>__pydantic_decorators__</code></strong></dt>
+<dd>Metadata containing the decorators defined on the model.
+This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
+<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
+<dd>Metadata for generic models; contains data used for a similar purpose to
+<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
+<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
+<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
+<dt><strong><code>__pydantic_post_init__</code></strong></dt>
+<dd>The name of the post-init method for the model, if defined.</dd>
+<dt><strong><code>__pydantic_root_model__</code></strong></dt>
+<dd>Whether the model is a <code>RootModel</code>.</dd>
+<dt><strong><code>__pydantic_serializer__</code></strong></dt>
+<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
+<dt><strong><code>__pydantic_validator__</code></strong></dt>
+<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
+<dt><strong><code>__pydantic_extra__</code></strong></dt>
+<dd>An instance attribute with the values of extra fields from validation when
+<code>model_config['extra'] == 'allow'</code>.</dd>
+<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
+<dd>An instance attribute with the names of fields explicitly set.</dd>
+<dt><strong><code>__pydantic_private__</code></strong></dt>
+<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
+</dl>
+<p>Create a new model by parsing and validating input data from keyword arguments.</p>
+<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
+validated to form a valid model.</p>
+<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class RoleDetails(BaseModel):
+    role: str
+    goal: str
+    backstory: str
+    tasks: Dict[str, TaskDetails]
+    tools: List[str]</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>pydantic.main.BaseModel</li>
+</ul>
+<h3>Class variables</h3>
+<dl>
+<dt id="praisonai.auto.RoleDetails.backstory"><code class="name">var <span class="ident">backstory</span> : str</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.goal"><code class="name">var <span class="ident">goal</span> : str</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.role"><code class="name">var <span class="ident">role</span> : str</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.tasks"><code class="name">var <span class="ident">tasks</span> : Dict[str, <a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a>]</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.RoleDetails.tools"><code class="name">var <span class="ident">tools</span> : List[str]</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</dd>
+<dt id="praisonai.auto.TaskDetails"><code class="flex name class">
+<span>class <span class="ident">TaskDetails</span></span>
+<span>(</span><span>**data: Any)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
+<p>A base class for creating Pydantic models.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>__class_vars__</code></strong></dt>
+<dd>The names of classvars defined on the model.</dd>
+<dt><strong><code>__private_attributes__</code></strong></dt>
+<dd>Metadata about the private attributes of the model.</dd>
+<dt><strong><code>__signature__</code></strong></dt>
+<dd>The signature for instantiating the model.</dd>
+<dt><strong><code>__pydantic_complete__</code></strong></dt>
+<dd>Whether model building is completed, or if there are still undefined fields.</dd>
+<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
+<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
+<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
+<dd>Whether the model has a custom <code>__init__</code> function.</dd>
+<dt><strong><code>__pydantic_decorators__</code></strong></dt>
+<dd>Metadata containing the decorators defined on the model.
+This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
+<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
+<dd>Metadata for generic models; contains data used for a similar purpose to
+<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
+<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
+<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
+<dt><strong><code>__pydantic_post_init__</code></strong></dt>
+<dd>The name of the post-init method for the model, if defined.</dd>
+<dt><strong><code>__pydantic_root_model__</code></strong></dt>
+<dd>Whether the model is a <code>RootModel</code>.</dd>
+<dt><strong><code>__pydantic_serializer__</code></strong></dt>
+<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
+<dt><strong><code>__pydantic_validator__</code></strong></dt>
+<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
+<dt><strong><code>__pydantic_extra__</code></strong></dt>
+<dd>An instance attribute with the values of extra fields from validation when
+<code>model_config['extra'] == 'allow'</code>.</dd>
+<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
+<dd>An instance attribute with the names of fields explicitly set.</dd>
+<dt><strong><code>__pydantic_private__</code></strong></dt>
+<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
+</dl>
+<p>Create a new model by parsing and validating input data from keyword arguments.</p>
+<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
+validated to form a valid model.</p>
+<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class TaskDetails(BaseModel):
+    description: str
+    expected_output: str</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>pydantic.main.BaseModel</li>
+</ul>
+<h3>Class variables</h3>
+<dl>
+<dt id="praisonai.auto.TaskDetails.description"><code class="name">var <span class="ident">description</span> : str</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TaskDetails.expected_output"><code class="name">var <span class="ident">expected_output</span> : str</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TaskDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TaskDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TaskDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</dd>
+<dt id="praisonai.auto.TeamStructure"><code class="flex name class">
+<span>class <span class="ident">TeamStructure</span></span>
+<span>(</span><span>**data: Any)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
+<p>A base class for creating Pydantic models.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>__class_vars__</code></strong></dt>
+<dd>The names of classvars defined on the model.</dd>
+<dt><strong><code>__private_attributes__</code></strong></dt>
+<dd>Metadata about the private attributes of the model.</dd>
+<dt><strong><code>__signature__</code></strong></dt>
+<dd>The signature for instantiating the model.</dd>
+<dt><strong><code>__pydantic_complete__</code></strong></dt>
+<dd>Whether model building is completed, or if there are still undefined fields.</dd>
+<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
+<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
+<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
+<dd>Whether the model has a custom <code>__init__</code> function.</dd>
+<dt><strong><code>__pydantic_decorators__</code></strong></dt>
+<dd>Metadata containing the decorators defined on the model.
+This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
+<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
+<dd>Metadata for generic models; contains data used for a similar purpose to
+<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
+<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
+<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
+<dt><strong><code>__pydantic_post_init__</code></strong></dt>
+<dd>The name of the post-init method for the model, if defined.</dd>
+<dt><strong><code>__pydantic_root_model__</code></strong></dt>
+<dd>Whether the model is a <code>RootModel</code>.</dd>
+<dt><strong><code>__pydantic_serializer__</code></strong></dt>
+<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
+<dt><strong><code>__pydantic_validator__</code></strong></dt>
+<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
+<dt><strong><code>__pydantic_extra__</code></strong></dt>
+<dd>An instance attribute with the values of extra fields from validation when
+<code>model_config['extra'] == 'allow'</code>.</dd>
+<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
+<dd>An instance attribute with the names of fields explicitly set.</dd>
+<dt><strong><code>__pydantic_private__</code></strong></dt>
+<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
+</dl>
+<p>Create a new model by parsing and validating input data from keyword arguments.</p>
+<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
+validated to form a valid model.</p>
+<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class TeamStructure(BaseModel):
+    roles: Dict[str, RoleDetails]</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>pydantic.main.BaseModel</li>
+</ul>
+<h3>Class variables</h3>
+<dl>
+<dt id="praisonai.auto.TeamStructure.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TeamStructure.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TeamStructure.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt id="praisonai.auto.TeamStructure.roles"><code class="name">var <span class="ident">roles</span> : Dict[str, <a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a>]</code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="praisonai.auto.AutoGenerator" href="#praisonai.auto.AutoGenerator">AutoGenerator</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.auto.AutoGenerator.convert_and_save" href="#praisonai.auto.AutoGenerator.convert_and_save">convert_and_save</a></code></li>
+<li><code><a title="praisonai.auto.AutoGenerator.generate" href="#praisonai.auto.AutoGenerator.generate">generate</a></code></li>
+<li><code><a title="praisonai.auto.AutoGenerator.get_user_content" href="#praisonai.auto.AutoGenerator.get_user_content">get_user_content</a></code></li>
+</ul>
+</li>
+<li>
+<h4><code><a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.auto.RoleDetails.backstory" href="#praisonai.auto.RoleDetails.backstory">backstory</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.goal" href="#praisonai.auto.RoleDetails.goal">goal</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.model_computed_fields" href="#praisonai.auto.RoleDetails.model_computed_fields">model_computed_fields</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.model_config" href="#praisonai.auto.RoleDetails.model_config">model_config</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.model_fields" href="#praisonai.auto.RoleDetails.model_fields">model_fields</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.role" href="#praisonai.auto.RoleDetails.role">role</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.tasks" href="#praisonai.auto.RoleDetails.tasks">tasks</a></code></li>
+<li><code><a title="praisonai.auto.RoleDetails.tools" href="#praisonai.auto.RoleDetails.tools">tools</a></code></li>
+</ul>
+</li>
+<li>
+<h4><code><a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.auto.TaskDetails.description" href="#praisonai.auto.TaskDetails.description">description</a></code></li>
+<li><code><a title="praisonai.auto.TaskDetails.expected_output" href="#praisonai.auto.TaskDetails.expected_output">expected_output</a></code></li>
+<li><code><a title="praisonai.auto.TaskDetails.model_computed_fields" href="#praisonai.auto.TaskDetails.model_computed_fields">model_computed_fields</a></code></li>
+<li><code><a title="praisonai.auto.TaskDetails.model_config" href="#praisonai.auto.TaskDetails.model_config">model_config</a></code></li>
+<li><code><a title="praisonai.auto.TaskDetails.model_fields" href="#praisonai.auto.TaskDetails.model_fields">model_fields</a></code></li>
+</ul>
+</li>
+<li>
+<h4><code><a title="praisonai.auto.TeamStructure" href="#praisonai.auto.TeamStructure">TeamStructure</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.auto.TeamStructure.model_computed_fields" href="#praisonai.auto.TeamStructure.model_computed_fields">model_computed_fields</a></code></li>
+<li><code><a title="praisonai.auto.TeamStructure.model_config" href="#praisonai.auto.TeamStructure.model_config">model_config</a></code></li>
+<li><code><a title="praisonai.auto.TeamStructure.model_fields" href="#praisonai.auto.TeamStructure.model_fields">model_fields</a></code></li>
+<li><code><a title="praisonai.auto.TeamStructure.roles" href="#praisonai.auto.TeamStructure.roles">roles</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

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+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.cli</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python"># praisonai/cli.py
+
+import sys
+from .version import __version__
+import yaml, os
+from rich import print
+from dotenv import load_dotenv
+from crewai import Agent, Task, Crew
+load_dotenv()
+import autogen
+import argparse
+from .auto import AutoGenerator
+from .agents_generator import AgentsGenerator
+from .inbuilt_tools import *
+
+try:
+    from chainlit.cli import chainlit_run
+    CHAINLIT_AVAILABLE = True
+except ImportError:
+    CHAINLIT_AVAILABLE = False
+
+try:
+    import gradio as gr
+    GRADIO_AVAILABLE = True
+except ImportError:
+    GRADIO_AVAILABLE = False
+
+class PraisonAI:
+    def __init__(self, agent_file=&#34;agents.yaml&#34;, framework=&#34;&#34;, auto=False, init=False):
+        &#34;&#34;&#34;
+        Initialize the PraisonAI object with default parameters.
+
+        Parameters:
+            agent_file (str): The default agent file to use. Defaults to &#34;agents.yaml&#34;.
+            framework (str): The default framework to use. Defaults to &#34;crewai&#34;.
+            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
+            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
+
+        Attributes:
+            config_list (list): A list of configuration dictionaries for the OpenAI API.
+            agent_file (str): The agent file to use.
+            framework (str): The framework to use.
+            auto (bool): A flag indicating whether to enable auto mode.
+            init (bool): A flag indicating whether to enable initialization mode.
+        &#34;&#34;&#34;
+        self.config_list = [
+            {
+                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
+                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
+                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
+            }
+        ]
+        self.agent_file = agent_file
+        self.framework = framework
+        self.auto = auto
+        self.init = init
+
+    def main(self):
+        &#34;&#34;&#34;
+        The main function of the PraisonAI object. It parses the command-line arguments,
+        initializes the necessary attributes, and then calls the appropriate methods based on the
+        provided arguments.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+    
+        Returns:
+            Any: Depending on the arguments provided, the function may return a result from the
+            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
+            the successful creation of a file.
+        &#34;&#34;&#34;
+        args = self.parse_args()
+        if args is None:
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+        if args.deploy:
+            from .deploy import CloudDeployer
+            deployer = CloudDeployer()
+            deployer.run_commands()
+            return
+        invocation_cmd = &#34;praisonai&#34;
+        version_string = f&#34;PraisonAI version {__version__}&#34;
+        
+        self.framework = args.framework or self.framework 
+        
+        if args.agent_file:
+            if args.agent_file.startswith(&#34;tests.test&#34;): # Argument used for testing purposes. eg: python -m unittest tests.test 
+                print(&#34;test&#34;)
+            else:
+                self.agent_file = args.agent_file
+        
+        
+        if args.auto or args.init:
+            temp_topic = &#39; &#39;.join(args.auto) if args.auto else &#39; &#39;.join(args.init)
+            self.topic = temp_topic
+        elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
+            self.topic = self.auto
+            
+        if args.auto or self.auto:
+            self.agent_file = &#34;test.yaml&#34;
+            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+            self.agent_file = generator.generate()
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+        elif args.init or self.init:
+            self.agent_file = &#34;agents.yaml&#34;
+            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+            self.agent_file = generator.generate()
+            print(&#34;File {} created successfully&#34;.format(self.agent_file))
+            return &#34;File {} created successfully&#34;.format(self.agent_file)
+        
+        if args.ui:
+            if args.ui == &#34;gradio&#34;:
+                self.create_gradio_interface()
+            elif args.ui == &#34;chainlit&#34;:
+                self.create_chainlit_interface()
+            else:
+                # Modify below code to allow default ui
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
+        else:
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+            
+    def parse_args(self):
+        &#34;&#34;&#34;
+        Parse the command-line arguments for the PraisonAI CLI.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            argparse.Namespace: An object containing the parsed command-line arguments.
+
+        Raises:
+            argparse.ArgumentError: If the arguments provided are invalid.
+
+        Example:
+            &gt;&gt;&gt; args = praison_ai.parse_args()
+            &gt;&gt;&gt; print(args.agent_file)  # Output: &#39;agents.yaml&#39;
+        &#34;&#34;&#34;
+        parser = argparse.ArgumentParser(prog=&#34;praisonai&#34;, description=&#34;praisonAI command-line interface&#34;)
+        parser.add_argument(&#34;--framework&#34;, choices=[&#34;crewai&#34;, &#34;autogen&#34;], help=&#34;Specify the framework&#34;)
+        parser.add_argument(&#34;--ui&#34;, choices=[&#34;chainlit&#34;, &#34;gradio&#34;], help=&#34;Specify the UI framework (gradio or chainlit).&#34;)
+        parser.add_argument(&#34;--auto&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+        parser.add_argument(&#34;--init&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+        parser.add_argument(&#34;agent_file&#34;, nargs=&#34;?&#34;, help=&#34;Specify the agent file&#34;)
+        parser.add_argument(&#34;--deploy&#34;, action=&#34;store_true&#34;, help=&#34;Deploy the application&#34;)  # New argument
+        args, unknown_args = parser.parse_known_args()
+
+        if unknown_args and unknown_args[0] == &#39;-b&#39; and unknown_args[1] == &#39;api:app&#39;:
+            args.agent_file = &#39;agents.yaml&#39;
+        if args.agent_file == &#39;api:app&#39; or args.agent_file == &#39;/app/api:app&#39;:
+            args.agent_file = &#39;agents.yaml&#39;
+        if args.agent_file == &#39;ui&#39;:
+            args.ui = &#39;chainlit&#39;
+
+        return args
+
+    def create_gradio_interface(self):
+        &#34;&#34;&#34;
+        Create a Gradio interface for generating agents and performing tasks.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            None: This method does not return any value. It launches the Gradio interface.
+
+        Raises:
+            None: This method does not raise any exceptions.
+
+        Example:
+            &gt;&gt;&gt; praison_ai.create_gradio_interface()
+        &#34;&#34;&#34;
+        if GRADIO_AVAILABLE:
+            def generate_crew_and_kickoff_interface(auto_args, framework):
+                &#34;&#34;&#34;
+                Generate a crew and kick off tasks based on the provided auto arguments and framework.
+
+                Args:
+                    auto_args (list): Topic.
+                    framework (str): The framework to use for generating agents.
+
+                Returns:
+                    str: A string representing the result of generating the crew and kicking off tasks.
+
+                Raises:
+                    None: This method does not raise any exceptions.
+
+                Example:
+                    &gt;&gt;&gt; result = generate_crew_and_kickoff_interface(&#34;Create a movie about Cat in Mars&#34;, &#34;crewai&#34;)
+                    &gt;&gt;&gt; print(result)
+                &#34;&#34;&#34;
+                self.framework = framework
+                self.agent_file = &#34;test.yaml&#34;
+                generator = AutoGenerator(topic=auto_args , framework=self.framework)
+                self.agent_file = generator.generate()
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
+
+            gr.Interface(
+                fn=generate_crew_and_kickoff_interface,
+                inputs=[gr.Textbox(lines=2, label=&#34;Auto Args&#34;), gr.Dropdown(choices=[&#34;crewai&#34;, &#34;autogen&#34;], label=&#34;Framework&#34;)],
+                outputs=&#34;textbox&#34;,
+                title=&#34;Praison AI Studio&#34;,
+                description=&#34;Create Agents and perform tasks&#34;,
+                theme=&#34;default&#34;
+            ).launch()
+        else:
+            print(&#34;ERROR: Gradio is not installed. Please install it with &#39;pip install gradio&#39; to use this feature.&#34;) 
+        
+    def create_chainlit_interface(self):
+        &#34;&#34;&#34;
+        Create a Chainlit interface for generating agents and performing tasks.
+
+        This function sets up a Chainlit application that listens for messages.
+        When a message is received, it runs PraisonAI with the provided message as the topic.
+        The generated agents are then used to perform tasks.
+
+        Returns:
+            None: This function does not return any value. It starts the Chainlit application.
+        &#34;&#34;&#34;
+        if CHAINLIT_AVAILABLE:
+            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8082&#34;  
+            import praisonai
+            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;chainlit_ui.py&#39;)
+            chainlit_run([chainlit_ui_path])
+        else:
+            print(&#34;ERROR: Chainlit is not installed. Please install it with &#39;pip install chainlit&#39; to use the UI.&#34;)        
+
+if __name__ == &#34;__main__&#34;:
+    praison_ai = PraisonAI()
+    praison_ai.main()</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="praisonai.cli.PraisonAI"><code class="flex name class">
+<span>class <span class="ident">PraisonAI</span></span>
+<span>(</span><span>agent_file='agents.yaml', framework='', auto=False, init=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Initialize the PraisonAI object with default parameters.</p>
+<h2 id="parameters">Parameters</h2>
+<p>agent_file (str): The default agent file to use. Defaults to "agents.yaml".
+framework (str): The default framework to use. Defaults to "crewai".
+auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
+init (bool): A flag indicating whether to enable initialization mode. Defaults to False.</p>
+<h2 id="attributes">Attributes</h2>
+<dl>
+<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
+<dd>A list of configuration dictionaries for the OpenAI API.</dd>
+<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
+<dd>The agent file to use.</dd>
+<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
+<dd>The framework to use.</dd>
+<dt><strong><code>auto</code></strong> :&ensp;<code>bool</code></dt>
+<dd>A flag indicating whether to enable auto mode.</dd>
+<dt><strong><code>init</code></strong> :&ensp;<code>bool</code></dt>
+<dd>A flag indicating whether to enable initialization mode.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class PraisonAI:
+    def __init__(self, agent_file=&#34;agents.yaml&#34;, framework=&#34;&#34;, auto=False, init=False):
+        &#34;&#34;&#34;
+        Initialize the PraisonAI object with default parameters.
+
+        Parameters:
+            agent_file (str): The default agent file to use. Defaults to &#34;agents.yaml&#34;.
+            framework (str): The default framework to use. Defaults to &#34;crewai&#34;.
+            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
+            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
+
+        Attributes:
+            config_list (list): A list of configuration dictionaries for the OpenAI API.
+            agent_file (str): The agent file to use.
+            framework (str): The framework to use.
+            auto (bool): A flag indicating whether to enable auto mode.
+            init (bool): A flag indicating whether to enable initialization mode.
+        &#34;&#34;&#34;
+        self.config_list = [
+            {
+                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
+                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
+                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
+            }
+        ]
+        self.agent_file = agent_file
+        self.framework = framework
+        self.auto = auto
+        self.init = init
+
+    def main(self):
+        &#34;&#34;&#34;
+        The main function of the PraisonAI object. It parses the command-line arguments,
+        initializes the necessary attributes, and then calls the appropriate methods based on the
+        provided arguments.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+    
+        Returns:
+            Any: Depending on the arguments provided, the function may return a result from the
+            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
+            the successful creation of a file.
+        &#34;&#34;&#34;
+        args = self.parse_args()
+        if args is None:
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+        if args.deploy:
+            from .deploy import CloudDeployer
+            deployer = CloudDeployer()
+            deployer.run_commands()
+            return
+        invocation_cmd = &#34;praisonai&#34;
+        version_string = f&#34;PraisonAI version {__version__}&#34;
+        
+        self.framework = args.framework or self.framework 
+        
+        if args.agent_file:
+            if args.agent_file.startswith(&#34;tests.test&#34;): # Argument used for testing purposes. eg: python -m unittest tests.test 
+                print(&#34;test&#34;)
+            else:
+                self.agent_file = args.agent_file
+        
+        
+        if args.auto or args.init:
+            temp_topic = &#39; &#39;.join(args.auto) if args.auto else &#39; &#39;.join(args.init)
+            self.topic = temp_topic
+        elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
+            self.topic = self.auto
+            
+        if args.auto or self.auto:
+            self.agent_file = &#34;test.yaml&#34;
+            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+            self.agent_file = generator.generate()
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+        elif args.init or self.init:
+            self.agent_file = &#34;agents.yaml&#34;
+            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+            self.agent_file = generator.generate()
+            print(&#34;File {} created successfully&#34;.format(self.agent_file))
+            return &#34;File {} created successfully&#34;.format(self.agent_file)
+        
+        if args.ui:
+            if args.ui == &#34;gradio&#34;:
+                self.create_gradio_interface()
+            elif args.ui == &#34;chainlit&#34;:
+                self.create_chainlit_interface()
+            else:
+                # Modify below code to allow default ui
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
+        else:
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+            
+    def parse_args(self):
+        &#34;&#34;&#34;
+        Parse the command-line arguments for the PraisonAI CLI.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            argparse.Namespace: An object containing the parsed command-line arguments.
+
+        Raises:
+            argparse.ArgumentError: If the arguments provided are invalid.
+
+        Example:
+            &gt;&gt;&gt; args = praison_ai.parse_args()
+            &gt;&gt;&gt; print(args.agent_file)  # Output: &#39;agents.yaml&#39;
+        &#34;&#34;&#34;
+        parser = argparse.ArgumentParser(prog=&#34;praisonai&#34;, description=&#34;praisonAI command-line interface&#34;)
+        parser.add_argument(&#34;--framework&#34;, choices=[&#34;crewai&#34;, &#34;autogen&#34;], help=&#34;Specify the framework&#34;)
+        parser.add_argument(&#34;--ui&#34;, choices=[&#34;chainlit&#34;, &#34;gradio&#34;], help=&#34;Specify the UI framework (gradio or chainlit).&#34;)
+        parser.add_argument(&#34;--auto&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+        parser.add_argument(&#34;--init&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+        parser.add_argument(&#34;agent_file&#34;, nargs=&#34;?&#34;, help=&#34;Specify the agent file&#34;)
+        parser.add_argument(&#34;--deploy&#34;, action=&#34;store_true&#34;, help=&#34;Deploy the application&#34;)  # New argument
+        args, unknown_args = parser.parse_known_args()
+
+        if unknown_args and unknown_args[0] == &#39;-b&#39; and unknown_args[1] == &#39;api:app&#39;:
+            args.agent_file = &#39;agents.yaml&#39;
+        if args.agent_file == &#39;api:app&#39; or args.agent_file == &#39;/app/api:app&#39;:
+            args.agent_file = &#39;agents.yaml&#39;
+        if args.agent_file == &#39;ui&#39;:
+            args.ui = &#39;chainlit&#39;
+
+        return args
+
+    def create_gradio_interface(self):
+        &#34;&#34;&#34;
+        Create a Gradio interface for generating agents and performing tasks.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            None: This method does not return any value. It launches the Gradio interface.
+
+        Raises:
+            None: This method does not raise any exceptions.
+
+        Example:
+            &gt;&gt;&gt; praison_ai.create_gradio_interface()
+        &#34;&#34;&#34;
+        if GRADIO_AVAILABLE:
+            def generate_crew_and_kickoff_interface(auto_args, framework):
+                &#34;&#34;&#34;
+                Generate a crew and kick off tasks based on the provided auto arguments and framework.
+
+                Args:
+                    auto_args (list): Topic.
+                    framework (str): The framework to use for generating agents.
+
+                Returns:
+                    str: A string representing the result of generating the crew and kicking off tasks.
+
+                Raises:
+                    None: This method does not raise any exceptions.
+
+                Example:
+                    &gt;&gt;&gt; result = generate_crew_and_kickoff_interface(&#34;Create a movie about Cat in Mars&#34;, &#34;crewai&#34;)
+                    &gt;&gt;&gt; print(result)
+                &#34;&#34;&#34;
+                self.framework = framework
+                self.agent_file = &#34;test.yaml&#34;
+                generator = AutoGenerator(topic=auto_args , framework=self.framework)
+                self.agent_file = generator.generate()
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
+
+            gr.Interface(
+                fn=generate_crew_and_kickoff_interface,
+                inputs=[gr.Textbox(lines=2, label=&#34;Auto Args&#34;), gr.Dropdown(choices=[&#34;crewai&#34;, &#34;autogen&#34;], label=&#34;Framework&#34;)],
+                outputs=&#34;textbox&#34;,
+                title=&#34;Praison AI Studio&#34;,
+                description=&#34;Create Agents and perform tasks&#34;,
+                theme=&#34;default&#34;
+            ).launch()
+        else:
+            print(&#34;ERROR: Gradio is not installed. Please install it with &#39;pip install gradio&#39; to use this feature.&#34;) 
+        
+    def create_chainlit_interface(self):
+        &#34;&#34;&#34;
+        Create a Chainlit interface for generating agents and performing tasks.
+
+        This function sets up a Chainlit application that listens for messages.
+        When a message is received, it runs PraisonAI with the provided message as the topic.
+        The generated agents are then used to perform tasks.
+
+        Returns:
+            None: This function does not return any value. It starts the Chainlit application.
+        &#34;&#34;&#34;
+        if CHAINLIT_AVAILABLE:
+            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8082&#34;  
+            import praisonai
+            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;chainlit_ui.py&#39;)
+            chainlit_run([chainlit_ui_path])
+        else:
+            print(&#34;ERROR: Chainlit is not installed. Please install it with &#39;pip install chainlit&#39; to use the UI.&#34;)        </code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="praisonai.cli.PraisonAI.create_chainlit_interface"><code class="name flex">
+<span>def <span class="ident">create_chainlit_interface</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Create a Chainlit interface for generating agents and performing tasks.</p>
+<p>This function sets up a Chainlit application that listens for messages.
+When a message is received, it runs PraisonAI with the provided message as the topic.
+The generated agents are then used to perform tasks.</p>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>None</code></dt>
+<dd>This function does not return any value. It starts the Chainlit application.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_chainlit_interface(self):
+    &#34;&#34;&#34;
+    Create a Chainlit interface for generating agents and performing tasks.
+
+    This function sets up a Chainlit application that listens for messages.
+    When a message is received, it runs PraisonAI with the provided message as the topic.
+    The generated agents are then used to perform tasks.
+
+    Returns:
+        None: This function does not return any value. It starts the Chainlit application.
+    &#34;&#34;&#34;
+    if CHAINLIT_AVAILABLE:
+        os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8082&#34;  
+        import praisonai
+        chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;chainlit_ui.py&#39;)
+        chainlit_run([chainlit_ui_path])
+    else:
+        print(&#34;ERROR: Chainlit is not installed. Please install it with &#39;pip install chainlit&#39; to use the UI.&#34;)        </code></pre>
+</details>
+</dd>
+<dt id="praisonai.cli.PraisonAI.create_gradio_interface"><code class="name flex">
+<span>def <span class="ident">create_gradio_interface</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Create a Gradio interface for generating agents and performing tasks.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
+<dd>An instance of the PraisonAI class.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>None</code></dt>
+<dd>This method does not return any value. It launches the Gradio interface.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>None</code></dt>
+<dd>This method does not raise any exceptions.</dd>
+</dl>
+<h2 id="example">Example</h2>
+<pre><code class="language-python-repl">&gt;&gt;&gt; praison_ai.create_gradio_interface()
+</code></pre></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_gradio_interface(self):
+    &#34;&#34;&#34;
+    Create a Gradio interface for generating agents and performing tasks.
+
+    Args:
+        self (PraisonAI): An instance of the PraisonAI class.
+
+    Returns:
+        None: This method does not return any value. It launches the Gradio interface.
+
+    Raises:
+        None: This method does not raise any exceptions.
+
+    Example:
+        &gt;&gt;&gt; praison_ai.create_gradio_interface()
+    &#34;&#34;&#34;
+    if GRADIO_AVAILABLE:
+        def generate_crew_and_kickoff_interface(auto_args, framework):
+            &#34;&#34;&#34;
+            Generate a crew and kick off tasks based on the provided auto arguments and framework.
+
+            Args:
+                auto_args (list): Topic.
+                framework (str): The framework to use for generating agents.
+
+            Returns:
+                str: A string representing the result of generating the crew and kicking off tasks.
+
+            Raises:
+                None: This method does not raise any exceptions.
+
+            Example:
+                &gt;&gt;&gt; result = generate_crew_and_kickoff_interface(&#34;Create a movie about Cat in Mars&#34;, &#34;crewai&#34;)
+                &gt;&gt;&gt; print(result)
+            &#34;&#34;&#34;
+            self.framework = framework
+            self.agent_file = &#34;test.yaml&#34;
+            generator = AutoGenerator(topic=auto_args , framework=self.framework)
+            self.agent_file = generator.generate()
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+
+        gr.Interface(
+            fn=generate_crew_and_kickoff_interface,
+            inputs=[gr.Textbox(lines=2, label=&#34;Auto Args&#34;), gr.Dropdown(choices=[&#34;crewai&#34;, &#34;autogen&#34;], label=&#34;Framework&#34;)],
+            outputs=&#34;textbox&#34;,
+            title=&#34;Praison AI Studio&#34;,
+            description=&#34;Create Agents and perform tasks&#34;,
+            theme=&#34;default&#34;
+        ).launch()
+    else:
+        print(&#34;ERROR: Gradio is not installed. Please install it with &#39;pip install gradio&#39; to use this feature.&#34;) </code></pre>
+</details>
+</dd>
+<dt id="praisonai.cli.PraisonAI.main"><code class="name flex">
+<span>def <span class="ident">main</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>The main function of the PraisonAI object. It parses the command-line arguments,
+initializes the necessary attributes, and then calls the appropriate methods based on the
+provided arguments.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
+<dd>An instance of the PraisonAI class.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>Any</code></dt>
+<dd>Depending on the arguments provided, the function may return a result from the</dd>
+</dl>
+<p>AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
+the successful creation of a file.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def main(self):
+    &#34;&#34;&#34;
+    The main function of the PraisonAI object. It parses the command-line arguments,
+    initializes the necessary attributes, and then calls the appropriate methods based on the
+    provided arguments.
+
+    Args:
+        self (PraisonAI): An instance of the PraisonAI class.
+
+    Returns:
+        Any: Depending on the arguments provided, the function may return a result from the
+        AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
+        the successful creation of a file.
+    &#34;&#34;&#34;
+    args = self.parse_args()
+    if args is None:
+        agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+        result = agents_generator.generate_crew_and_kickoff()
+        return result
+    if args.deploy:
+        from .deploy import CloudDeployer
+        deployer = CloudDeployer()
+        deployer.run_commands()
+        return
+    invocation_cmd = &#34;praisonai&#34;
+    version_string = f&#34;PraisonAI version {__version__}&#34;
+    
+    self.framework = args.framework or self.framework 
+    
+    if args.agent_file:
+        if args.agent_file.startswith(&#34;tests.test&#34;): # Argument used for testing purposes. eg: python -m unittest tests.test 
+            print(&#34;test&#34;)
+        else:
+            self.agent_file = args.agent_file
+    
+    
+    if args.auto or args.init:
+        temp_topic = &#39; &#39;.join(args.auto) if args.auto else &#39; &#39;.join(args.init)
+        self.topic = temp_topic
+    elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
+        self.topic = self.auto
+        
+    if args.auto or self.auto:
+        self.agent_file = &#34;test.yaml&#34;
+        generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+        self.agent_file = generator.generate()
+        agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+        result = agents_generator.generate_crew_and_kickoff()
+        return result
+    elif args.init or self.init:
+        self.agent_file = &#34;agents.yaml&#34;
+        generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
+        self.agent_file = generator.generate()
+        print(&#34;File {} created successfully&#34;.format(self.agent_file))
+        return &#34;File {} created successfully&#34;.format(self.agent_file)
+    
+    if args.ui:
+        if args.ui == &#34;gradio&#34;:
+            self.create_gradio_interface()
+        elif args.ui == &#34;chainlit&#34;:
+            self.create_chainlit_interface()
+        else:
+            # Modify below code to allow default ui
+            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+            result = agents_generator.generate_crew_and_kickoff()
+            return result
+    else:
+        agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+        result = agents_generator.generate_crew_and_kickoff()
+        return result</code></pre>
+</details>
+</dd>
+<dt id="praisonai.cli.PraisonAI.parse_args"><code class="name flex">
+<span>def <span class="ident">parse_args</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Parse the command-line arguments for the PraisonAI CLI.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
+<dd>An instance of the PraisonAI class.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>argparse.Namespace</code></dt>
+<dd>An object containing the parsed command-line arguments.</dd>
+</dl>
+<h2 id="raises">Raises</h2>
+<dl>
+<dt><code>argparse.ArgumentError</code></dt>
+<dd>If the arguments provided are invalid.</dd>
+</dl>
+<h2 id="example">Example</h2>
+<pre><code class="language-python-repl">&gt;&gt;&gt; args = praison_ai.parse_args()
+&gt;&gt;&gt; print(args.agent_file)  # Output: 'agents.yaml'
+</code></pre></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def parse_args(self):
+    &#34;&#34;&#34;
+    Parse the command-line arguments for the PraisonAI CLI.
+
+    Args:
+        self (PraisonAI): An instance of the PraisonAI class.
+
+    Returns:
+        argparse.Namespace: An object containing the parsed command-line arguments.
+
+    Raises:
+        argparse.ArgumentError: If the arguments provided are invalid.
+
+    Example:
+        &gt;&gt;&gt; args = praison_ai.parse_args()
+        &gt;&gt;&gt; print(args.agent_file)  # Output: &#39;agents.yaml&#39;
+    &#34;&#34;&#34;
+    parser = argparse.ArgumentParser(prog=&#34;praisonai&#34;, description=&#34;praisonAI command-line interface&#34;)
+    parser.add_argument(&#34;--framework&#34;, choices=[&#34;crewai&#34;, &#34;autogen&#34;], help=&#34;Specify the framework&#34;)
+    parser.add_argument(&#34;--ui&#34;, choices=[&#34;chainlit&#34;, &#34;gradio&#34;], help=&#34;Specify the UI framework (gradio or chainlit).&#34;)
+    parser.add_argument(&#34;--auto&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+    parser.add_argument(&#34;--init&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
+    parser.add_argument(&#34;agent_file&#34;, nargs=&#34;?&#34;, help=&#34;Specify the agent file&#34;)
+    parser.add_argument(&#34;--deploy&#34;, action=&#34;store_true&#34;, help=&#34;Deploy the application&#34;)  # New argument
+    args, unknown_args = parser.parse_known_args()
+
+    if unknown_args and unknown_args[0] == &#39;-b&#39; and unknown_args[1] == &#39;api:app&#39;:
+        args.agent_file = &#39;agents.yaml&#39;
+    if args.agent_file == &#39;api:app&#39; or args.agent_file == &#39;/app/api:app&#39;:
+        args.agent_file = &#39;agents.yaml&#39;
+    if args.agent_file == &#39;ui&#39;:
+        args.ui = &#39;chainlit&#39;
+
+    return args</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.cli.PraisonAI.create_chainlit_interface" href="#praisonai.cli.PraisonAI.create_chainlit_interface">create_chainlit_interface</a></code></li>
+<li><code><a title="praisonai.cli.PraisonAI.create_gradio_interface" href="#praisonai.cli.PraisonAI.create_gradio_interface">create_gradio_interface</a></code></li>
+<li><code><a title="praisonai.cli.PraisonAI.main" href="#praisonai.cli.PraisonAI.main">main</a></code></li>
+<li><code><a title="praisonai.cli.PraisonAI.parse_args" href="#praisonai.cli.PraisonAI.parse_args">parse_args</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/deploy.html

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+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
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+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.deploy</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">import subprocess
+import os
+from dotenv import load_dotenv
+
+class CloudDeployer:
+    &#34;&#34;&#34;
+    A class for deploying a cloud-based application.
+
+    Attributes:
+        None
+
+    Methods:
+        __init__(self):
+            Loads environment variables from .env file or system and sets them.
+
+    &#34;&#34;&#34;
+    def __init__(self):
+        &#34;&#34;&#34;
+        Loads environment variables from .env file or system and sets them.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        &#34;&#34;&#34;
+        # Load environment variables from .env file or system
+        load_dotenv()
+        self.set_environment_variables()
+
+    def create_dockerfile(self):
+        &#34;&#34;&#34;
+        Creates a Dockerfile for the application.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method creates a Dockerfile in the current directory with the specified content.
+        The Dockerfile is used to build a Docker image for the application.
+        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
+        set the working directory to /app, copy the current directory contents into the container,
+        install the required Python packages (flask, praisonai, gunicorn, and markdown),
+        expose port 8080, and run the application using Gunicorn.
+        &#34;&#34;&#34;
+        with open(&#34;Dockerfile&#34;, &#34;w&#34;) as file:
+            file.write(&#34;FROM python:3.11-slim\n&#34;)
+            file.write(&#34;WORKDIR /app\n&#34;)
+            file.write(&#34;COPY . .\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;EXPOSE 8080\n&#34;)
+            file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
+            
+    def create_api_file(self):
+        &#34;&#34;&#34;
+        Creates an API file for the application.
+
+        Parameters:
+            self (CloudDeployer): An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        This method creates an API file named &#34;api.py&#34; in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path (&#34;/&#34;) and uses the Markdown library to format the output.
+        &#34;&#34;&#34;
+        with open(&#34;api.py&#34;, &#34;w&#34;) as file:
+            file.write(&#34;from flask import Flask\n&#34;)
+            file.write(&#34;from praisonai import PraisonAI\n&#34;)
+            file.write(&#34;import markdown\n\n&#34;)
+            file.write(&#34;app = Flask(__name__)\n\n&#34;)
+            file.write(&#34;def basic():\n&#34;)
+            file.write(&#34;    praison_ai = PraisonAI(agent_file=\&#34;agents.yaml\&#34;)\n&#34;)
+            file.write(&#34;    return praison_ai.main()\n\n&#34;)
+            file.write(&#34;@app.route(&#39;/&#39;)\n&#34;)
+            file.write(&#34;def home():\n&#34;)
+            file.write(&#34;    output = basic()\n&#34;)
+            file.write(&#34;    html_output = markdown.markdown(output)\n&#34;)
+            file.write(&#34;    return f&#39;&lt;html&gt;&lt;body&gt;{html_output}&lt;/body&gt;&lt;/html&gt;&#39;\n\n&#34;)
+            file.write(&#34;if __name__ == \&#34;__main__\&#34;:\n&#34;)
+            file.write(&#34;    app.run(debug=True)\n&#34;)
+    
+    def set_environment_variables(self):
+        &#34;&#34;&#34;Sets environment variables with fallback to .env values or defaults.&#34;&#34;&#34;
+        os.environ[&#34;OPENAI_MODEL_NAME&#34;] = os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
+        os.environ[&#34;OPENAI_API_KEY&#34;] = os.getenv(&#34;OPENAI_API_KEY&#34;, &#34;Enter your API key&#34;)
+        os.environ[&#34;OPENAI_API_BASE&#34;] = os.getenv(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)
+
+    def run_commands(self):
+        &#34;&#34;&#34;
+        Sets environment variables with fallback to .env values or defaults.
+
+        Parameters:
+            None
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
+
+        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to &#34;gpt-4o&#34;.
+        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to &#34;Enter your API key&#34;.
+        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to &#34;https://api.openai.com/v1&#34;.
+        &#34;&#34;&#34;
+        self.create_api_file()
+        self.create_dockerfile()
+        &#34;&#34;&#34;Runs a sequence of shell commands for deployment, continues on error.&#34;&#34;&#34;
+        commands = [
+            &#34;yes | gcloud auth configure-docker us-central1-docker.pkg.dev&#34;,
+            &#34;gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1&#34;,
+            &#34;docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .&#34;,
+            &#34;docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+            &#34;docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+            &#34;gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}&#34;
+        ]
+
+        for cmd in commands:
+            try:
+                subprocess.run(cmd, shell=True, check=True)
+            except subprocess.CalledProcessError as e:
+                print(f&#34;ERROR: Command &#39;{e.cmd}&#39; failed with exit status {e.returncode}&#34;)
+                print(f&#34;Continuing with the next command...&#34;)
+
+# Usage
+if __name__ == &#34;__main__&#34;:
+    deployer = CloudDeployer()
+    deployer.run_commands()</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="praisonai.deploy.CloudDeployer"><code class="flex name class">
+<span>class <span class="ident">CloudDeployer</span></span>
+</code></dt>
+<dd>
+<div class="desc"><p>A class for deploying a cloud-based application.</p>
+<h2 id="attributes">Attributes</h2>
+<p>None</p>
+<h2 id="methods">Methods</h2>
+<p><strong>init</strong>(self):
+Loads environment variables from .env file or system and sets them.</p>
+<p>Loads environment variables from .env file or system and sets them.</p>
+<h2 id="parameters">Parameters</h2>
+<p>self: An instance of the CloudDeployer class.</p>
+<h2 id="returns">Returns</h2>
+<p>None</p>
+<h2 id="raises">Raises</h2>
+<p>None</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class CloudDeployer:
+    &#34;&#34;&#34;
+    A class for deploying a cloud-based application.
+
+    Attributes:
+        None
+
+    Methods:
+        __init__(self):
+            Loads environment variables from .env file or system and sets them.
+
+    &#34;&#34;&#34;
+    def __init__(self):
+        &#34;&#34;&#34;
+        Loads environment variables from .env file or system and sets them.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        &#34;&#34;&#34;
+        # Load environment variables from .env file or system
+        load_dotenv()
+        self.set_environment_variables()
+
+    def create_dockerfile(self):
+        &#34;&#34;&#34;
+        Creates a Dockerfile for the application.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method creates a Dockerfile in the current directory with the specified content.
+        The Dockerfile is used to build a Docker image for the application.
+        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
+        set the working directory to /app, copy the current directory contents into the container,
+        install the required Python packages (flask, praisonai, gunicorn, and markdown),
+        expose port 8080, and run the application using Gunicorn.
+        &#34;&#34;&#34;
+        with open(&#34;Dockerfile&#34;, &#34;w&#34;) as file:
+            file.write(&#34;FROM python:3.11-slim\n&#34;)
+            file.write(&#34;WORKDIR /app\n&#34;)
+            file.write(&#34;COPY . .\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+            file.write(&#34;EXPOSE 8080\n&#34;)
+            file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
+            
+    def create_api_file(self):
+        &#34;&#34;&#34;
+        Creates an API file for the application.
+
+        Parameters:
+            self (CloudDeployer): An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        This method creates an API file named &#34;api.py&#34; in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path (&#34;/&#34;) and uses the Markdown library to format the output.
+        &#34;&#34;&#34;
+        with open(&#34;api.py&#34;, &#34;w&#34;) as file:
+            file.write(&#34;from flask import Flask\n&#34;)
+            file.write(&#34;from praisonai import PraisonAI\n&#34;)
+            file.write(&#34;import markdown\n\n&#34;)
+            file.write(&#34;app = Flask(__name__)\n\n&#34;)
+            file.write(&#34;def basic():\n&#34;)
+            file.write(&#34;    praison_ai = PraisonAI(agent_file=\&#34;agents.yaml\&#34;)\n&#34;)
+            file.write(&#34;    return praison_ai.main()\n\n&#34;)
+            file.write(&#34;@app.route(&#39;/&#39;)\n&#34;)
+            file.write(&#34;def home():\n&#34;)
+            file.write(&#34;    output = basic()\n&#34;)
+            file.write(&#34;    html_output = markdown.markdown(output)\n&#34;)
+            file.write(&#34;    return f&#39;&lt;html&gt;&lt;body&gt;{html_output}&lt;/body&gt;&lt;/html&gt;&#39;\n\n&#34;)
+            file.write(&#34;if __name__ == \&#34;__main__\&#34;:\n&#34;)
+            file.write(&#34;    app.run(debug=True)\n&#34;)
+    
+    def set_environment_variables(self):
+        &#34;&#34;&#34;Sets environment variables with fallback to .env values or defaults.&#34;&#34;&#34;
+        os.environ[&#34;OPENAI_MODEL_NAME&#34;] = os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
+        os.environ[&#34;OPENAI_API_KEY&#34;] = os.getenv(&#34;OPENAI_API_KEY&#34;, &#34;Enter your API key&#34;)
+        os.environ[&#34;OPENAI_API_BASE&#34;] = os.getenv(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)
+
+    def run_commands(self):
+        &#34;&#34;&#34;
+        Sets environment variables with fallback to .env values or defaults.
+
+        Parameters:
+            None
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
+
+        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to &#34;gpt-4o&#34;.
+        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to &#34;Enter your API key&#34;.
+        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to &#34;https://api.openai.com/v1&#34;.
+        &#34;&#34;&#34;
+        self.create_api_file()
+        self.create_dockerfile()
+        &#34;&#34;&#34;Runs a sequence of shell commands for deployment, continues on error.&#34;&#34;&#34;
+        commands = [
+            &#34;yes | gcloud auth configure-docker us-central1-docker.pkg.dev&#34;,
+            &#34;gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1&#34;,
+            &#34;docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .&#34;,
+            &#34;docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+            &#34;docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+            &#34;gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}&#34;
+        ]
+
+        for cmd in commands:
+            try:
+                subprocess.run(cmd, shell=True, check=True)
+            except subprocess.CalledProcessError as e:
+                print(f&#34;ERROR: Command &#39;{e.cmd}&#39; failed with exit status {e.returncode}&#34;)
+                print(f&#34;Continuing with the next command...&#34;)</code></pre>
+</details>
+<h3>Methods</h3>
+<dl>
+<dt id="praisonai.deploy.CloudDeployer.create_api_file"><code class="name flex">
+<span>def <span class="ident">create_api_file</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Creates an API file for the application.</p>
+<h2 id="parameters">Parameters</h2>
+<p>self (CloudDeployer): An instance of the CloudDeployer class.</p>
+<h2 id="returns">Returns</h2>
+<p>None
+This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_api_file(self):
+    &#34;&#34;&#34;
+    Creates an API file for the application.
+
+    Parameters:
+        self (CloudDeployer): An instance of the CloudDeployer class.
+
+    Returns:
+        None
+
+    This method creates an API file named &#34;api.py&#34; in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path (&#34;/&#34;) and uses the Markdown library to format the output.
+    &#34;&#34;&#34;
+    with open(&#34;api.py&#34;, &#34;w&#34;) as file:
+        file.write(&#34;from flask import Flask\n&#34;)
+        file.write(&#34;from praisonai import PraisonAI\n&#34;)
+        file.write(&#34;import markdown\n\n&#34;)
+        file.write(&#34;app = Flask(__name__)\n\n&#34;)
+        file.write(&#34;def basic():\n&#34;)
+        file.write(&#34;    praison_ai = PraisonAI(agent_file=\&#34;agents.yaml\&#34;)\n&#34;)
+        file.write(&#34;    return praison_ai.main()\n\n&#34;)
+        file.write(&#34;@app.route(&#39;/&#39;)\n&#34;)
+        file.write(&#34;def home():\n&#34;)
+        file.write(&#34;    output = basic()\n&#34;)
+        file.write(&#34;    html_output = markdown.markdown(output)\n&#34;)
+        file.write(&#34;    return f&#39;&lt;html&gt;&lt;body&gt;{html_output}&lt;/body&gt;&lt;/html&gt;&#39;\n\n&#34;)
+        file.write(&#34;if __name__ == \&#34;__main__\&#34;:\n&#34;)
+        file.write(&#34;    app.run(debug=True)\n&#34;)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.deploy.CloudDeployer.create_dockerfile"><code class="name flex">
+<span>def <span class="ident">create_dockerfile</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Creates a Dockerfile for the application.</p>
+<h2 id="parameters">Parameters</h2>
+<p>self: An instance of the CloudDeployer class.</p>
+<h2 id="returns">Returns</h2>
+<p>None</p>
+<h2 id="raises">Raises</h2>
+<p>None
+This method creates a Dockerfile in the current directory with the specified content.
+The Dockerfile is used to build a Docker image for the application.
+The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
+set the working directory to /app, copy the current directory contents into the container,
+install the required Python packages (flask, praisonai, gunicorn, and markdown),
+expose port 8080, and run the application using Gunicorn.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_dockerfile(self):
+    &#34;&#34;&#34;
+    Creates a Dockerfile for the application.
+
+    Parameters:
+        self: An instance of the CloudDeployer class.
+
+    Returns:
+        None
+
+    Raises:
+        None
+
+    This method creates a Dockerfile in the current directory with the specified content.
+    The Dockerfile is used to build a Docker image for the application.
+    The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
+    set the working directory to /app, copy the current directory contents into the container,
+    install the required Python packages (flask, praisonai, gunicorn, and markdown),
+    expose port 8080, and run the application using Gunicorn.
+    &#34;&#34;&#34;
+    with open(&#34;Dockerfile&#34;, &#34;w&#34;) as file:
+        file.write(&#34;FROM python:3.11-slim\n&#34;)
+        file.write(&#34;WORKDIR /app\n&#34;)
+        file.write(&#34;COPY . .\n&#34;)
+        file.write(&#34;RUN pip install flask praisonai==0.0.30 gunicorn markdown\n&#34;)
+        file.write(&#34;EXPOSE 8080\n&#34;)
+        file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.deploy.CloudDeployer.run_commands"><code class="name flex">
+<span>def <span class="ident">run_commands</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p>
+<h2 id="parameters">Parameters</h2>
+<p>None</p>
+<h2 id="returns">Returns</h2>
+<p>None</p>
+<h2 id="raises">Raises</h2>
+<p>None
+This method sets environment variables for the application. It uses the <code>os.environ</code> dictionary to set the following environment variables:</p>
+<ul>
+<li><code>OPENAI_MODEL_NAME</code>: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".</li>
+<li><code>OPENAI_API_KEY</code>: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".</li>
+<li><code>OPENAI_API_BASE</code>: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".</li>
+</ul></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def run_commands(self):
+    &#34;&#34;&#34;
+    Sets environment variables with fallback to .env values or defaults.
+
+    Parameters:
+        None
+
+    Returns:
+        None
+
+    Raises:
+        None
+
+    This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
+
+    - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to &#34;gpt-4o&#34;.
+    - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to &#34;Enter your API key&#34;.
+    - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to &#34;https://api.openai.com/v1&#34;.
+    &#34;&#34;&#34;
+    self.create_api_file()
+    self.create_dockerfile()
+    &#34;&#34;&#34;Runs a sequence of shell commands for deployment, continues on error.&#34;&#34;&#34;
+    commands = [
+        &#34;yes | gcloud auth configure-docker us-central1-docker.pkg.dev&#34;,
+        &#34;gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1&#34;,
+        &#34;docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .&#34;,
+        &#34;docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+        &#34;docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
+        &#34;gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}&#34;
+    ]
+
+    for cmd in commands:
+        try:
+            subprocess.run(cmd, shell=True, check=True)
+        except subprocess.CalledProcessError as e:
+            print(f&#34;ERROR: Command &#39;{e.cmd}&#39; failed with exit status {e.returncode}&#34;)
+            print(f&#34;Continuing with the next command...&#34;)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.deploy.CloudDeployer.set_environment_variables"><code class="name flex">
+<span>def <span class="ident">set_environment_variables</span></span>(<span>self)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def set_environment_variables(self):
+    &#34;&#34;&#34;Sets environment variables with fallback to .env values or defaults.&#34;&#34;&#34;
+    os.environ[&#34;OPENAI_MODEL_NAME&#34;] = os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
+    os.environ[&#34;OPENAI_API_KEY&#34;] = os.getenv(&#34;OPENAI_API_KEY&#34;, &#34;Enter your API key&#34;)
+    os.environ[&#34;OPENAI_API_BASE&#34;] = os.getenv(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="praisonai.deploy.CloudDeployer" href="#praisonai.deploy.CloudDeployer">CloudDeployer</a></code></h4>
+<ul class="">
+<li><code><a title="praisonai.deploy.CloudDeployer.create_api_file" href="#praisonai.deploy.CloudDeployer.create_api_file">create_api_file</a></code></li>
+<li><code><a title="praisonai.deploy.CloudDeployer.create_dockerfile" href="#praisonai.deploy.CloudDeployer.create_dockerfile">create_dockerfile</a></code></li>
+<li><code><a title="praisonai.deploy.CloudDeployer.run_commands" href="#praisonai.deploy.CloudDeployer.run_commands">run_commands</a></code></li>
+<li><code><a title="praisonai.deploy.CloudDeployer.set_environment_variables" href="#praisonai.deploy.CloudDeployer.set_environment_variables">set_environment_variables</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/inbuilt_tools/autogen_tools.html

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+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
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+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.inbuilt_tools.autogen_tools</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python"># praisonai/inbuilt_tools/autogen_tools.py
+
+from crewai_tools import (
+    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
+    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
+    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
+    YoutubeVideoSearchTool
+)
+from typing import Any
+from autogen import register_function
+import os
+import importlib
+from pathlib import Path
+import os
+import inspect
+import sys
+
+def create_autogen_tool_function(tool_name):
+    def autogen_tool(assistant, user_proxy):
+        def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+            def tool_func(query: str) -&gt; Any:
+                tool_instance = tool_class()
+                return tool_instance.run(query=query)
+            register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+        tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+        if os.path.isfile(tools_py_path):
+            print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+            tool_module = importlib.import_module(&#34;tools&#34;)
+        elif tools_dir_path.is_dir():
+            print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+            tool_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+        else:
+            raise ImportError(&#34;Neither tools.py nor tools directory found in the root directory.&#34;)
+
+        Tool = getattr(tool_module, tool_name)
+
+        register_tool(Tool, tool_name, f&#34;Description for {tool_name}&#34;, assistant, user_proxy)
+
+    return autogen_tool
+
+# Load tools.py
+sys.path.insert(0, os.getcwd())
+root_directory = os.getcwd()
+tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+tools_module = None
+
+if os.path.isfile(tools_py_path):
+    print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+    tools_module = importlib.import_module(&#34;tools&#34;)
+elif tools_dir_path.is_dir():
+    print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+    tools_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+
+# Create autogen_TOOL_NAME_HERE function for each tool
+if tools_module is not None:
+    for name, obj in inspect.getmembers(tools_module):
+        if inspect.isclass(obj):
+            globals()[f&#34;autogen_{name}&#34;] = create_autogen_tool_function(name)
+
+def autogen_CodeDocsSearchTool(assistant, user_proxy):
+    def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(docs_url: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_code_docs_search_tool(CodeDocsSearchTool, &#34;code_docs_search_tool&#34;, &#34;Search a Code Docs content(search_query: &#39;string&#39;, docs_url: &#39;string&#39;) - A tool that can be used to semantic search a query from a Code Docs content.&#34;, assistant, user_proxy)
+
+def autogen_CSVSearchTool(assistant, user_proxy):
+    def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(csv: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(csv=csv, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_csv_search_tool(CSVSearchTool, &#34;csv_search_tool&#34;, &#34;Search a CSV&#39;s content(search_query: &#39;string&#39;, csv: &#39;string&#39;) - A tool that can be used to semantic search a query from a CSV&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_DirectorySearchTool(assistant, user_proxy):
+    def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(directory: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(directory=directory, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_directory_search_tool(DirectorySearchTool, &#34;directory_search_tool&#34;, &#34;Search a directory&#39;s content(search_query: &#39;string&#39;, directory: &#39;string&#39;) - A tool that can be used to semantic search a query from a directory&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_DOCXSearchTool(assistant, user_proxy):
+    def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(docx: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(docx=docx, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_docx_search_tool(DOCXSearchTool, &#34;docx_search_tool&#34;, &#34;Search a DOCX&#39;s content(search_query: &#39;string&#39;, docx: &#39;string&#39;) - A tool that can be used to semantic search a query from a DOCX&#39;s content.&#34;, assistant, user_proxy)
+
+# DirectoryReadTool
+def autogen_DirectoryReadTool(assistant, user_proxy):
+    def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(directory: str) -&gt; Any:
+            tool_instance = tool_class(directory=directory)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_directory_read_tool(DirectoryReadTool, &#34;directory_read_tool&#34;, &#34;List files in directory(directory: &#39;string&#39;) - A tool that can be used to recursively list a directory&#39;s content.&#34;, assistant, user_proxy)
+
+# FileReadTool
+def autogen_FileReadTool(assistant, user_proxy):
+    def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(file_path: str) -&gt; Any:
+            tool_instance = tool_class(file_path=file_path)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_file_read_tool(FileReadTool, &#34;file_read_tool&#34;, &#34;Read a file&#39;s content(file_path: &#39;string&#39;) - A tool that can be used to read a file&#39;s content.&#34;, assistant, user_proxy)
+
+# TXTSearchTool
+def autogen_TXTSearchTool(assistant, user_proxy):
+    def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(txt: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(txt=txt, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_txt_search_tool(TXTSearchTool, &#34;txt_search_tool&#34;, &#34;Search a txt&#39;s content(search_query: &#39;string&#39;, txt: &#39;string&#39;) - A tool that can be used to semantic search a query from a txt&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_JSONSearchTool(assistant, user_proxy):
+    def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(json_path: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(json_path=json_path, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_json_search_tool(JSONSearchTool, &#34;json_search_tool&#34;, &#34;Search a JSON&#39;s content(search_query: &#39;string&#39;, json_path: &#39;string&#39;) - A tool that can be used to semantic search a query from a JSON&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_MDXSearchTool(assistant, user_proxy):
+    def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(mdx: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(mdx=mdx, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_mdx_search_tool(MDXSearchTool, &#34;mdx_search_tool&#34;, &#34;Search a MDX&#39;s content(search_query: &#39;string&#39;, mdx: &#39;string&#39;) - A tool that can be used to semantic search a query from a MDX&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_PDFSearchTool(assistant, user_proxy):
+    def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(pdf: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(pdf=pdf, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_pdf_search_tool(PDFSearchTool, &#34;pdf_search_tool&#34;, &#34;Search a PDF&#39;s content(search_query: &#39;string&#39;, pdf: &#39;string&#39;) - A tool that can be used to semantic search a query from a PDF&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_RagTool(assistant, user_proxy):
+    def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(query: str, data: Any) -&gt; Any:
+            tool_instance = tool_class(query=query, data=data)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_rag_tool(RagTool, &#34;rag_tool&#34;, &#34;Knowledge base(query: &#39;string&#39;, data: Any) - A knowledge base that can be used to answer questions.&#34;, assistant, user_proxy)
+
+def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
+    def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website_url: str, element_query: str) -&gt; Any:
+            tool_instance = tool_class(website_url=website_url, element_query=element_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, &#34;scrape_element_from_website_tool&#34;, &#34;Scrape an element from a website(element_query: &#39;string&#39;, website_url: &#39;string&#39;) - A tool that can be used to scrape an element from a website.&#34;, assistant, user_proxy)
+
+def autogen_ScrapeWebsiteTool(assistant, user_proxy):
+    def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website_url: str) -&gt; Any:
+            tool_instance = tool_class(website_url=website_url)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_scrape_website_tool(ScrapeWebsiteTool, &#34;scrape_website_tool&#34;, &#34;Read website content(website_url: &#39;string&#39;) - A tool that can be used to read content from a specified website.&#34;, assistant, user_proxy)
+
+def autogen_WebsiteSearchTool(assistant, user_proxy):
+    def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(website=website, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_website_search_tool(WebsiteSearchTool, &#34;website_search_tool&#34;, &#34;Search in a specific website(search_query: &#39;string&#39;, website: &#39;string&#39;) - A tool that can be used to semantic search a query from a specific URL content.&#34;, assistant, user_proxy)
+
+def autogen_XMLSearchTool(assistant, user_proxy):
+    def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(xml: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(xml=xml, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_xml_search_tool(XMLSearchTool, &#34;xml_search_tool&#34;, &#34;Search a XML&#39;s content(search_query: &#39;string&#39;, xml: &#39;string&#39;) - A tool that can be used to semantic search a query from a XML&#39;s content.&#34;, assistant, user_proxy)
+
+def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
+    def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(youtube_channel_handle: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_youtube_channel_search_tool(YoutubeChannelSearchTool, &#34;youtube_channel_search_tool&#34;, &#34;Search a Youtube Channels content(search_query: &#39;string&#39;, youtube_channel_handle: &#39;string&#39;) - A tool that can be used to semantic search a query from a Youtube Channels content.&#34;, assistant, user_proxy)
+
+def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
+    def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(youtube_video_url: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_youtube_video_search_tool(YoutubeVideoSearchTool, &#34;youtube_video_search_tool&#34;, &#34;Search a Youtube Video content(search_query: &#39;string&#39;, youtube_video_url: &#39;string&#39;) - A tool that can be used to semantic search a query from a Youtube Video content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool"><code class="name flex">
+<span>def <span class="ident">autogen_BaseTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_tool(assistant, user_proxy):
+    def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(query: str) -&gt; Any:
+            tool_instance = tool_class()
+            return tool_instance.run(query=query)
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+    root_directory = os.getcwd()
+    tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+    tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+    if os.path.isfile(tools_py_path):
+        print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+        tool_module = importlib.import_module(&#34;tools&#34;)
+    elif tools_dir_path.is_dir():
+        print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+        tool_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+    else:
+        raise ImportError(&#34;Neither tools.py nor tools directory found in the root directory.&#34;)
+
+    Tool = getattr(tool_module, tool_name)
+
+    register_tool(Tool, tool_name, f&#34;Description for {tool_name}&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_CSVSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_CSVSearchTool(assistant, user_proxy):
+    def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(csv: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(csv=csv, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_csv_search_tool(CSVSearchTool, &#34;csv_search_tool&#34;, &#34;Search a CSV&#39;s content(search_query: &#39;string&#39;, csv: &#39;string&#39;) - A tool that can be used to semantic search a query from a CSV&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_CodeDocsSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_CodeDocsSearchTool(assistant, user_proxy):
+    def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(docs_url: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_code_docs_search_tool(CodeDocsSearchTool, &#34;code_docs_search_tool&#34;, &#34;Search a Code Docs content(search_query: &#39;string&#39;, docs_url: &#39;string&#39;) - A tool that can be used to semantic search a query from a Code Docs content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS"><code class="name flex">
+<span>def <span class="ident">autogen_DDGS</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_tool(assistant, user_proxy):
+    def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(query: str) -&gt; Any:
+            tool_instance = tool_class()
+            return tool_instance.run(query=query)
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+    root_directory = os.getcwd()
+    tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+    tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+    if os.path.isfile(tools_py_path):
+        print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+        tool_module = importlib.import_module(&#34;tools&#34;)
+    elif tools_dir_path.is_dir():
+        print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+        tool_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+    else:
+        raise ImportError(&#34;Neither tools.py nor tools directory found in the root directory.&#34;)
+
+    Tool = getattr(tool_module, tool_name)
+
+    register_tool(Tool, tool_name, f&#34;Description for {tool_name}&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_DOCXSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_DOCXSearchTool(assistant, user_proxy):
+    def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(docx: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(docx=docx, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_docx_search_tool(DOCXSearchTool, &#34;docx_search_tool&#34;, &#34;Search a DOCX&#39;s content(search_query: &#39;string&#39;, docx: &#39;string&#39;) - A tool that can be used to semantic search a query from a DOCX&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool"><code class="name flex">
+<span>def <span class="ident">autogen_DirectoryReadTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_DirectoryReadTool(assistant, user_proxy):
+    def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(directory: str) -&gt; Any:
+            tool_instance = tool_class(directory=directory)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_directory_read_tool(DirectoryReadTool, &#34;directory_read_tool&#34;, &#34;List files in directory(directory: &#39;string&#39;) - A tool that can be used to recursively list a directory&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_DirectorySearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_DirectorySearchTool(assistant, user_proxy):
+    def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(directory: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(directory=directory, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_directory_search_tool(DirectorySearchTool, &#34;directory_search_tool&#34;, &#34;Search a directory&#39;s content(search_query: &#39;string&#39;, directory: &#39;string&#39;) - A tool that can be used to semantic search a query from a directory&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool"><code class="name flex">
+<span>def <span class="ident">autogen_FileReadTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_FileReadTool(assistant, user_proxy):
+    def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(file_path: str) -&gt; Any:
+            tool_instance = tool_class(file_path=file_path)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_file_read_tool(FileReadTool, &#34;file_read_tool&#34;, &#34;Read a file&#39;s content(file_path: &#39;string&#39;) - A tool that can be used to read a file&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_InternetSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_tool(assistant, user_proxy):
+    def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(query: str) -&gt; Any:
+            tool_instance = tool_class()
+            return tool_instance.run(query=query)
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+    root_directory = os.getcwd()
+    tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+    tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+    if os.path.isfile(tools_py_path):
+        print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+        tool_module = importlib.import_module(&#34;tools&#34;)
+    elif tools_dir_path.is_dir():
+        print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+        tool_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+    else:
+        raise ImportError(&#34;Neither tools.py nor tools directory found in the root directory.&#34;)
+
+    Tool = getattr(tool_module, tool_name)
+
+    register_tool(Tool, tool_name, f&#34;Description for {tool_name}&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_JSONSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_JSONSearchTool(assistant, user_proxy):
+    def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(json_path: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(json_path=json_path, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_json_search_tool(JSONSearchTool, &#34;json_search_tool&#34;, &#34;Search a JSON&#39;s content(search_query: &#39;string&#39;, json_path: &#39;string&#39;) - A tool that can be used to semantic search a query from a JSON&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_MDXSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_MDXSearchTool(assistant, user_proxy):
+    def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(mdx: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(mdx=mdx, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_mdx_search_tool(MDXSearchTool, &#34;mdx_search_tool&#34;, &#34;Search a MDX&#39;s content(search_query: &#39;string&#39;, mdx: &#39;string&#39;) - A tool that can be used to semantic search a query from a MDX&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_PDFSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_PDFSearchTool(assistant, user_proxy):
+    def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(pdf: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(pdf=pdf, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_pdf_search_tool(PDFSearchTool, &#34;pdf_search_tool&#34;, &#34;Search a PDF&#39;s content(search_query: &#39;string&#39;, pdf: &#39;string&#39;) - A tool that can be used to semantic search a query from a PDF&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool"><code class="name flex">
+<span>def <span class="ident">autogen_RagTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_RagTool(assistant, user_proxy):
+    def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(query: str, data: Any) -&gt; Any:
+            tool_instance = tool_class(query=query, data=data)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_rag_tool(RagTool, &#34;rag_tool&#34;, &#34;Knowledge base(query: &#39;string&#39;, data: Any) - A knowledge base that can be used to answer questions.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool"><code class="name flex">
+<span>def <span class="ident">autogen_ScrapeElementFromWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
+    def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website_url: str, element_query: str) -&gt; Any:
+            tool_instance = tool_class(website_url=website_url, element_query=element_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, &#34;scrape_element_from_website_tool&#34;, &#34;Scrape an element from a website(element_query: &#39;string&#39;, website_url: &#39;string&#39;) - A tool that can be used to scrape an element from a website.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool"><code class="name flex">
+<span>def <span class="ident">autogen_ScrapeWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_ScrapeWebsiteTool(assistant, user_proxy):
+    def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website_url: str) -&gt; Any:
+            tool_instance = tool_class(website_url=website_url)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_scrape_website_tool(ScrapeWebsiteTool, &#34;scrape_website_tool&#34;, &#34;Read website content(website_url: &#39;string&#39;) - A tool that can be used to read content from a specified website.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_TXTSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_TXTSearchTool(assistant, user_proxy):
+    def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(txt: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(txt=txt, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_txt_search_tool(TXTSearchTool, &#34;txt_search_tool&#34;, &#34;Search a txt&#39;s content(search_query: &#39;string&#39;, txt: &#39;string&#39;) - A tool that can be used to semantic search a query from a txt&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_WebsiteSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_WebsiteSearchTool(assistant, user_proxy):
+    def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(website: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(website=website, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_website_search_tool(WebsiteSearchTool, &#34;website_search_tool&#34;, &#34;Search in a specific website(search_query: &#39;string&#39;, website: &#39;string&#39;) - A tool that can be used to semantic search a query from a specific URL content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_XMLSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_XMLSearchTool(assistant, user_proxy):
+    def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(xml: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(xml=xml, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_xml_search_tool(XMLSearchTool, &#34;xml_search_tool&#34;, &#34;Search a XML&#39;s content(search_query: &#39;string&#39;, xml: &#39;string&#39;) - A tool that can be used to semantic search a query from a XML&#39;s content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_YoutubeChannelSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
+    def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(youtube_channel_handle: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_youtube_channel_search_tool(YoutubeChannelSearchTool, &#34;youtube_channel_search_tool&#34;, &#34;Search a Youtube Channels content(search_query: &#39;string&#39;, youtube_channel_handle: &#39;string&#39;) - A tool that can be used to semantic search a query from a Youtube Channels content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool"><code class="name flex">
+<span>def <span class="ident">autogen_YoutubeVideoSearchTool</span></span>(<span>assistant, user_proxy)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
+    def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+        def tool_func(youtube_video_url: str, search_query: str) -&gt; Any:
+            tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
+            return tool_instance.run()
+        register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+    register_youtube_video_search_tool(YoutubeVideoSearchTool, &#34;youtube_video_search_tool&#34;, &#34;Search a Youtube Video content(search_query: &#39;string&#39;, youtube_video_url: &#39;string&#39;) - A tool that can be used to semantic search a query from a Youtube Video content.&#34;, assistant, user_proxy)</code></pre>
+</details>
+</dd>
+<dt id="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function"><code class="name flex">
+<span>def <span class="ident">create_autogen_tool_function</span></span>(<span>tool_name)</span>
+</code></dt>
+<dd>
+<div class="desc"></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_autogen_tool_function(tool_name):
+    def autogen_tool(assistant, user_proxy):
+        def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+            def tool_func(query: str) -&gt; Any:
+                tool_instance = tool_class()
+                return tool_instance.run(query=query)
+            register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
+        tools_dir_path = Path(root_directory) / &#39;tools&#39;
+
+        if os.path.isfile(tools_py_path):
+            print(f&#34;{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.&#34;)
+            tool_module = importlib.import_module(&#34;tools&#34;)
+        elif tools_dir_path.is_dir():
+            print(f&#34;tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.&#34;)
+            tool_module = importlib.import_module(f&#34;tools.{tool_name}&#34;)
+        else:
+            raise ImportError(&#34;Neither tools.py nor tools directory found in the root directory.&#34;)
+
+        Tool = getattr(tool_module, tool_name)
+
+        register_tool(Tool, tool_name, f&#34;Description for {tool_name}&#34;, assistant, user_proxy)
+
+    return autogen_tool</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai.inbuilt_tools" href="index.html">praisonai.inbuilt_tools</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool">autogen_BaseTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">autogen_CSVSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">autogen_CodeDocsSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DDGS">autogen_DDGS</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">autogen_DOCXSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">autogen_DirectoryReadTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">autogen_DirectorySearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">autogen_FileReadTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool">autogen_InternetSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">autogen_JSONSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">autogen_MDXSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">autogen_PDFSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">autogen_RagTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">autogen_ScrapeElementFromWebsiteTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">autogen_ScrapeWebsiteTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">autogen_TXTSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">autogen_WebsiteSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">autogen_XMLSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">autogen_YoutubeChannelSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">autogen_YoutubeVideoSearchTool</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function" href="#praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function">create_autogen_tool_function</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/inbuilt_tools/index.html

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+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.inbuilt_tools</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python"># from .base_tool import BaseTool, Tool, tool
+from .autogen_tools import *</code></pre>
+</details>
+</section>
+<section>
+<h2 class="section-title" id="header-submodules">Sub-modules</h2>
+<dl>
+<dt><code class="name"><a title="praisonai.inbuilt_tools.autogen_tools" href="autogen_tools.html">praisonai.inbuilt_tools.autogen_tools</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="../index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-submodules">Sub-modules</a></h3>
+<ul>
+<li><code><a title="praisonai.inbuilt_tools.autogen_tools" href="autogen_tools.html">praisonai.inbuilt_tools.autogen_tools</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/index.html

@@ -0,0 +1,97 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+<meta name="generator" content="pdoc 0.10.0" />
+<title>praisonai API documentation</title>
+<meta name="description" content="" />
+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Package <code>praisonai</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">from .cli import PraisonAI
+from .version import __version__</code></pre>
+</details>
+</section>
+<section>
+<h2 class="section-title" id="header-submodules">Sub-modules</h2>
+<dl>
+<dt><code class="name"><a title="praisonai.agents_generator" href="agents_generator.html">praisonai.agents_generator</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.auto" href="auto.html">praisonai.auto</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.cli" href="cli.html">praisonai.cli</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.deploy" href="deploy.html">praisonai.deploy</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.inbuilt_tools" href="inbuilt_tools/index.html">praisonai.inbuilt_tools</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.test" href="test.html">praisonai.test</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="praisonai.version" href="version.html">praisonai.version</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-submodules">Sub-modules</a></h3>
+<ul>
+<li><code><a title="praisonai.agents_generator" href="agents_generator.html">praisonai.agents_generator</a></code></li>
+<li><code><a title="praisonai.auto" href="auto.html">praisonai.auto</a></code></li>
+<li><code><a title="praisonai.cli" href="cli.html">praisonai.cli</a></code></li>
+<li><code><a title="praisonai.deploy" href="deploy.html">praisonai.deploy</a></code></li>
+<li><code><a title="praisonai.inbuilt_tools" href="inbuilt_tools/index.html">praisonai.inbuilt_tools</a></code></li>
+<li><code><a title="praisonai.test" href="test.html">praisonai.test</a></code></li>
+<li><code><a title="praisonai.version" href="version.html">praisonai.version</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/test.html

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+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+<meta name="generator" content="pdoc 0.10.0" />
+<title>praisonai.test API documentation</title>
+<meta name="description" content="" />
+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
+<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.test</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">import yaml
+import os
+from rich import print
+from dotenv import load_dotenv
+from crewai import Agent, Task, Crew
+load_dotenv()
+import autogen
+config_list = [
+    {
+        &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-3.5-turbo&#34;),
+        &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
+        &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
+    }
+]
+
+def generate_crew_and_kickoff(agent_file):
+    &#34;&#34;&#34;
+    This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
+
+    Parameters:
+    agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
+
+    Returns:
+    str: The result of the last task executed by the crew.
+    &#34;&#34;&#34;
+
+    with open(agent_file, &#39;r&#39;) as f:
+        config = yaml.safe_load(f)
+
+    topic = config[&#39;topic&#39;]  
+    framework = config[&#39;framework&#39;]
+
+    agents = {}
+    tasks = []
+    if framework == &#34;autogen&#34;:
+        # Load the LLM configuration dynamically
+        print(config_list)
+        llm_config = {&#34;config_list&#34;: config_list}
+        
+        for role, details in config[&#39;roles&#39;].items():
+            agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
+            agent_goal = details[&#39;goal&#39;].format(topic=topic)
+            # Creating an AssistantAgent for each role dynamically
+            agents[role] = autogen.AssistantAgent(
+                name=agent_name,
+                llm_config=llm_config,
+                system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
+            )
+
+            # Preparing tasks for initiate_chats
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+                
+                chat_task = {
+                    &#34;recipient&#34;: agents[role],
+                    &#34;message&#34;: description_filled,
+                    &#34;summary_method&#34;: &#34;last_msg&#34;,  # Customize as needed
+                    # Additional fields like carryover can be added based on dependencies
+                }
+                tasks.append(chat_task)
+
+        # Assuming the user proxy agent is set up as per your requirements
+        user = autogen.UserProxyAgent(
+            name=&#34;User&#34;,
+            human_input_mode=&#34;NEVER&#34;,
+            is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().endswith(&#34;TERMINATE&#34;),
+            code_execution_config={
+                &#34;work_dir&#34;: &#34;coding&#34;,
+                &#34;use_docker&#34;: False,
+            },
+            # additional setup for the user proxy agent
+        )
+        response = user.initiate_chats(tasks)
+        result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
+    else:
+        for role, details in config[&#39;roles&#39;].items():
+            role_filled = details[&#39;role&#39;].format(topic=topic)
+            goal_filled = details[&#39;goal&#39;].format(topic=topic)
+            backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
+            
+            # Assume tools are loaded and handled here as per your requirements
+            agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
+            agents[role] = agent
+
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+
+                task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
+                tasks.append(task)
+
+        crew = Crew(
+            agents=list(agents.values()),
+            tasks=tasks,
+            verbose=2
+        )
+
+        result = crew.kickoff()
+    return result
+
+if __name__ == &#34;__main__&#34;:
+    agent_file = &#34;agents.yaml&#34;
+    result = generate_crew_and_kickoff(agent_file)
+    print(result)</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="praisonai.test.generate_crew_and_kickoff"><code class="name flex">
+<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>agent_file)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.</p>
+<p>Parameters:
+agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.</p>
+<p>Returns:
+str: The result of the last task executed by the crew.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def generate_crew_and_kickoff(agent_file):
+    &#34;&#34;&#34;
+    This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
+
+    Parameters:
+    agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
+
+    Returns:
+    str: The result of the last task executed by the crew.
+    &#34;&#34;&#34;
+
+    with open(agent_file, &#39;r&#39;) as f:
+        config = yaml.safe_load(f)
+
+    topic = config[&#39;topic&#39;]  
+    framework = config[&#39;framework&#39;]
+
+    agents = {}
+    tasks = []
+    if framework == &#34;autogen&#34;:
+        # Load the LLM configuration dynamically
+        print(config_list)
+        llm_config = {&#34;config_list&#34;: config_list}
+        
+        for role, details in config[&#39;roles&#39;].items():
+            agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
+            agent_goal = details[&#39;goal&#39;].format(topic=topic)
+            # Creating an AssistantAgent for each role dynamically
+            agents[role] = autogen.AssistantAgent(
+                name=agent_name,
+                llm_config=llm_config,
+                system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
+            )
+
+            # Preparing tasks for initiate_chats
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+                
+                chat_task = {
+                    &#34;recipient&#34;: agents[role],
+                    &#34;message&#34;: description_filled,
+                    &#34;summary_method&#34;: &#34;last_msg&#34;,  # Customize as needed
+                    # Additional fields like carryover can be added based on dependencies
+                }
+                tasks.append(chat_task)
+
+        # Assuming the user proxy agent is set up as per your requirements
+        user = autogen.UserProxyAgent(
+            name=&#34;User&#34;,
+            human_input_mode=&#34;NEVER&#34;,
+            is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().endswith(&#34;TERMINATE&#34;),
+            code_execution_config={
+                &#34;work_dir&#34;: &#34;coding&#34;,
+                &#34;use_docker&#34;: False,
+            },
+            # additional setup for the user proxy agent
+        )
+        response = user.initiate_chats(tasks)
+        result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
+    else:
+        for role, details in config[&#39;roles&#39;].items():
+            role_filled = details[&#39;role&#39;].format(topic=topic)
+            goal_filled = details[&#39;goal&#39;].format(topic=topic)
+            backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
+            
+            # Assume tools are loaded and handled here as per your requirements
+            agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
+            agents[role] = agent
+
+            for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
+                description_filled = task_details[&#39;description&#39;].format(topic=topic)
+                expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
+
+                task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
+                tasks.append(task)
+
+        crew = Crew(
+            agents=list(agents.values()),
+            tasks=tasks,
+            verbose=2
+        )
+
+        result = crew.kickoff()
+    return result</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="praisonai.test.generate_crew_and_kickoff" href="#praisonai.test.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/api/praisonai/version.html

@@ -0,0 +1,59 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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+<title>praisonai.version API documentation</title>
+<meta name="description" content="" />
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+<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>praisonai.version</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">__version__ = &#34;0.0.1&#34;</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>

docs/auto.md

@@ -0,0 +1,6 @@
+# Auto
+## Full Automatic Mode
+
+```bash
+praisonai --auto create a movie script about Dog in Moon
+```

docs/conf.py

@@ -0,0 +1,28 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'PraisonAI'
+copyright = '2024, Mervin Praison'
+author = 'Mervin Praison'
+release = '7'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx_rtd_theme']
+
+templates_path = ['_templates']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']

docs/contributing.md

@@ -0,0 +1,10 @@
+# Contributing
+
+- Fork on GitHub: Use the "Fork" button on the repository page.
+- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
+- Create a branch: `git checkout -b new-feature`
+- Make changes and commit: `git commit -am "Add some feature"`
+- Push to your fork: `git push origin new-feature`
+- Submit a pull request via GitHub's web interface.
+- Await feedback from project maintainers.
+

docs/create_custom_tools.md

@@ -0,0 +1,60 @@
+# Create Custom Tools
+## TL;DR to Create a Custom Tool
+
+```bash
+pip install praisonai duckduckgo-search
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init research about the latest AI News and prepare a detailed report
+```
+
+- Add `- InternetSearchTool` in the agents.yaml file in the tools section. 
+- Create a file called tools.py and add this code [tools.py](./tools.py)
+
+```bash
+praisonai
+```
+
+## Pre-requisite to Create a Custom Tool
+`agents.yaml` file should be present in the current directory. 
+
+If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
+
+### Step 1 to Create a Custom Tool
+
+Create a file called tools.py in the same directory as the agents.yaml file.
+
+```python
+# example tools.py
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+```
+
+### Step 2 to Create a Custom Tool
+
+Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
+
+```yaml
+framework: crewai
+topic: research about the latest AI News and prepare a detailed report
+roles:
+  research_analyst:
+    backstory: Experienced in gathering and analyzing data related to AI news trends.
+    goal: Analyze AI News trends
+    role: Research Analyst
+    tasks:
+      gather_data:
+        description: Conduct in-depth research on the latest AI News trends from reputable
+          sources.
+        expected_output: Comprehensive report on current AI News trends.
+    tools:
+    - InternetSearchTool
+```

docs/deploy.md

@@ -0,0 +1,31 @@
+# Deploy 
+
+## Google Cloud
+
+```bash
+gcloud init
+gcloud services enable run.googleapis.com
+gcloud services enable containerregistry.googleapis.com
+gcloud services enable cloudbuild.googleapis.com
+
+export OPENAI_MODEL_NAME="gpt-4o"
+export OPENAI_API_KEY="Enter your API key"
+export OPENAI_API_BASE="https://api.openai.com/v1"
+
+yes | gcloud auth configure-docker us-central1-docker.pkg.dev 
+gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
+
+PROJECT_ID=$(gcloud config get-value project)
+TAG="latest"
+docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
+docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
+docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
+
+gcloud run deploy praisonai-service \
+    --image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
+    --platform managed \
+    --region us-central1 \
+    --allow-unauthenticated \
+    --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
+```
+

docs/generate_docs.sh

@@ -0,0 +1,11 @@
+#!/bin/sh
+set -e  # Exit if any command fails
+
+# Install necessary packages
+pip install pdoc3 mkdocs mkdocs-material 
+
+# Generate HTML documentation with pdoc
+pdoc --html --skip-errors -o docs/api praisonai --force
+
+# Build the MkDocs site
+mkdocs build

docs/generate_from_readme.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Ensure we are in the docs folder
+cd "$(dirname "$0")"
+
+# Define the sections and their corresponding filenames
+sections=(
+  "Praison AI:index.md"
+  "TL;DR:tldr.md"
+  "Installation:installation.md"
+  "Initialise:initialise.md"
+  "Run:run.md"
+  "Create Custom Tools:create_custom_tools.md"
+  "Test:test.md"
+  "Agents Playbook:agents_playbook.md"
+  "Include praisonai package in your project:include_package.md"
+  "Deploy:deploy.md"
+  "Other Models:other_models.md"
+  "Contributing:contributing.md"
+)
+
+# Function to extract section content
+extract_section() {
+  section_name="$1"
+  input_file="$2"
+  output_file="$3"
+  
+  # Escape special characters in section name for use in regex
+  section_name_escaped=$(echo "$section_name" | sed 's/[.[\*^$(){}?+|]/\\&/g')
+  
+  # Extract content
+  if command -v gsed > /dev/null; then
+    gsed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | gsed '$d' >> "$output_file"
+  else
+    sed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | sed '$d' >> "$output_file"
+  fi
+}
+
+# Extract content above the first section from README.md
+if command -v gsed > /dev/null; then
+  gsed -n '1,/^## /{p;}' ../README.md | gsed '$d' > index.md
+else
+  sed -n '1,/^## /{p;}' ../README.md | sed '$d' > index.md
+fi
+
+# Create the files and add content
+for section in "${sections[@]}"; do
+  section_name="${section%%:*}"
+  filename="${section##*:}"
+  if [[ "$filename" == "index.md" ]]; then
+    extract_section "$section_name" ../README.md index.md
+  elif [[ "$section_name" == "TL;DR" ]]; then
+    extract_section "$section_name" ../README.md index.md
+  else
+    extract_section "$section_name" ../README.md "$filename"
+  fi
+done
+
+# Special handling for the last section (Contributing)
+if command -v gsed > /dev/null; then
+  gsed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
+else
+  sed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
+fi

docs/index.md

@@ -0,0 +1,26 @@
+# Praison AI
+
+Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+
+## Prerequisite:
+### Export API KEY
+
+```
+export OPENAI_API_KEY="Enter your API key"
+```
+
+## Installation
+
+```bash
+pip install praisonai
+```
+
+## Automatically Create Agents to Perform a Task
+
+```
+praisonai --init create a movie script about dog in moon
+```
+## Run
+```
+praisonai
+```

docs/index.rst

@@ -0,0 +1,20 @@
+.. PraisonAI documentation master file, created by
+   sphinx-quickstart on Mon Jun 10 12:11:57 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to PraisonAI's documentation!
+=====================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/initialise.md

@@ -0,0 +1,14 @@
+# Initialise
+
+```bash
+export OPENAI_API_KEY="Enter your API key"
+```
+
+Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
+
+Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
+    
+```bash
+praisonai --init create a movie script about dog in moon
+```
+This will automatically create agents.yaml file in the current directory.

docs/installation.md

@@ -0,0 +1,5 @@
+# Installation
+
+```bash
+pip install praisonai
+```

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/other_models.md

@@ -0,0 +1,24 @@
+# Other Models
+
+```bash
+Ollama
+OPENAI_API_BASE='http://localhost:11434/v1'
+OPENAI_MODEL_NAME='mistral'
+OPENAI_API_KEY='NA'
+
+FastChat¶
+OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51'
+OPENAI_API_KEY=NA
+
+LM Studio¶
+OPENAI_API_BASE="http://localhost:8000/v1"
+OPENAI_MODEL_NAME=NA
+OPENAI_API_KEY=NA
+
+Mistral API¶
+OPENAI_API_BASE=https://api.mistral.ai/v1
+OPENAI_MODEL_NAME="mistral-small"
+OPENAI_API_KEY=your-mistral-api-key
+```
+

docs/requirements.in

@@ -0,0 +1,12 @@
+sphinx
+sphinx_rtd_theme
+sphinx-autodoc-typehints
+mkdocs
+markdown-include
+mkdocs-material
+mkdocs-jupyter
+pymdown-extensions
+Pygments
+pdoc3
+xmlrunner
+unittest2

docs/requirements.txt

@@ -0,0 +1,24 @@
+sphinx
+sphinx_rtd_theme
+sphinx-autodoc-typehints
+mkdocs
+markdown-include
+mkdocs-material
+mkdocs-jupyter
+pymdown-extensions
+Pygments
+mkdocstrings 
+mkdocs-apidoc 
+mkdocstrings-python
+mkdocs-jupyter
+pdoc3
+rich
+pyautogen
+crewai
+gradio
+crewai_tools
+duckduckgo_search
+praisonai_tools
+open-interpreter
+xmlrunner
+unittest2

docs/run.md

@@ -0,0 +1,23 @@
+# Run
+
+```bash
+praisonai
+```
+
+or 
+    
+```bash
+python -m praisonai
+```
+
+## Specify the agent framework (Optional):
+
+```bash
+praisonai --framework autogen
+```
+
+## Full Automatic Mode
+
+```bash
+praisonai --auto create a movie script about Dog in Moon
+```

docs/test.md

@@ -0,0 +1,5 @@
+# Test
+    
+```bash
+python -m unittest tests.test 
+```

docs/tldr.md

@@ -0,0 +1,7 @@
+# TL;DR
+```bash
+pip install praisonai
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init create a movie script about dog in moon
+praisonai
+```

docs/ui.md

@@ -0,0 +1,38 @@
+# PraisonAI User Interface (UI)
+
+## Chainlit
+```bash
+pip install "praisonai[ui]"
+export OPENAI_API_KEY="Enter your API key"
+chainlit create-secret
+export CHAINLIT_AUTH_SECRET=xxxxxxxx
+praisonai ui
+```
+
+Default Username: admin
+Default Password: admin
+
+### To Change Username and Password
+
+create .env file in the root folder of the project
+Add below Variables and required Username/Password
+```
+CHAINLIT_USERNAME=admin
+CHAINLIT_USERNAME=admin
+```
+
+## Gradio
+```bash
+pip install "praisonai[gradio]"
+export OPENAI_API_KEY="Enter your API key"
+praisonai --ui gradio
+```
+
+## Streamlit
+
+```
+git clone https://github.com/leporejoseph/PraisonAi-Streamlit
+cd PraisonAi-Streamlit
+pip install -r requirements.txt
+streamlit run app.py
+```

docs/wrapper.md

@@ -0,0 +1,45 @@
+## Include praisonai package in your project
+
+Note: Please create agents.yaml file before hand. 
+
+```python
+from praisonai import PraisonAI
+
+def basic(): # Basic Mode
+    praison_ai = PraisonAI(agent_file="agents.yaml")
+    praison_ai.main()
+
+if __name__ == "__main__":
+    basic()
+```
+
+## All in one
+
+```python
+from praisonai import PraisonAI
+
+def basic(): # Basic Mode
+    praison_ai = PraisonAI(agent_file="agents.yaml")
+    praison_ai.main()
+    
+def advanced(): # Advanced Mode with options
+    praison_ai = PraisonAI(
+        agent_file="agents.yaml",
+        framework="autogen",
+    )
+    praison_ai.main()
+    
+def auto(): # Full Automatic Mode
+    praison_ai = PraisonAI(
+        auto="Create a movie script about car in mars",
+        framework="autogen"
+    )
+    print(praison_ai.framework)
+    praison_ai.main()
+
+if __name__ == "__main__":
+    basic()
+    advanced()
+    auto()
+```
+

hub/tools/tools.py

@@ -0,0 +1,72 @@
+# tools.py
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+    
+from langchain_community.tools.pubmed.tool import PubmedQueryRun
+from langchain_community.tools.reddit_search.tool import RedditSearchRun
+# pip install tavily-python
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+from langchain_community.utilities.you import YouSearchAPIWrapper
+# pip install youtube_search
+from langchain_community.tools import YouTubeSearchTool
+# pip install wikipedia
+from langchain_community.utilities import WikipediaAPIWrapper
+
+class TavilyTool(BaseTool):
+    name: str = "TavilyTool"
+    description: str = "Search Tavily for relevant information based on a query."
+
+    def _run(self, query: str):
+        api_wrapper = TavilySearchAPIWrapper()
+        results = api_wrapper.results(query=query, max_results=5)
+        return results
+
+class YouSearchTool(BaseTool):
+    name: str = "You Search Tool"
+    description: str = "Search You.com for relevant information based on a query."
+
+    def _run(self, query: str):
+        api_wrapper = YouSearchAPIWrapper()
+        results = api_wrapper.results(query=query, max_results=5)
+        return results
+
+class WikipediaSearchTool(BaseTool):
+    name: str = "WikipediaSearchTool"
+    description: str = "Search Wikipedia for relevant information based on a query."
+
+    def _run(self, query: str):
+        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
+        results = api_wrapper.load(query=query)
+        return results
+
+# pip install open-interpreter
+from interpreter import interpreter
+from praisonai_tools import BaseTool
+
+class OpenInterpreterTool(BaseTool):
+    name: str = "Open Interpreter Tool"
+    description: str = "Use Open Interpreter configured to create and execute code based on natural language commands."
+
+    def __init__(self):
+        super().__init__()
+        interpreter.auto_run = True
+
+    def _run(self, code: str):
+        result = interpreter.chat(code)
+        return result
+
+# Example usage
+if __name__ == "__main__":
+    tool = OpenInterpreterTool()
+    code = "Plot AAPL and META's normalized stock prices"
+    output = tool._run(code)
+    print(output)

mkdocs.yml

@@ -0,0 +1,53 @@
+# Website Info
+site_name: PraisonAI Documentation
+site_url: https://docs.praison.ai
+site_author: MervinPraison
+
+# Repository
+repo_name: MervinPraison/PraisonAI
+repo_url: https://github.com/MervinPraison/PraisonAI
+
+nav:
+  - Home: index.md
+  - TL;DR: tldr.md
+  - Installation: installation.md
+  - Initialise: initialise.md
+  - Run: run.md
+  - User Interface: ui.md
+  - Create Custom Tools: create_custom_tools.md
+  - Test: test.md
+  - Agents Playbook: agents_playbook.md
+  - Wrapper: wrapper.md
+  - Deploy: deploy.md
+  - Other Models: other_models.md
+  - Contributing: contributing.md
+  - API Reference: api.md
+  - API Code: api/praisonai/index.html
+
+theme:
+  name: material
+
+markdown_extensions:
+  - admonition
+  - toc:
+      permalink: true
+  - pymdownx.superfences
+  - pymdownx.details
+
+plugins:
+  - search
+  - mkdocstrings:
+      handlers:
+        python:
+          selection:
+            docstring_style: google
+  # - gen-files:
+  #     scripts:
+  #     - scripts/gen_ref_pages.py  
+  # - mkdocs_apidoc:
+  #       function_template: |
+  #         ### {{ name }}
+
+  #         {{ signature }}
+
+  #         {{ docstring }}

praisonai/agents_generator.py

@@ -1,4 +1,4 @@
-# agents_generator.py
+# praisonai/agents_generator.py
 
 import sys
 from .version import __version__
@@ -6,21 +6,145 @@ import yaml, os
 from rich import print
 from dotenv import load_dotenv
 from crewai import Agent, Task, Crew
+from crewai.telemetry import Telemetry
 load_dotenv()
 import autogen
-import gradio as gr
 import argparse
 from .auto import AutoGenerator
-from crewai_tools import *
-from .tools import *
+from crewai_tools import (
+    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
+    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
+    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
+    YoutubeVideoSearchTool
+)
+from .inbuilt_tools import *
+import inspect
+from pathlib import Path
+import importlib
+import importlib.util
+from praisonai_tools import BaseTool
+import os
+
+os.environ["OTEL_SDK_DISABLED"] = "true"
+
+def noop(*args, **kwargs):
+    pass
+
+def disable_crewai_telemetry():
+    for attr in dir(Telemetry):
+        if callable(getattr(Telemetry, attr)) and not attr.startswith("__"):
+            setattr(Telemetry, attr, noop)
+            
+disable_crewai_telemetry()
 
 class AgentsGenerator:
     def __init__(self, agent_file, framework, config_list):
+        """
+        Initialize the AgentsGenerator object.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Attributes:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+        """
         self.agent_file = agent_file
         self.framework = framework
         self.config_list = config_list
+        
+    def is_function_or_decorated(self, obj):
+        """
+        Checks if the given object is a function or has a __call__ method.
+
+        Parameters:
+            obj (object): The object to be checked.
+
+        Returns:
+            bool: True if the object is a function or has a __call__ method, False otherwise.
+        """
+        return inspect.isfunction(obj) or hasattr(obj, '__call__')
+
+    def load_tools_from_module(self, module_path):
+        """
+        Loads tools from a specified module path.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        """
+        spec = importlib.util.spec_from_file_location("tools_module", module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
+    
+    def load_tools_from_module_class(self, module_path):
+        """
+        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
+
+        Parameters:
+            module_path (str): The path to the module containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified module path does not exist.
+        """
+        spec = importlib.util.spec_from_file_location("tools_module", module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}
+
+    def load_tools_from_package(self, package_path):
+        """
+        Loads tools from a specified package path containing modules with functions or classes.
+
+        Parameters:
+            package_path (str): The path to the package containing the tools.
+
+        Returns:
+            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+
+        Raises:
+            FileNotFoundError: If the specified package path does not exist.
+
+        This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
+        """
+        tools_dict = {}
+        for module_file in os.listdir(package_path):
+            if module_file.endswith('.py') and not module_file.startswith('__'):
+                module_name = f"{package_path.name}.{module_file[:-3]}"  # Remove .py for import
+                module = importlib.import_module(module_name)
+                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
+                    tools_dict[name] = obj
+        return tools_dict
 
     def generate_crew_and_kickoff(self):
+        """
+        Generates a crew of agents and initiates tasks based on the provided configuration.
+
+        Parameters:
+            agent_file (str): The path to the agent file.
+            framework (str): The framework to be used for the agents.
+            config_list (list): A list of configurations for the agents.
+
+        Returns:
+            str: The output of the tasks performed by the crew of agents.
+
+        Raises:
+            FileNotFoundError: If the specified agent file does not exist.
+
+        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
+        """
         if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
             self.agent_file = 'agents.yaml'
         try:
@@ -51,9 +175,19 @@ class AgentsGenerator:
             'WebsiteSearchTool': WebsiteSearchTool(),
             'XMLSearchTool': XMLSearchTool(),
             'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
-            'YoutubeVideoSearchTool': YoutubeVideoSearchTool() 
+            'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
         }
-        # config['tools'] = [tools_dict[tool] for tool in config.get('tools', []) if tool in tools_dict]
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, 'tools.py')
+        tools_dir_path = Path(root_directory) / 'tools'
+        
+        if os.path.isfile(tools_py_path):
+            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
+            # print("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
+        elif tools_dir_path.is_dir():
+            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
+            # print("tools folder exists in the root directory")
+        
         framework = self.framework or config.get('framework')
 
         agents = {}
@@ -86,7 +220,12 @@ class AgentsGenerator:
                 )
                 for tool in details.get('tools', []):
                     if tool in tools_dict:
-                        tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
+                        try:
+                            tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
+                            print(f"Found {tool_class.__name__} for {tool}")
+                        except KeyError:
+                            print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
+                            continue
                         tool_class(agents[role], user_proxy)
 
                 # Preparing tasks for initiate_chats
@@ -121,7 +260,6 @@ class AgentsGenerator:
 
                     task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
                     tasks.append(task)
-            # print(agents)
             crew = Crew(
                 agents=list(agents.values()),
                 tasks=tasks,
@@ -131,91 +269,3 @@ class AgentsGenerator:
             response = crew.kickoff()
             result = f"### Task Output ###\n{response}"
         return result
-    
-
-# # AutoGen Tools example below
-# from typing import Any, Optional
-# import os
-# from autogen import ConversableAgent
-# from autogen_tools import autogen_ScrapeWebsiteTool
-
-# assistant = ConversableAgent(
-#     name="Assistant",
-#     system_message="You are a helpful AI assistant. "
-#     "You can help with website scraping. "
-#     "Return 'TERMINATE' when the task is done.",
-#     llm_config={"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}]},
-# )
-
-# user_proxy = ConversableAgent(
-#     name="User",
-#     llm_config=False,
-#     is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
-#     human_input_mode="NEVER",
-# )
-
-# autogen_ScrapeWebsiteTool(assistant, user_proxy)
-
-# chat_result = user_proxy.initiate_chat(assistant, message="Scrape the official Nodejs website.")
-
-# # CrewAI Tools example below 
-# import os
-# from crewai import Agent, Task, Crew
-# # Importing crewAI tools
-# from crewai_tools import (
-#     DirectoryReadTool,
-#     FileReadTool,
-#     SerperDevTool,
-#     WebsiteSearchTool
-# )
-
-# # Set up API keys
-# os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-# os.environ["OPENAI_API_KEY"] = "Your Key"
-
-# # Instantiate tools
-# docs_tool = DirectoryReadTool(directory='./blog-posts')
-# file_tool = FileReadTool()
-# search_tool = SerperDevTool()
-# web_rag_tool = WebsiteSearchTool()
-
-# # Create agents
-# researcher = Agent(
-#     role='Market Research Analyst',
-#     goal='Provide up-to-date market analysis of the AI industry',
-#     backstory='An expert analyst with a keen eye for market trends.',
-#     tools=[search_tool, web_rag_tool],
-#     verbose=True
-# )
-
-# writer = Agent(
-#     role='Content Writer',
-#     goal='Craft engaging blog posts about the AI industry',
-#     backstory='A skilled writer with a passion for technology.',
-#     tools=[docs_tool, file_tool],
-#     verbose=True
-# )
-
-# # Define tasks
-# research = Task(
-#     description='Research the latest trends in the AI industry and provide a summary.',
-#     expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
-#     agent=researcher
-# )
-
-# write = Task(
-#     description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
-#     expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
-#     agent=writer,
-#     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
-# )
-
-# # Assemble a crew
-# crew = Crew(
-#     agents=[researcher, writer],
-#     tasks=[research, write],
-#     verbose=2
-# )
-
-# # Execute tasks
-# crew.kickoff()

praisonai/auto.py

@@ -1,6 +1,6 @@
 from openai import OpenAI
 from pydantic import BaseModel
-from typing import Dict, List
+from typing import Dict, List, Optional
 import instructor
 import os
 import json
@@ -23,16 +23,34 @@ class TeamStructure(BaseModel):
     roles: Dict[str, RoleDetails]
 
 class AutoGenerator:
-    def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai"):
-        self.config_list = [
+    def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai", config_list: Optional[List[Dict]] = None):
+        """
+        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
+        Note: autogen framework is different from this AutoGenerator class.
+
+        Args:
+            topic (str, optional): The topic for the generated team structure. Defaults to "Movie Story writing about AI".
+            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to "test.yaml".
+            framework (str, optional): The framework for the generated team structure. Defaults to "crewai".
+            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
+                                                          If None, it defaults to using environment variables or hardcoded values.
+        Attributes:
+            config_list (list): A list containing the configuration details for the OpenAI API.
+            topic (str): The specified topic for the generated team structure.
+            agent_file (str): The specified name of the YAML file to save the generated team structure.
+            framework (str): The specified framework for the generated team structure.
+            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
+        """
+        self.config_list = config_list or [
             {
-                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4-turbo-preview"),
+                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
                 'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                'api_key': os.environ.get("OPENAI_API_KEY")
             }
         ]
         self.topic = topic
         self.agent_file = agent_file
-        self.framework = framework
+        self.framework = framework or "crewai"
         self.client = instructor.patch(
             OpenAI(
                 base_url=self.config_list[0]['base_url'],
@@ -42,6 +60,23 @@ class AutoGenerator:
         )
 
     def generate(self):
+        """
+        Generates a team structure for the specified topic.
+
+        Args:
+            None
+
+        Returns:
+            str: The full path of the YAML file containing the generated team structure.
+
+        Raises:
+            Exception: If the generation process fails.
+
+        Usage:
+            generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
+            path = generator.generate()
+            print(path)
+        """
         response = self.client.chat.completions.create(
             model=self.config_list[0]['model'],
             response_model=TeamStructure,
@@ -93,6 +128,20 @@ class AutoGenerator:
             yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
 
     def get_user_content(self):
+        """
+        Generates a prompt for the OpenAI API to generate a team structure.
+
+        Args:
+            None
+
+        Returns:
+            str: The prompt for the OpenAI API.
+
+        Usage:
+            generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
+            prompt = generator.get_user_content()
+            print(prompt)
+        """
         user_content = """Generate a team structure for  \"""" + self.topic + """\" task. 
 No Input data will be provided to the team.
 The team will work in sequence. First role will pass the output to the next role, and so on.
@@ -135,5 +184,5 @@ The final role you create should satisfy the provided task: """ + self.topic + "
         return user_content
 
     
-# generator = AutoGenerator(framework="crewai", topic="Create a snake game in python")
+# generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
 # print(generator.generate())

praisonai/chainlit_ui.py

@@ -0,0 +1,240 @@
+# praisonai/chainlit_ui.py
+from praisonai.agents_generator import AgentsGenerator 
+from praisonai.auto import AutoGenerator 
+import chainlit as cl
+import os
+from chainlit.types import ThreadDict
+from chainlit.input_widget import Select, TextInput
+from typing import Optional
+from dotenv import load_dotenv
+load_dotenv()
+
+framework = "crewai"
+config_list = [
+            {
+                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
+                'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                'api_key': os.environ.get("OPENAI_API_KEY")
+            }
+        ]
+agent_file = "test.yaml"
+
+actions=[
+    cl.Action(name="run", value="run", label="✅ Run"),
+    cl.Action(name="modify", value="modify", label="🔧 Modify"),
+]
+
+@cl.action_callback("run")
+async def on_run(action):
+    await main(cl.Message(content=""))
+
+@cl.action_callback("modify")
+async def on_modify(action):
+    await cl.Message(content="Modify the agents and tools from below settings").send()
+    
+
+@cl.set_chat_profiles
+async def set_profiles(current_user: cl.User):
+    return [
+        cl.ChatProfile(
+            name="Auto",
+            markdown_description="Automatically generate agents and tasks based on your input.",
+            starters=[
+                cl.Starter(
+                    label="Create a movie script",
+                    message="Create a movie script about a futuristic society where AI and humans coexist, focusing on the conflict and resolution between them. Start with an intriguing opening scene.",
+                    icon="/public/movie.svg",
+                ),
+                cl.Starter(
+                    label="Design a fantasy world",
+                    message="Design a detailed fantasy world with unique geography, cultures, and magical systems. Start by describing the main continent and its inhabitants.",
+                    icon="/public/fantasy.svg",
+                ),
+                cl.Starter(
+                    label="Write a futuristic political thriller",
+                    message="Write a futuristic political thriller involving a conspiracy within a global government. Start with a high-stakes meeting that sets the plot in motion.",
+                    icon="/public/thriller.svg",
+                ),
+                cl.Starter(
+                    label="Develop a new board game",
+                    message="Develop a new, innovative board game. Describe the game's objective, rules, and unique mechanics. Create a scenario to illustrate gameplay.",
+                    icon="/public/game.svg",
+                ),
+            ]
+        ),
+        cl.ChatProfile(
+            name="Manual",
+            markdown_description="Manually define your agents and tasks using a YAML file.",
+        ),
+    ]
+
+
+@cl.on_chat_start
+async def start_chat():
+    cl.user_session.set(
+        "message_history",
+        [{"role": "system", "content": "You are a helpful assistant."}],
+    )
+    
+    # Create tools.py if it doesn't exist
+    if not os.path.exists("tools.py"):
+        with open("tools.py", "w") as f:
+            f.write("# Add your custom tools here\n")
+    
+    settings = await cl.ChatSettings(
+        [
+            TextInput(id="Model", label="OpenAI - Model", initial=config_list[0]['model']),
+            TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
+            TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
+            Select(
+                id="Framework",
+                label="Framework",
+                values=["crewai", "autogen"],
+                initial_index=0,
+            ),
+        ]
+    ).send()
+    cl.user_session.set("settings", settings)
+    chat_profile = cl.user_session.get("chat_profile")
+    if chat_profile=="Manual":
+        
+        agent_file = "agents.yaml"
+        full_agent_file_path = os.path.abspath(agent_file)  # Get full path
+        if os.path.exists(full_agent_file_path):
+            with open(full_agent_file_path, 'r') as f:
+                yaml_content = f.read()
+            msg = cl.Message(content=yaml_content, language="yaml")
+            await msg.send()
+            
+                
+        full_tools_file_path = os.path.abspath("tools.py")  # Get full path
+        if os.path.exists(full_tools_file_path):
+            with open(full_tools_file_path, 'r') as f:
+                tools_content = f.read()
+            msg = cl.Message(content=tools_content, language="python")
+            await msg.send()
+
+        settings = await cl.ChatSettings(
+            [
+                TextInput(id="Model", label="OpenAI - Model", initial=config_list[0]['model']),
+                TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
+                TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
+                Select(
+                    id="Framework",
+                    label="Framework",
+                    values=["crewai", "autogen"],
+                    initial_index=0,
+                ),
+                TextInput(id="agents", label="agents.yaml", initial=yaml_content, multiline=True),
+                TextInput(id="tools", label="tools.py", initial=tools_content, multiline=True),
+            ]
+        ).send()
+        cl.user_session.set("settings", settings)
+        
+        res = await cl.AskActionMessage(
+            content="Pick an action!",
+            actions=actions,
+        ).send()
+        if res and res.get("value") == "modify":
+            await cl.Message(content="Modify the agents and tools from below settings", actions=actions).send()
+        elif res and res.get("value") == "run":
+            await main(cl.Message(content="", actions=actions))
+
+    await on_settings_update(settings)
+    
+@cl.on_settings_update
+async def on_settings_update(settings):
+    """Handle updates to the ChatSettings form."""
+    global config_list, framework
+    config_list[0]['model'] = settings["Model"]
+    config_list[0]['base_url'] = settings["BaseUrl"]
+    config_list[0]['api_key'] = settings["ApiKey"]
+    os.environ["OPENAI_API_KEY"] = config_list[0]['api_key']
+    os.environ["OPENAI_MODEL_NAME"] = config_list[0]['model']
+    os.environ["OPENAI_API_BASE"] = config_list[0]['base_url']
+    framework = settings["Framework"]
+    
+    if "agents" in settings:
+        with open("agents.yaml", "w") as f:
+            f.write(settings["agents"])
+    if "tools" in settings:
+        with open("tools.py", "w") as f:
+            f.write(settings["tools"])
+    
+    print("Settings updated")
+
+@cl.on_chat_resume
+async def on_chat_resume(thread: ThreadDict):
+    message_history = cl.user_session.get("message_history", [])
+    root_messages = [m for m in thread["steps"] if m["parentId"] is None]
+    for message in root_messages:
+        if message["type"] == "user_message":
+            message_history.append({"role": "user", "content": message["output"]})
+        elif message["type"] == "ai_message":
+            message_history.append({"role": "assistant", "content": message["content"]})
+    cl.user_session.set("message_history", message_history)
+
+# @cl.step(type="tool")
+# async def tool(data: Optional[str] = None, language: Optional[str] = None):
+#     return cl.Message(content=data, language=language)
+
+@cl.on_message
+async def main(message: cl.Message):
+    """Run PraisonAI with the provided message as the topic."""
+    message_history = cl.user_session.get("message_history")
+    message_history.append({"role": "user", "content": message.content})
+    topic = message.content
+    chat_profile = cl.user_session.get("chat_profile")
+
+    if chat_profile == "Auto":
+        agent_file = "agents.yaml"
+        generator = AutoGenerator(topic=topic, agent_file=agent_file, framework=framework, config_list=config_list)
+        agent_file = generator.generate()
+        agents_generator = AgentsGenerator(agent_file, framework, config_list)
+        result = agents_generator.generate_crew_and_kickoff()
+        msg = cl.Message(content=result)
+        await msg.send()
+        message_history.append({"role": "assistant", "content": message.content})
+    else:  # chat_profile == "Manual"
+        agent_file = "agents.yaml"
+        full_agent_file_path = os.path.abspath(agent_file)  # Get full path
+        full_tools_file_path = os.path.abspath("tools.py")  
+        if os.path.exists(full_agent_file_path):
+            with open(full_agent_file_path, 'r') as f:
+                yaml_content = f.read()
+            # tool_res = await tool()
+            msg_agents = cl.Message(content=yaml_content, language="yaml")
+            await msg_agents.send()
+            if os.path.exists(full_tools_file_path):
+                with open(full_tools_file_path, 'r') as f:
+                    tools_content = f.read()
+                msg_tools = cl.Message(content=tools_content, language="python")
+                await msg_tools.send()
+        else:
+            # If the file doesn't exist, follow the same process as "Auto"
+            generator = AutoGenerator(topic=topic, agent_file=agent_file, framework=framework, config_list=config_list)
+            agent_file = generator.generate()
+
+        agents_generator = AgentsGenerator(agent_file, framework, config_list)
+        result = agents_generator.generate_crew_and_kickoff()
+        msg = cl.Message(content=result, actions=actions)
+        await msg.send()
+        message_history.append({"role": "assistant", "content": message.content})
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get username and password from environment variables
+username = os.getenv("CHAINLIT_USERNAME", "admin")  # Default to "admin" if not found
+password = os.getenv("CHAINLIT_PASSWORD", "admin")  # Default to "admin" if not found
+
+@cl.password_auth_callback
+def auth_callback(username: str, password: str):
+    # Fetch the user matching username from your database
+    # and compare the hashed password with the value stored in the database
+    if (username, password) == (username, password):
+        return cl.User(
+            identifier=username, metadata={"role": "ADMIN", "provider": "credentials"}
+        )
+    else:
+        return None

praisonai/cli.py

@@ -8,19 +8,46 @@ from dotenv import load_dotenv
 from crewai import Agent, Task, Crew
 load_dotenv()
 import autogen
-import gradio as gr
 import argparse
 from .auto import AutoGenerator
 from .agents_generator import AgentsGenerator
-from crewai_tools import *
-from .tools import *
+from .inbuilt_tools import *
+
+try:
+    from chainlit.cli import chainlit_run
+    CHAINLIT_AVAILABLE = True
+except ImportError:
+    CHAINLIT_AVAILABLE = False
+
+try:
+    import gradio as gr
+    GRADIO_AVAILABLE = True
+except ImportError:
+    GRADIO_AVAILABLE = False
 
 class PraisonAI:
-    def __init__(self, agent_file="agents.yaml", framework="crewai", auto=False, init=False):
+    def __init__(self, agent_file="agents.yaml", framework="", auto=False, init=False):
+        """
+        Initialize the PraisonAI object with default parameters.
+
+        Parameters:
+            agent_file (str): The default agent file to use. Defaults to "agents.yaml".
+            framework (str): The default framework to use. Defaults to "crewai".
+            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
+            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
+
+        Attributes:
+            config_list (list): A list of configuration dictionaries for the OpenAI API.
+            agent_file (str): The agent file to use.
+            framework (str): The framework to use.
+            auto (bool): A flag indicating whether to enable auto mode.
+            init (bool): A flag indicating whether to enable initialization mode.
+        """
         self.config_list = [
             {
-                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4-turbo-preview"),
+                'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
                 'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                'api_key': os.environ.get("OPENAI_API_KEY")
             }
         ]
         self.agent_file = agent_file
@@ -29,6 +56,19 @@ class PraisonAI:
         self.init = init
 
     def main(self):
+        """
+        The main function of the PraisonAI object. It parses the command-line arguments,
+        initializes the necessary attributes, and then calls the appropriate methods based on the
+        provided arguments.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+    
+        Returns:
+            Any: Depending on the arguments provided, the function may return a result from the
+            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
+            the successful creation of a file.
+        """
         args = self.parse_args()
         if args is None:
             agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
@@ -42,20 +82,14 @@ class PraisonAI:
         invocation_cmd = "praisonai"
         version_string = f"PraisonAI version {__version__}"
         
-        if args.framework:
-            self.framework = args.framework
+        self.framework = args.framework or self.framework 
         
-        ui = args.ui
-
         if args.agent_file:
-            if args.agent_file == "tests.test": # Argument used for testing purposes
-                full_path = os.path.abspath("agents.yaml")
+            if args.agent_file.startswith("tests.test"): # Argument used for testing purposes. eg: python -m unittest tests.test 
+                print("test")
             else:
-                full_path = os.path.abspath(args.agent_file)
-            self.agent_file = full_path
-        else:
-            full_path = os.path.abspath(self.agent_file)
-            self.agent_file = full_path
+                self.agent_file = args.agent_file
+        
         
         if args.auto or args.init:
             temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
@@ -77,88 +111,129 @@ class PraisonAI:
             print("File {} created successfully".format(self.agent_file))
             return "File {} created successfully".format(self.agent_file)
         
-        if ui:
-            self.create_gradio_interface()
+        if args.ui:
+            if args.ui == "gradio":
+                self.create_gradio_interface()
+            elif args.ui == "chainlit":
+                self.create_chainlit_interface()
+            else:
+                # Modify below code to allow default ui
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
         else:
             agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
             result = agents_generator.generate_crew_and_kickoff()
             return result
             
     def parse_args(self):
+        """
+        Parse the command-line arguments for the PraisonAI CLI.
+
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            argparse.Namespace: An object containing the parsed command-line arguments.
+
+        Raises:
+            argparse.ArgumentError: If the arguments provided are invalid.
+
+        Example:
+            >>> args = praison_ai.parse_args()
+            >>> print(args.agent_file)  # Output: 'agents.yaml'
+        """
         parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
-        parser.add_argument("--framework", choices=["crewai", "autogen"], default="crewai", help="Specify the framework")
-        parser.add_argument("--ui", action="store_true", help="Enable UI mode")
+        parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
+        parser.add_argument("--ui", choices=["chainlit", "gradio"], help="Specify the UI framework (gradio or chainlit).")
         parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
         parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
         parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
         parser.add_argument("--deploy", action="store_true", help="Deploy the application")  # New argument
-
         args, unknown_args = parser.parse_known_args()
 
         if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
             args.agent_file = 'agents.yaml'
         if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
             args.agent_file = 'agents.yaml'
+        if args.agent_file == 'ui':
+            args.ui = 'chainlit'
 
         return args
 
     def create_gradio_interface(self):
-        def generate_crew_and_kickoff_interface(auto_args, framework):
-            self.framework = framework
-            self.agent_file = "test.yaml"
-            generator = AutoGenerator(topic=auto_args , framework=self.framework)
-            self.agent_file = generator.generate()
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
+        """
+        Create a Gradio interface for generating agents and performing tasks.
 
-        gr.Interface(
-            fn=generate_crew_and_kickoff_interface,
-            inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
-            outputs="textbox",
-            title="Praison AI Studio",
-            description="Create Agents and perform tasks",
-            theme="default"
-        ).launch()
+        Args:
+            self (PraisonAI): An instance of the PraisonAI class.
+
+        Returns:
+            None: This method does not return any value. It launches the Gradio interface.
+
+        Raises:
+            None: This method does not raise any exceptions.
+
+        Example:
+            >>> praison_ai.create_gradio_interface()
+        """
+        if GRADIO_AVAILABLE:
+            def generate_crew_and_kickoff_interface(auto_args, framework):
+                """
+                Generate a crew and kick off tasks based on the provided auto arguments and framework.
+
+                Args:
+                    auto_args (list): Topic.
+                    framework (str): The framework to use for generating agents.
+
+                Returns:
+                    str: A string representing the result of generating the crew and kicking off tasks.
+
+                Raises:
+                    None: This method does not raise any exceptions.
+
+                Example:
+                    >>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
+                    >>> print(result)
+                """
+                self.framework = framework
+                self.agent_file = "test.yaml"
+                generator = AutoGenerator(topic=auto_args , framework=self.framework)
+                self.agent_file = generator.generate()
+                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
+                result = agents_generator.generate_crew_and_kickoff()
+                return result
+
+            gr.Interface(
+                fn=generate_crew_and_kickoff_interface,
+                inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
+                outputs="textbox",
+                title="Praison AI Studio",
+                description="Create Agents and perform tasks",
+                theme="default"
+            ).launch()
+        else:
+            print("ERROR: Gradio is not installed. Please install it with 'pip install gradio' to use this feature.") 
+        
+    def create_chainlit_interface(self):
+        """
+        Create a Chainlit interface for generating agents and performing tasks.
+
+        This function sets up a Chainlit application that listens for messages.
+        When a message is received, it runs PraisonAI with the provided message as the topic.
+        The generated agents are then used to perform tasks.
+
+        Returns:
+            None: This function does not return any value. It starts the Chainlit application.
+        """
+        if CHAINLIT_AVAILABLE:
+            os.environ["CHAINLIT_PORT"] = "8082"  
+            import praisonai
+            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), 'chainlit_ui.py')
+            chainlit_run([chainlit_ui_path])
+        else:
+            print("ERROR: Chainlit is not installed. Please install it with 'pip install chainlit' to use the UI.")        
 
 if __name__ == "__main__":
     praison_ai = PraisonAI()
-    praison_ai.main()
-
-
-
-## agents.yaml
-# framework: autogen
-# topic: create movie script about cat in mars
-# roles:
-#   concept_developer:
-#     backstory: Experienced in creating captivating and original story concepts.
-#     goal: Generate a unique concept for a movie script about a cat in Mars
-#     role: Concept Developer
-#     tools:
-#     - search_tool
-#     tasks:
-#       concept_generation:
-#         description: Develop a unique and engaging concept for a movie script about
-#           a cat in Mars.
-#         expected_output: A detailed concept document for the movie script.
-#   scriptwriter:
-#     backstory: Expert in dialogue and script structure, translating concepts into
-#       scripts.
-#     goal: Write a script based on the movie concept
-#     role: Scriptwriter
-#     tasks:
-#       scriptwriting_task:
-#         description: Turn the movie concept into a script, including dialogue and
-#           scenes.
-#         expected_output: A production-ready script for the movie about a cat in Mars.
-#   director:
-#     backstory: Experienced in visualizing scripts and creating compelling storyboards.
-#     goal: Create a storyboard and visualize the script
-#     role: Director
-#     tasks:
-#       storyboard_creation:
-#         description: Create a storyboard for the movie script about a cat in Mars.
-#         expected_output: A detailed storyboard for the movie about a cat in Mars.
-# dependencies: []
-
+    praison_ai.main()

praisonai/deploy.py

@@ -3,21 +3,75 @@ import os
 from dotenv import load_dotenv
 
 class CloudDeployer:
+    """
+    A class for deploying a cloud-based application.
+
+    Attributes:
+        None
+
+    Methods:
+        __init__(self):
+            Loads environment variables from .env file or system and sets them.
+
+    """
     def __init__(self):
+        """
+        Loads environment variables from .env file or system and sets them.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        """
         # Load environment variables from .env file or system
         load_dotenv()
         self.set_environment_variables()
 
     def create_dockerfile(self):
+        """
+        Creates a Dockerfile for the application.
+
+        Parameters:
+            self: An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method creates a Dockerfile in the current directory with the specified content.
+        The Dockerfile is used to build a Docker image for the application.
+        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
+        set the working directory to /app, copy the current directory contents into the container,
+        install the required Python packages (flask, praisonai, gunicorn, and markdown),
+        expose port 8080, and run the application using Gunicorn.
+        """
         with open("Dockerfile", "w") as file:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==0.0.18 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==0.0.30 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             
     def create_api_file(self):
+        """
+        Creates an API file for the application.
+
+        Parameters:
+            self (CloudDeployer): An instance of the CloudDeployer class.
+
+        Returns:
+            None
+
+        This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
+        """
         with open("api.py", "w") as file:
             file.write("from flask import Flask\n")
             file.write("from praisonai import PraisonAI\n")
@@ -36,11 +90,29 @@ class CloudDeployer:
     
     def set_environment_variables(self):
         """Sets environment variables with fallback to .env values or defaults."""
-        os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4-turbo-preview")
+        os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
         os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
         os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
 
     def run_commands(self):
+        """
+        Sets environment variables with fallback to .env values or defaults.
+
+        Parameters:
+            None
+
+        Returns:
+            None
+
+        Raises:
+            None
+
+        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
+
+        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
+        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
+        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
+        """
         self.create_api_file()
         self.create_dockerfile()
         """Runs a sequence of shell commands for deployment, continues on error."""

praisonai/inbuilt_tools/__init__.py

@@ -0,0 +1,2 @@
+# from .base_tool import BaseTool, Tool, tool
+from .autogen_tools import *

praisonai/inbuilt_tools/autogen_tools.py

@@ -1,3 +1,5 @@
+# praisonai/inbuilt_tools/autogen_tools.py
+
 from crewai_tools import (
     CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
     FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
@@ -6,6 +8,60 @@ from crewai_tools import (
 )
 from typing import Any
 from autogen import register_function
+import os
+import importlib
+from pathlib import Path
+import os
+import inspect
+import sys
+
+def create_autogen_tool_function(tool_name):
+    def autogen_tool(assistant, user_proxy):
+        def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
+            def tool_func(query: str) -> Any:
+                tool_instance = tool_class()
+                return tool_instance.run(query=query)
+            register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
+
+        root_directory = os.getcwd()
+        tools_py_path = os.path.join(root_directory, 'tools.py')
+        tools_dir_path = Path(root_directory) / 'tools'
+
+        if os.path.isfile(tools_py_path):
+            print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
+            tool_module = importlib.import_module("tools")
+        elif tools_dir_path.is_dir():
+            print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
+            tool_module = importlib.import_module(f"tools.{tool_name}")
+        else:
+            raise ImportError("Neither tools.py nor tools directory found in the root directory.")
+
+        Tool = getattr(tool_module, tool_name)
+
+        register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)
+
+    return autogen_tool
+
+# Load tools.py
+sys.path.insert(0, os.getcwd())
+root_directory = os.getcwd()
+tools_py_path = os.path.join(root_directory, 'tools.py')
+tools_dir_path = Path(root_directory) / 'tools'
+
+tools_module = None
+
+if os.path.isfile(tools_py_path):
+    print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
+    tools_module = importlib.import_module("tools")
+elif tools_dir_path.is_dir():
+    print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
+    tools_module = importlib.import_module(f"tools.{tool_name}")
+
+# Create autogen_TOOL_NAME_HERE function for each tool
+if tools_module is not None:
+    for name, obj in inspect.getmembers(tools_module):
+        if inspect.isclass(obj):
+            globals()[f"autogen_{name}"] = create_autogen_tool_function(name)
 
 def autogen_CodeDocsSearchTool(assistant, user_proxy):
     def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):

praisonai/test.py

@@ -1,4 +1,5 @@
-import yaml, os
+import yaml
+import os
 from rich import print
 from dotenv import load_dotenv
 from crewai import Agent, Task, Crew
@@ -8,10 +9,21 @@ config_list = [
     {
         'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo"),
         'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+        'api_key': os.environ.get("OPENAI_API_KEY")
     }
 ]
 
 def generate_crew_and_kickoff(agent_file):
+    """
+    This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
+
+    Parameters:
+    agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
+
+    Returns:
+    str: The result of the last task executed by the crew.
+    """
+
     with open(agent_file, 'r') as f:
         config = yaml.safe_load(f)
 

praisonai/tools/__init__.py

@@ -1,2 +0,0 @@
-from .base_tool import BaseTool, Tool, tool
-from .autogen_tools import *

praisonai/tools/base_tool.py

@@ -1,146 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Optional, Type
-
-from langchain_core.tools import StructuredTool
-from pydantic import BaseModel, ConfigDict, Field, validator
-from pydantic.v1 import BaseModel as V1BaseModel
-
-
-class BaseTool(BaseModel, ABC):
-    class _ArgsSchemaPlaceholder(V1BaseModel):
-        pass
-
-    model_config = ConfigDict()
-
-    name: str
-    """The unique name of the tool that clearly communicates its purpose."""
-    description: str
-    """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[V1BaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
-    """The schema for the arguments that the tool accepts."""
-    description_updated: bool = False
-    """Flag to check if the description has been updated."""
-    cache_function: Optional[Callable] = lambda _args, _result: True
-    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
-
-    @validator("args_schema", always=True, pre=True)
-    def _default_args_schema(cls, v: Type[V1BaseModel]) -> Type[V1BaseModel]:
-        if not isinstance(v, cls._ArgsSchemaPlaceholder):
-            return v
-
-        return type(
-            f"{cls.__name__}Schema",
-            (V1BaseModel,),
-            {
-                "__annotations__": {
-                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
-                },
-            },
-        )
-
-    def model_post_init(self, __context: Any) -> None:
-        self._generate_description()
-
-        super().model_post_init(__context)
-
-    def run(
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        print(f"Using Tool: {self.name}")
-        return self._run(*args, **kwargs)
-
-    @abstractmethod
-    def _run(
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        """Here goes the actual implementation of the tool."""
-
-    def to_langchain(self) -> StructuredTool:
-        self._set_args_schema()
-        return StructuredTool(
-            name=self.name,
-            description=self.description,
-            args_schema=self.args_schema,
-            func=self._run,
-        )
-
-    def _set_args_schema(self):
-        if self.args_schema is None:
-            class_name = f"{self.__class__.__name__}Schema"
-            self.args_schema = type(
-                class_name,
-                (V1BaseModel,),
-                {
-                    "__annotations__": {
-                        k: v
-                        for k, v in self._run.__annotations__.items()
-                        if k != "return"
-                    },
-                },
-            )
-
-    def _generate_description(self):
-        args = []
-        for arg, attribute in self.args_schema.schema()["properties"].items():
-            if "type" in attribute:
-                args.append(f"{arg}: '{attribute['type']}'")
-
-        description = self.description.replace("\n", " ")
-        self.description = f"{self.name}({', '.join(args)}) - {description}"
-
-
-class Tool(BaseTool):
-    func: Callable
-    """The function that will be executed when the tool is called."""
-
-    def _run(self, *args: Any, **kwargs: Any) -> Any:
-        return self.func(*args, **kwargs)
-
-
-def to_langchain(
-    tools: list[BaseTool | StructuredTool],
-) -> list[StructuredTool]:
-    return [t.to_langchain() if isinstance(t, BaseTool) else t for t in tools]
-
-
-def tool(*args):
-    """
-    Decorator to create a tool from a function.
-    """
-
-    def _make_with_name(tool_name: str) -> Callable:
-        def _make_tool(f: Callable) -> BaseTool:
-            if f.__doc__ is None:
-                raise ValueError("Function must have a docstring")
-            if f.__annotations__ is None:
-                raise ValueError("Function must have type annotations")
-
-            class_name = "".join(tool_name.split()).title()
-            args_schema = type(
-                class_name,
-                (V1BaseModel,),
-                {
-                    "__annotations__": {
-                        k: v for k, v in f.__annotations__.items() if k != "return"
-                    },
-                },
-            )
-
-            return Tool(
-                name=tool_name,
-                description=f.__doc__,
-                func=f,
-                args_schema=args_schema,
-            )
-
-        return _make_tool
-
-    if len(args) == 1 and callable(args[0]):
-        return _make_with_name(args[0].__name__)(args[0])
-    if len(args) == 1 and isinstance(args[0], str):
-        return _make_with_name(args[0])
-    raise ValueError("Invalid arguments")

praisonai/tools/find_params.py

@@ -1,116 +0,0 @@
-from crewai_tools import (
-    CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
-    FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
-    ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
-    YoutubeVideoSearchTool
-)
-import inspect
-
-# Assuming the tool classes are imported correctly as in the original request
-
-tools_classes = {
-    'CodeDocsSearchTool': CodeDocsSearchTool,
-    'CSVSearchTool': CSVSearchTool,
-    'DirectorySearchTool': DirectorySearchTool,
-    'DOCXSearchTool': DOCXSearchTool,
-    'DirectoryReadTool': DirectoryReadTool,
-    'FileReadTool': FileReadTool,
-    'TXTSearchTool': TXTSearchTool,
-    'JSONSearchTool': JSONSearchTool,
-    'MDXSearchTool': MDXSearchTool,
-    'PDFSearchTool': PDFSearchTool,
-    'RagTool': RagTool,
-    'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool,
-    'ScrapeWebsiteTool': ScrapeWebsiteTool,
-    'WebsiteSearchTool': WebsiteSearchTool,
-    'XMLSearchTool': XMLSearchTool,
-    'YoutubeChannelSearchTool': YoutubeChannelSearchTool,
-    'YoutubeVideoSearchTool': YoutubeVideoSearchTool,
-}
-
-for tool_name, tool_class in tools_classes.items():
-    # Inspect the constructor (__init__) to get its parameters, excluding 'self'
-    constructor_params = inspect.signature(tool_class.__init__).parameters
-    param_names = [p for p in constructor_params if p != 'self']
-
-    # Attempt to instantiate the tool with dummy or default values for parameters
-    try:
-        # Creating a dummy parameters dict with None or empty values based on parameter name
-        dummy_params = {param: None if param != 'website_url' else 'http://example.com' for param in param_names}
-        instance = tool_class(**dummy_params)
-        tool_description = getattr(instance, "description", "No description attribute")
-    except Exception as e:
-        tool_description = f"Error instantiating tool for description extraction: {str(e)}"
-
-    # Printing tool class name, its constructor parameters, and description
-    print(f"Tool Class: {tool_name}")
-    print(f"Constructor Parameters: {', '.join(param_names)}")
-    print(f"Description: {tool_description}\n")
-
-# Tool Class: CodeDocsSearchTool
-# Constructor Parameters: docs_url, kwargs
-# Description: Search a Code Docs content(search_query: 'string', docs_url: 'string') - A tool that can be used to semantic search a query from a Code Docs content.
-
-# Tool Class: CSVSearchTool
-# Constructor Parameters: csv, kwargs
-# Description: Search a CSV's content(search_query: 'string', csv: 'string') - A tool that can be used to semantic search a query from a CSV's content.
-
-# Tool Class: DirectorySearchTool
-# Constructor Parameters: directory, kwargs
-# Description: Search a directory's content(search_query: 'string', directory: 'string') - A tool that can be used to semantic search a query from a directory's content.
-
-# Tool Class: DOCXSearchTool
-# Constructor Parameters: docx, kwargs
-# Description: Search a DOCX's content(search_query: 'string', docx: 'string') - A tool that can be used to semantic search a query from a DOCX's content.
-
-# Tool Class: DirectoryReadTool
-# Constructor Parameters: directory, kwargs
-# Description: List files in directory(directory: 'string') - A tool that can be used to recursively list a directory's content.
-
-# Tool Class: FileReadTool
-# Constructor Parameters: file_path, kwargs
-# Description: Read a file's content(file_path: 'string') - A tool that can be used to read a file's content.
-
-# Tool Class: TXTSearchTool
-# Constructor Parameters: txt, kwargs
-# Description: Search a txt's content(search_query: 'string', txt: 'string') - A tool that can be used to semantic search a query from a txt's content.
-
-# Tool Class: JSONSearchTool
-# Constructor Parameters: json_path, kwargs
-# Description: Search a JSON's content(search_query: 'string', json_path: 'string') - A tool that can be used to semantic search a query from a JSON's content.
-
-# Tool Class: MDXSearchTool
-# Constructor Parameters: mdx, kwargs
-# Description: Search a MDX's content(search_query: 'string', mdx: 'string') - A tool that can be used to semantic search a query from a MDX's content.
-
-# Tool Class: PDFSearchTool
-# Constructor Parameters: pdf, kwargs
-# Description: Search a PDF's content(query: 'string', pdf: 'string') - A tool that can be used to semantic search a query from a PDF's content.
-
-# Tool Class: RagTool
-# Constructor Parameters: data
-# Description: Knowledge base(query: 'string') - A knowledge base that can be used to answer questions.
-
-# Tool Class: ScrapeElementFromWebsiteTool
-# Constructor Parameters: website_url, cookies, css_element, kwargs
-# Description: Read a website content() - A tool that can be used to read http://example.com's content.
-
-# Tool Class: ScrapeWebsiteTool
-# Constructor Parameters: website_url, cookies, kwargs
-# Description: Read website content() - A tool that can be used to read http://example.com's content.
-
-# Tool Class: WebsiteSearchTool
-# Constructor Parameters: website, kwargs
-# Description: Search in a specific website(search_query: 'string', website: 'string') - A tool that can be used to semantic search a query from a specific URL content.
-
-# Tool Class: XMLSearchTool
-# Constructor Parameters: xml, kwargs
-# Description: Search a XML's content(search_query: 'string', xml: 'string') - A tool that can be used to semantic search a query from a XML's content.
-
-# Tool Class: YoutubeChannelSearchTool
-# Constructor Parameters: youtube_channel_handle, kwargs
-# Description: Search a Youtube Channels content(search_query: 'string', youtube_channel_handle: 'string') - A tool that can be used to semantic search a query from a Youtube Channels content.
-
-# Tool Class: YoutubeVideoSearchTool
-# Constructor Parameters: youtube_video_url, kwargs
-# Description: Search a Youtube Video content(search_query: 'string', youtube_video_url: 'string') - A tool that can be used to semantic search a query from a Youtube Video content.

praisonai/tools/tools-autogen-decorator-example-1.py

@@ -1,38 +0,0 @@
-from typing import Any, Optional
-import os
-from autogen import ConversableAgent
-from scrape_website_tool.scrape_website_tool import ScrapeWebsiteTool
-
-assistant = ConversableAgent(
-    name="Assistant",
-    system_message="You are a helpful AI assistant. "
-    "You can help with website scraping. "
-    "Return 'TERMINATE' when the task is done.",
-    llm_config={"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}]},
-)
-
-user_proxy = ConversableAgent(
-    name="User",
-    llm_config=False,
-    is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
-    human_input_mode="NEVER",
-)
-
-from autogen import register_function
-from typing import Any
-
-def register_tool(tool_class, tool_name, tool_description):
-    def tool_func(website_url: str) -> Any:
-        tool_instance = tool_class(website_url=website_url)
-        return tool_instance.run()
-
-    register_function(
-        tool_func,
-        caller=assistant,  # The assistant agent can suggest calls to the tool.
-        executor=user_proxy,  # The user proxy agent can execute the tool calls.
-        name=tool_name,  # By default, the function name is used as the tool name.
-        description=tool_description,  # A description of the tool.
-    )
-register_tool(ScrapeWebsiteTool, "scrape_website_tool", "A tool for scraping specific websites")
-
-chat_result = user_proxy.initiate_chat(assistant, message="Scrape the official Nodejs website.")

praisonai/tools/tools-autogen-example-2.py

@@ -1,35 +0,0 @@
-from typing import Any, Optional
-import os
-from autogen import ConversableAgent
-from scrape_website_tool.scrape_website_tool import ScrapeWebsiteTool
-
-def scrape_website_tool(website_url: str) -> Any:
-    scrape_tool = ScrapeWebsiteTool(website_url=website_url)
-    return scrape_tool.run()
-
-assistant = ConversableAgent(
-    name="Assistant",
-    system_message="You are a helpful AI assistant. "
-    "You can help with website scraping. "
-    "Return 'TERMINATE' when the task is done.",
-    llm_config={"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}]},
-)
-
-user_proxy = ConversableAgent(
-    name="User",
-    llm_config=False,
-    is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
-    human_input_mode="NEVER",
-)
-
-from autogen import register_function
-# Register the ScrapeWebsiteTool function to the agent and user proxy to add a website scraping tool.
-register_function(
-    scrape_website_tool,
-    caller=assistant,  # The assistant agent can suggest calls to the scrape website tool.
-    executor=user_proxy,  # The user proxy agent can execute the scrape website tool calls.
-    name="scrape_website_tool",  # By default, the function name is used as the tool name.
-    description="A tool for scraping specific websites",  # A description of the tool.
-)
-
-chat_result = user_proxy.initiate_chat(assistant, message="Scrape the official Python website.")

praisonai/tools/tools-autogen-example.py

@@ -1,23 +0,0 @@
-from typing import Any, Optional
-import os
-from autogen import ConversableAgent
-from autogen_tools import autogen_ScrapeWebsiteTool
-
-assistant = ConversableAgent(
-    name="Assistant",
-    system_message="You are a helpful AI assistant. "
-    "You can help with website scraping. "
-    "Return 'TERMINATE' when the task is done.",
-    llm_config={"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}]},
-)
-
-user_proxy = ConversableAgent(
-    name="User",
-    llm_config=False,
-    is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
-    human_input_mode="NEVER",
-)
-
-autogen_ScrapeWebsiteTool(assistant, user_proxy)
-
-chat_result = user_proxy.initiate_chat(assistant, message="Scrape the official Nodejs website.")

praisonai/tools/tools-crewai-example.py

@@ -1,134 +0,0 @@
-
-import os
-from crewai import Agent, Task, Crew
-from crewai_tools import (
-    DirectoryReadTool,
-    FileReadTool,
-    SerperDevTool,
-    WebsiteSearchTool
-)
-
-os.environ["SERPER_API_KEY"] = "Your Key"
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
-docs_tool = DirectoryReadTool(directory='./blog-posts')
-file_tool = FileReadTool()
-search_tool = SerperDevTool()
-web_rag_tool = WebsiteSearchTool()
-
-researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[search_tool, web_rag_tool],
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
-    tools=[docs_tool, file_tool],
-    verbose=True
-)
-
-research = Task(
-    description='Research the latest trends in the AI industry and provide a summary.',
-    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
-    agent=researcher
-)
-
-write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst\'s summary. Draw inspiration from the latest blog posts in the directory.',
-    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
-    agent=writer,
-    output_file='blog-posts/new_post.md'
-)
-
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research, write],
-    verbose=2
-)
-
-crew.kickoff()
-
-from crewai_tools import DirectoryReadTool
-
-tool = DirectoryReadTool()
-
-toos = DirectoryReadTool(directory='./directory')
-
-from crewai_tools import BaseTool
-
-class MyCustomTool(BaseTool):
-    name: str = "Name of my tool"
-    description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
-
-    def _run(self, argument: str) -> str:
-        return "Result from custom tool"
-
-from crewai_tools import tool
-
-@tool("Name of my tool")
-def my_tool(question: str) -> str:
-    return "Result from my tool"
-
-import json
-import requests
-from crewai import Agent
-from crewai.tools import tool
-from unstructured.partition.html import partition_html
-
-@tool("Integration with a given API")
-def integration_tool(argument: str) -> str:
-    return "Results from integration tool"
-
-agent = Agent(
-    role='Research Analyst',
-    goal='Provide up-to-date market analysis',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[integration_tool]
-)
-
-from crewai import Agent
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
-
-os.environ["SERPER_API_KEY"] = "Your Key"
-
-search = GoogleSerperAPIWrapper()
-
-serper_tool = Tool(
-  name="Intermediate Answer",
-  func=search.run,
-  description="Useful for search-based queries",
-)
-
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[serper_tool]
-)
-# Tool	Description
-# CodeDocsSearchTool	A RAG tool optimized for searching through code documentation and related technical documents.
-# CSVSearchTool	A RAG tool designed for searching within CSV files, tailored to handle structured data.
-# DirectorySearchTool	A RAG tool for searching within directories, useful for navigating through file systems.
-# DOCXSearchTool	A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.
-# DirectoryReadTool	Facilitates reading and processing of directory structures and their contents.
-# FileReadTool	Enables reading and extracting data from files, supporting various file formats.
-# GithubSearchTool	A RAG tool for searching within GitHub repositories, useful for code and documentation search.
-# SeperDevTool	A specialized tool for development purposes, with specific functionalities under development.
-# TXTSearchTool	A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.
-# JSONSearchTool	A RAG tool designed for searching within JSON files, catering to structured data handling.
-# MDXSearchTool	A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.
-# PDFSearchTool	A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.
-# PGSearchTool	A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries.
-# RagTool	A general-purpose RAG tool capable of handling various data sources and types.
-# ScrapeElementFromWebsiteTool	Enables scraping specific elements from websites, useful for targeted data extraction.
-# ScrapeWebsiteTool	Facilitates scraping entire websites, ideal for comprehensive data collection.
-# WebsiteSearchTool	A RAG tool for searching website content, optimized for web data extraction.
-# XMLSearchTool	A RAG tool designed for searching within XML files, suitable for structured data formats.
-# YoutubeChannelSearchTool	A RAG tool for searching within YouTube channels, useful for video content analysis.
-# YoutubeVideoSearchTool	A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.

public/fantasy.svg

@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M840.5 798.2L662.3 599.5l-151 173.7-173.7-173.7-167.7 201c-21 30.4 0.9 71.8 37.9 71.6l594.7-3.3c36.2-0.1 57.8-40.3 38-70.6z" fill="#FFB89A" /><path d="M741.6 647.3l-52.3-47.7c-12.2-11.2-31.2-10.3-42.4 1.9s-10.3 31.2 1.9 42.4l52.3 47.7c5.8 5.3 13 7.8 20.2 7.8 8.1 0 16.2-3.3 22.2-9.8 11.2-12.1 10.3-31.1-1.9-42.3zM631.2 546.5c-12.4-11-31.4-9.8-42.3 2.6l-98.8 111.7-171-165.7L87.9 724.7c-11.8 11.7-11.8 30.7-0.1 42.4 5.9 5.9 13.6 8.9 21.3 8.9 7.6 0 15.3-2.9 21.1-8.7l189.4-188.1 173.8 168.5L633.8 589c11-12.5 9.8-31.5-2.6-42.5z" fill="#33CC99" /><path d="M721.3 342.8m-35.1 0a35.1 35.1 0 1 0 70.2 0 35.1 35.1 0 1 0-70.2 0Z" fill="#33CC99" /><path d="M743.2 175.1H191.6c-70.6 0-128.3 57.7-128.3 128.3v499.2c0 70.6 57.7 128.3 128.3 128.3h551.5c70.6 0 128.3-57.7 128.3-128.3V303.5c0.1-70.6-57.7-128.4-128.2-128.4z m68.3 627.6c0 18.1-7.1 35.2-20.1 48.2-13 13-30.1 20.1-48.2 20.1H191.6c-18.1 0-35.2-7.1-48.2-20.1-13-13-20.1-30.1-20.1-48.2V303.5c0-18.1 7.1-35.2 20.1-48.2 13-13 30.1-20.1 48.2-20.1h551.5c18.1 0 35.2 7.1 48.2 20.1 13 13 20.1 30.1 20.1 48.2v499.2z" fill="#45484C" /><path d="M799.7 90.9H237.2c-16.6 0-30 13.4-30 30s13.4 30 30 30h562.4c26.1 0 50.8 10.3 69.4 28.9 18.6 18.6 28.9 43.3 28.9 69.4v482.4c0 16.6 13.4 30 30 30s30-13.4 30-30V249.2C958 161.9 887 90.9 799.7 90.9z" fill="#45484C" /></svg>

public/game.svg

@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M570.2 842c-50.6 0-278.7-180-278.7-401.9 0-58.8-2.9-133.1-1-183.9-50.8 3.2-91.4 45.7-91.4 97.3v272.1c37.4 194.7 137.5 334 255.2 334 69.5 0 132.9-48.6 180.9-128.5-20.8 7.1-42.6 10.9-65 10.9z" fill="#FFB89A" /><path d="M926.1 191.8C900.5 74.1 817.9 62.1 704.9 62.1c-29.1 0-60.3 0.8-93 0.8-36 0-70.5-1.1-102.5-1.1-109.7 0-189.8 12.5-201.3 123.7-20.4 198.3 30 617.1 306.1 617.1S939 414.3 926.1 191.8z m-76.9 268.5c-9.5 47.9-22.3 90.8-38.1 127.7-16.8 39.2-37 71.4-60 95.8-37.3 39.5-82.1 58.7-137 58.7-53.4 0-97.6-20.1-134.9-61.6-45.5-50.5-79.8-131.5-99-234.2-15.6-83.5-20.3-178.9-12.4-255.2 1.8-17.3 5.7-30.7 11.6-39.8 4.4-6.8 10.1-11.7 18.7-15.8 25.8-12.5 70.8-14.2 111.4-14.2 15 0 30.7 0.2 47.3 0.5 17.8 0.3 36.2 0.6 55.2 0.6 17.2 0 33.9-0.2 50-0.4 15.1-0.2 29.3-0.4 43.1-0.4 44.5 0 89.5 1.8 118 15.1 15.9 7.4 33.4 20.8 43.6 63 2.6 53.3 3.6 153.5-17.5 260.2z" fill="#4E5155" /><path d="M532 841.7c-32.5 22.3-70.6 33.7-113.2 33.7-29.7 0-57.3-6-82.1-17.7-23.2-11-44.7-27.4-63.9-48.7-46-50.9-80.3-131.3-99.2-232.4-15.1-80.6-19.6-172.9-12-246.8 3-29.5 12-50.2 27.5-63.2 14.2-12 35.1-19.2 65.8-22.9 16.5-2 28.2-16.9 26.3-33.3-2-16.5-16.9-28.2-33.3-26.3-42.9 5.1-73.8 16.7-97.4 36.5-27.9 23.5-43.8 57.2-48.5 103-8.2 79.3-3.4 178.1 12.7 264 9.7 51.9 23.4 99.4 40.6 141.2 19.8 48.1 44.4 88.6 73 120.4 51.6 57.2 115.7 86.2 190.6 86.2 55 0 104.5-14.9 147.2-44.2 13.7-9.4 17.1-28.1 7.7-41.7-9.4-13.7-28.1-17.2-41.8-7.8z" fill="#4E5155" /><path d="M519.7 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.5-30-30-30zM299.5 385.5c0-16.6-13.4-30-30-30s-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3zM754.6 248.5c-16.6 0-30 13.4-30 30v91.3c0 16.6 13.4 30 30 30s30-13.4 30-30v-91.3c0-16.6-13.4-30-30-30zM716.7 554.5c0-16.6-13.4-30-30-30H551v30c0 58.5 38.1 123.7 92.8 123.7 22.9 0 45-11.9 62.2-33.6 10.3-13 8.1-31.9-4.9-42.1-13-10.3-31.9-8.1-42.1 4.9-5.3 6.7-11.1 10.9-15.1 10.9-4.3 0-11.9-5.1-19.1-16.4-3.3-5.3-6.2-11.2-8.4-17.4h70.4c16.4 0 29.9-13.4 29.9-30zM401.6 704c-25.4 0-46.1-24.2-46.1-53.9 0-16.6-13.4-30-30-30s-30 13.4-30 30c0 62.8 47.6 113.9 106.1 113.9 16.6 0 30-13.4 30-30s-13.5-30-30-30z" fill="#33CC99" /></svg>

public/movie.svg

@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M861.9 383.8H218.1c-36.4 0-66.1-29.8-66.1-66.1V288c0-36.4 29.8-66.1 66.1-66.1h643.8c36.4 0 66.1 29.8 66.1 66.1v29.7c0 36.3-29.8 66.1-66.1 66.1z" fill="#FFB89A" /><path d="M822.9 129.2H199.8c-77.2 0-140.4 63.2-140.4 140.4v487.2c0 77.2 63.2 140.4 140.4 140.4h623.1c77.2 0 140.4-63.2 140.4-140.4V269.6c0-77.2-63.2-140.4-140.4-140.4z m80.4 177H760.4L864.6 201c5.4 3.3 10.4 7.3 15 11.8 15.3 15.3 23.7 35.4 23.7 56.8v36.6z m-673.3 0l104-117h61.3l-109.1 117H230z m247.4-117h169.2L532 306.2H368.3l109.1-117z m248.8 0h65.6L676 306.2h-60l112.5-114.8-2.3-2.2zM143 212.9c15.3-15.3 35.4-23.7 56.8-23.7h53.9l-104 117h-30.4v-36.5c0.1-21.4 8.5-41.5 23.7-56.8z m736.6 600.7c-15.3 15.3-35.4 23.7-56.8 23.7h-623c-21.3 0-41.5-8.4-56.8-23.7-15.3-15.3-23.7-35.4-23.7-56.8V366.2h783.9v390.6c0.1 21.3-8.3 41.5-23.6 56.8z" fill="#45484C" /><path d="M400.5 770.6V430.9L534.1 508c14.3 8.3 19.3 26.6 11 41-8.3 14.3-26.6 19.3-41 11l-43.6-25.2v131.8l114.1-65.9-7.5-4.3c-14.3-8.3-19.3-26.6-11-41 8.3-14.3 26.6-19.3 41-11l97.5 56.3-294.1 169.9z" fill="#33CC99" /></svg>

public/thriller.svg

@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 1024 1024" class="icon"  version="1.1" xmlns="http://www.w3.org/2000/svg"><path d="M188.3 766.5a94.4 135.8 0 1 0 188.8 0 94.4 135.8 0 1 0-188.8 0Z" fill="#FFB89A" /><path d="M931.5 397s0-0.1 0 0c-34.2-82.6-119.3-141-218.8-141-129.7 0-234.9 99.3-234.9 221.9 0 52.1 19.1 100.1 50.9 138 1 14.5 1.8 29.1 1.8 43.6 0 148.5 98.1 269 219.2 269 121 0 219.2-120.4 219.2-269 0-70.1-1.7-214.7-37.4-262.5z m-36.6 347.5c-8.7 25.3-21.1 47.9-36.8 67.1-29.8 36.5-68.3 56.7-108.5 56.7s-78.7-20.1-108.5-56.7c-15.7-19.2-28-41.8-36.8-67.1-9.3-26.9-13.9-55.5-13.9-85.1 0-16.8-1-33.5-2-47.7l-1.3-19.5-12.6-15c-24.1-28.6-36.8-63-36.8-99.3 0-89.3 78.5-161.9 174.9-161.9 36.4 0 71.4 10.3 101 29.7 28.4 18.7 65.5 81.7 65.5 81.7s17.9 27.5 24.7 98.2c4.5 46.5 5 95.9 5 133.8 0.1 29.6-4.6 58.2-13.9 85.1zM377.1 219.9c-51.8 0-93.8 42-93.8 93.8s42 93.8 93.8 93.8 93.8-42 93.8-93.8-42-93.8-93.8-93.8z m0 127.5c-18.6 0-33.8-15.2-33.8-33.8 0-18.6 15.2-33.8 33.8-33.8 18.6 0 33.8 15.2 33.8 33.8 0 18.7-15.1 33.8-33.8 33.8z" fill="#45484C" /><path d="M521.2 206.7m-50.3 0a50.3 50.3 0 1 0 100.6 0 50.3 50.3 0 1 0-100.6 0Z" fill="#45484C" /><path d="M653 156.4m-50.3 0a50.3 50.3 0 1 0 100.6 0 50.3 50.3 0 1 0-100.6 0Z" fill="#45484C" /><path d="M781.9 158.4m-50.3 0a50.3 50.3 0 1 0 100.6 0 50.3 50.3 0 1 0-100.6 0Z" fill="#45484C" /><path d="M909 206.7m-50.3 0a50.3 50.3 0 1 0 100.6 0 50.3 50.3 0 1 0-100.6 0Z" fill="#45484C" /><path d="M263.9 602.7c44.7 0 81 31.5 81 70.3 0 20.9-10.2 35.9-18.7 44.8l-15.9 19.7-0.5 27.2c0.7 7.2 0.6 16.9 0.6 24.7v4.8c0 33.7-27.4 61.2-61.2 61.2-14.9 0-33.3-9.6-48.1-25-15.2-15.9-24.6-35.9-24.6-52.3v-3.2c0-12.7 0-36.2 1-60.2 1.4-33 7.4-57.3 7.4-57.3 3.9-14.7 13.4-28.2 26.8-38 14.8-11 32.8-16.7 52.2-16.7m0-60c-66.4 0-122 42.4-137 99.4-10.9 23-10.4 112.6-10.4 135.9 0 66.9 65.8 137.3 132.7 137.3 66.9 0 121.2-54.3 121.2-121.2 0-9.2 0.3-23-0.8-34.9 22-23 35.4-53.2 35.4-86.3-0.1-71.9-63.2-130.2-141.1-130.2zM444.4 559.9c-26.4 0-47.8 21.4-47.8 47.8s21.4 47.8 47.8 47.8 47.8-21.4 47.8-47.8-21.4-47.8-47.8-47.8zM377.1 494.5c-15.2 0-27.5 12.3-27.5 27.5s12.3 27.5 27.5 27.5 27.5-12.3 27.5-27.5c0-15.3-12.3-27.5-27.5-27.5zM288.1 471.5c-15.2 0-27.5 12.3-27.5 27.5s12.3 27.5 27.5 27.5 27.5-12.3 27.5-27.5-12.4-27.5-27.5-27.5zM188.3 477.9c-15.2 0-27.5 12.3-27.5 27.5s12.3 27.5 27.5 27.5 27.5-12.3 27.5-27.5-12.3-27.5-27.5-27.5zM100.6 538.4c-15.2 0-27.5 12.3-27.5 27.5s12.3 27.5 27.5 27.5 27.5-12.3 27.5-27.5c-0.1-15.2-12.4-27.5-27.5-27.5z" fill="#45484C" /><path d="M670.1 584.6c-41.4 0-80.2-20.3-103.9-54.3-9.5-13.6-6.2-32.3 7.4-41.8 13.6-9.5 32.3-6.2 41.8 7.4 12.5 17.9 33 28.6 54.7 28.6 36.8 0 66.7-29.9 66.7-66.7 0-19.8-8.7-38.4-23.9-51.2-12.7-10.6-14.4-29.6-3.7-42.3s29.6-14.4 42.3-3.7c28.9 24.2 45.4 59.6 45.4 97.2-0.1 70-56.9 126.8-126.8 126.8z" fill="#33CC99" /><path d="M853 556.4c-26 0-49.6-14.5-60.1-36.9-7-15-0.6-32.9 14.4-39.9s32.9-0.6 39.9 14.4c0.3 0.6 2.2 2.4 5.8 2.4 1.2 0 2.3-0.2 3.3-0.6 15.5-5.9 32.8 1.8 38.7 17.3 5.9 15.5-1.8 32.8-17.3 38.7-7.9 3.1-16.2 4.6-24.7 4.6z" fill="#33CC99" /></svg>

pyproject.toml

@@ -1,27 +1,30 @@
 [tool.poetry]
-name = "praisonAI"
-version = "0.0.18"
-description = "praisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
+name = "PraisonAI"
+version = "0.0.30"
+description = "PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
 readme = "README.md"
 
 [tool.poetry.urls]
-Homepage = "https://github.com/mervinpraison/praisonAI"
-Repository = "https://github.com/mervinpraison/praisonAI"
+Homepage = "https://docs.praison.ai"
+Repository = "https://github.com/mervinpraison/PraisonAI"
 
 [tool.setuptools]
-packages = ["praisonAI"]
+packages = ["PraisonAI"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<3.13"
 rich = ">=13.7"
 pyautogen = ">=0.2.19"
-crewai = ">=0.22.5"
-gradio = ">=4.20.0"
-Flask = ">=3.0.0"
+crewai = ">=0.30.4"
 markdown = ">=3.5"
-crewai-tools = "^0.1.1"
+crewai-tools = "^0.2.6"
+praisonai-tools = ">=0.0.4"
+blinker = ">=1.8.2"
+Flask = ">=3.0.0"
+chainlit = {version = "^1.1.301", optional = true}
+gradio = {version = ">=4.26.0", optional = true}
 
 [tool.poetry.dev-dependencies]
 pytest = "^8.0.0"
@@ -32,4 +35,8 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
-praisonai = "praisonai.__main__:main"
+praisonai = "praisonai.__main__:main"
+
+[tool.poetry.extras]
+ui = ["chainlit"]
+gradio = ["gradio"]

requirements.txt

@@ -1,4 +1,8 @@
 rich>=13.7
 pyautogen>=0.2.19
-crewai>=0.22.5
-gradio>=4.20.0
+crewai>=0.30.4
+gradio>=4.20.0
+chainlit>=1.1.301
+crewai_tools
+duckduckgo_search
+praisonai_tools

switch_llm_provider.sh

@@ -17,4 +17,14 @@ elif [ "$1" = "openai" ] || [ -z "$1" ]; then
   export OPENAI_API_BASE=https://api.openai.com/v1
   export OPENAI_API_KEY=$(echo $OAI_API_KEY)
   echo "OpenAI configuration initiated with model $MODEL_NAME."
+elif [ "$1" = "ollama" ]; then
+  # Check for a second argument for the model name, default to a custom model if not provided
+  MODEL_NAME=${2:-default-ollama-model}
+  # Set environment variables for Ollama local configuration with dynamic model name
+  export OPENAI_MODEL_NAME=$MODEL_NAME
+  export OPENAI_API_BASE=http://127.0.0.1:11434
+  export OPENAI_API_KEY=$(echo $OLLAMA_API_KEY)
+  # Set up SSH public key
+  export SSH_PUBLIC_KEY="ssh-ed25519 AAAA..."
+  echo "Ollama local model configuration initiated with model $MODEL_NAME.
 fi

tests/advanced_example.py

@@ -5,7 +5,8 @@ def advanced():
         agent_file="agents.yaml",
         framework="autogen",
     )
-    praison_ai.main()
+    print(praison_ai)
+    return praison_ai.main()
 
 if __name__ == "__main__":
-    advanced()
+    print(advanced())

tests/auto_example.py

@@ -6,7 +6,7 @@ def auto():
         framework="autogen"
     )
     print(praison_ai.framework)
-    praison_ai.main()
+    return praison_ai.main()
 
 if __name__ == "__main__":
-    auto()
+    print(auto())

tests/basic_example.py

@@ -2,7 +2,7 @@ from praisonai import PraisonAI
 
 def main():
     praison_ai = PraisonAI(agent_file="agents.yaml")
-    praison_ai.main()
+    return praison_ai.main()
 
 if __name__ == "__main__":
-    main()
+    print(main())

tests/inbuilt-tool-agents.yaml

@@ -0,0 +1,42 @@
+framework: crewai
+topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
+roles:
+  story_developer:
+    backstory: Experienced in crafting compelling narratives for a diverse audience.
+    goal: Develop an engaging storyline for a movie about cats in Mars
+    role: Story Developer
+    tasks:
+      storyline_creation:
+        description: Create a captivating storyline for a movie about cats in Mars
+          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
+        expected_output: Detailed storyline with key plot points, character motivations,
+          and setting descriptions.
+    tools:
+    - ScrapeWebsiteTool
+  screenwriter:
+    backstory: Proficient in translating complex narratives into engaging screenplay
+      format.
+    goal: Transform the storyline into a script
+    role: Screenwriter
+    tasks:
+      script_creation:
+        description: Transform the developed storyline into a script for the movie
+          about cats in Mars.
+        expected_output: Complete script with dialogues, scenes, and stage directions
+          ready for production.
+    tools:
+    - ''
+  director:
+    backstory: Experienced in visual storytelling and bringing scripts to life on
+      screen.
+    goal: Oversee the production of the movie
+    role: Director
+    tasks:
+      movie_production:
+        description: Take the script and lead the team to produce the movie 'Cats
+          in Mars' based on the provided content and script.
+        expected_output: Completed movie ready for distribution with compelling visuals
+          and engaging storytelling.
+    tools:
+    - ''
+dependencies: []

tests/search-tool-agents.yaml

@@ -0,0 +1,42 @@
+framework: crewai
+topic: create a movie about cat in mars
+roles:
+  story_developer:
+    backstory: Experienced in crafting compelling narratives for a diverse audience.
+    goal: Develop an engaging storyline for a movie about cats in Mars
+    role: Story Developer
+    tasks:
+      storyline_creation:
+        description: Create a captivating storyline for a movie about cats in Mars.
+          Use Internet Search Tool to understand the latest story writing ideas.
+        expected_output: Detailed storyline with key plot points, character motivations,
+          and setting descriptions.
+    tools:
+    - internet_search_tool
+  screenwriter:
+    backstory: Proficient in translating complex narratives into engaging screenplay
+      format.
+    goal: Transform the storyline into a script
+    role: Screenwriter
+    tasks:
+      script_creation:
+        description: Transform the developed storyline into a script for the movie
+          about cats in Mars.
+        expected_output: Complete script with dialogues, scenes, and stage directions
+          ready for production.
+    tools:
+    - ''
+  director:
+    backstory: Experienced in visual storytelling and bringing scripts to life on
+      screen.
+    goal: Oversee the production of the movie
+    role: Director
+    tasks:
+      movie_production:
+        description: Take the script and lead the team to produce the movie 'Cats
+          in Mars' based on the provided content and script.
+        expected_output: Completed movie ready for distribution with compelling visuals
+          and engaging storytelling.
+    tools:
+    - ''
+dependencies: []

tests/source/autogen_function_tools.py

@@ -41,4 +41,4 @@ register_function(
     description="A simple calculator",  # A description of the tool.
 )
 
-chat_result = user_proxy.initiate_chat(assistant, message="What is (44232 + 13312 / (232 - 32)) * 5?")
+chat_result = user_proxy.initiate_chat(assistant, message="What is (44232 + 13312 / (232 - 32)) * 5?")

tests/source/autogen_langchain_tools.py

@@ -0,0 +1,99 @@
+import math
+import os
+from typing import Optional, Type
+
+# Starndard Langchain example
+from langchain.agents import create_spark_sql_agent
+from langchain.agents.agent_toolkits import SparkSQLToolkit
+from langchain.chat_models import ChatOpenAI
+
+# Import things that are needed generically
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.tools import BaseTool
+from langchain.tools.file_management.read import ReadFileTool
+from langchain.utilities.spark_sql import SparkSQL
+
+import autogen
+
+class CircumferenceToolInput(BaseModel):
+    radius: float = Field()
+
+
+class CircumferenceTool(BaseTool):
+    name = "circumference_calculator"
+    description = "Use this tool when you need to calculate a circumference using the radius of a circle"
+    args_schema: Type[BaseModel] = CircumferenceToolInput
+
+    def _run(self, radius: float):
+        return float(radius) * 2.0 * math.pi
+
+
+def get_file_path_of_example():
+    current_dir = os.path.dirname(__file__)
+    file_path = os.path.join(current_dir, "radius.txt")
+    return file_path
+
+# Define a function to generate llm_config from a LangChain tool
+def generate_llm_config(tool):
+    # Define the function schema based on the tool's args_schema
+    function_schema = {
+        "name": tool.name.lower().replace(" ", "_"),
+        "description": tool.description,
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    }
+
+    if tool.args is not None:
+        function_schema["parameters"]["properties"] = tool.args
+
+    return function_schema
+
+
+# Instantiate the ReadFileTool
+read_file_tool = ReadFileTool()
+custom_tool = CircumferenceTool()
+
+# Construct the llm_config
+llm_config = {
+    # Generate functions config for the Tool
+    "functions": [
+        generate_llm_config(custom_tool),
+        generate_llm_config(read_file_tool),
+    ],
+    "config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}],
+    "timeout": 120,
+}
+
+user_proxy = autogen.UserProxyAgent(
+    name="user_proxy",
+    is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
+    human_input_mode="NEVER",
+    max_consecutive_auto_reply=10,
+    code_execution_config={
+        "work_dir": "coding",
+        "use_docker": False,
+    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
+)
+
+# Register the tool and start the conversation
+user_proxy.register_function(
+    function_map={
+        custom_tool.name: custom_tool._run,
+        read_file_tool.name: read_file_tool._run,
+    }
+)
+
+chatbot = autogen.AssistantAgent(
+    name="chatbot",
+    system_message="For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.",
+    llm_config=llm_config,
+)
+
+user_proxy.initiate_chat(
+    chatbot,
+    message=f"Read the file with the path {get_file_path_of_example()}, then calculate the circumference of a circle that has a radius of that files contents.",  # 7.81mm in the file
+    llm_config=llm_config,
+)

tests/source/crewai_tools.py

@@ -0,0 +1,71 @@
+import os
+from crewai import Agent, Task, Crew
+from duckduckgo_search import DDGS
+from langchain.tools import tool
+
+@tool("Internet Search Tool")
+def internet_search_tool(query: str) -> list:
+    """Search Internet for relevant information based on a query."""
+    ddgs = DDGS()
+    results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+    return results
+
+# Define agents
+researcher = Agent(
+    role='Researcher',
+    goal='Gather and analyze information on specific topics',
+    verbose=True,
+    backstory=(
+        "As a seasoned researcher, you have a keen eye for detail and a "
+        "deep understanding of your field. You're adept at sifting through "
+        "information to find the most relevant and accurate data."
+    ),
+    tools=[internet_search_tool],
+    allow_delegation=True
+)
+
+writer = Agent(
+    role='Writer',
+    goal='Compose informative and engaging articles based on research findings',
+    verbose=True,
+    backstory=(
+        "With a talent for storytelling, you translate complex ideas into "
+        "clear, compelling narratives. Your articles are well-researched, "
+        "thought-provoking, and accessible to a broad audience."
+    ),
+    tools=[internet_search_tool],
+    allow_delegation=False
+)
+
+# Define tasks
+research_task = Task(
+    description=(
+        "Investigate the latest trends in renewable energy technologies. "
+        "Identify key advancements, challenges, and potential impacts on "
+        "global energy policies."
+    ),
+    expected_output='A detailed report summarizing the findings.',
+    tools=[internet_search_tool],
+    agent=researcher,
+)
+
+writing_task = Task(
+    description=(
+        "Based on the research findings, write an article that highlights "
+        "the importance of renewable energy innovations. The article should "
+        "educate the public on how these technologies can contribute to "
+        "sustainable development."
+    ),
+    expected_output='An engaging and informative article suitable for publication.',
+    tools=[internet_search_tool],
+    agent=writer,
+)
+
+# Create and kick off the crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task]
+)
+
+result = crew.kickoff(inputs={'topic': 'renewable energy'})
+print(result)

tests/source/radius.txt

@@ -0,0 +1 @@
+7.81mm

tests/test.py

@@ -1,47 +1,64 @@
 # tests/test.py
-# from .debug import * # Uncomment this line to import the debug.py file for debugging
 import unittest
-import os
+import subprocess
 from praisonai.cli import PraisonAI
 from .advanced_example import advanced
 from .basic_example import main
 from .auto_example import auto
+from xmlrunner import XMLTestRunner
 
-class TestGenerateCrewAndKickoff(unittest.TestCase):
-    def test_generate_crew_and_kickoff_with_autogen_framework(self):
+class TestPraisonAIFramework(unittest.TestCase):
+    def test_main_with_autogen_framework(self):
         praison_ai = PraisonAI(agent_file='tests/autogen-agents.yaml')
-        result = praison_ai.generate_crew_and_kickoff()
-        # Assert the result
+        result = praison_ai.main()
         self.assertIn('### Task Output ###', result)
 
-    def test_generate_crew_and_kickoff_with_custom_framework(self):
+    def test_main_with_custom_framework(self):
         praison_ai = PraisonAI(agent_file='tests/crewai-agents.yaml')
-        result = praison_ai.generate_crew_and_kickoff()
-        # Assert the result
+        result = praison_ai.main()
         self.assertIn('### Task Output ###', result)
-class TestPraisonAICommand(unittest.TestCase):
+
+    def test_main_with_internet_search_tool(self):
+        praison_ai = PraisonAI(agent_file='tests/search-tool-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)
+
+    def test_main_with_built_in_tool(self):
+        praison_ai = PraisonAI(agent_file='tests/inbuilt-tool-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)
+
+class TestPraisonAICommandLine(unittest.TestCase):
+    def run_command(self, command):
+        result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, text=True)
+        return result.stdout
+
     def test_praisonai_command(self):
         command = "praisonai --framework autogen --auto create movie script about cat in mars"
-        result = os.popen(command).read()
-        self.assertIn('### Task Output ###', result)
-        
-class TestPraisonAIInitCommand(unittest.TestCase):
+        result = self.run_command(command)
+        self.assertIn('TERMINATE', result)
+
     def test_praisonai_init_command(self):
         command = "praisonai --framework autogen --init create movie script about cat in mars"
-        result = os.popen(command).read()
-        self.assertIn('### Task Output ###', result)
+        result = self.run_command(command)
+        self.assertIn('created successfully', result)
 
-class TestBasicExample(unittest.TestCase):
+class TestExamples(unittest.TestCase):
     def test_basic_example(self):
-        main()
+        # Assuming main() has been adjusted to return a value for assertion
+        result = main()
+        self.assertIsNotNone(result)  # Adjust this assertion based on the expected outcome of main()
 
-class TestAdvancedExample(unittest.TestCase):
     def test_advanced_example(self):
-        advanced()
-        
-class TestAutoExample(unittest.TestCase):
+        # Assuming advanced() returns a value suitable for assertion
+        result = advanced()
+        self.assertIsNotNone(result)  # Adjust this assertion as needed
+
     def test_auto_example(self):
-        auto()
+        # Assuming auto() returns a value that can be asserted
+        result = auto()
+        self.assertIsNotNone(result)  # Adjust this assertion according to what auto() is expected to do
 
 if __name__ == '__main__':
-    unittest.main()
+    with open('unittest_report.xml', 'wb') as output:
+        unittest.main(testRunner=XMLTestRunner(output=output))

tests/test_agents_playbook.py

@@ -0,0 +1,25 @@
+# tests/test_agents_playbook.py
+import unittest
+import subprocess
+from praisonai import PraisonAI
+
+class TestPraisonAIFramework(unittest.TestCase):
+    def test_main_with_autogen_framework(self):
+        praison_ai = PraisonAI(agent_file='tests/autogen-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)
+
+    def test_main_with_custom_framework(self):
+        praison_ai = PraisonAI(agent_file='tests/crewai-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)
+
+    def test_main_with_internet_search_tool(self):
+        praison_ai = PraisonAI(agent_file='tests/search-tool-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)
+
+    def test_main_with_built_in_tool(self):
+        praison_ai = PraisonAI(agent_file='tests/inbuilt-tool-agents.yaml')
+        result = praison_ai.main()
+        self.assertIn('### Task Output ###', result)

tests/tools/internet_search.py

@@ -0,0 +1,9 @@
+from duckduckgo_search import DDGS
+from langchain.tools import tool
+
+@tool("Internet Search Tool")
+def internet_search_tool(query: str) -> list:
+    """Search Internet for relevant information based on a query."""
+    ddgs = DDGS()
+    results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+    return results

tools.py

@@ -1,8 +1,12 @@
+# tools.py
 from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
 
-@tool("Internet Search Tool")
-def internet_search_tool(query: str) -> list:
-    """Search Internet for relevant information based on a query."""
-    ddgs = DDGS()
-    results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-    return results
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results

tools_test/internet_search.py

@@ -0,0 +1,11 @@
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "Internet Search Tool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results