Merge pull request #41 from MervinPraison/develop

v0.0.21 final

.gitignore

@@ -13,4 +13,21 @@ assets/*
 praisonAI.egg-info
 flagged
 test.yaml
-db
+db
+praisonai_prompt.txt
+watch.sh
+docs.sh
+# 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 +1,35 @@
+# Read the Docs configuration file for Sphinx projects
+# 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"
+    # You can also specify other tool versions:
+    # nodejs: "20"
+    # rust: "1.70"
+    # golang: "1.20"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+  # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs
+  # builder: "dirhtml"
+  # Fail on all warnings to avoid broken references
+  # fail_on_warning: true
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#   - pdf
+#   - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+# 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==0.0.21 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
@@ -169,9 +247,11 @@ 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 +271,7 @@ Mistral API¶
 OPENAI_API_BASE=https://api.mistral.ai/v1
 OPENAI_MODEL_NAME="mistral-small"
 OPENAI_API_KEY=your-mistral-api-key
+```
 
 ## Contributing
 
@@ -200,4 +281,4 @@ OPENAI_API_KEY=your-mistral-api-key
 - 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.
+- 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/
+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
+    - '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:
-      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: []

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/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 = '0.0.21'
+
+# -- 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 = 'alabaster'
+html_static_path = ['_static']

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/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

hub/tools/tools.py

@@ -0,0 +1,49 @@
+# 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

praisonai/agents_generator.py

@@ -1,4 +1,4 @@
-# agents_generator.py
+# praisonai/agents_generator.py
 
 import sys
 from .version import __version__
@@ -11,14 +11,49 @@ 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
 
 class AgentsGenerator:
     def __init__(self, agent_file, framework, config_list):
         self.agent_file = agent_file
         self.framework = framework
         self.config_list = config_list
+        
+    def is_function_or_decorated(self, obj):
+        return inspect.isfunction(obj) or hasattr(obj, '__call__')
+
+    def load_tools_from_module(self, module_path):
+        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):
+        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):
+        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):
         if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
@@ -51,9 +86,19 @@ 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 +131,12 @@ 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 +171,6 @@ 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,
@@ -130,92 +179,4 @@ 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()
+        return result

praisonai/cli.py

@@ -12,8 +12,7 @@ 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 *
 
 class PraisonAI:
     def __init__(self, agent_file="agents.yaml", framework="crewai", auto=False, init=False):
@@ -41,21 +40,15 @@ class PraisonAI:
             return
         invocation_cmd = "praisonai"
         version_string = f"PraisonAI version {__version__}"
-        
         if args.framework:
             self.framework = args.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
+                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)
@@ -86,7 +79,7 @@ class PraisonAI:
             
     def parse_args(self):
         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("--framework", choices=["crewai", "autogen"], help="Specify the framework")
         parser.add_argument("--ui", action="store_true", help="Enable UI mode")
         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")
@@ -124,41 +117,3 @@ class PraisonAI:
 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: []
-

praisonai/deploy.py

@@ -13,7 +13,7 @@ class CloudDeployer:
             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==0.0.21 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

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/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.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "praisonAI"
-version = "0.0.18"
+version = "0.0.21"
 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 = ""
@@ -22,6 +22,7 @@ gradio = ">=4.20.0"
 Flask = ">=3.0.0"
 markdown = ">=3.5"
 crewai-tools = "^0.1.1"
+praisonai-tools = ">=0.0.4"
 
 [tool.poetry.dev-dependencies]
 pytest = "^8.0.0"

requirements.txt

@@ -1,4 +1,7 @@
 rich>=13.7
 pyautogen>=0.2.19
 crewai>=0.22.5
-gradio>=4.20.0
+gradio>=4.20.0
+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 +1,63 @@
 # 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
 
-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()

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