diff --git a/Dockerfile b/Dockerfile
index ed7bc00c..c04c8ba7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,6 +1,6 @@
FROM python:3.11-slim
WORKDIR /app
COPY . .
-RUN pip install flask praisonai==2.0.45 gunicorn markdown
+RUN pip install flask praisonai==2.0.46 gunicorn markdown
EXPOSE 8080
CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
diff --git a/agents/data-analyst-agent.py b/agents/data-analyst-agent.py
new file mode 100644
index 00000000..203f14f5
--- /dev/null
+++ b/agents/data-analyst-agent.py
@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+import os
+
+agent = Agent(instructions="You are a Data Analyst Agent", tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+agent.start(f"""
+ Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+ Analyse the data and give me the insights
+ read_csv to read the file
+""")
\ No newline at end of file
diff --git a/agents/finance-agent.py b/agents/finance-agent.py
new file mode 100644
index 00000000..78eff629
--- /dev/null
+++ b/agents/finance-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+agent = Agent(instructions="You are a Research Agent", tools=[get_stock_price, get_stock_info, get_historical_data])
+agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
\ No newline at end of file
diff --git a/agents/image-agent.py b/agents/image-agent.py
new file mode 100644
index 00000000..9ea8e1da
--- /dev/null
+++ b/agents/image-agent.py
@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+ name="ImageAnalyst",
+ role="Image Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+ name="analyze_landmark",
+ description="Describe this famous landmark and its architectural features.",
+ expected_output="Detailed description of the landmark's architecture and significance",
+ agent=image_agent,
+ images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+ name="analyze_local_image",
+ description="What objects can you see in this image? Describe their arrangement.",
+ expected_output="Detailed description of objects and their spatial relationships",
+ agent=image_agent,
+ images=["image.jpg"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[task1, task2],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/agents/image-to-text-agent.py b/agents/image-to-text-agent.py
new file mode 100644
index 00000000..9ea8e1da
--- /dev/null
+++ b/agents/image-to-text-agent.py
@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+ name="ImageAnalyst",
+ role="Image Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+ name="analyze_landmark",
+ description="Describe this famous landmark and its architectural features.",
+ expected_output="Detailed description of the landmark's architecture and significance",
+ agent=image_agent,
+ images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+ name="analyze_local_image",
+ description="What objects can you see in this image? Describe their arrangement.",
+ expected_output="Detailed description of objects and their spatial relationships",
+ agent=image_agent,
+ images=["image.jpg"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[task1, task2],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/agents/markdown-agent.py b/agents/markdown-agent.py
new file mode 100644
index 00000000..f0366c46
--- /dev/null
+++ b/agents/markdown-agent.py
@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")
\ No newline at end of file
diff --git a/agents/planning-agent.py b/agents/planning-agent.py
new file mode 100644
index 00000000..cf7df8a7
--- /dev/null
+++ b/agents/planning-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
\ No newline at end of file
diff --git a/agents/praisonaiagents/agent/agent.py b/agents/praisonaiagents/agent/agent.py
index c273a4eb..db4fbd7b 100644
--- a/agents/praisonaiagents/agent/agent.py
+++ b/agents/praisonaiagents/agent/agent.py
@@ -762,4 +762,8 @@ async def _achat_completion(self, response, tools):
def run(self):
"""Alias for start() method"""
- return self.start()
\ No newline at end of file
+ return self.start()
+
+ def start(self, prompt: str, **kwargs):
+ """Start the agent with a prompt. This is a convenience method that wraps chat()."""
+ return self.chat(prompt, **kwargs)
\ No newline at end of file
diff --git a/agents/praisonaiagents/tools/pandas_tools.py b/agents/praisonaiagents/tools/pandas_tools.py
index 55245db8..58c617ae 100644
--- a/agents/praisonaiagents/tools/pandas_tools.py
+++ b/agents/praisonaiagents/tools/pandas_tools.py
@@ -160,6 +160,9 @@ def get_summary(self, df: pd.DataFrame) -> Dict[str, Any]:
Dict: Summary statistics and information
"""
try:
+ if not isinstance(df, pd.DataFrame):
+ raise TypeError(f"Expected pandas DataFrame, got {type(df).__name__}")
+
numeric_summary = df.describe().to_dict()
# Convert numpy types to native Python types
for col in numeric_summary:
diff --git a/agents/praisonaiagents/tools/yfinance_tools.py b/agents/praisonaiagents/tools/yfinance_tools.py
index f85a02fc..efe159b2 100644
--- a/agents/praisonaiagents/tools/yfinance_tools.py
+++ b/agents/praisonaiagents/tools/yfinance_tools.py
@@ -145,7 +145,15 @@ def get_historical_data(
return [{"error": "yfinance package not available"}]
hist = ticker.history(period=period, interval=interval, start=start, end=end)
- return hist.reset_index().to_dict('records')
+ data = hist.reset_index().to_dict('records')
+
+ # Convert timestamps to ISO format strings
+ for record in data:
+ if 'Date' in record and hasattr(record['Date'], 'isoformat'):
+ record['Date'] = record['Date'].isoformat()
+ if 'Datetime' in record and hasattr(record['Datetime'], 'isoformat'):
+ record['Datetime'] = record['Datetime'].isoformat()
+ return data
except Exception as e:
error_msg = f"Error fetching historical data: {str(e)}"
logging.error(error_msg)
diff --git a/agents/programming-agent.py b/agents/programming-agent.py
new file mode 100644
index 00000000..dac126f7
--- /dev/null
+++ b/agents/programming-agent.py
@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+agent = Agent(
+ instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10,
+ tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+)
+agent.start(
+ "Write a python script using yfinance to find the stock price of Tesla"
+ "First check if required packages are installed"
+ "Run it using execute_code"
+ "execute_command if you want to run any terminal command"
+ "search internet using duckduckgo if you want to know update python package information"
+ "Analyse the output using analyze_code and fix error if required"
+ "if no package is installed, install it"
+ "then run the code"
+)
\ No newline at end of file
diff --git a/agents/pyproject.toml b/agents/pyproject.toml
index 74a659e4..e7873a76 100644
--- a/agents/pyproject.toml
+++ b/agents/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "praisonaiagents"
-version = "0.0.35"
+version = "0.0.36"
description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
authors = [
{ name="Mervin Praison" }
diff --git a/agents/recommendation-agent.py b/agents/recommendation-agent.py
new file mode 100644
index 00000000..b53c135d
--- /dev/null
+++ b/agents/recommendation-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
\ No newline at end of file
diff --git a/agents/research-agent.py b/agents/research-agent.py
new file mode 100644
index 00000000..6e50da3e
--- /dev/null
+++ b/agents/research-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
\ No newline at end of file
diff --git a/agents/shopping-agent.py b/agents/shopping-agent.py
new file mode 100644
index 00000000..c7839f3f
--- /dev/null
+++ b/agents/shopping-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
\ No newline at end of file
diff --git a/agents/single-agent.py b/agents/single-agent.py
new file mode 100644
index 00000000..f0366c46
--- /dev/null
+++ b/agents/single-agent.py
@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")
\ No newline at end of file
diff --git a/agents/tesla-stock-price.csv b/agents/tesla-stock-price.csv
new file mode 100644
index 00000000..68e0cdce
--- /dev/null
+++ b/agents/tesla-stock-price.csv
@@ -0,0 +1,758 @@
+"date","close","volume","open","high","low"
+"11:34","270.49","4,787,699","264.50","273.88","262.24"
+"2018/10/15","259.5900","6189026.0000","259.0600","263.2800","254.5367"
+"2018/10/12","258.7800","7189257.0000","261.0000","261.9900","252.0100"
+"2018/10/11","252.2300","8128184.0000","257.5300","262.2500","249.0300"
+"2018/10/10","256.8800","12781560.0000","264.6100","265.5100","247.7700"
+"2018/10/09","262.8000","12037780.0000","255.2500","266.7700","253.3000"
+"2018/10/08","250.5600","13371180.0000","264.5200","267.7599","249.0000"
+"2018/10/05","261.9500","17900710.0000","274.6500","274.8800","260.0000"
+"2018/10/04","281.8300","9638885.0000","293.9500","294.0000","277.6700"
+"2018/10/03","294.8000","7982272.0000","303.3300","304.6000","291.5700"
+"2018/10/02","301.0200","11699690.0000","313.9500","316.8400","299.1500"
+"2018/10/01","310.7000","21714210.0000","305.7700","311.4400","301.0500"
+"2018/09/28","264.7700","33597290.0000","270.2600","278.0000","260.5550"
+"2018/09/27","307.5200","7337760.0000","312.9000","314.9600","306.9100"
+"2018/09/26","309.5800","7835863.0000","301.9100","313.8900","301.1093"
+"2018/09/25","300.9900","4472287.0000","300.0000","304.6000","296.5000"
+"2018/09/24","299.6800","4834384.0000","298.4800","302.9993","293.5800"
+"2018/09/21","299.1000","5038497.0000","297.7000","300.5800","295.3700"
+"2018/09/20","298.3300","7332477.0000","303.5600","305.9800","293.3300"
+"2018/09/19","299.0200","8264353.0000","280.5100","300.0000","280.5000"
+"2018/09/18","284.9600","16494280.0000","296.6900","302.6400","275.5000"
+"2018/09/17","294.8400","6866208.0000","290.0400","300.8700","288.1300"
+"2018/09/14","295.2000","6757652.0000","288.7600","297.3256","286.5200"
+"2018/09/13","289.4600","6333281.0000","288.0200","295.0000","285.1800"
+"2018/09/12","290.5400","10002150.0000","281.4400","292.5000","278.6463"
+"2018/09/11","279.4400","9161061.0000","279.4700","282.0000","273.5500"
+"2018/09/10","285.5000","14232250.0000","273.2600","286.0300","271.0000"
+"2018/09/07","263.2400","22442310.0000","260.1000","268.3500","252.2548"
+"2018/09/06","280.9500","7472756.0000","284.8000","291.1700","278.8800"
+"2018/09/05","280.7400","7707342.0000","285.0500","286.7800","277.1800"
+"2018/09/04","288.9500","8304571.0000","296.9400","298.1900","288.0000"
+"2018/08/31","301.6600","5367048.0000","302.0000","305.3082","298.6000"
+"2018/08/30","303.1500","7210625.0000","302.2600","304.6000","297.7200"
+"2018/08/29","305.0100","7410538.0000","310.2700","311.8500","303.6900"
+"2018/08/28","311.8600","7639453.0000","318.4100","318.8800","311.1900"
+"2018/08/27","319.2700","13062650.0000","318.0000","322.4350","308.8100"
+"2018/08/24","322.8200","3592734.0000","320.7000","323.8500","319.4000"
+"2018/08/23","320.1000","5138685.0000","319.1400","327.3200","318.1000"
+"2018/08/22","321.6400","5935853.0000","320.8700","323.8800","314.6700"
+"2018/08/21","321.9000","13147770.0000","310.6100","324.7900","309.0000"
+"2018/08/20","308.4400","17380430.0000","291.7040","308.5000","288.2000"
+"2018/08/17","305.5000","18895880.0000","323.5000","326.7700","303.5300"
+"2018/08/16","335.4500","6045392.0000","339.9100","342.2800","333.8200"
+"2018/08/15","338.6900","9073033.0000","341.9060","344.4900","332.1400"
+"2018/08/14","347.6400","6948511.0000","358.4500","359.1995","347.1000"
+"2018/08/13","356.4100","10444920.0000","361.1300","363.1900","349.0200"
+"2018/08/10","355.4900","11530510.0000","354.0000","360.0000","346.0000"
+"2018/08/09","352.4500","16951800.0000","365.5500","367.0100","345.7300"
+"2018/08/08","370.3400","24535420.0000","369.0900","382.6400","367.1200"
+"2018/08/07","379.5700","30596400.0000","343.8400","387.4600","339.1501"
+"2018/08/06","341.9900","8536766.0000","345.4600","354.9800","341.8200"
+"2018/08/03","348.1700","13646640.0000","347.8100","355.0000","342.5300"
+"2018/08/02","349.5400","23159160.0000","328.4400","349.9900","323.1600"
+"2018/08/01","300.8400","8768099.0000","297.9900","303.0000","293.0000"
+"2018/07/31","298.1400","5055094.0000","292.2500","298.3200","289.0700"
+"2018/07/30","290.1700","6799581.0000","295.9000","296.1000","286.1300"
+"2018/07/27","297.1800","5694690.0000","307.2500","307.6933","295.3404"
+"2018/07/26","306.6500","4619567.0000","304.8500","310.7000","303.6411"
+"2018/07/25","308.7400","7031382.0000","296.7400","309.6200","294.5025"
+"2018/07/24","297.4300","9586186.0000","304.4209","307.7171","292.5452"
+"2018/07/23","303.2000","10974820.0000","301.8400","305.5000","292.8601"
+"2018/07/20","313.5800","5159831.0000","321.2300","323.2400","311.7093"
+"2018/07/19","320.2300","5907116.0000","316.3300","323.5400","314.0100"
+"2018/07/18","323.8500","5610085.0000","325.0000","325.5000","316.2500"
+"2018/07/17","322.6900","6987782.0000","308.8100","324.7400","308.5000"
+"2018/07/16","310.1000","7800454.0000","311.7100","315.1600","306.2500"
+"2018/07/13","318.8700","5866776.0000","315.5800","319.5849","309.2500"
+"2018/07/12","316.7100","5711851.0000","321.4300","323.2300","312.7700"
+"2018/07/11","318.9600","4878595.0000","315.8000","321.9400","315.0700"
+"2018/07/10","322.4700","9437557.0000","324.5600","327.6771","319.2000"
+"2018/07/09","318.5100","7579541.0000","311.9900","318.5200","308.0000"
+"2018/07/06","308.9000","8860198.0000","304.9550","312.0700","302.0000"
+"2018/07/05","309.1600","17457570.0000","313.7600","314.3900","296.2200"
+"2018/07/03","310.8600","12282640.0000","331.7500","332.4900","309.6900"
+"2018/07/02","335.0700","18732710.0000","360.0700","364.7800","329.8500"
+"2018/06/29","342.9500","6475903.0000","353.3300","353.8600","342.4100"
+"2018/06/28","349.9300","8388172.0000","348.6600","357.0200","346.1100"
+"2018/06/27","344.5000","8313817.0000","345.0000","350.7900","339.5000"
+"2018/06/26","342.0000","7434749.0000","336.0540","343.5500","325.7990"
+"2018/06/25","333.0100","6925089.0000","330.1200","338.4700","327.5000"
+"2018/06/22","333.6300","10253770.0000","351.5400","352.2500","332.0000"
+"2018/06/21","347.5100","7949846.0000","362.0000","366.2139","346.2700"
+"2018/06/20","362.2200","8370268.0000","358.0400","364.3800","352.0000"
+"2018/06/19","352.5500","12734840.0000","365.1600","370.0000","346.2500"
+"2018/06/18","370.8300","12025450.0000","355.4000","373.7300","354.5000"
+"2018/06/15","358.1700","10827560.0000","353.8400","364.6700","351.2500"
+"2018/06/14","357.7200","10961150.0000","347.6300","358.7500","346.6000"
+"2018/06/13","344.7800","9401467.0000","346.7060","347.2000","339.8000"
+"2018/06/12","342.7700","22256430.0000","344.7000","354.9700","338.0000"
+"2018/06/11","332.1000","13166050.0000","322.5100","334.6600","322.5000"
+"2018/06/08","317.6600","8192175.0000","319.0000","324.4800","317.1500"
+"2018/06/07","316.0900","14336110.0000","316.1500","330.0000","313.5800"
+"2018/06/06","319.5000","18742400.0000","300.5000","322.1700","297.4800"
+"2018/06/05","291.1300","5877873.0000","297.7000","297.8000","286.7400"
+"2018/06/04","296.7400","4793787.0000","294.3400","299.0000","293.5480"
+"2018/06/01","291.8200","5417984.0000","285.8600","291.9500","283.8390"
+"2018/05/31","284.7300","5912825.0000","287.2100","290.3700","282.9300"
+"2018/05/30","291.7200","7473590.0000","283.2900","295.0050","281.6000"
+"2018/05/29","283.7600","5657153.0000","278.5100","286.5000","276.1500"
+"2018/05/25","278.8500","3872621.0000","277.6250","279.6400","275.6100"
+"2018/05/24","277.8500","4170789.0000","278.4000","281.1100","274.8900"
+"2018/05/23","279.0700","5972237.0000","277.7600","279.9099","274.0000"
+"2018/05/22","275.0100","8938501.0000","287.7620","288.0000","273.4220"
+"2018/05/21","284.4900","9167844.0000","281.3300","291.4900","281.3000"
+"2018/05/18","276.8200","7243699.0000","284.6500","284.6500","274.0000"
+"2018/05/17","284.5400","4418258.0000","285.9000","289.1873","283.9700"
+"2018/05/16","286.4800","5669644.0000","283.8300","288.8100","281.5600"
+"2018/05/15","284.1800","9509753.0000","285.0100","286.9600","280.5000"
+"2018/05/14","291.9700","7265501.0000","303.3200","304.9400","291.6200"
+"2018/05/11","301.0600","4665321.0000","307.7000","308.8800","299.0800"
+"2018/05/10","305.0200","5642771.0000","307.4997","312.9900","304.1119"
+"2018/05/09","306.8500","5712255.0000","300.4100","307.0100","300.0500"
+"2018/05/08","301.9700","5927447.0000","300.7950","307.7500","299.0000"
+"2018/05/07","302.7700","8673090.0000","297.5000","305.9588","295.1700"
+"2018/05/04","294.0900","8565506.0000","283.0000","296.8600","279.5200"
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diff --git a/agents/uv.lock b/agents/uv.lock
index fc2bb4dc..77c3e3c9 100644
--- a/agents/uv.lock
+++ b/agents/uv.lock
@@ -990,7 +990,7 @@ wheels = [
[[package]]
name = "praisonaiagents"
-version = "0.0.35"
+version = "0.0.36"
source = { editable = "." }
dependencies = [
{ name = "openai" },
diff --git a/agents/video-agent.py b/agents/video-agent.py
new file mode 100644
index 00000000..a35b0c09
--- /dev/null
+++ b/agents/video-agent.py
@@ -0,0 +1,36 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Video Analysis Agent
+video_agent = Agent(
+ name="VideoAnalyst",
+ role="Video Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# Task with Video File
+task1 = Task(
+ name="analyze_video",
+ description="""Watch this video and provide:
+ 1. A summary of the main events
+ 2. Key objects and people visible
+ 3. Any text or important information shown
+ 4. The overall context and setting""",
+ expected_output="Comprehensive analysis of the video content",
+ agent=video_agent,
+ images=["video.mp4"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[video_agent],
+ tasks=[task1],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/agents/websearch-agent.py b/agents/websearch-agent.py
new file mode 100644
index 00000000..b53d60ea
--- /dev/null
+++ b/agents/websearch-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Web Search Agent", tools=[duckduckgo])
+agent.start("Search about AI 2024")
\ No newline at end of file
diff --git a/agents/wikipedia-agent.py b/agents/wikipedia-agent.py
new file mode 100644
index 00000000..a34a1644
--- /dev/null
+++ b/agents/wikipedia-agent.py
@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+ instructions="You are a Wikipedia Agent",
+ tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+ self_reflect=True,
+ min_reflect=3,
+ max_reflect=5,
+)
+agent.start(
+ "What is the history of AI?"
+ "First search the history of AI"
+ "Read the page of the history of AI"
+ "Get the summary of the page"
+)
\ No newline at end of file
diff --git a/docs/agents/data-analyst.mdx b/docs/agents/data-analyst.mdx
new file mode 100644
index 00000000..4f340d53
--- /dev/null
+++ b/docs/agents/data-analyst.mdx
@@ -0,0 +1,120 @@
+---
+title: "Data Analyst Agent"
+sidebarTitle: "Data Analyst"
+description: "Learn how to create AI agents for data analysis and insights generation."
+icon: "chart-line"
+---
+
+```mermaid
+flowchart LR
+ In[Data Source] --> Reader[Data Reader]
+ Reader --> Analyzer[Data Analyzer]
+ Analyzer --> Generator[Insights Generator]
+ Generator --> Out[Output]
+
+ style In fill:#8B0000,color:#fff
+ style Reader fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `data_analysis.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import (
+ read_csv, read_excel, write_csv, write_excel,
+ filter_data, get_summary, group_by, pivot_table
+ )
+
+ agent = Agent(
+ instructions="You are a Data Analyst Agent",
+ tools=[
+ read_csv, read_excel, write_csv, write_excel,
+ filter_data, get_summary, group_by, pivot_table
+ ]
+ )
+
+ # Start analysis with a specific task
+ agent.start(f"""
+ Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+ Analyse the data and give me the insights
+ read_csv to read the file
+ """)
+ ```
+
+
+
+## Understanding Data Analysis Workflow
+
+The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here's how it works:
+
+1. **Data Reading**: The agent can read data from various sources:
+ - CSV files using `read_csv`
+ - Excel files using `read_excel`
+
+2. **Data Analysis**: Multiple analysis tools are available:
+ - `filter_data`: Filter datasets based on conditions
+ - `get_summary`: Generate statistical summaries
+ - `group_by`: Group data by specific columns
+ - `pivot_table`: Create pivot tables for analysis
+
+3. **Data Export**: Results can be exported to:
+ - CSV format using `write_csv`
+ - Excel format using `write_excel`
+
+## Features
+
+
+
+ Support for both CSV and Excel file formats.
+
+
+ Comprehensive suite of analysis tools including filtering, summarization, and pivoting.
+
+
+ Export capabilities to various formats.
+
+
+ Automatic generation of data insights and patterns.
+
+
+
+## Example Usage
+
+```python
+# Example: Analyzing stock data
+agent.start("""
+ 1. Read 'stock_data.csv'
+ 2. Filter data for the last 30 days
+ 3. Calculate daily returns
+ 4. Generate summary statistics
+ 5. Export results to 'analysis_results.xlsx'
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out other specialized agents like the [Finance Agent](/agents/finance) for specific use cases
diff --git a/docs/agents/finance.mdx b/docs/agents/finance.mdx
new file mode 100644
index 00000000..e7f9d01f
--- /dev/null
+++ b/docs/agents/finance.mdx
@@ -0,0 +1,97 @@
+---
+title: "Finance Agent"
+sidebarTitle: "Finance"
+description: "Learn how to create AI agents for financial analysis and investment recommendations."
+icon: "money-bill-trend-up"
+---
+
+```mermaid
+flowchart LR
+ In[Request] --> Price[Stock Price]
+ Price --> Info[Stock Info]
+ Info --> History[Historical Data]
+ History --> Out[Investment Advice]
+
+ style In fill:#8B0000,color:#fff
+ style Price fill:#2E8B57,color:#fff
+ style Info fill:#2E8B57,color:#fff
+ style History fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Finance Agent can analyze stock market data and provide investment recommendations.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `finance_analysis.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+ agent = Agent(
+ instructions="You are a Research Agent",
+ tools=[get_stock_price, get_stock_info, get_historical_data]
+ )
+ agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
+ ```
+
+
+
+## Understanding Financial Analysis
+
+The Finance Agent specializes in stock market analysis and investment recommendations using three key components:
+
+1. **Real-time Price Data**: Uses `get_stock_price` to fetch current market prices
+2. **Company Information**: Retrieves company details using `get_stock_info`
+3. **Historical Analysis**: Analyzes trends with `get_historical_data`
+
+## Features
+
+
+
+ Access to current stock market prices and information.
+
+
+ Detailed company information and fundamentals.
+
+
+ Analysis of historical price movements and patterns.
+
+
+ AI-powered investment recommendations.
+
+
+
+## Example Usage
+
+```python
+# Example: Analyzing specific stocks
+agent.start("""
+ 1. Get current prices for AAPL and GOOGL
+ 2. Retrieve company information
+ 3. Analyze 6-month historical data
+ 4. Provide investment recommendations
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex financial analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Data Analyst Agent](/agents/data-analyst) for detailed data analysis capabilities
diff --git a/docs/agents/image-to-text.mdx b/docs/agents/image-to-text.mdx
new file mode 100644
index 00000000..70897e52
--- /dev/null
+++ b/docs/agents/image-to-text.mdx
@@ -0,0 +1,140 @@
+---
+title: "Image to Text Agent"
+sidebarTitle: "Image to Text"
+description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
+icon: "image-to-text"
+---
+
+```mermaid
+flowchart LR
+ In[Image Input] --> OCR[Text Extractor]
+ OCR --> Analyzer[Content Analyzer]
+ Analyzer --> Generator[Description Generator]
+ Generator --> Out[Text Output]
+
+ style In fill:#8B0000,color:#fff
+ style OCR fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image-to-Text Agent can extract text from images and generate comprehensive descriptions.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `image_to_text.py`:
+ ```python
+ from praisonaiagents import Agent, Task, PraisonAIAgents
+
+ # Create Image-to-Text Agent
+ image_text_agent = Agent(
+ name="ImageTextConverter",
+ role="Image Text Extraction Specialist",
+ goal="Convert image content to textual descriptions and extract text",
+ backstory="""You are an expert in OCR and image understanding.
+ You excel at extracting text from images and generating detailed descriptions.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+ )
+
+ # Create text extraction task
+ extraction_task = Task(
+ name="extract_text",
+ description="Extract all text from this image and describe its layout.",
+ expected_output="Extracted text and layout description",
+ agent=image_text_agent,
+ images=["document.jpg"]
+ )
+
+ # Create description task
+ description_task = Task(
+ name="generate_description",
+ description="Generate a detailed description of the image content.",
+ expected_output="Comprehensive description of visual elements",
+ agent=image_text_agent,
+ images=["scene.jpg"]
+ )
+
+ # Create PraisonAIAgents instance
+ agents = PraisonAIAgents(
+ agents=[image_text_agent],
+ tasks=[extraction_task, description_task],
+ process="sequential",
+ verbose=1
+ )
+
+ # Run analysis
+ agents.start()
+ ```
+
+
+
+## Understanding Image-to-Text Conversion
+
+The Image-to-Text Agent combines multiple capabilities to convert visual content into textual form:
+
+1. **OCR Processing**: Extracts text from images using optical character recognition
+2. **Layout Analysis**: Understands the spatial arrangement of text and visual elements
+3. **Content Description**: Generates natural language descriptions of image content
+4. **Text Formatting**: Preserves text formatting and structure where possible
+
+## Features
+
+
+
+ Advanced OCR capabilities for text extraction.
+
+
+ Analysis of text and content layout.
+
+
+ Detailed descriptions of visual content.
+
+
+ Maintains text formatting and structure.
+
+
+
+## Example Usage
+
+```python
+# Example: Processing a document image
+document_task = Task(
+ name="process_document",
+ description="Extract text and analyze document layout",
+ expected_output="Extracted text with layout information",
+ agent=image_text_agent,
+ images=["business_document.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+ agents=[image_text_agent],
+ tasks=[document_task],
+ process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document processing
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving text extraction accuracy
+- Check out the [Image Agent](/agents/image) for pure image analysis capabilities
diff --git a/docs/agents/image.mdx b/docs/agents/image.mdx
new file mode 100644
index 00000000..ac1c1e63
--- /dev/null
+++ b/docs/agents/image.mdx
@@ -0,0 +1,139 @@
+---
+title: "Image Analysis Agent"
+sidebarTitle: "Image Analysis"
+description: "Learn how to create AI agents for image analysis and visual content understanding."
+icon: "image"
+---
+
+```mermaid
+flowchart LR
+ In[Image Input] --> Analyzer[Image Analyzer]
+ Analyzer --> Detector[Object Detector]
+ Detector --> Generator[Description Generator]
+ Generator --> Out[Analysis Output]
+
+ style In fill:#8B0000,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Detector fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `image_analysis.py`:
+ ```python
+ from praisonaiagents import Agent, Task, PraisonAIAgents
+
+ # Create Image Analysis Agent
+ image_agent = Agent(
+ name="ImageAnalyst",
+ role="Image Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+ )
+
+ # Create tasks for different types of analysis
+ task1 = Task(
+ name="analyze_landmark",
+ description="Describe this famous landmark and its architectural features.",
+ expected_output="Detailed description of the landmark's architecture and significance",
+ agent=image_agent,
+ images=["https://example.com/landmark.jpg"]
+ )
+
+ task2 = Task(
+ name="analyze_local_image",
+ description="What objects can you see in this image? Describe their arrangement.",
+ expected_output="Detailed description of objects and their spatial relationships",
+ agent=image_agent,
+ images=["local_image.jpg"]
+ )
+
+ # Create PraisonAIAgents instance
+ agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[task1, task2],
+ process="sequential",
+ verbose=1
+ )
+
+ # Run analysis
+ agents.start()
+ ```
+
+
+
+## Understanding Image Analysis
+
+The Image Analysis Agent is designed to process and understand visual content through several key capabilities:
+
+1. **Image Input**: Supports both URL and local file inputs
+2. **Object Detection**: Identifies and locates objects within images
+3. **Architectural Analysis**: Specialized in analyzing architectural features
+4. **Spatial Understanding**: Describes spatial relationships between objects
+
+## Features
+
+
+
+ Support for both URL and local image files.
+
+
+ Advanced object detection and recognition.
+
+
+ Specialized analysis of architectural features.
+
+
+ Generation of comprehensive visual descriptions.
+
+
+
+## Example Usage
+
+```python
+# Example: Analyzing a landmark image
+landmark_task = Task(
+ name="analyze_famous_landmark",
+ description="Analyze this historical landmark",
+ expected_output="Architectural details and historical significance",
+ agent=image_agent,
+ images=["https://example.com/landmark.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[landmark_task],
+ process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex image analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Video Agent](/agents/video) for video content analysis
diff --git a/docs/agents/markdown.mdx b/docs/agents/markdown.mdx
new file mode 100644
index 00000000..1a935adb
--- /dev/null
+++ b/docs/agents/markdown.mdx
@@ -0,0 +1,120 @@
+---
+title: "Markdown Agent"
+sidebarTitle: "Markdown"
+description: "Learn how to create AI agents for generating and formatting content in Markdown."
+icon: "markdown"
+---
+
+```mermaid
+flowchart LR
+ In[Content Request] --> Generator[Content Generator]
+ Generator --> Formatter[Markdown Formatter]
+ Formatter --> Validator[Format Validator]
+ Validator --> Out[Markdown Output]
+
+ style In fill:#8B0000,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Formatter fill:#2E8B57,color:#fff
+ style Validator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Markdown Agent can generate and format content in Markdown syntax.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `markdown_generator.py`:
+ ```python
+ from praisonaiagents import Agent
+
+ # Create Markdown Agent
+ markdown_agent = Agent(
+ name="MarkdownWriter",
+ role="Markdown Content Specialist",
+ goal="Generate well-formatted content in Markdown syntax",
+ instructions="You are a Markdown Agent, output in markdown format",
+ llm="gpt-4o-mini",
+ self_reflect=False
+ )
+
+ # Generate content
+ response = markdown_agent.start(
+ "Write a blog post about artificial intelligence"
+ )
+
+ # Save to file
+ with open('blog_post.md', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Markdown Generation
+
+The Markdown Agent specializes in creating properly formatted Markdown content:
+
+1. **Content Generation**: Creates original content based on prompts
+2. **Markdown Formatting**: Applies proper Markdown syntax
+3. **Structure Validation**: Ensures correct formatting
+4. **Document Organization**: Creates well-structured documents
+
+## Features
+
+
+
+ Generates original, well-structured content.
+
+
+ Proper implementation of Markdown formatting.
+
+
+ Organized document hierarchy and sections.
+
+
+ Ensures correct Markdown syntax usage.
+
+
+
+## Example Usage
+
+```python
+# Example: Generate a technical documentation
+agent = Agent(
+ instructions="You are a Markdown Agent, output in markdown format"
+)
+
+# Generate API documentation
+response = agent.start("""
+ Create technical documentation for a REST API with:
+ - Introduction
+ - Authentication
+ - Endpoints
+ - Examples
+""")
+
+# Save documentation
+with open('api_docs.md', 'w') as f:
+ f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document generation
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving content quality
+- Check out the [Documentation Agent](/agents/documentation) for specialized documentation generation
diff --git a/docs/agents/planning.mdx b/docs/agents/planning.mdx
new file mode 100644
index 00000000..94d5e4fb
--- /dev/null
+++ b/docs/agents/planning.mdx
@@ -0,0 +1,109 @@
+---
+title: "Planning Agent"
+sidebarTitle: "Planning"
+description: "Learn how to create AI agents for trip planning and itinerary generation."
+icon: "calendar"
+---
+
+```mermaid
+flowchart LR
+ In[Request] --> Search[Web Search]
+ Search --> Analyzer[Options Analyzer]
+ Analyzer --> Generator[Plan Generator]
+ Generator --> Out[Itinerary]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Planning Agent can search for travel options and create detailed itineraries.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `travel_planner.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import duckduckgo
+
+ # Create Planning Agent
+ planning_agent = Agent(
+ name="TravelPlanner",
+ role="Travel Planning Specialist",
+ goal="Create comprehensive travel plans and itineraries",
+ instructions="You are a Planning Agent",
+ tools=[duckduckgo]
+ )
+
+ # Generate travel plan
+ response = planning_agent.start(
+ "I want to go to London next week, find me a good hotel and flight"
+ )
+
+ # Save plan
+ with open('travel_plan.txt', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Travel Planning
+
+The Planning Agent combines multiple capabilities to create comprehensive travel plans:
+
+1. **Web Search**: Uses DuckDuckGo to find current travel options
+2. **Options Analysis**: Evaluates and compares different choices
+3. **Plan Generation**: Creates detailed itineraries
+4. **Recommendations**: Provides personalized suggestions
+
+## Features
+
+
+
+ Finds and compares flight options.
+
+
+ Recommends suitable accommodations.
+
+
+ Generates detailed travel schedules.
+
+
+ Uses current pricing and availability.
+
+
+
+## Example Usage
+
+```python
+# Example: Create a weekend trip plan
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex travel planning
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendations
+- Check out the [Research Agent](/agents/research) for detailed destination research
diff --git a/docs/agents/programming.mdx b/docs/agents/programming.mdx
new file mode 100644
index 00000000..d6893d44
--- /dev/null
+++ b/docs/agents/programming.mdx
@@ -0,0 +1,125 @@
+---
+title: "Programming Agent"
+sidebarTitle: "Programming"
+description: "Learn how to create AI agents for code development, analysis, and execution."
+icon: "code"
+---
+
+```mermaid
+flowchart LR
+ In[Code Request] --> Analyzer[Code Analyzer]
+ Analyzer --> Generator[Code Generator]
+ Generator --> Executor[Code Executor]
+ Executor --> Debugger[Code Debugger]
+ Debugger --> Out[Output]
+
+ style In fill:#8B0000,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Executor fill:#2E8B57,color:#fff
+ style Debugger fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Programming Agent can analyze, generate, execute, and debug code.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `code_assistant.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+ from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+ from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+ agent = Agent(
+ instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10,
+ tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+ )
+ agent.start(
+ "Write a python script using yfinance to find the stock price of Tesla"
+ "First check if required packages are installed"
+ "Run it using execute_code"
+ "execute_command if you want to run any terminal command"
+ "search internet using duckduckgo if you want to know update python package information"
+ "Analyse the output using analyze_code and fix error if required"
+ "if no package is installed, install it"
+ "then run the code"
+ )
+ ```
+
+
+
+## Understanding Code Development
+
+The Programming Agent combines multiple tools for comprehensive code development:
+
+1. **Code Tools**:
+ - `execute_code`: Run Python code
+ - `analyze_code`: Analyze code structure
+ - `format_code`: Format code to standards
+ - `lint_code`: Check code quality
+ - `disassemble_code`: View bytecode
+
+2. **Shell Tools**:
+ - `execute_command`: Run terminal commands
+ - `list_processes`: View running processes
+ - `kill_process`: Terminate processes
+ - `get_system_info`: System information
+
+3. **Web Tools**:
+ - `duckduckgo`: Search for programming resources
+
+## Features
+
+
+
+ Run and test code directly.
+
+
+ Analyze and improve code quality.
+
+
+ Execute system commands and manage processes.
+
+
+ Handle dependencies and installations.
+
+
+
+## Example Usage
+
+```python
+# Example: Create and run a data analysis script
+response = agent.start("""
+ 1. Create a script that:
+ - Uses pandas for data analysis
+ - Reads a CSV file
+ - Performs basic statistics
+ - Creates a visualization
+ 2. Check and install required packages
+ 3. Execute and analyze the results
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex development workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving code quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for specialized data analysis
diff --git a/docs/agents/recommendation.mdx b/docs/agents/recommendation.mdx
new file mode 100644
index 00000000..9fd27a4f
--- /dev/null
+++ b/docs/agents/recommendation.mdx
@@ -0,0 +1,109 @@
+---
+title: "Recommendation Agent"
+sidebarTitle: "Recommendation"
+description: "Learn how to create AI agents for personalized recommendations across various domains."
+icon: "thumbs-up"
+---
+
+```mermaid
+flowchart LR
+ In[User Preferences] --> Search[Content Search]
+ Search --> Analyzer[Preference Analyzer]
+ Analyzer --> Generator[Recommendation Generator]
+ Generator --> Out[Recommendations]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Generator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `recommendation_system.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import duckduckgo
+
+ # Create Recommendation Agent
+ recommendation_agent = Agent(
+ name="RecommendationEngine",
+ role="Recommendation Specialist",
+ goal="Generate personalized recommendations based on preferences",
+ instructions="You are a Recommendation Agent",
+ tools=[duckduckgo]
+ )
+
+ # Get recommendations
+ response = recommendation_agent.start(
+ "Recommend me a good movie to watch in 2025"
+ )
+
+ # Save recommendations
+ with open('recommendations.txt', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Recommendation System
+
+The Recommendation Agent uses multiple approaches to generate personalized suggestions:
+
+1. **Content Search**: Uses DuckDuckGo to find current options
+2. **Preference Analysis**: Understands user preferences
+3. **Recommendation Generation**: Creates personalized suggestions
+4. **Content Filtering**: Filters based on relevance and quality
+
+## Features
+
+
+
+ Tailored recommendations based on preferences.
+
+
+ Up-to-date content and information.
+
+
+ Recommendations across various categories.
+
+
+ Quality-based content selection.
+
+
+
+## Example Usage
+
+```python
+# Example: Get personalized recommendations
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex recommendation systems
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Research Agent](/agents/research) for detailed content research
diff --git a/docs/agents/research.mdx b/docs/agents/research.mdx
new file mode 100644
index 00000000..6aedc9a4
--- /dev/null
+++ b/docs/agents/research.mdx
@@ -0,0 +1,108 @@
+---
+title: "Research Agent"
+sidebarTitle: "Research"
+description: "Learn how to create AI agents for conducting comprehensive research and analysis."
+icon: "magnifying-glass-chart"
+---
+
+```mermaid
+flowchart LR
+ In[Research Query] --> Search[Web Search]
+ Search --> Analyzer[Content Analyzer]
+ Analyzer --> Synthesizer[Information Synthesizer]
+ Synthesizer --> Out[Research Report]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Synthesizer fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Research Agent can gather, analyze, and synthesize information from various sources.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `research_assistant.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import duckduckgo
+
+ # Create Research Agent
+ research_agent = Agent(
+ name="ResearchAssistant",
+ role="Research Specialist",
+ goal="Conduct comprehensive research and analysis",
+ instructions="You are a Research Agent",
+ tools=[duckduckgo]
+ )
+
+ # Conduct research
+ response = research_agent.start(
+ "Research about AI developments in 2024"
+ )
+
+ # Save research findings
+ with open('research_report.md', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Research Process
+
+The Research Agent employs a systematic approach to information gathering and analysis:
+
+1. **Web Search**: Uses DuckDuckGo to find relevant sources
+2. **Content Analysis**: Evaluates source credibility and relevance
+3. **Information Synthesis**: Combines findings into coherent insights
+4. **Report Generation**: Creates structured research reports
+
+## Features
+
+
+
+ Comprehensive online information gathering.
+
+
+ In-depth evaluation of sources.
+
+
+ Integration of multiple sources.
+
+
+ Structured research documentation.
+
+
+
+## Example Usage
+
+```python
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex research projects
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for data-driven research
diff --git a/docs/agents/shopping.mdx b/docs/agents/shopping.mdx
new file mode 100644
index 00000000..79f95bf1
--- /dev/null
+++ b/docs/agents/shopping.mdx
@@ -0,0 +1,109 @@
+---
+title: "Shopping Agent"
+sidebarTitle: "Shopping"
+description: "Learn how to create AI agents for price comparison and shopping assistance."
+icon: "shopping-cart"
+---
+
+```mermaid
+flowchart LR
+ In[Product Query] --> Search[Store Search]
+ Search --> Analyzer[Price Analyzer]
+ Analyzer --> Comparator[Price Comparator]
+ Comparator --> Out[Price Report]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Comparator fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Shopping Agent can search for products, compare prices, and provide shopping recommendations.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `shopping_assistant.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import duckduckgo
+
+ # Create Shopping Agent
+ shopping_agent = Agent(
+ name="ShoppingAssistant",
+ role="Shopping Specialist",
+ goal="Find the best prices and deals across stores",
+ instructions="You are a Shopping Agent",
+ tools=[duckduckgo]
+ )
+
+ # Search for product prices
+ response = shopping_agent.start(
+ "I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table"
+ )
+
+ # Save price comparison
+ with open('price_comparison.md', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Shopping Assistant
+
+The Shopping Agent combines multiple capabilities for effective price comparison:
+
+1. **Store Search**: Uses DuckDuckGo to find retailers
+2. **Price Analysis**: Gathers pricing information
+3. **Price Comparison**: Compares prices across stores
+4. **Deal Finding**: Identifies special offers and discounts
+
+## Features
+
+
+
+ Compare prices across multiple stores.
+
+
+ Find special offers and discounts.
+
+
+ Evaluate retailer reliability.
+
+
+ Monitor price changes over time.
+
+
+
+## Example Usage
+
+```python
+# Example: Compare product prices
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex shopping research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving price accuracy
+- Check out the [Research Agent](/agents/research) for detailed product research
diff --git a/docs/agents/single.mdx b/docs/agents/single.mdx
new file mode 100644
index 00000000..9df300c4
--- /dev/null
+++ b/docs/agents/single.mdx
@@ -0,0 +1,102 @@
+---
+title: "Single Agent"
+sidebarTitle: "Single"
+description: "Learn how to create a basic single-purpose AI agent for simple tasks."
+icon: "circle-1"
+---
+
+```mermaid
+flowchart LR
+ In[Input] --> Agent[Single Agent]
+ Agent --> Out[Output]
+
+ style In fill:#8B0000,color:#fff
+ style Agent fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how to create and use a simple single-purpose agent.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `simple_agent.py`:
+ ```python
+ from praisonaiagents import Agent
+
+ agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+ agent.start("Write a blog post about AI")
+ ```
+
+
+
+## Understanding Single Agent
+
+The Single Agent is the simplest form of PraisonAI agent:
+
+1. **Simple Setup**: Minimal configuration required
+2. **Single Purpose**: Focused on one specific task
+3. **Direct Execution**: Straightforward input-output flow
+4. **No Dependencies**: No external tools required
+
+## Features
+
+
+
+ Minimal configuration needed.
+
+
+ Single-purpose execution.
+
+
+ Rapid implementation.
+
+
+ No external dependencies.
+
+
+
+## Example Usage
+
+```python
+# Example: Create a content generation agent
+from praisonaiagents import Agent
+
+# Create content agent
+agent = Agent(
+ instructions="You are a Content Generation Agent, create engaging content"
+)
+
+# Generate content
+response = agent.start("""
+ Write a short story about:
+ - A future world
+ - With advanced AI
+ - And human collaboration
+""")
+
+# Save the story
+with open('ai_story.txt', 'w') as f:
+ f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for more complex workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving output quality
+- Check out other specialized agents like the [Markdown Agent](/agents/markdown) for specific use cases
diff --git a/docs/agents/video.mdx b/docs/agents/video.mdx
new file mode 100644
index 00000000..7a29f476
--- /dev/null
+++ b/docs/agents/video.mdx
@@ -0,0 +1,149 @@
+---
+title: "Video Agent"
+sidebarTitle: "Video"
+description: "Learn how to create AI agents for video analysis and content understanding."
+icon: "video"
+---
+
+```mermaid
+flowchart LR
+ In[Video Input] --> Analyzer[Content Analyzer]
+ Analyzer --> Detector[Object Detector]
+ Detector --> Transcriber[Text Transcriber]
+ Transcriber --> Out[Analysis Report]
+
+ style In fill:#8B0000,color:#fff
+ style Analyzer fill:#2E8B57,color:#fff
+ style Detector fill:#2E8B57,color:#fff
+ style Transcriber fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Video Agent can analyze video content, detect objects, and extract meaningful information.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `video_analyzer.py`:
+ ```python
+ from praisonaiagents import Agent, Task, PraisonAIAgents
+
+ # Create Video Analysis Agent
+ video_agent = Agent(
+ name="VideoAnalyst",
+ role="Video Analysis Specialist",
+ goal="Analyze videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and video analysis.
+ You excel at describing content, detecting objects, and understanding context.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+ )
+
+ # Create video analysis task
+ analysis_task = Task(
+ name="analyze_video",
+ description="""Analyze this video and provide:
+ 1. Summary of main events
+ 2. Key objects and people
+ 3. Text and important information
+ 4. Context and setting""",
+ expected_output="Comprehensive video analysis",
+ agent=video_agent,
+ images=["video.mp4"]
+ )
+
+ # Create PraisonAIAgents instance
+ agents = PraisonAIAgents(
+ agents=[video_agent],
+ tasks=[analysis_task],
+ process="sequential",
+ verbose=1
+ )
+
+ # Run analysis
+ agents.start()
+ ```
+
+
+
+## Understanding Video Analysis
+
+The Video Agent combines multiple capabilities for comprehensive video understanding:
+
+1. **Content Analysis**: Analyzes video scenes and events
+2. **Object Detection**: Identifies objects and people
+3. **Text Extraction**: Captures text shown in videos
+4. **Context Understanding**: Interprets settings and situations
+
+## Features
+
+
+
+ Detailed analysis of video scenes.
+
+
+ Identification of objects and people.
+
+
+ Capture of text and captions.
+
+
+ Understanding of video context.
+
+
+
+## Example Usage
+
+```python
+# Example: Analyze a presentation video
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+video_agent = Agent(
+ name="VideoAnalyst",
+ role="Video Analysis Specialist",
+ goal="Extract information from presentation videos",
+ llm="gpt-4o-mini"
+)
+
+# Create presentation analysis task
+presentation_task = Task(
+ name="analyze_presentation",
+ description="""Analyze this presentation video:
+ 1. Extract key points
+ 2. Capture slide content
+ 3. Note speaker's main arguments
+ 4. Summarize Q&A session""",
+ expected_output="Detailed presentation summary",
+ agent=video_agent,
+ images=["presentation.mp4"]
+)
+
+# Run analysis
+agents = PraisonAIAgents(
+ agents=[video_agent],
+ tasks=[presentation_task],
+ process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex video analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Image Agent](/agents/image) for still image analysis
diff --git a/docs/agents/websearch.mdx b/docs/agents/websearch.mdx
new file mode 100644
index 00000000..bc89bab7
--- /dev/null
+++ b/docs/agents/websearch.mdx
@@ -0,0 +1,124 @@
+---
+title: "Web Search Agent"
+sidebarTitle: "Web Search"
+description: "Learn how to create AI agents for intelligent web searching and information gathering."
+icon: "globe"
+---
+
+```mermaid
+flowchart LR
+ In[Search Query] --> Search[Web Search]
+ Search --> Filter[Content Filter]
+ Filter --> Summarizer[Content Summarizer]
+ Summarizer --> Out[Search Results]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Filter fill:#2E8B57,color:#fff
+ style Summarizer fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Web Search Agent can perform intelligent searches and process web content.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `web_search.py`:
+ ```python
+ from praisonaiagents import Agent, Tools
+ from praisonaiagents.tools import duckduckgo
+
+ # Create Web Search Agent
+ search_agent = Agent(
+ name="WebSearcher",
+ role="Web Search Specialist",
+ goal="Perform intelligent web searches and gather information",
+ instructions="You are a Web Search Agent",
+ tools=[duckduckgo]
+ )
+
+ # Perform search
+ response = search_agent.start(
+ "Search about AI developments in 2024"
+ )
+
+ # Save search results
+ with open('search_results.md', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Web Search
+
+The Web Search Agent employs sophisticated search strategies:
+
+1. **Query Processing**: Optimizes search queries
+2. **Content Filtering**: Filters relevant results
+3. **Information Extraction**: Extracts key information
+4. **Result Summarization**: Summarizes findings
+
+## Features
+
+
+
+ Intelligent query processing.
+
+
+ Relevance-based filtering.
+
+
+ Key information extraction.
+
+
+ Concise result summaries.
+
+
+
+## Example Usage
+
+```python
+# Example: Perform topic research
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(
+ instructions="You are a Web Search Agent",
+ tools=[duckduckgo]
+)
+
+# Research specific topic
+response = agent.start("""
+ Search for information about quantum computing:
+ - Recent breakthroughs
+ - Leading companies
+ - Current applications
+ - Future predictions
+""")
+
+# Save research results
+with open('quantum_computing_research.md', 'w') as f:
+ f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex search workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving search relevance
+- Check out the [Research Agent](/agents/research) for comprehensive research capabilities
diff --git a/docs/agents/wikipedia.mdx b/docs/agents/wikipedia.mdx
new file mode 100644
index 00000000..c531bffe
--- /dev/null
+++ b/docs/agents/wikipedia.mdx
@@ -0,0 +1,139 @@
+---
+title: "Wikipedia Agent"
+sidebarTitle: "Wikipedia"
+description: "Learn how to create AI agents for searching and extracting information from Wikipedia."
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+ In[Query] --> Search[Wiki Search]
+ Search --> Page[Page Retrieval]
+ Page --> Summary[Content Summary]
+ Summary --> Out[Knowledge Output]
+
+ style In fill:#8B0000,color:#fff
+ style Search fill:#2E8B57,color:#fff
+ style Page fill:#2E8B57,color:#fff
+ style Summary fill:#2E8B57,color:#fff
+ style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Wikipedia Agent can search, retrieve, and summarize Wikipedia content.
+
+## Quick Start
+
+
+
+ First, install the PraisonAI Agents package:
+ ```bash
+ pip install praisonaiagents
+ ```
+
+
+
+ Set your OpenAI API key as an environment variable:
+ ```bash
+ export OPENAI_API_KEY=your_api_key_here
+ ```
+
+
+
+ Create a new file `wikipedia_assistant.py`:
+ ```python
+ from praisonaiagents import Agent, Task, PraisonAIAgents
+ from praisonaiagents.tools import (
+ wiki_search,
+ wiki_summary,
+ wiki_page,
+ wiki_random,
+ wiki_language
+ )
+
+ # Create Wikipedia Agent
+ wiki_agent = Agent(
+ name="WikipediaAssistant",
+ role="Wikipedia Research Specialist",
+ goal="Extract and summarize Wikipedia content",
+ instructions="You are a Wikipedia Agent",
+ tools=[
+ wiki_search,
+ wiki_summary,
+ wiki_page,
+ wiki_random,
+ wiki_language
+ ],
+ self_reflect=True,
+ min_reflect=3,
+ max_reflect=5
+ )
+
+ # Research a topic
+ response = wiki_agent.start("""
+ What is the history of AI?
+ First search the history of AI
+ Read the page of the history of AI
+ Get the summary of the page
+ """)
+
+ # Save research results
+ with open('ai_history.md', 'w') as f:
+ f.write(response)
+ ```
+
+
+
+## Understanding Wikipedia Research
+
+The Wikipedia Agent provides comprehensive Wikipedia access through multiple tools:
+
+1. **Wiki Search**: `wiki_search` for finding relevant articles
+2. **Page Access**: `wiki_page` for retrieving full articles
+3. **Summarization**: `wiki_summary` for concise overviews
+4. **Random Articles**: `wiki_random` for discovering content
+5. **Language Support**: `wiki_language` for multilingual access
+
+## Features
+
+
+
+ Find relevant Wikipedia articles.
+
+
+ Access complete article content.
+
+
+ Generate concise article summaries.
+
+
+ Access content in multiple languages.
+
+
+
+## Example Usage
+
+```python
+# Example: Research a scientific topic
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+ instructions="You are a Wikipedia Agent",
+ tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+ self_reflect=True,
+ min_reflect=3,
+ max_reflect=5,
+)
+agent.start(
+ "What is the history of AI?"
+ "First search the history of AI"
+ "Read the page of the history of AI"
+ "Get the summary of the page"
+)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex Wikipedia research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Research Agent](/agents/research) for broader research capabilities
diff --git a/docs/api/praisonai/deploy.html b/docs/api/praisonai/deploy.html
index 4d98b48f..e951a42f 100644
--- a/docs/api/praisonai/deploy.html
+++ b/docs/api/praisonai/deploy.html
@@ -110,7 +110,7 @@ Raises
file.write("FROM python:3.11-slim\n")
file.write("WORKDIR /app\n")
file.write("COPY . .\n")
- file.write("RUN pip install flask praisonai==2.0.45 gunicorn markdown\n")
+ file.write("RUN pip install flask praisonai==2.0.46 gunicorn markdown\n")
file.write("EXPOSE 8080\n")
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
diff --git a/docs/mint.json b/docs/mint.json
index e723e857..f9b6472d 100644
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -62,10 +62,12 @@
{
"name": "Use Cases",
"url": "/usecases"
+ },
+ {
+ "name": "Agents",
+ "url": "/agents"
}
-
- ]
- ,
+ ],
"navigation": [
{
"group": "",
@@ -146,6 +148,25 @@
"usecases/adaptive-learning"
]
},
+ {
+ "group": "Agents",
+ "pages": [
+ "agents/data-analyst",
+ "agents/finance",
+ "agents/image",
+ "agents/image-to-text",
+ "agents/markdown",
+ "agents/planning",
+ "agents/programming",
+ "agents/shopping",
+ "agents/single",
+ "agents/video",
+ "agents/websearch",
+ "agents/wikipedia",
+ "agents/research",
+ "agents/recommendation"
+ ]
+ },
{
"group": "Tools",
"pages": [
diff --git a/cookbooks/.gitignore b/examples/.gitignore
similarity index 100%
rename from cookbooks/.gitignore
rename to examples/.gitignore
diff --git a/examples/agents/data-analyst-agent.py b/examples/agents/data-analyst-agent.py
new file mode 100644
index 00000000..203f14f5
--- /dev/null
+++ b/examples/agents/data-analyst-agent.py
@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+import os
+
+agent = Agent(instructions="You are a Data Analyst Agent", tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+agent.start(f"""
+ Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+ Analyse the data and give me the insights
+ read_csv to read the file
+""")
\ No newline at end of file
diff --git a/examples/agents/finance-agent.py b/examples/agents/finance-agent.py
new file mode 100644
index 00000000..78eff629
--- /dev/null
+++ b/examples/agents/finance-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+agent = Agent(instructions="You are a Research Agent", tools=[get_stock_price, get_stock_info, get_historical_data])
+agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
\ No newline at end of file
diff --git a/examples/agents/image-agent.py b/examples/agents/image-agent.py
new file mode 100644
index 00000000..9ea8e1da
--- /dev/null
+++ b/examples/agents/image-agent.py
@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+ name="ImageAnalyst",
+ role="Image Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+ name="analyze_landmark",
+ description="Describe this famous landmark and its architectural features.",
+ expected_output="Detailed description of the landmark's architecture and significance",
+ agent=image_agent,
+ images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+ name="analyze_local_image",
+ description="What objects can you see in this image? Describe their arrangement.",
+ expected_output="Detailed description of objects and their spatial relationships",
+ agent=image_agent,
+ images=["image.jpg"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[task1, task2],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/examples/agents/image-to-text-agent.py b/examples/agents/image-to-text-agent.py
new file mode 100644
index 00000000..9ea8e1da
--- /dev/null
+++ b/examples/agents/image-to-text-agent.py
@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+ name="ImageAnalyst",
+ role="Image Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+ name="analyze_landmark",
+ description="Describe this famous landmark and its architectural features.",
+ expected_output="Detailed description of the landmark's architecture and significance",
+ agent=image_agent,
+ images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+ name="analyze_local_image",
+ description="What objects can you see in this image? Describe their arrangement.",
+ expected_output="Detailed description of objects and their spatial relationships",
+ agent=image_agent,
+ images=["image.jpg"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[image_agent],
+ tasks=[task1, task2],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/examples/agents/markdown-agent.py b/examples/agents/markdown-agent.py
new file mode 100644
index 00000000..f0366c46
--- /dev/null
+++ b/examples/agents/markdown-agent.py
@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")
\ No newline at end of file
diff --git a/examples/agents/planning-agent.py b/examples/agents/planning-agent.py
new file mode 100644
index 00000000..cf7df8a7
--- /dev/null
+++ b/examples/agents/planning-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
\ No newline at end of file
diff --git a/examples/agents/programming-agent.py b/examples/agents/programming-agent.py
new file mode 100644
index 00000000..dac126f7
--- /dev/null
+++ b/examples/agents/programming-agent.py
@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+agent = Agent(
+ instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10,
+ tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+)
+agent.start(
+ "Write a python script using yfinance to find the stock price of Tesla"
+ "First check if required packages are installed"
+ "Run it using execute_code"
+ "execute_command if you want to run any terminal command"
+ "search internet using duckduckgo if you want to know update python package information"
+ "Analyse the output using analyze_code and fix error if required"
+ "if no package is installed, install it"
+ "then run the code"
+)
\ No newline at end of file
diff --git a/examples/agents/recommendation-agent.py b/examples/agents/recommendation-agent.py
new file mode 100644
index 00000000..b53c135d
--- /dev/null
+++ b/examples/agents/recommendation-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
\ No newline at end of file
diff --git a/examples/agents/research-agent.py b/examples/agents/research-agent.py
new file mode 100644
index 00000000..6e50da3e
--- /dev/null
+++ b/examples/agents/research-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
\ No newline at end of file
diff --git a/examples/agents/shopping-agent.py b/examples/agents/shopping-agent.py
new file mode 100644
index 00000000..c7839f3f
--- /dev/null
+++ b/examples/agents/shopping-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
\ No newline at end of file
diff --git a/examples/agents/single-agent.py b/examples/agents/single-agent.py
new file mode 100644
index 00000000..f0366c46
--- /dev/null
+++ b/examples/agents/single-agent.py
@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")
\ No newline at end of file
diff --git a/examples/agents/video-agent.py b/examples/agents/video-agent.py
new file mode 100644
index 00000000..a35b0c09
--- /dev/null
+++ b/examples/agents/video-agent.py
@@ -0,0 +1,36 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Video Analysis Agent
+video_agent = Agent(
+ name="VideoAnalyst",
+ role="Video Analysis Specialist",
+ goal="Analyze images and videos to extract meaningful information",
+ backstory="""You are an expert in computer vision and image analysis.
+ You excel at describing images, detecting objects, and understanding visual content.""",
+ llm="gpt-4o-mini",
+ self_reflect=False
+)
+
+# Task with Video File
+task1 = Task(
+ name="analyze_video",
+ description="""Watch this video and provide:
+ 1. A summary of the main events
+ 2. Key objects and people visible
+ 3. Any text or important information shown
+ 4. The overall context and setting""",
+ expected_output="Comprehensive analysis of the video content",
+ agent=video_agent,
+ images=["video.mp4"]
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+ agents=[video_agent],
+ tasks=[task1],
+ process="sequential",
+ verbose=1
+)
+
+# Run all tasks
+agents.start()
\ No newline at end of file
diff --git a/examples/agents/websearch-agent.py b/examples/agents/websearch-agent.py
new file mode 100644
index 00000000..b53d60ea
--- /dev/null
+++ b/examples/agents/websearch-agent.py
@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Web Search Agent", tools=[duckduckgo])
+agent.start("Search about AI 2024")
\ No newline at end of file
diff --git a/examples/agents/wikipedia-agent.py b/examples/agents/wikipedia-agent.py
new file mode 100644
index 00000000..a34a1644
--- /dev/null
+++ b/examples/agents/wikipedia-agent.py
@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+ instructions="You are a Wikipedia Agent",
+ tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+ self_reflect=True,
+ min_reflect=3,
+ max_reflect=5,
+)
+agent.start(
+ "What is the history of AI?"
+ "First search the history of AI"
+ "Read the page of the history of AI"
+ "Get the summary of the page"
+)
\ No newline at end of file
diff --git a/cookbooks/general/async_example.py b/examples/general/async_example.py
similarity index 100%
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diff --git a/cookbooks/general/mini_agents_example.py b/examples/general/mini_agents_example.py
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diff --git a/cookbooks/general/orchestrator-workers.py b/examples/general/orchestrator-workers.py
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rename to examples/general/orchestrator-workers.py
diff --git a/cookbooks/general/parallelisation.py b/examples/general/parallelisation.py
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diff --git a/cookbooks/general/prompt_chaining.py b/examples/general/prompt_chaining.py
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diff --git a/cookbooks/general/structured_agents_example.py b/examples/general/structured_agents_example.py
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diff --git a/cookbooks/general/structured_response_example.py b/examples/general/structured_response_example.py
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diff --git a/cookbooks/general/tools-class.py b/examples/general/tools-class.py
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diff --git a/cookbooks/general/tools_example.py b/examples/general/tools_example.py
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diff --git a/cookbooks/general/workflow_example_basic.py b/examples/general/workflow_example_basic.py
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diff --git a/cookbooks/general/workflow_example_detailed.py b/examples/general/workflow_example_detailed.py
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diff --git a/cookbooks/tools/cli/app.py b/examples/tools/cli/app.py
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diff --git a/cookbooks/tools/e2b/agents.yaml b/examples/tools/e2b/agents.yaml
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diff --git a/cookbooks/tools/e2b/app.py b/examples/tools/e2b/app.py
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diff --git a/cookbooks/tools/e2b/single_agent.py b/examples/tools/e2b/single_agent.py
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diff --git a/cookbooks/tools/e2b/tools.py b/examples/tools/e2b/tools.py
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diff --git a/cookbooks/usecases/adaptive-learning.py b/examples/usecases/adaptive-learning.py
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diff --git a/cookbooks/usecases/code-review.py b/examples/usecases/code-review.py
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diff --git a/cookbooks/usecases/customer-service.py b/examples/usecases/customer-service.py
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diff --git a/cookbooks/usecases/emergency-response.py b/examples/usecases/emergency-response.py
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diff --git a/cookbooks/usecases/multilingual-content.py b/examples/usecases/multilingual-content.py
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rename to examples/usecases/multilingual-content.py
diff --git a/cookbooks/usecases/predictive-maintenance.py b/examples/usecases/predictive-maintenance.py
similarity index 100%
rename from cookbooks/usecases/predictive-maintenance.py
rename to examples/usecases/predictive-maintenance.py
diff --git a/cookbooks/usecases/smart-city.py b/examples/usecases/smart-city.py
similarity index 100%
rename from cookbooks/usecases/smart-city.py
rename to examples/usecases/smart-city.py
diff --git a/cookbooks/usecases/supply-chain.py b/examples/usecases/supply-chain.py
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diff --git a/praisonai.rb b/praisonai.rb
index 5d35826e..de8d6127 100644
--- a/praisonai.rb
+++ b/praisonai.rb
@@ -3,7 +3,7 @@ class Praisonai < Formula
desc "AI tools for various AI applications"
homepage "https://github.com/MervinPraison/PraisonAI"
- url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.45.tar.gz"
+ url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.46.tar.gz"
sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32" # Replace with actual SHA256 checksum
license "MIT"
diff --git a/praisonai/deploy.py b/praisonai/deploy.py
index efa95cc9..b1c3cbd6 100644
--- a/praisonai/deploy.py
+++ b/praisonai/deploy.py
@@ -56,7 +56,7 @@ def create_dockerfile(self):
file.write("FROM python:3.11-slim\n")
file.write("WORKDIR /app\n")
file.write("COPY . .\n")
- file.write("RUN pip install flask praisonai==2.0.45 gunicorn markdown\n")
+ file.write("RUN pip install flask praisonai==2.0.46 gunicorn markdown\n")
file.write("EXPOSE 8080\n")
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
diff --git a/pyproject.toml b/pyproject.toml
index a9f92167..398adf69 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "PraisonAI"
-version = "2.0.45"
+version = "2.0.46"
description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
readme = "README.md"
license = ""
@@ -12,7 +12,7 @@ dependencies = [
"rich>=13.7",
"markdown>=3.5",
"pyparsing>=3.0.0",
- "praisonaiagents>=0.0.35",
+ "praisonaiagents>=0.0.36",
"python-dotenv>=0.19.0",
"instructor>=1.3.3",
"PyYAML>=6.0",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
[tool.poetry]
name = "PraisonAI"
-version = "2.0.45"
+version = "2.0.46"
description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
authors = ["Mervin Praison"]
license = ""
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
rich = ">=13.7"
markdown = ">=3.5"
pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.35"
+praisonaiagents = ">=0.0.36"
python-dotenv = ">=0.19.0"
instructor = ">=1.3.3"
PyYAML = ">=6.0"
diff --git a/uv.lock b/uv.lock
index 8581cf9a..6e542294 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3060,7 +3060,7 @@ wheels = [
[[package]]
name = "praisonai"
-version = "2.0.45"
+version = "2.0.46"
source = { editable = "." }
dependencies = [
{ name = "instructor" },
@@ -3197,7 +3197,7 @@ requires-dist = [
{ name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
{ name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.7" },
{ name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.7" },
- { name = "praisonaiagents", specifier = ">=0.0.35" },
+ { name = "praisonaiagents", specifier = ">=0.0.36" },
{ name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
{ name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
{ name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -3250,16 +3250,16 @@ wheels = [
[[package]]
name = "praisonaiagents"
-version = "0.0.35"
+version = "0.0.36"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "openai" },
{ name = "pydantic" },
{ name = "rich" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/58/2e/f7899ee05c74b0f2ddd237490f361865b85be404f7888c6229e5292a9af4/praisonaiagents-0.0.35.tar.gz", hash = "sha256:23fdda1320f29cdc86162a8a0b8e77bad07ad793a2f08d6d8d7f7729a01f7ab7", size = 69306 }
+sdist = { url = "https://files.pythonhosted.org/packages/95/42/4c41d4fd2467d6ec156ec53609a8ff176ec3915c7c196fcb4b8ff2faff42/praisonaiagents-0.0.36.tar.gz", hash = "sha256:a13fd30ec2be40d05c4e689f37dcb4bbbce6dced5118ed5b2e130bc11443029f", size = 69554 }
wheels = [
- { url = "https://files.pythonhosted.org/packages/d2/11/678bfe9b57d8d0a3b0f5f01c2cdb52d12ed6dcd157cc6324b084f265ba1b/praisonaiagents-0.0.35-py3-none-any.whl", hash = "sha256:0c0f9650cb67449261738cd7225d5815434bc042cca319e7e7230196b47807cf", size = 86057 },
+ { url = "https://files.pythonhosted.org/packages/49/e0/f34256aed4573aa92bcee1a28f6e3322368be49633dfe9e00fe805d1b37d/praisonaiagents-0.0.36-py3-none-any.whl", hash = "sha256:1767f30fdf3d4c27383742a8392af99e742fad80d740f8847679a2274909070d", size = 86287 },
]
[[package]]