Merge pull request #291 from MervinPraison/develop

Add parallelisation feature and update documentation

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.43 gunicorn markdown
+RUN pip install flask praisonai==2.0.44 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/parallelization.py

@@ -13,31 +13,27 @@ agent1 = Agent(
     name="Processor 1",
     role="Time collector",
     goal="Get the time and return it",
-    tools=[process_time],
-    verbose=0
+    tools=[process_time]
 )
 
 agent2 = Agent(
     name="Processor 2",
     role="Time collector",
     goal="Get the time and return it",
-    tools=[process_time],
-    verbose=0
+    tools=[process_time]
 )
 
 agent3 = Agent(
     name="Processor 3",
     role="Time collector",
     goal="Get the time and return it",
-    tools=[process_time],
-    verbose=0
+    tools=[process_time]
 )
 
 aggregator = Agent(
     name="Aggregator",
     role="Result aggregator",
-    goal="Collect all the processed time from all tasks",
-    verbose=0
+    goal="Collect all the processed time from all tasks"
 )
 
 # Create parallel tasks with memory disabled
@@ -82,8 +78,7 @@ async def main():
     workflow = PraisonAIAgents(
         agents=[agent1, agent2, agent3, aggregator],
         tasks=[task1, task2, task3, aggregate_task],
-        process="workflow",
-        verbose=0
+        process="workflow"
     )
 
     # Run parallel workflow

agents/praisonaiagents/agent/agent.py

@@ -16,7 +16,8 @@ from ..main import (
     display_self_reflection,
     ReflectionOutput,
     client,
-    error_logs
+    error_logs,
+    adisplay_instruction
 )
 import inspect
 

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.33"
+version = "0.0.34"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/uv.lock

@@ -990,7 +990,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.33"
+version = "0.0.34"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

cookbooks/general/parallelisation.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from datetime import datetime
+import asyncio
+
+def process_time():
+    """Simulate processing"""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"Processing at: {current_time}")
+    return f"Processed at {current_time}"
+
+# Create parallel processing agents
+agent1 = Agent(
+    name="Processor 1",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent2 = Agent(
+    name="Processor 2",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent3 = Agent(
+    name="Processor 3",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+aggregator = Agent(
+    name="Aggregator",
+    role="Result aggregator",
+    goal="Collect all the processed time from all tasks"
+)
+
+# Create parallel tasks with memory disabled
+task1 = Task(
+    name="process_1",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent1,
+    is_start=True,
+    async_execution=True
+)
+
+task2 = Task(
+    name="process_2",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent2,
+    is_start=True,
+    async_execution=True
+)
+
+task3 = Task(
+    name="process_3",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent3,
+    is_start=True,
+    async_execution=True
+)
+
+aggregate_task = Task(
+    name="aggregate",
+    description="Collect all the processed time from all tasks",
+    expected_output="Output all the processed time from all tasks and just the time",
+    agent=aggregator,
+    context=[task1, task2, task3]
+)
+
+async def main():
+
+    # Create workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3, aggregator],
+        tasks=[task1, task2, task3, aggregate_task],
+        process="workflow"
+    )
+
+    # Run parallel workflow
+    results = await workflow.astart()
+
+    # Print results
+    print("\nParallel Processing Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.43 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.44 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/features/parallelisation.mdx

@@ -0,0 +1,231 @@
+---
+title: "Parallelisation"
+description: "Learn how to create AI agents that can execute tasks in parallel for improved performance."
+icon: "arrows-split-up-and-left"
+---
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        from datetime import datetime
+        import asyncio
+
+        def process_time():
+            """Simulate processing"""
+            current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            print(f"Processing at: {current_time}")
+            return f"Processed at {current_time}"
+
+        # Create parallel processing agents
+        agent1 = Agent(
+            name="Processor 1",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        agent2 = Agent(
+            name="Processor 2",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        agent3 = Agent(
+            name="Processor 3",
+            role="Time collector",
+            goal="Get the time and return it",
+            tools=[process_time]
+        )
+
+        aggregator = Agent(
+            name="Aggregator",
+            role="Result aggregator",
+            goal="Collect all the processed time from all tasks"
+        )
+
+        # Create parallel tasks with memory disabled
+        task1 = Task(
+            name="process_1",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent1,
+            is_start=True,
+            async_execution=True
+        )
+
+        task2 = Task(
+            name="process_2",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent2,
+            is_start=True,
+            async_execution=True
+        )
+
+        task3 = Task(
+            name="process_3",
+            description="Use process_time tool to get the time",
+            expected_output="processed time",
+            agent=agent3,
+            is_start=True,
+            async_execution=True
+        )
+
+        aggregate_task = Task(
+            name="aggregate",
+            description="Collect all the processed time from all tasks",
+            expected_output="Output all the processed time from all tasks and just the time",
+            agent=aggregator,
+            context=[task1, task2, task3]
+        )
+
+        async def main():
+
+            # Create workflow manager
+            workflow = PraisonAIAgents(
+                agents=[agent1, agent2, agent3, aggregator],
+                tasks=[task1, task2, task3, aggregate_task],
+                process="workflow"
+            )
+
+            # Run parallel workflow
+            results = await workflow.astart()
+
+            # Print results
+            print("\nParallel Processing Results:")
+            for task_id, result in results["task_results"].items():
+                if result:
+                    print(f"Task {task_id}: {result.raw}")
+
+        # Run the async main function
+        if __name__ == "__main__":
+            asyncio.run(main())
+        ```
+    </Step>
+
+    <Step title="Start Agents">
+        Type this in your terminal to run your agents:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python and async programming
+</Note>
+
+## Understanding Parallelisation
+
+<Card title="What is Parallelisation?" icon="question">
+  Parallelisation enables:
+  - Concurrent execution of multiple tasks
+  - Improved performance through parallel processing
+  - Efficient handling of independent operations
+  - Aggregation of parallel task results
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Parallel Execution" icon="arrows-split-up-and-left">
+    Run multiple tasks simultaneously for improved performance.
+  </Card>
+  <Card title="Async Support" icon="bolt">
+    Built-in support for asynchronous execution.
+  </Card>
+  <Card title="Result Aggregation" icon="layer-group">
+    Combine results from parallel tasks efficiently.
+  </Card>
+  <Card title="Process Control" icon="sliders">
+    Monitor and manage parallel task execution.
+  </Card>
+</CardGroup>
+
+## Configuration Options
+
+```python
+# Create a parallel task
+task = Task(
+    name="parallel_task",
+    description="Task to run in parallel",
+    expected_output="Task result",
+    agent=agent,
+    is_start=True,
+    async_execution=True  # Enable parallel execution
+)
+
+# Create an aggregator task
+aggregator_task = Task(
+    name="aggregate",
+    description="Aggregate results from parallel tasks",
+    expected_output="Combined results",
+    agent=aggregator,
+    context=[task1, task2, task3]  # Reference parallel tasks
+)
+
+# Async workflow execution
+async def run_workflow():
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, aggregator],
+        tasks=[task1, task2, aggregator_task],
+        process="workflow",
+        verbose=True
+    )
+    results = await workflow.astart()
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Execution Issues" icon="triangle-exclamation">
+    If parallel execution fails:
+    - Check async configuration
+    - Verify task independence
+    - Monitor resource usage
+  </Card>
+
+  <Card title="Result Aggregation" icon="diagram-project">
+    If aggregation is incorrect:
+    - Review task outputs
+    - Check context connections
+    - Verify aggregator logic
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, ensure your parallel tasks are truly independent and your system has sufficient resources to handle concurrent execution.
+</Note>

docs/mint.json

@@ -92,7 +92,8 @@
         "features/structured",
         "features/callbacks",
         "features/agenticrouting",
-        "features/promptchaining"
+        "features/promptchaining",
+        "features/parallelisation"
       ]
     },
     {

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/2.0.43.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.44.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ 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==2.0.43 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.44 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.43"
+version = "2.0.44"
 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>=0.0.33",
+    "praisonaiagents>=0.0.34",
     "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 = "2.0.43"
+version = "2.0.44"
 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 = ">=0.0.33"
+praisonaiagents = ">=0.0.34"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.43"
+version = "2.0.44"
 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 = ">=0.0.33" },
+    { name = "praisonaiagents", specifier = ">=0.0.34" },
     { 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 = "0.0.33"
+version = "0.0.34"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f2/8a/c61b36bf73bebb89054ef83a511bcf2f43c6219e9471514109e238f77c0f/praisonaiagents-0.0.33.tar.gz", hash = "sha256:cf3df6ab44b9f7b38461e6f75e19ff792dc3d1f6a05c8854439c282a2aa701ce", size = 69163 }
+sdist = { url = "https://files.pythonhosted.org/packages/cc/98/00ef0d9d50a810f38710647fe2dd6c198b084b787eae5e43307de1b6118b/praisonaiagents-0.0.34.tar.gz", hash = "sha256:1e6e3c37145a1a62030151d014f4f399d9d08a77ee0fd14bcf49f0482bd041fb", size = 69170 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ce/2a/ded102807af9efb0b3e0de86a906d5a0d621eb8aab2be30f9c897aad8054/praisonaiagents-0.0.33-py3-none-any.whl", hash = "sha256:f4491ded47ed879bd39bdfa0cad6589aa69fb6a72ba0f1b1e7d0c9a997824921", size = 85889 },
+    { url = "https://files.pythonhosted.org/packages/19/4c/dbb750a2156753ceff051f5ac7b6bba284d1fc57fc9a1aa7fded855009e5/praisonaiagents-0.0.34-py3-none-any.whl", hash = "sha256:a895ee63c73f129d9fa33bbdbe4f13bc5de50509c5565247f640141ef4b8aa8c", size = 85896 },
 ]
 
 [[package]]