Merge pull request #306 from MervinPraison/develop

Develop

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.49 gunicorn markdown
+RUN pip install flask praisonai==2.0.50 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/context-agents.py

@@ -0,0 +1,53 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    technical_skills: List[str]
+    soft_skills: List[str]
+    skill_gaps: List[str]
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+
+# Create single CV analysis agent
+cv_analyzer = Agent(
+    role="CV Analysis Expert",
+    goal="Provide comprehensive CV evaluation and recommendations",
+    backstory="""You are an expert CV analyst with deep knowledge of industry requirements, 
+    market trends, and talent assessment. You excel at evaluating skills, experience, 
+    and providing actionable recommendations.""",
+    verbose=True
+)
+
+# Single comprehensive analysis task
+cv_analysis_task = Task(
+    description="""Analyze the CV comprehensively including:
+    1. Technical and soft skills assessment
+    2. Experience and achievements evaluation
+    3. Overall CV evaluation with recommendations
+    Provide detailed insights and actionable recommendations.""",
+    expected_output="Complete CV analysis report",
+    agent=cv_analyzer,
+    output_pydantic=CVAnalysisReport
+)
+
+# Create and run the agent
+agents = PraisonAIAgents(
+    agents=[cv_analyzer],
+    tasks=[cv_analysis_task]
+)
+
+# Read and pass CV content through start()
+with open('cv.txt', 'r') as file:
+    cv_content = file.read()
+
+# Start the analysis by passing CV content
+result = agents.start(cv_content)
+
+print(result)

agents/context-multi-tasks.py

@@ -0,0 +1,61 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    technical_skills: List[str]
+    soft_skills: List[str]
+    skill_gaps: List[str]
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+
+# Create single CV analysis agent
+cv_analyzer = Agent(
+    role="CV Analysis Expert",
+    goal="Provide comprehensive CV evaluation and recommendations",
+    backstory="""You are an expert CV analyst with deep knowledge of industry requirements, 
+    market trends, and talent assessment. You excel at evaluating skills, experience, 
+    and providing actionable recommendations.""",
+    verbose=True
+)
+
+# Read CV content first
+with open('cv.txt', 'r') as file:
+    cv_content = file.read()
+
+cv_key_strengths = Task(
+    description="List the key strengths to look",
+    expected_output="List of key strengths",
+    agent=cv_analyzer,
+)
+
+# Single comprehensive analysis task with CV content in context
+cv_analysis_task = Task(
+    description="""Analyze the CV comprehensively including:
+    1. Technical and soft skills assessment
+    2. Experience and achievements evaluation
+    3. Overall CV evaluation with recommendations
+    Provide detailed insights and actionable recommendations.""",
+    expected_output="Complete CV analysis report",
+    agent=cv_analyzer,
+    output_pydantic=CVAnalysisReport,
+    context=[cv_content, cv_key_strengths]  # Pass CV content directly in task context
+)
+
+# Create and run the agent
+agents = PraisonAIAgents(
+    agents=[cv_analyzer],
+    tasks=[cv_key_strengths,cv_analysis_task],
+    process="sequential"
+)
+
+# Start the analysis - no need to pass content again since it's in task context
+result = agents.start()
+
+print(result)

agents/crewai-tools-example.py

@@ -23,6 +23,5 @@ agent = Agent(
 )
 
 # Run the agent
-
 result = PraisonAIAgents(agents=[agent], verbose=10).start()
 print(result)

agents/cv.txt

@@ -0,0 +1,57 @@
+Name: Alex Johnson
+Location: London, UK
+
+Professional Summary
+Dynamic and results-oriented leader with a proven track record of driving innovation, scaling operations, and achieving transformational growth. Over 15 years of experience in engineering, strategic management, and executive leadership in the automotive and renewable energy sectors. Passionate about sustainable technologies and visionary leadership.
+
+Professional Experience
+Chief Operating Officer (COO)
+NextGen Mobility Ltd. | Palo Alto, CA | 2020 – Present
+
+Spearheaded global operations for an electric vehicle company, scaling production by 200% in three years.
+Introduced automation-driven supply chain strategies, reducing costs by 15%.
+Collaborated with R&D to launch three new EV models, driving 25% revenue growth annually.
+Vice President of Engineering
+ElectroDrive Systems | San Francisco, CA | 2015 – 2020
+
+Led a team of 500+ engineers in developing next-generation EV batteries, achieving a 30% increase in energy efficiency.
+Secured $50M in government funding for sustainable energy innovation.
+Drove cross-functional collaboration, aligning product development with market needs.
+Director of Renewable Energy Initiatives
+GreenTech Innovations | London, UK | 2011 – 2015
+
+Oversaw £100M projects in solar and wind energy deployment across Europe.
+Established partnerships with policymakers to influence renewable energy legislation.
+Pioneered a smart energy grid system, enhancing energy distribution efficiency by 40%.
+Senior Project Manager
+Innovate Auto Ltd. | Munich, Germany | 2008 – 2011
+
+Managed R&D projects for autonomous driving technologies.
+Delivered projects 20% under budget through strategic resource allocation.
+Filed 10+ patents in automotive AI and renewable integration.
+Mechanical Engineer
+AutoFusion Technologies | Berlin, Germany | 2005 – 2008
+
+Designed core systems for EV powertrains, optimising energy consumption by 15%.
+Collaborated with suppliers to improve material efficiency, cutting costs by £5M annually.
+Education
+MBA in Strategic Management
+Harvard Business School | Cambridge, MA | 2015
+
+MSc in Renewable Energy Engineering
+Imperial College London | London, UK | 2007
+
+BSc in Mechanical Engineering
+Technical University of Munich | Munich, Germany | 2005
+
+Skills
+Strategic Leadership & Visionary Thinking
+Electric Vehicle Engineering
+Renewable Energy Systems
+Global Operations Management
+Policy Advocacy & Government Relations
+Supply Chain Optimisation
+Achievements
+Featured in Forbes 30 Under 40 for innovations in EV technology.
+Author of The Future of Sustainable Mobility.
+Keynote speaker at World Energy Summit, 2023.

agents/praisonaiagents/agent/agent.py

@@ -485,9 +485,9 @@ Your Role: {self.role}\n
 Your Goal: {self.goal}
             """
             if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
             elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
         else:
             system_prompt = None
 
@@ -677,9 +677,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             # Build system prompt
             system_prompt = self.system_prompt
             if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
             elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
 
             # Build messages
             if isinstance(prompt, str):

agents/praisonaiagents/agents/agents.py

@@ -229,15 +229,28 @@ You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
 """
         if task.context:
-            context_results = ""
-            for context_task in task.context:
-                if context_task.result:
-                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
-                else:
-                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            context_results = []  # Use list to avoid duplicates
+            for context_item in task.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        context_results.append(
+                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
+                        )
+            
+            # Join unique context results
+            unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
             task_prompt += f"""
-Here are the results of previous tasks that might be useful:\n
-{context_results}
+Context:
+
+{'  '.join(unique_contexts)}
 """
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
@@ -427,8 +440,16 @@ Here are the results of previous tasks that might be useful:\n
                 else:
                     self.run_task(task_id)
 
-    async def astart(self):
+    async def astart(self, content=None, **kwargs):
         """Async version of start method"""
+        if content:
+            # Add content to context of all tasks
+            for task in self.tasks.values():
+                if isinstance(content, (str, list)):
+                    if not task.context:
+                        task.context = []
+                    task.context.append(content)
+
         await self.arun_all_tasks()
         return {
             "task_status": self.get_all_tasks_status(),
@@ -485,16 +506,30 @@ You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
 """
         if task.context:
-            context_results = ""
-            for context_task in task.context:
-                if context_task.result:
-                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
-                else:
-                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            context_results = []  # Use list to avoid duplicates
+            for context_item in task.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        context_results.append(
+                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
+                        )
+            
+            # Join unique context results
+            unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
             task_prompt += f"""
-Here are the results of previous tasks that might be useful:\n
-{context_results}
+Context:
+
+{'  '.join(unique_contexts)}
 """
+
         # Add memory context if available
         if task.memory:
             try:
@@ -714,12 +749,24 @@ Here are the results of previous tasks that might be useful:\n
             return str(agent[0])
         return None
 
-    def start(self):
+    def start(self, content=None, **kwargs):
+        """Start agent execution with optional content and config"""
+        if content:
+            # Add content to context of all tasks
+            for task in self.tasks.values():
+                if isinstance(content, (str, list)):
+                    # If context is empty, initialize it
+                    if not task.context:
+                        task.context = []
+                    # Add content to context
+                    task.context.append(content)
+                
+        # Run tasks as before
         self.run_all_tasks()
         return {
             "task_status": self.get_all_tasks_status(),
             "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
-        } 
+        }
 
     def set_state(self, key: str, value: Any) -> None:
         """Set a state value"""

agents/praisonaiagents/task/task.py

@@ -19,7 +19,7 @@ class Task:
         agent: Optional[Agent] = None,
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-        context: Optional[List["Task"]] = None,
+        context: Optional[List[Union[str, List, 'Task']]] = None,
         async_execution: Optional[bool] = False,
         config: Optional[Dict[str, Any]] = None,
         output_file: Optional[str] = None,
@@ -219,4 +219,33 @@ class Task:
                     self.callback(task_output)
             except Exception as e:
                 logger.error(f"Task {self.id}: Failed to execute callback: {e}")
-                logger.exception(e)
+                logger.exception(e)
+
+        task_prompt = f"""
+You need to do the following task: {self.description}.
+Expected Output: {self.expected_output}.
+"""
+        if self.context:
+            context_results = []  # Use list to avoid duplicates
+            for context_item in self.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        context_results.append(
+                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
+                        )
+            
+            # Join unique context results
+            unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
+            task_prompt += f"""
+Context:
+
+{'  '.join(unique_contexts)}
+"""

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.40"
+version = "0.0.41"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/uv.lock

@@ -1558,7 +1558,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.40"
+version = "0.0.41"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

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==2.0.49 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.50 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;)
             

examples/usecases/analysis/cv-analysis.py

@@ -0,0 +1,108 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+
+# Define structured output models
+class SkillAnalysis(BaseModel):
+    technical_skills: List[str]
+    soft_skills: List[str]
+    skill_gaps: List[str]
+    recommendations: List[str]
+
+class ExperienceAnalysis(BaseModel):
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+    improvement_suggestions: List[str]
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    skill_analysis: SkillAnalysis
+    experience_analysis: ExperienceAnalysis
+
+# Create specialized agents
+skill_analyst = Agent(
+    role="Skills Analysis Expert",
+    goal="Analyze technical and soft skills in CV",
+    backstory="""You are an experienced skills analyst with deep knowledge of industry requirements 
+    and current market trends. You excel at identifying valuable skills and potential skill gaps.""",
+    verbose=True
+)
+
+experience_analyst = Agent(
+    role="Experience Analysis Expert",
+    goal="Analyze professional experience and achievements",
+    backstory="""You are an expert in evaluating professional experience and achievements. 
+    You can identify impactful contributions and quantifiable results.""",
+    verbose=True
+)
+
+cv_evaluator = Agent(
+    role="Senior CV Evaluator",
+    goal="Provide comprehensive CV evaluation and recommendations",
+    backstory="""You are a senior CV evaluation expert with years of experience in talent assessment. 
+    You provide detailed analysis and actionable recommendations for CV improvement.""",
+    verbose=True
+)
+
+# Create tasks with structured outputs
+skills_analysis_task = Task(
+    description="""Analyze the CV's technical and soft skills. Identify skill gaps and provide recommendations 
+    for skill development based on current market trends.""",
+    expected_output="Detailed analysis of skills with recommendations",
+    agent=skill_analyst,
+    output_pydantic=SkillAnalysis,
+    context=["test"],
+)
+
+experience_analysis_task = Task(
+    description="""Evaluate professional experience, achievements, and impact. 
+    Identify key accomplishments and areas of expertise.""",
+    expected_output="Comprehensive analysis of professional experience",
+    agent=experience_analyst,
+    output_pydantic=ExperienceAnalysis
+)
+
+final_evaluation_task = Task(
+    description="""Review all analyses and provide a comprehensive evaluation of the CV. 
+    Include overall assessment, key strengths, areas for improvement, and specific recommendations.""",
+    expected_output="Final CV evaluation report",
+    agent=cv_evaluator,
+    context=[skills_analysis_task, experience_analysis_task],
+    output_pydantic=CVAnalysisReport
+)
+
+# Create and run the agents
+agents = PraisonAIAgents(
+    agents=[skill_analyst, experience_analyst, cv_evaluator],
+    tasks=[skills_analysis_task, experience_analysis_task, final_evaluation_task],
+    process="sequential",
+    verbose=True
+)
+
+# Start the analysis
+result = agents.start()
+
+# Access the structured results
+print("\nCV Analysis Results:")
+print(f"\nOverall Score: {result.pydantic.overall_score}/100")
+print("\nKey Strengths:")
+for strength in result.pydantic.key_strengths:
+    print(f"- {strength}")
+
+print("\nSkill Analysis:")
+print("\nTechnical Skills:")
+for skill in result.pydantic.skill_analysis.technical_skills:
+    print(f"- {skill}")
+
+print("\nExperience Highlights:")
+for achievement in result.pydantic.experience_analysis.key_achievements:
+    print(f"- {achievement}")
+
+print("\nRecommendations:")
+for recommendation in result.pydantic.recommendations:
+    print(f"- {recommendation}")

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/2.0.49.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.50.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==2.0.49 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.50 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 = "2.0.49"
+version = "2.0.50"
 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>=0.0.40",
+    "praisonaiagents>=0.0.41",
     "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 = "2.0.49"
+version = "2.0.50"
 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 = ">=0.0.40"
+praisonaiagents = ">=0.0.41"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.49"
+version = "2.0.50"
 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 = ">=0.0.40" },
+    { name = "praisonaiagents", specifier = ">=0.0.41" },
     { 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 = "0.0.40"
+version = "0.0.41"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
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