feat: Add 10 Grok4 Intelligent Agent Examples #890
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Contributor
WalkthroughThis update introduces a suite of new Python scripts, each defining a specialized AI agent using the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Agent (domain-specific)
participant PraisonAIAgents Library
participant xai/grok-4 Model
User->>Agent (domain-specific): Provide domain-specific prompt
Agent (domain-specific)->>PraisonAIAgents Library: Instantiate with instructions and model
PraisonAIAgents Library->>xai/grok-4 Model: Send prompt and instructions
xai/grok-4 Model-->>PraisonAIAgents Library: Generate response
PraisonAIAgents Library-->>Agent (domain-specific): Return response
Agent (domain-specific)-->>User: Deliver domain-specific output
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Summary of Changes
Hello @Dhivya-Bharathy, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly expands the collection of example PraisonAI agents by introducing 10 new intelligent agents powered by Grok4. The primary goal is to showcase the broad applicability and versatility of PraisonAI agents across a wide range of domains, from business and finance to creative design and education, providing ready-to-use demonstrations for various AI-driven tasks.
Highlights
- New Grok4 Intelligent Agent Examples: I've added 10 new Python files, each defining a distinct PraisonAI agent. These agents are configured to use
xai/grok-4as their underlying large language model and include an example prompt to demonstrate their core functionality. - Diverse Agent Categories: The newly added agents cover a wide range of applications, categorized into Multi-Agent Teams (e.g., competitor intelligence, legal, recruitment), Multimodal Agents (coding, design), Specialized Domain Agents (game design, finance), and an Integration Agent (Multi MCP).
- Consistent PraisonAI Pattern: All new agents adhere to the established PraisonAI patterns, ensuring consistency and ease of use within the existing framework.
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| response = agent.start( | ||
| "I need to analyze my main competitor's pricing strategy and market positioning. " | ||
| "Can you help me gather insights?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, making the script less demonstrative. Printing the response allows users to see the agent's output.
Suggested change
| response = agent.start( | |
| "I need to analyze my main competitor's pricing strategy and market positioning. " | |
| "Can you help me gather insights?" | |
| ) | |
| response = agent.start( | |
| "I need to analyze my main competitor's pricing strategy and market positioning. " | |
| "Can you help me gather insights?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to analyze my investment portfolio and create a diversification strategy. " | ||
| "Can you help me optimize my financial planning?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which reduces the script's demonstrative value. Printing the response will show the agent's output to users.
Suggested change
| response = agent.start( | |
| "I need to analyze my investment portfolio and create a diversification strategy. " | |
| "Can you help me optimize my financial planning?" | |
| ) | |
| response = agent.start( | |
| "I need to analyze my investment portfolio and create a diversification strategy. " | |
| "Can you help me optimize my financial planning?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I want to create a mobile puzzle game concept. " | ||
| "Can you help me design engaging gameplay mechanics and story elements?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, making it difficult to understand the script's output. Printing the response will display the agent's output.
Suggested change
| response = agent.start( | |
| "I want to create a mobile puzzle game concept. " | |
| "Can you help me design engaging gameplay mechanics and story elements?" | |
| ) | |
| response = agent.start( | |
| "I want to create a mobile puzzle game concept. " | |
| "Can you help me design engaging gameplay mechanics and story elements?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to review a software licensing agreement. " | ||
| "Can you help me identify potential risks and key terms?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which diminishes the script's demonstrative purpose. Printing the response will allow users to see the agent's output.
Suggested change
| response = agent.start( | |
| "I need to review a software licensing agreement. " | |
| "Can you help me identify potential risks and key terms?" | |
| ) | |
| response = agent.start( | |
| "I need to review a software licensing agreement. " | |
| "Can you help me identify potential risks and key terms?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to create a job posting for a senior software engineer position. " | ||
| "Can you help me write an attractive and comprehensive job description?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which reduces the script's usefulness as a demonstration. Printing the response will display the agent's output.
Suggested change
| response = agent.start( | |
| "I need to create a job posting for a senior software engineer position. " | |
| "Can you help me write an attractive and comprehensive job description?" | |
| ) | |
| response = agent.start( | |
| "I need to create a job posting for a senior software engineer position. " | |
| "Can you help me write an attractive and comprehensive job description?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need help with my startup's marketing strategy and content creation. " | ||
| "Can you provide a comprehensive plan?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which makes the script less effective as a demonstration. Printing the response will show the agent's output.
Suggested change
| response = agent.start( | |
| "I need help with my startup's marketing strategy and content creation. " | |
| "Can you provide a comprehensive plan?" | |
| ) | |
| response = agent.start( | |
| "I need help with my startup's marketing strategy and content creation. " | |
| "Can you provide a comprehensive plan?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to create a lesson plan for teaching Python programming to beginners. " | ||
| "Can you help me design an engaging curriculum?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which limits the script's demonstrative value. Printing the response will display the agent's output.
Suggested change
| response = agent.start( | |
| "I need to create a lesson plan for teaching Python programming to beginners. " | |
| "Can you help me design an engaging curriculum?" | |
| ) | |
| response = agent.start( | |
| "I need to create a lesson plan for teaching Python programming to beginners. " | |
| "Can you help me design an engaging curriculum?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to coordinate multiple tools and services for my project. " | ||
| "Can you help me set up an integrated workflow using MCP agents?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which reduces the script's effectiveness as a demonstration. Printing the response will show the agent's output.
Suggested change
| response = agent.start( | |
| "I need to coordinate multiple tools and services for my project. " | |
| "Can you help me set up an integrated workflow using MCP agents?" | |
| ) | |
| response = agent.start( | |
| "I need to coordinate multiple tools and services for my project. " | |
| "Can you help me set up an integrated workflow using MCP agents?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I have a screenshot of a UI design and need to generate the corresponding HTML/CSS code. " | ||
| "Can you help me create the frontend code?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which diminishes the script's demonstrative impact. Printing the response will allow users to see the agent's output.
Suggested change
| response = agent.start( | |
| "I have a screenshot of a UI design and need to generate the corresponding HTML/CSS code. " | |
| "Can you help me create the frontend code?" | |
| ) | |
| response = agent.start( | |
| "I have a screenshot of a UI design and need to generate the corresponding HTML/CSS code. " | |
| "Can you help me create the frontend code?" | |
| ) | |
| print(response) |
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| response = agent.start( | ||
| "I need to create a logo for my tech startup. " | ||
| "Can you help me design a modern and professional logo concept?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
The agent's response is not being printed, which limits the script's ability to demonstrate the agent's capabilities. Printing the response will display the agent's output.
Suggested change
| response = agent.start( | |
| "I need to create a logo for my tech startup. " | |
| "Can you help me design a modern and professional logo concept?" | |
| ) | |
| response = agent.start( | |
| "I need to create a logo for my tech startup. " | |
| "Can you help me design a modern and professional logo concept?" | |
| ) | |
| print(response) |
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Additional details and impacted files@@ Coverage Diff @@
## main #890 +/- ##
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Coverage 14.23% 14.23%
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Files 25 25
Lines 2571 2571
Branches 367 367
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Hits 366 366
Misses 2189 2189
Partials 16 16
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Actionable comments posted: 2
🧹 Nitpick comments (4)
examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_coding_agent.py (1)
1-13: LGTM with note on multimodal capabilities.The agent setup follows the correct pattern and the instructions appropriately define a multimodal coding agent. However, consider that the prompt mentions analyzing screenshots but no actual image processing mechanism is demonstrated in this example.
For completeness, consider adding a comment or example showing how to actually pass visual inputs to the agent, or clarify that this is a text-only demonstration of the multimodal agent concept.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_services_agency.py (1)
1-13: LGTM! Well-structured agent implementation.The agent configuration is clear and domain-appropriate. The instructions properly define the AI services agency role, and the startup marketing prompt aligns well with the agent's capabilities.
Consider enhancing the script for production use:
from praisonaiagents import Agent +import logging agent = Agent( instructions="You are an AI services agency agent. " "Help users with comprehensive business services including marketing, " "content creation, strategy, and operational optimization using CrewAI framework.", llm="xai/grok-4" ) -response = agent.start( - "I need help with my startup's marketing strategy and content creation. " - "Can you provide a comprehensive plan?" -) +try: + response = agent.start( + "I need help with my startup's marketing strategy and content creation. " + "Can you provide a comprehensive plan?" + ) + + # Process the response + if response: + print("AI Services Agency Response:") + print(response) + else: + print("No response received from agent") + +except Exception as e: + logging.error(f"Error running AI services agent: {e}") + print(f"Failed to get response: {e}")examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_design_agent.py (1)
1-13: LGTM! Good multimodal design agent setup.The agent configuration properly defines the multimodal design role covering graphic design, UI/UX, and visual content creation.
Consider enhancing the example to better demonstrate multimodal capabilities:
from praisonaiagents import Agent +import logging agent = Agent( instructions="You are a multimodal design AI agent. " "Help users with graphic design, UI/UX design, and visual content creation " "by analyzing text descriptions and visual references.", llm="xai/grok-4" ) -response = agent.start( - "I need to create a logo for my tech startup. " - "Can you help me design a modern and professional logo concept?" -) +try: + # Example with text-only prompt (current) + response = agent.start( + "I need to create a logo for my tech startup called 'DataFlow'. " + "The company specializes in real-time data analytics. " + "Can you help me design a modern and professional logo concept? " + "Please describe the visual elements, color scheme, typography, and overall aesthetic." + ) + + if response: + print("🎨 Design Concept:") + print(response) + else: + print("No design concept received") + +except Exception as e: + logging.error(f"Error running design agent: {e}") + print(f"Failed to get design concept: {e}")examples/python/tools/exa-tool/grok4_intelligent_agents/ai_game_design_agent.py (1)
1-13: LGTM! Well-designed game development agent.The agent configuration effectively covers key game design areas including concept development, mechanics design, and storytelling across multiple platforms.
Consider enhancing the script for a more comprehensive game design workflow:
from praisonaiagents import Agent +import logging agent = Agent( instructions="You are a game design AI agent. " "Help users with game concept development, mechanics design, " "and creative storytelling for various gaming platforms and genres.", llm="xai/grok-4" ) -response = agent.start( - "I want to create a mobile puzzle game concept. " - "Can you help me design engaging gameplay mechanics and story elements?" -) +try: + response = agent.start( + "I want to create a mobile puzzle game concept for casual players aged 25-45. " + "The theme should be space exploration. " + "Can you help me design engaging gameplay mechanics, progression system, " + "story elements, and monetization strategy? " + "Please structure your response with clear sections for each aspect." + ) + + if response: + print("🎮 Game Design Concept:") + print(response) + + # Could extend to save design doc + # with open(f"game_concept_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md", "w") as f: + # f.write(response) + else: + print("No game concept received") + +except Exception as e: + logging.error(f"Error running game design agent: {e}") + print(f"Failed to get game concept: {e}")
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📒 Files selected for processing (10)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_competitor_intelligence_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_finance_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_game_design_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_legal_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_recruitment_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_services_agency.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/ai_teaching_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/multi_mcp_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_coding_agent.py(1 hunks)examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_design_agent.py(1 hunks)
🧰 Additional context used
🧠 Learnings (11)
📓 Common learnings
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.windsurfrules:0-0
Timestamp: 2025-06-30T10:06:44.129Z
Learning: Applies to src/praisonai-ts/src/{llm,agent,agents,task}/**/*.ts : Use the 'aisdk' library for all large language model (LLM) calls in TypeScript, such as using 'generateText' for text generation.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use conda environment activation (`conda activate praisonai-agents`) before running development or tests.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_finance_agent.py (5)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the unified LLM wrapper in `praisonaiagents/llm/` for integrating with multiple LLM providers.
examples/python/tools/exa-tool/grok4_intelligent_agents/multi_mcp_agent.py (5)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/praisonaiagents/mcp/**/*.py : Implement MCP server and SSE support for distributed execution and real-time communication in `praisonaiagents/mcp/`.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_competitor_intelligence_agent.py (4)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_legal_agent.py (4)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_services_agency.py (5)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.windsurfrules:0-0
Timestamp: 2025-06-30T10:06:44.129Z
Learning: Applies to src/praisonai-ts/src/{llm,agent,agents,task}/**/*.ts : Use the 'aisdk' library for all large language model (LLM) calls in TypeScript, such as using 'generateText' for text generation.
examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_coding_agent.py (4)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_teaching_agent.py (5)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/llm/llm.ts : The 'LLM' class in 'llm.ts' should wrap 'aisdk.generateText' calls for generating text responses.
examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_design_agent.py (4)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_recruitment_agent.py (6)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.windsurfrules:0-0
Timestamp: 2025-06-30T10:06:44.129Z
Learning: Applies to src/praisonai-ts/src/{llm,agent,agents,task}/**/*.ts : Use the 'aisdk' library for all large language model (LLM) calls in TypeScript, such as using 'generateText' for text generation.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_game_design_agent.py (5)
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Use the `Agent` class from `praisonaiagents/agent/` for core agent implementations, supporting LLM integration, tool calling, and self-reflection.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/autoagents.ts : The 'AutoAgents' class in 'src/agents/autoagents.ts' should provide high-level convenience for automatically generating agent/task configuration from user instructions, using 'aisdk' to parse config.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agent/agent.ts : The 'Agent' class in 'src/agent/agent.ts' should encapsulate a single agent's role, name, and methods for calling the LLM using 'aisdk'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-ts/.cursorrules:0-0
Timestamp: 2025-06-30T10:05:51.843Z
Learning: Applies to src/praisonai-ts/src/agents/agents.ts : The 'PraisonAIAgents' class in 'src/agents/agents.ts' should manage multiple agents, tasks, memory, and process type, mirroring the Python 'agents.py'.
Learnt from: CR
PR: MervinPraison/PraisonAI#0
File: src/praisonai-agents/CLAUDE.md:0-0
Timestamp: 2025-06-30T10:06:17.673Z
Learning: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
🧬 Code Graph Analysis (7)
examples/python/tools/exa-tool/grok4_intelligent_agents/multi_mcp_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_competitor_intelligence_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_legal_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_services_agency.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_coding_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/multimodal_design_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_recruitment_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(1715-1820)
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- GitHub Check: quick-test
🔇 Additional comments (4)
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_finance_agent.py (1)
1-13: LGTM! Well-scoped financial analysis agent.The agent configuration is appropriate with comprehensive instructions covering key financial domains (analysis, investment strategies, budget planning, market trends). The example prompt demonstrates practical usage for portfolio optimization.
examples/python/tools/exa-tool/grok4_intelligent_agents/multi_mcp_agent.py (1)
4-6: MCP integration confirmed – no changes needed.The praisonaiagents framework includes a full MCP implementation (src/praisonai-agents/praisonaiagents/mcp/ with both stdio and SSE clients), and there are comprehensive integration tests under src/praisonai/tests/integration/test_mcp_integration.py. Your multi_mcp_agent example can safely rely on these capabilities.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_competitor_intelligence_agent.py (1)
1-13: LGTM! Well-defined business intelligence agent.The agent configuration appropriately covers competitor analysis, market trends, and business intelligence. The example prompt demonstrates practical usage for competitive strategy analysis.
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_teaching_agent.py (1)
1-13: LGTM! Comprehensive educational agent setup.The agent configuration appropriately covers educational content creation, lesson planning, and personalized learning across subjects. The example prompt demonstrates practical usage for curriculum development.
Note: All five agent examples follow a consistent, well-structured pattern that demonstrates the versatility of the PraisonAI framework across different domains.
Comment on lines
+1
to
+13
| from praisonaiagents import Agent | ||
|
|
||
| agent = Agent( | ||
| instructions="You are a legal AI agent. " | ||
| "Help users with legal document analysis, contract review, " | ||
| "and legal advice while ensuring compliance and risk assessment.", | ||
| llm="xai/grok-4" | ||
| ) | ||
|
|
||
| response = agent.start( | ||
| "I need to review a software licensing agreement. " | ||
| "Can you help me identify potential risks and key terms?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
🛠️ Refactor suggestion
LGTM! Clear legal agent implementation.
The agent configuration appropriately defines the legal analysis role, and the software licensing prompt is well-suited for the agent's capabilities.
For legal domain applications, consider adding important disclaimers and robustness:
from praisonaiagents import Agent
+import logging
agent = Agent(
- instructions="You are a legal AI agent. "
- "Help users with legal document analysis, contract review, "
- "and legal advice while ensuring compliance and risk assessment.",
+ instructions="You are a legal AI agent. "
+ "Help users with legal document analysis, contract review, "
+ "and legal advice while ensuring compliance and risk assessment. "
+ "IMPORTANT: This is AI-generated analysis for informational purposes only. "
+ "Always consult with qualified legal professionals for binding legal advice.",
llm="xai/grok-4"
)
-response = agent.start(
- "I need to review a software licensing agreement. "
- "Can you help me identify potential risks and key terms?"
-)
+try:
+ response = agent.start(
+ "I need to review a software licensing agreement. "
+ "Can you help me identify potential risks and key terms?"
+ )
+
+ if response:
+ print("⚖️ Legal Analysis (AI-Generated - Consult Legal Professional):")
+ print(response)
+ else:
+ print("No legal analysis received")
+
+except Exception as e:
+ logging.error(f"Error running legal agent: {e}")
+ print(f"Failed to get legal analysis: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| from praisonaiagents import Agent | |
| agent = Agent( | |
| instructions="You are a legal AI agent. " | |
| "Help users with legal document analysis, contract review, " | |
| "and legal advice while ensuring compliance and risk assessment.", | |
| llm="xai/grok-4" | |
| ) | |
| response = agent.start( | |
| "I need to review a software licensing agreement. " | |
| "Can you help me identify potential risks and key terms?" | |
| ) | |
| from praisonaiagents import Agent | |
| import logging | |
| agent = Agent( | |
| instructions="You are a legal AI agent. " | |
| "Help users with legal document analysis, contract review, " | |
| "and legal advice while ensuring compliance and risk assessment. " | |
| "IMPORTANT: This is AI-generated analysis for informational purposes only. " | |
| "Always consult with qualified legal professionals for binding legal advice.", | |
| llm="xai/grok-4" | |
| ) | |
| try: | |
| response = agent.start( | |
| "I need to review a software licensing agreement. " | |
| "Can you help me identify potential risks and key terms?" | |
| ) | |
| if response: | |
| print("⚖️ Legal Analysis (AI-Generated - Consult Legal Professional):") | |
| print(response) | |
| else: | |
| print("No legal analysis received") | |
| except Exception as e: | |
| logging.error(f"Error running legal agent: {e}") | |
| print(f"Failed to get legal analysis: {e}") |
🤖 Prompt for AI Agents
In examples/python/tools/exa-tool/grok4_intelligent_agents/ai_legal_agent.py
lines 1 to 13, although the legal AI agent is well implemented, add a disclaimer
in the instructions to clarify that the agent does not provide official legal
advice and users should consult a qualified attorney. Also, consider adding
error handling around the agent.start call to manage unexpected inputs or
failures gracefully, improving robustness for legal domain use.
Comment on lines
+1
to
+13
| from praisonaiagents import Agent | ||
|
|
||
| agent = Agent( | ||
| instructions="You are a recruitment AI agent. " | ||
| "Help users with candidate screening, job posting optimization, " | ||
| "and recruitment process automation to find the best talent.", | ||
| llm="xai/grok-4" | ||
| ) | ||
|
|
||
| response = agent.start( | ||
| "I need to create a job posting for a senior software engineer position. " | ||
| "Can you help me write an attractive and comprehensive job description?" | ||
| ) No newline at end of file |
Contributor
There was a problem hiding this comment.
🛠️ Refactor suggestion
LGTM! Comprehensive recruitment agent setup.
The agent configuration properly addresses key recruitment functions including candidate screening, job posting optimization, and process automation.
For recruitment applications, consider adding bias mitigation and compliance considerations:
from praisonaiagents import Agent
+import logging
agent = Agent(
instructions="You are a recruitment AI agent. "
"Help users with candidate screening, job posting optimization, "
- "and recruitment process automation to find the best talent.",
+ "and recruitment process automation to find the best talent. "
+ "Ensure all recommendations comply with employment laws and promote "
+ "diversity, equity, and inclusion. Avoid bias in language and requirements.",
llm="xai/grok-4"
)
-response = agent.start(
- "I need to create a job posting for a senior software engineer position. "
- "Can you help me write an attractive and comprehensive job description?"
-)
+try:
+ response = agent.start(
+ "I need to create a job posting for a senior software engineer position. "
+ "Can you help me write an attractive and comprehensive job description? "
+ "Please ensure the language is inclusive, requirements are justified, "
+ "and the posting attracts diverse candidates. Include salary range if possible."
+ )
+
+ if response:
+ print("👥 Job Posting (Review for Compliance):")
+ print(response)
+ print("\n⚠️ Please review for legal compliance and bias before posting")
+ else:
+ print("No job posting received")
+
+except Exception as e:
+ logging.error(f"Error running recruitment agent: {e}")
+ print(f"Failed to generate job posting: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| from praisonaiagents import Agent | |
| agent = Agent( | |
| instructions="You are a recruitment AI agent. " | |
| "Help users with candidate screening, job posting optimization, " | |
| "and recruitment process automation to find the best talent.", | |
| llm="xai/grok-4" | |
| ) | |
| response = agent.start( | |
| "I need to create a job posting for a senior software engineer position. " | |
| "Can you help me write an attractive and comprehensive job description?" | |
| ) | |
| from praisonaiagents import Agent | |
| import logging | |
| agent = Agent( | |
| instructions="You are a recruitment AI agent. " | |
| "Help users with candidate screening, job posting optimization, " | |
| "and recruitment process automation to find the best talent. " | |
| "Ensure all recommendations comply with employment laws and promote " | |
| "diversity, equity, and inclusion. Avoid bias in language and requirements.", | |
| llm="xai/grok-4" | |
| ) | |
| try: | |
| response = agent.start( | |
| "I need to create a job posting for a senior software engineer position. " | |
| "Can you help me write an attractive and comprehensive job description? " | |
| "Please ensure the language is inclusive, requirements are justified, " | |
| "and the posting attracts diverse candidates. Include salary range if possible." | |
| ) | |
| if response: | |
| print("👥 Job Posting (Review for Compliance):") | |
| print(response) | |
| print("\n⚠️ Please review for legal compliance and bias before posting") | |
| else: | |
| print("No job posting received") | |
| except Exception as e: | |
| logging.error(f"Error running recruitment agent: {e}") | |
| print(f"Failed to generate job posting: {e}") |
🤖 Prompt for AI Agents
In
examples/python/tools/exa-tool/grok4_intelligent_agents/ai_recruitment_agent.py
around lines 1 to 13, the recruitment agent setup is good but lacks bias
mitigation and compliance considerations. Enhance the agent's instructions by
explicitly including guidelines to avoid bias in candidate screening and ensure
compliance with relevant hiring laws and regulations. This can be done by adding
clear directives in the instructions string to promote fairness and legal
adherence during recruitment processes.
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🚀 Overview
This PR adds 10 new intelligent agent examples using Grok4 (xai/grok-4) to demonstrate the versatility of PraisonAI agents across different domains.
📋 Added Agents
�� Multi-Agent Teams
�� Multimodal Agents
�� Specialized Domain Agents
�� Integration Agents
✨ Features
xai/grok-4as the LLM provider�� Testing
Each agent includes a sample prompt that demonstrates its core functionality and can be run immediately for testing.
📁 File Structure
examples/python/tools/exa-tool/grok4_intelligent_agents/
├── ai_competitor_intelligence_agent.py
├── ai_legal_agent.py
├── ai_recruitment_agent.py
├── ai_services_agency.py
├── ai_teaching_agent.py
├── multimodal_coding_agent.py
├── multimodal_design_agent.py
├── ai_game_design_agent.py
├── ai_finance_agent.py
└── multi_mcp_agent.py
Summary by CodeRabbit