feat: Add 10 new agents to Kimi2_Intelligent_Agents (aerospace, bioinformatics, blockchain, energy, IoT, metaverse, nanotechnology, quantum AI, quantum computing, robotics) #948
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…ics, blockchain, energy, IoT, metaverse, nanotechnology, quantum AI, quantum computing, robotics)
WalkthroughTen new Python scripts were added, each defining an AI agent specialized in a different advanced technology domain. These agents are instantiated using the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant AgentScript
participant Agent
participant LLM
User->>AgentScript: Run script
AgentScript->>Agent: Instantiate with domain instructions and LLM
AgentScript->>Agent: Start with greeting prompt
Agent->>LLM: Send greeting prompt
LLM-->>Agent: Generate greeting response
Agent-->>AgentScript: Return greeting response
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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 Kimi2_Intelligent_Agents collection by adding ten new, highly specialized AI agents. These additions aim to broaden the system's capabilities, allowing it to address a wider range of domain-specific inquiries and tasks, from aerospace to quantum computing, without modifying any existing codebase.
Highlights
- New Agent Additions: This pull request introduces 10 new specialized AI agents, expanding the
Kimi2_Intelligent_Agentscollection. These agents cover diverse domains including Aerospace Engineering, Bioinformatics, Blockchain, Energy, IoT, Metaverse, Nanotechnology, Quantum AI, Quantum Computing, and Robotics. - Agent Implementation: Each new agent is implemented as a standalone Python file within the
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/directory. They are configured using thepraisonaiagentslibrary, with specificinstructionstailored to their respective domains and all utilize theopenrouter/moonshotai/kimi-k2Large Language Model. - No Existing File Modifications: As stated in the PR description, only new agent files were added, ensuring no existing codebase was altered by these changes.
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Lines 2571 2664 +93
Branches 367 384 +17
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+ Hits 370 379 +9
- Misses 2185 2269 +84
Partials 16 16
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Code Review
This pull request adds 10 new agent examples. The overall structure is good, but each example script has some issues that prevent it from being fully functional and from following standard Python conventions. Specifically, the agent's response is never printed, the code is at the module level, and the files are missing a final newline.
I've left a detailed comment with a code suggestion on each new file to address these points. Applying these suggestions will make the examples runnable and align them with best practices. Great work on expanding the agent library!
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| agent = Agent( | ||
| instructions="You are an aerospace engineering AI agent. " | ||
| "Help users understand aerospace engineering concepts, " | ||
| "aircraft design, and space technology. Provide guidance on " | ||
| "aerodynamics, propulsion systems, materials science, " | ||
| "and satellite technology.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your aerospace engineering assistant. " | ||
| "How can I help you with aerospace engineering " | ||
| "and space technology today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are an aerospace engineering AI agent. " | |
| "Help users understand aerospace engineering concepts, " | |
| "aircraft design, and space technology. Provide guidance on " | |
| "aerodynamics, propulsion systems, materials science, " | |
| "and satellite technology.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your aerospace engineering assistant. " | |
| "How can I help you with aerospace engineering " | |
| "and space technology today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are an aerospace engineering AI agent. " | |
| "Help users understand aerospace engineering concepts, " | |
| "aircraft design, and space technology. Provide guidance on " | |
| "aerodynamics, propulsion systems, materials science, " | |
| "and satellite technology.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your aerospace engineering assistant. " | |
| "How can I help you with aerospace engineering " | |
| "and space technology today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a bioinformatics and genomics AI agent. " | ||
| "Help users understand bioinformatics, genomics, " | ||
| "and computational biology. Provide guidance on " | ||
| "DNA sequence analysis, protein structure prediction, " | ||
| "genetic algorithms, and biological data mining.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your bioinformatics and genomics assistant. " | ||
| "How can I help you with bioinformatics " | ||
| "and genomics research today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a bioinformatics and genomics AI agent. " | |
| "Help users understand bioinformatics, genomics, " | |
| "and computational biology. Provide guidance on " | |
| "DNA sequence analysis, protein structure prediction, " | |
| "genetic algorithms, and biological data mining.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your bioinformatics and genomics assistant. " | |
| "How can I help you with bioinformatics " | |
| "and genomics research today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a bioinformatics and genomics AI agent. " | |
| "Help users understand bioinformatics, genomics, " | |
| "and computational biology. Provide guidance on " | |
| "DNA sequence analysis, protein structure prediction, " | |
| "genetic algorithms, and biological data mining.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your bioinformatics and genomics assistant. " | |
| "How can I help you with bioinformatics " | |
| "and genomics research today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a blockchain and DeFi AI agent. " | ||
| "Help users understand blockchain technology, " | ||
| "decentralized finance, and cryptocurrency. Provide guidance on " | ||
| "smart contract development, DeFi protocols, " | ||
| "tokenomics, and blockchain security.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your blockchain and DeFi assistant. " | ||
| "How can I help you explore blockchain technology " | ||
| "and decentralized finance today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a blockchain and DeFi AI agent. " | |
| "Help users understand blockchain technology, " | |
| "decentralized finance, and cryptocurrency. Provide guidance on " | |
| "smart contract development, DeFi protocols, " | |
| "tokenomics, and blockchain security.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your blockchain and DeFi assistant. " | |
| "How can I help you explore blockchain technology " | |
| "and decentralized finance today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a blockchain and DeFi AI agent. " | |
| "Help users understand blockchain technology, " | |
| "decentralized finance, and cryptocurrency. Provide guidance on " | |
| "smart contract development, DeFi protocols, " | |
| "tokenomics, and blockchain security.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your blockchain and DeFi assistant. " | |
| "How can I help you explore blockchain technology " | |
| "and decentralized finance today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are an energy and renewables AI agent. " | ||
| "Help users understand renewable energy technologies, " | ||
| "energy systems, and sustainability. Provide guidance on " | ||
| "solar power, wind energy, energy storage, " | ||
| "and green technology solutions.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your energy and renewables assistant. " | ||
| "How can I help you with renewable energy " | ||
| "and sustainability today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are an energy and renewables AI agent. " | |
| "Help users understand renewable energy technologies, " | |
| "energy systems, and sustainability. Provide guidance on " | |
| "solar power, wind energy, energy storage, " | |
| "and green technology solutions.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your energy and renewables assistant. " | |
| "How can I help you with renewable energy " | |
| "and sustainability today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are an energy and renewables AI agent. " | |
| "Help users understand renewable energy technologies, " | |
| "energy systems, and sustainability. Provide guidance on " | |
| "solar power, wind energy, energy storage, " | |
| "and green technology solutions.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your energy and renewables assistant. " | |
| "How can I help you with renewable energy " | |
| "and sustainability today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are an IoT and smart cities AI agent. " | ||
| "Help users understand Internet of Things, smart city " | ||
| "technologies, and connected systems. Provide guidance on " | ||
| "IoT device integration, smart infrastructure, " | ||
| "sensor networks, and urban technology solutions.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your IoT and smart cities assistant. " | ||
| "How can I help you with IoT and smart city " | ||
| "technologies today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are an IoT and smart cities AI agent. " | |
| "Help users understand Internet of Things, smart city " | |
| "technologies, and connected systems. Provide guidance on " | |
| "IoT device integration, smart infrastructure, " | |
| "sensor networks, and urban technology solutions.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your IoT and smart cities assistant. " | |
| "How can I help you with IoT and smart city " | |
| "technologies today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are an IoT and smart cities AI agent. " | |
| "Help users understand Internet of Things, smart city " | |
| "technologies, and connected systems. Provide guidance on " | |
| "IoT device integration, smart infrastructure, " | |
| "sensor networks, and urban technology solutions.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your IoT and smart cities assistant. " | |
| "How can I help you with IoT and smart city " | |
| "technologies today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a metaverse and virtual reality AI agent. " | ||
| "Help users understand metaverse concepts, VR development, " | ||
| "and immersive technologies. Provide guidance on " | ||
| "3D modeling, VR programming, digital twins, " | ||
| "and virtual world design.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your metaverse and VR assistant. " | ||
| "How can I help you explore the metaverse " | ||
| "and virtual reality today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a metaverse and virtual reality AI agent. " | |
| "Help users understand metaverse concepts, VR development, " | |
| "and immersive technologies. Provide guidance on " | |
| "3D modeling, VR programming, digital twins, " | |
| "and virtual world design.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your metaverse and VR assistant. " | |
| "How can I help you explore the metaverse " | |
| "and virtual reality today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a metaverse and virtual reality AI agent. " | |
| "Help users understand metaverse concepts, VR development, " | |
| "and immersive technologies. Provide guidance on " | |
| "3D modeling, VR programming, digital twins, " | |
| "and virtual world design.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your metaverse and VR assistant. " | |
| "How can I help you explore the metaverse " | |
| "and virtual reality today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a nanotechnology and materials AI agent. " | ||
| "Help users understand nanotechnology, advanced materials, " | ||
| "and material science. Provide guidance on " | ||
| "nanomaterials, material properties, fabrication techniques, " | ||
| "and applications in various industries.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your nanotechnology and materials assistant. " | ||
| "How can I help you with nanotechnology " | ||
| "and materials science today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a nanotechnology and materials AI agent. " | |
| "Help users understand nanotechnology, advanced materials, " | |
| "and material science. Provide guidance on " | |
| "nanomaterials, material properties, fabrication techniques, " | |
| "and applications in various industries.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your nanotechnology and materials assistant. " | |
| "How can I help you with nanotechnology " | |
| "and materials science today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a nanotechnology and materials AI agent. " | |
| "Help users understand nanotechnology, advanced materials, " | |
| "and material science. Provide guidance on " | |
| "nanomaterials, material properties, fabrication techniques, " | |
| "and applications in various industries.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your nanotechnology and materials assistant. " | |
| "How can I help you with nanotechnology " | |
| "and materials science today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a quantum AI agent. " | ||
| "Help users understand quantum artificial intelligence, " | ||
| "quantum machine learning, and quantum neural networks. " | ||
| "Provide guidance on quantum algorithms for AI, " | ||
| "quantum optimization, and hybrid quantum-classical systems.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your quantum AI assistant. " | ||
| "How can I help you explore quantum artificial " | ||
| "intelligence today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a quantum AI agent. " | |
| "Help users understand quantum artificial intelligence, " | |
| "quantum machine learning, and quantum neural networks. " | |
| "Provide guidance on quantum algorithms for AI, " | |
| "quantum optimization, and hybrid quantum-classical systems.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your quantum AI assistant. " | |
| "How can I help you explore quantum artificial " | |
| "intelligence today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a quantum AI agent. " | |
| "Help users understand quantum artificial intelligence, " | |
| "quantum machine learning, and quantum neural networks. " | |
| "Provide guidance on quantum algorithms for AI, " | |
| "quantum optimization, and hybrid quantum-classical systems.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your quantum AI assistant. " | |
| "How can I help you explore quantum artificial " | |
| "intelligence today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a quantum computing AI agent. " | ||
| "Help users understand quantum computing concepts, " | ||
| "quantum algorithms, and quantum programming. Provide guidance on " | ||
| "quantum circuit design, quantum error correction, " | ||
| "quantum machine learning, and quantum cryptography.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your quantum computing assistant. " | ||
| "How can I help you explore quantum computing " | ||
| "and quantum programming today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a quantum computing AI agent. " | |
| "Help users understand quantum computing concepts, " | |
| "quantum algorithms, and quantum programming. Provide guidance on " | |
| "quantum circuit design, quantum error correction, " | |
| "quantum machine learning, and quantum cryptography.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your quantum computing assistant. " | |
| "How can I help you explore quantum computing " | |
| "and quantum programming today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a quantum computing AI agent. " | |
| "Help users understand quantum computing concepts, " | |
| "quantum algorithms, and quantum programming. Provide guidance on " | |
| "quantum circuit design, quantum error correction, " | |
| "quantum machine learning, and quantum cryptography.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your quantum computing assistant. " | |
| "How can I help you explore quantum computing " | |
| "and quantum programming today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
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| agent = Agent( | ||
| instructions="You are a robotics and automation AI agent. " | ||
| "Help users understand robotics, automation systems, " | ||
| "and industrial applications. Provide guidance on " | ||
| "robot programming, automation workflows, " | ||
| "sensor integration, and robotic process automation.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your robotics and automation assistant. " | ||
| "How can I help you with robotics and " | ||
| "automation systems today?") |
There was a problem hiding this comment.
This example script can be improved in a few ways to follow Python best practices and be more useful:
- Show Output: The
responsefrom the agent is never printed, so running the script doesn't show any result. - Executable Code Scope: It's standard practice to wrap executable script logic in a
mainfunction and call it under anif __name__ == "__main__":guard. This prevents code from running when the file is imported by other modules. - Missing Final Newline: The file should end with a newline character as per PEP 8.
The suggested change below addresses all these points.
Suggested change
| agent = Agent( | |
| instructions="You are a robotics and automation AI agent. " | |
| "Help users understand robotics, automation systems, " | |
| "and industrial applications. Provide guidance on " | |
| "robot programming, automation workflows, " | |
| "sensor integration, and robotic process automation.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your robotics and automation assistant. " | |
| "How can I help you with robotics and " | |
| "automation systems today?") | |
| def main(): | |
| agent = Agent( | |
| instructions="You are a robotics and automation AI agent. " | |
| "Help users understand robotics, automation systems, " | |
| "and industrial applications. Provide guidance on " | |
| "robot programming, automation workflows, " | |
| "sensor integration, and robotic process automation.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your robotics and automation assistant. " | |
| "How can I help you with robotics and " | |
| "automation systems today?") | |
| print(response) | |
| if __name__ == "__main__": | |
| main() |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 3
♻️ Duplicate comments (3)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/blockchain_defi_agent.py (1)
1-14: Comprehensive blockchain/DeFi agent with consistent patternThe instructions effectively cover key blockchain and DeFi concepts including smart contracts, tokenomics, and security. The agent follows the same implementation pattern as other agents in this collection.
Similar to the aerospace engineering agent, consider:
- The
responsevariable is captured but not used- Add error handling for robust operation
- Consider displaying or logging the initial response
The same refactoring pattern from the aerospace agent applies here for improved error handling and response utilization.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/robotics_automation_agent.py (1)
1-14: Solid robotics and automation agent implementationThe instructions comprehensively cover robotics and automation topics including robot programming, sensor integration, and industrial applications. The implementation maintains consistency with the other specialized agents.
This agent follows the same pattern as the previous agents and has the same opportunities for improvement:
- Unused
responsevariable- Missing error handling
- No validation of agent initialization
Consider applying similar error handling and response utilization improvements as suggested for the aerospace engineering agent.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/energy_renewables_agent.py (1)
1-14: Well-defined energy and renewables agentThe instructions effectively address key renewable energy topics including solar power, wind energy, energy storage, and green technology solutions. The agent maintains the consistent implementation pattern.
This agent exhibits the same implementation pattern and opportunities for improvement as the other specialized agents:
- The
responsevariable is not utilized after capture- No error handling for potential initialization failures
- Could benefit from the same error handling improvements suggested for the aerospace engineering agent
🧹 Nitpick comments (4)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/bioinformatics_genomics_agent.py (1)
1-14: Good domain specialization with room for structural improvements.The bioinformatics and genomics instructions are well-crafted and comprehensive, covering key areas like DNA sequence analysis, protein structure prediction, and biological data mining. The implementation correctly uses the
Agentclass.However, the same structural issues exist as in other agent files:
- Unused
responsevariable- No error handling
- Consider code reuse pattern
Consider applying similar improvements as suggested in the quantum AI agent for consistency:
+try: + agent = Agent( + instructions="You are a bioinformatics and genomics AI agent. " + "Help users understand bioinformatics, genomics, " + "and computational biology. Provide guidance on " + "DNA sequence analysis, protein structure prediction, " + "genetic algorithms, and biological data mining.", + llm="openrouter/moonshotai/kimi-k2" + ) + + response = agent.start("Hello! I'm your bioinformatics and genomics assistant. " + "How can I help you with bioinformatics " + "and genomics research today?") + print(response) +except Exception as e: + print(f"Error initializing bioinformatics agent: {e}")examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/metaverse_vr_agent.py (1)
1-14: Well-scoped metaverse and VR domain coverage.The instructions effectively cover the metaverse and VR domain, including 3D modeling, VR programming, digital twins, and virtual world design. The implementation maintains the established pattern consistency.
The domain expertise is appropriate for current metaverse and VR development needs.
Consider adding augmented reality (AR) and mixed reality (MR) to the instructions since these are closely related technologies:
agent = Agent( - instructions="You are a metaverse and virtual reality AI agent. " - "Help users understand metaverse concepts, VR development, " - "and immersive technologies. Provide guidance on " - "3D modeling, VR programming, digital twins, " - "and virtual world design.", + instructions="You are a metaverse and virtual reality AI agent. " + "Help users understand metaverse concepts, VR/AR/MR development, " + "and immersive technologies. Provide guidance on " + "3D modeling, VR programming, digital twins, " + "virtual world design, and cross-platform compatibility.", llm="openrouter/moonshotai/kimi-k2" )examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/nanotechnology_materials_agent.py (1)
1-14: Comprehensive materials science domain coverage with consistent implementation.The nanotechnology and materials science instructions are well-crafted, covering nanomaterials, material properties, fabrication techniques, and industrial applications. This completes a solid collection of domain-specific AI agents.
Overall Assessment: All 5 agents demonstrate consistent implementation patterns and appropriate domain expertise. The collection provides comprehensive coverage of advanced technology domains.
Recommendations for the complete agent collection:
- Implement centralized configuration for the shared LLM model to facilitate future updates
- Add comprehensive error handling across all agents
- Consider agent orchestration capabilities for multi-domain queries
- Add documentation explaining the agent architecture and usage patterns
- Implement testing framework for agent response quality validation
# config.py DEFAULT_LLM_MODEL = "openrouter/moonshotai/kimi-k2" # agent_base.py class DomainAgentBase: def __init__(self, instructions, greeting_message, domain_name): self.domain_name = domain_name self.instructions = instructions self.greeting_message = greeting_message def initialize(self): try: self.agent = Agent( instructions=self.instructions, llm=DEFAULT_LLM_MODEL ) return self.agent.start(self.greeting_message) except Exception as e: print(f"Error initializing {self.domain_name} agent: {e}") return Noneexamples/python/tools/exa-tool/Kimi2_Intelligent_Agents/aerospace_engineering_agent.py (1)
1-14: Well-structured domain-specific agent implementation with room for improvementThe agent creation follows good practices with clear, comprehensive instructions covering key aerospace engineering topics. However, consider the following improvements:
- The
responsevariable is unused after capturing the agent's initial response- No error handling for potential issues during agent creation or startup
- Consider adding validation for the LLM model availability
+import logging + from praisonaiagents import Agent +try: agent = Agent( instructions="You are an aerospace engineering AI agent. " "Help users understand aerospace engineering concepts, " "aircraft design, and space technology. Provide guidance on " "aerodynamics, propulsion systems, materials science, " "and satellite technology.", llm="openrouter/moonshotai/kimi-k2" ) response = agent.start("Hello! I'm your aerospace engineering assistant. " "How can I help you with aerospace engineering " "and space technology today?") + print(response) # Display the response or handle it appropriately +except Exception as e: + logging.error(f"Failed to initialize aerospace engineering agent: {e}") + raise
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📒 Files selected for processing (10)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/aerospace_engineering_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/bioinformatics_genomics_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/blockchain_defi_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/energy_renewables_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/iot_smart_cities_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/metaverse_vr_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/nanotechnology_materials_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_ai_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_computing_agent.py(1 hunks)examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/robotics_automation_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-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-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-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.
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/tests/**/*.py : Test files should be placed in the `tests/` directory and demonstrate specific usage patterns, serving as both test and documentation.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_ai_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/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'.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_computing_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/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/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/Kimi2_Intelligent_Agents/nanotechnology_materials_agent.py (5)
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/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/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: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/robotics_automation_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/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'.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/energy_renewables_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-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/Kimi2_Intelligent_Agents/bioinformatics_genomics_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/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/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: Applies to src/praisonai-agents/praisonaiagents/{memory,knowledge}/**/*.py : Place memory-related implementations in `praisonaiagents/memory/` and knowledge/document processing in `praisonaiagents/knowledge/`.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/blockchain_defi_agent.py (6)
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-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.
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 conda environment activation (`conda activate praisonai-agents`) before running development or tests.
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/metaverse_vr_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/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/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/Kimi2_Intelligent_Agents/aerospace_engineering_agent.py (6)
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 `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/.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/Kimi2_Intelligent_Agents/iot_smart_cities_agent.py (5)
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'.
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 (8)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_ai_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/nanotechnology_materials_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/robotics_automation_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/bioinformatics_genomics_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/blockchain_defi_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/metaverse_vr_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/aerospace_engineering_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/iot_smart_cities_agent.py (1)
src/praisonai-agents/praisonaiagents/llm/llm.py (1)
response(2171-2267)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (3)
- GitHub Check: Run tests and collect coverage
- GitHub Check: quick-test
- GitHub Check: test-core (3.11)
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| from praisonaiagents import Agent | ||
|
|
||
| agent = Agent( | ||
| instructions="You are a quantum AI agent. " | ||
| "Help users understand quantum artificial intelligence, " | ||
| "quantum machine learning, and quantum neural networks. " | ||
| "Provide guidance on quantum algorithms for AI, " | ||
| "quantum optimization, and hybrid quantum-classical systems.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your quantum AI assistant. " | ||
| "How can I help you explore quantum artificial " | ||
| "intelligence today?") |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify LLM model availability and consider improvements.
The implementation follows the correct pattern using the Agent class from praisonaiagents. However, there are several areas for improvement:
- Model validation: Ensure the LLM model "openrouter/moonshotai/kimi-k2" is valid and accessible
- Unused response: The
responsevariable is assigned but never used - Missing error handling: No exception handling around agent creation or startup
Consider these improvements:
from praisonaiagents import Agent
-agent = Agent(
- instructions="You are a quantum AI agent. "
- "Help users understand quantum artificial intelligence, "
- "quantum machine learning, and quantum neural networks. "
- "Provide guidance on quantum algorithms for AI, "
- "quantum optimization, and hybrid quantum-classical systems.",
- llm="openrouter/moonshotai/kimi-k2"
-)
+try:
+ agent = Agent(
+ instructions="You are a quantum AI agent. "
+ "Help users understand quantum artificial intelligence, "
+ "quantum machine learning, and quantum neural networks. "
+ "Provide guidance on quantum algorithms for AI, "
+ "quantum optimization, and hybrid quantum-classical systems.",
+ llm="openrouter/moonshotai/kimi-k2"
+ )
+
+ response = agent.start("Hello! I'm your quantum AI assistant. "
+ "How can I help you explore quantum artificial "
+ "intelligence today?")
+ print(response) # Actually use the response
+except Exception as e:
+ print(f"Error initializing quantum AI agent: {e}")
-
-response = agent.start("Hello! I'm your quantum AI assistant. "
- "How can I help you explore quantum artificial "
- "intelligence today?") 🌐 Web query:
Is "openrouter/moonshotai/kimi-k2" a valid LLM model available through the OpenRouter API?
💡 Result:
Yes, "openrouter/moonshotai/kimi-k2" is a valid LLM model available through the OpenRouter API. The correct model identifier you should use in your API calls is moonshotai/kimi-k2 (not "openrouter/moonshotai/kimi-k2"; drop the "openrouter/" prefix when specifying the model name in requests)[1][4][10].
Key details:
- moonshotai/kimi-k2 refers specifically to the Kimi K2 model provided by Moonshot AI, a state-of-the-art 1 trillion parameter Mixture-of-Experts language model[1][6][10].
- Available on OpenRouter: You can access this model via OpenRouter’s API endpoint (
https://openrouter.ai/api/v1/chat/completions), specifying"model": "moonshotai/kimi-k2"in your OpenAI-compatible request body[1][4][10]. - Documentation and guides, including third-party integrations, reference this exact model name and endpoint, and provide working code examples[1][4][8][10].
For example, an API request targeting Kimi K2 would look like this in Python (using the OpenAI SDK, with a custom API base URL):
import openai
client = openai.OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_OPENROUTER_API_KEY",
)
response = client.chat.completions.create(
model="moonshotai/kimi-k2",
messages=[
{"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
{"role": "user", "content": "Write a short story about a robot who discovers music."},
],
)
print(response.choices[0].message.content)[1][4][10]
Summary:
- The model is correctly specified as moonshotai/kimi-k2 on OpenRouter, not "openrouter/moonshotai/kimi-k2"[1][4][10].
- It is a live, officially supported model accessible by developers for chat/completion tasks using the standard OpenAI-compatible API.
Citations:
- 1: https://dev.to/lynn_mikami_e94e5b9ad7daf/kimi-k2-the-open-source-llm-powering-the-next-generation-of-ai-agents-345f
- 2: https://github.com/simonw/llm-openrouter
- 3: https://feedland.org/?river=true&screenname=gwthompson
- 4: https://openrouter.ai/announcements/use-openrouter-models-in-cursor-try-it-with-moonshot-ais-kimi-k2
- 5: https://llm.datasette.io/en/stable/other-models.html
- 6: https://github.com/MoonshotAI/Kimi-K2
- 7: https://docs.typingmind.com/manage-and-connect-ai-models/openrouter-models
- 8: https://huggingface.co/blog/francesca-petracci/kimi-k2-claude-code
- 9: https://openrouter.ai/models/?q=free
- 10: https://openrouter.ai/moonshotai/kimi-k2
Update LLM identifier, use the response, and add error handling
The model "openrouter/moonshotai/kimi-k2" is valid on OpenRouter, but when calling the API you should drop the "openrouter/" prefix and use "moonshotai/kimi-k2". Also, the response is never used and there’s no error handling around your agent calls.
File: examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_ai_agent.py
Changes needed:
- Correct the model name to
"moonshotai/kimi-k2" - Wrap agent creation and startup in a
try/except - Actually use (e.g. print) the
response
from praisonaiagents import Agent
- agent = Agent(
- instructions="You are a quantum AI agent. "
- "Help users understand quantum artificial intelligence, "
- "quantum machine learning, and quantum neural networks. "
- "Provide guidance on quantum algorithms for AI, "
- "quantum optimization, and hybrid quantum-classical systems.",
- llm="openrouter/moonshotai/kimi-k2"
- )
-
- response = agent.start("Hello! I'm your quantum AI assistant. "
- "How can I help you explore quantum artificial "
- "intelligence today?")
+try:
+ agent = Agent(
+ instructions="You are a quantum AI agent. "
+ "Help users understand quantum artificial intelligence, "
+ "quantum machine learning, and quantum neural networks. "
+ "Provide guidance on quantum algorithms for AI, "
+ "quantum optimization, and hybrid quantum-classical systems.",
+ llm="moonshotai/kimi-k2"
+ )
+
+ response = agent.start(
+ "Hello! I'm your quantum AI assistant. "
+ "How can I help you explore quantum artificial intelligence today?"
+ )
+ print(response) # Use the agent’s response
+except Exception as e:
+ print(f"Error initializing quantum AI agent: {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 quantum AI agent. " | |
| "Help users understand quantum artificial intelligence, " | |
| "quantum machine learning, and quantum neural networks. " | |
| "Provide guidance on quantum algorithms for AI, " | |
| "quantum optimization, and hybrid quantum-classical systems.", | |
| llm="openrouter/moonshotai/kimi-k2" | |
| ) | |
| response = agent.start("Hello! I'm your quantum AI assistant. " | |
| "How can I help you explore quantum artificial " | |
| "intelligence today?") | |
| from praisonaiagents import Agent | |
| try: | |
| agent = Agent( | |
| instructions="You are a quantum AI agent. " | |
| "Help users understand quantum artificial intelligence, " | |
| "quantum machine learning, and quantum neural networks. " | |
| "Provide guidance on quantum algorithms for AI, " | |
| "quantum optimization, and hybrid quantum-classical systems.", | |
| llm="moonshotai/kimi-k2" | |
| ) | |
| response = agent.start( | |
| "Hello! I'm your quantum AI assistant. " | |
| "How can I help you explore quantum artificial intelligence today?" | |
| ) | |
| print(response) # Use the agent’s response | |
| except Exception as e: | |
| print(f"Error initializing quantum AI agent: {e}") |
🤖 Prompt for AI Agents
In examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_ai_agent.py
lines 1 to 14, update the LLM identifier by removing the "openrouter/" prefix so
it reads "moonshotai/kimi-k2". Wrap the agent creation and the call to
agent.start in a try/except block to handle potential errors gracefully.
Finally, use the response variable by printing it or otherwise outputting it to
ensure the result is utilized.
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| from praisonaiagents import Agent | ||
|
|
||
| agent = Agent( | ||
| instructions="You are an IoT and smart cities AI agent. " | ||
| "Help users understand Internet of Things, smart city " | ||
| "technologies, and connected systems. Provide guidance on " | ||
| "IoT device integration, smart infrastructure, " | ||
| "sensor networks, and urban technology solutions.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your IoT and smart cities assistant. " | ||
| "How can I help you with IoT and smart city " | ||
| "technologies today?") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consistent implementation with IoT-specific domain expertise.
The IoT and smart cities instructions effectively cover the domain including device integration, sensor networks, and urban technology solutions. The implementation maintains consistency with the established pattern.
Since all agent files share the same structure and issues, consider implementing a factory pattern or base configuration to reduce code duplication across the entire agent collection.
Consider creating a shared agent factory to reduce duplication:
# agent_factory.py
from praisonaiagents import Agent
def create_domain_agent(domain_name, instructions, greeting_message):
"""Factory function to create domain-specific agents with consistent error handling."""
try:
agent = Agent(
instructions=instructions,
llm="openrouter/moonshotai/kimi-k2"
)
response = agent.start(greeting_message)
print(response)
return agent
except Exception as e:
print(f"Error initializing {domain_name} agent: {e}")
return NoneThen use it in each agent file:
from agent_factory import create_domain_agent
agent = create_domain_agent(
"IoT and Smart Cities",
"You are an IoT and smart cities AI agent. ...",
"Hello! I'm your IoT and smart cities assistant. ..."
)🤖 Prompt for AI Agents
In
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/iot_smart_cities_agent.py
lines 1 to 14, the agent initialization code duplicates the pattern used across
multiple agent files. To reduce code duplication and improve maintainability,
refactor by creating a shared factory function (e.g., in agent_factory.py) that
accepts domain-specific parameters like instructions and greeting messages,
handles agent creation and error management, and returns the initialized agent.
Then update this file to use that factory function for cleaner, consistent agent
setup.
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| from praisonaiagents import Agent | ||
|
|
||
| agent = Agent( | ||
| instructions="You are a quantum computing AI agent. " | ||
| "Help users understand quantum computing concepts, " | ||
| "quantum algorithms, and quantum programming. Provide guidance on " | ||
| "quantum circuit design, quantum error correction, " | ||
| "quantum machine learning, and quantum cryptography.", | ||
| llm="openrouter/moonshotai/kimi-k2" | ||
| ) | ||
|
|
||
| response = agent.start("Hello! I'm your quantum computing assistant. " | ||
| "How can I help you explore quantum computing " | ||
| "and quantum programming today?") |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Comprehensive quantum computing agent with advanced topic coverage
The instructions effectively cover sophisticated quantum computing topics including quantum algorithms, circuit design, error correction, machine learning, and cryptography. This completes the consistent pattern across all specialized agents.
This agent follows the same implementation pattern as the other specialized agents. Given the specialized nature of quantum computing, consider verifying that the chosen LLM model has adequate knowledge in this domain:
The same improvement suggestions apply: handle the unused response variable and add error handling as demonstrated in the aerospace engineering agent review.
🏁 Script executed:
#!/bin/bash
# Check if there are any existing quantum computing examples or tests in the repository
echo "Searching for existing quantum computing references..."
rg -i "quantum" --type py -A 3 -B 3
echo "Searching for LLM model references..."
rg "openrouter/moonshotai/kimi-k2" --type py -A 2 -B 2Length of output: 32443
Quantum computing agent: verify LLM choice and add response handling
There’s strong quantum‐computing coverage in this agent (and across the repo—tests, use-cases, tools), but we should ensure the LLM has the required domain expertise. Also, the example code currently assigns response without using it and lacks error handling.
Files to update:
- examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_computing_agent.py
- Verify that
openrouter/moonshotai/kimi-k2performs well on quantum topics (see existing quantum references in tests and examples). - Handle the
response(e.g., print or further process it). - Wrap the
agent.start(...)call in a try/except block.
- Verify that
Suggested diff:
-agent = Agent(
+agent = Agent(
instructions="You are a quantum computing AI agent. "
"Help users understand quantum computing concepts, "
"quantum algorithms, and quantum programming. Provide guidance on "
"quantum circuit design, quantum error correction, "
"quantum machine learning, and quantum cryptography.",
llm="openrouter/moonshotai/kimi-k2"
)
-response = agent.start("Hello! I'm your quantum computing assistant. "
- "How can I help you explore quantum computing "
- "and quantum programming today?")
+try:
+ response = agent.start(
+ "Hello! I'm your quantum computing assistant. "
+ "How can I help you explore quantum computing "
+ "and quantum programming today?"
+ )
+ print(response)
+except Exception as e:
+ print(f"Error during agent execution: {e}")🤖 Prompt for AI Agents
In
examples/python/tools/exa-tool/Kimi2_Intelligent_Agents/quantum_computing_agent.py
lines 1 to 14, verify that the LLM "openrouter/moonshotai/kimi-k2" is suitable
for quantum computing topics by checking existing tests or documentation. Then,
wrap the agent.start call in a try/except block to handle potential errors
gracefully. Finally, ensure the response variable is used meaningfully, such as
printing the response or processing it further, instead of just assigning it
without use.
Dhivya-Bharathy
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This pull request adds 10 new specialized agents to the Kimi2_Intelligent_Agents folder:
Each agent is designed to address unique domain-specific tasks and can be further integrated or extended as needed.
No existing files were modified. Only new agent files were added.
Please review and let me know if any changes are required!
Summary by CodeRabbit