| Name | agentCores JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | https://github.com/Leoleojames1/agentCores |
| Summary | A flexible framework for creating and managing AI agent configurations |
| upload_time | 2024-12-18 06:57:50 |
| maintainer | None |
| docs_url | None |
| author | Leo Borcherding |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Borch Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
ai
agents
llm
framework
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# agentCores
<p align="center">
<img src="https://raw.githubusercontent.com/Leoleojames1/agentCores/main/src/agentCores/data/agentCoresLogoFix.png" alt="agentCores logo" width="450"/>
</p>
## Overview
agentCores is a powerful MongoDB-based system for creating, managing, and deploying AI agents. It provides a comprehensive framework for handling complex agent configurations, making it easier to work with multiple AI models and agent types.
### What agentCores Does
1. **Agent Configuration Management**:
- Create, store, and manage AI agent configurations in MongoDB
- Version control and unique identifiers for each agent
- Template-based agent creation with extensive customization
2. **Database Integration**:
- MongoDB collections for agent data, conversations, and knowledge
- Customizable collection structures and configurations
- Built-in database management and optimization
3. **Model and Prompt Management**:
- Support for multiple AI models (language, vision, speech)
- Structured prompt templates and version control
- Integration with Ollama and other model providers
4. **Research and Development Tools**:
- Built-in research assistant capabilities
- Paper and code repository integration
- Knowledge base management
5. **Command-line Interface**:
- Interactive agent management
- Real-time chat capabilities
- Database administration tools
## Installation
```bash
pip install agentCores
```
### Dependencies
- Python 3.8+
- MongoDB
- ollama (optional, for LLM functionality)
- duckduckgo_search (optional, for research capabilities)
## MongoDB Structure
agentCores uses a single MongoDB database named `agentCores` with organized collections:
```
MongoDB Database: agentCores/
├── System Collections:
│ ├── agent_matrix # Main agent configurations
│ ├── documentation # System documentation
│ ├── templates # Agent templates
│ └── template_files # Template resources
├── Agent-Specific Collections:
│ ├── conversations_{agent_id} # Chat history
│ ├── knowledge_{agent_id} # Knowledge base
│ ├── embeddings_{agent_id} # Vector embeddings
│ ├── research_{agent_id} # Research data
│ └── design_patterns_{agent_id} # Agent patterns
└── Shared Collections:
├── global_knowledge # Shared knowledge base
├── model_configs # Model configurations
└── prompt_templates # Prompt templates
```
## Quick Start
### Basic Usage
```python
from agentCores import agentCores
# Initialize with default MongoDB connection
core = agentCores()
# Or specify custom MongoDB connection
core = agentCores(connection_uri="mongodb://localhost:27017/")
# Create a simple agent
agent = core.mintAgent(
agent_id="my_assistant",
model_config={
"largeLanguageModel": {
"names": ["llama2"],
"instances": None,
"concurrency": [],
"parrallelModels": None
}
},
prompt_config={
"userInput": "You are a helpful assistant",
"agent": {
"llmSystem": "Provide clear and concise answers",
"llmBooster": "Use examples when helpful"
}
}
)
# Start chatting
core.chat_with_agent("my_assistant")
```
### Research Assistant
```python
from agentCores import researchAssistant
# Initialize research assistant with MongoDB connection
assistant = researchAssistant(
agent_id="research_agent",
connection_uri="mongodb://localhost:27017/"
)
# Process a research query
response = assistant.process_query("What is quantum computing?")
# Start interactive research session
assistant.start_chat()
```
## Core Features
### Agent Configuration Structure
```python
{
"agentCore": {
"identifyers": {
"agent_id": str, # Unique identifier
"uid": str, # Generated hash
"version": int, # Version number
"creationDate": float, # Creation timestamp
"cpuNoiseHex": str # Hardware identifier
},
"models": {
"largeLanguageModel": {
"names": list, # Model names
"instances": None, # Instance count
"concurrency": list, # Concurrency settings
"parrallelModels": None,
"model_config_template": dict
},
"largeLanguageAndVisionAssistant": dict,
"yoloVision": dict,
"speechRecognitionSTT": dict,
"voiceGenerationTTS": dict,
"embedding": dict
},
"prompts": {
"userInput": str,
"agent": {
"llmSystem": str,
"llmBooster": str,
"visionSystem": str,
"visionBooster": str
}
},
"modalityFlags": {
"TTS_FLAG": bool, # Text-to-speech
"STT_FLAG": bool, # Speech-to-text
"CHUNK_AUDIO_FLAG": bool,
"AUTO_SPEECH_FLAG": bool,
"LLAVA_FLAG": bool,
"SCREEN_SHOT_FLAG": bool,
"SPLICE_VIDEO_FLAG": bool,
"AUTO_COMMANDS_FLAG": bool,
"CLEAR_MEMORY_FLAG": bool,
"ACTIVE_AGENT_FLAG": bool
},
"evolutionarySettings": {
"mutation": float,
"pain": float,
"hunger": float,
"fasting": bool,
"rationalizationFactor": float
}
}
}
```
### Command-line Interface
Start the interface:
```bash
python -m agentCores
```
Available commands:
```
/help Show available commands
/agentCores List all agent cores
/showAgent <agent_id> Show agent configuration
/createAgent <template_id> <new_agent_id> Create new agent
/createCustomAgent Interactive agent creation
/createCollection <name> Create MongoDB collection
/linkCollection <agent_id> <collection_name> Link collection to agent
/storeAgent <file_path> Import agent from JSON
/exportAgent <agent_id> Export agent to JSON
/deleteAgent <agent_id> Delete an agent
/resetAgent <agent_id> Reset agent to base template
/chat <agent_id> Start chat session
/knowledge <agent_id> View knowledge base
/conversations <agent_id> View conversation history
```
## Advanced Usage
### Custom Model Configuration
```python
model_config = {
"largeLanguageModel": {
"instances": 2,
"names": ["llama2", "codellama"],
"concurrency": ["parallel", "sequential"],
"parrallelModels": True,
"model_config_template": {
"temperature": 0.7,
"context_window": 4096,
"streaming": True
}
}
}
agent = core.mintAgent(
agent_id="advanced_agent",
model_config=model_config
)
```
### Custom Collection Configuration
```python
# Create collection with indexes
core.create_collection_with_schema(
"research_papers",
indexes=[
[("paper_id", 1)],
[("title", "text")]
]
)
# Link collection to agent
core.linkDatabase(
"research_agent",
"research_papers"
)
```
### Research Integration
```python
from agentCores import researchAssistant
# Initialize with custom configuration
assistant = researchAssistant(
agent_id="research_agent",
connection_uri="mongodb://localhost:27017/"
)
# Create research collection
assistant.core.create_collection_with_schema(
"papers",
indexes=[
[("paper_id", 1)],
[("title", "text")]
]
)
# Process research queries
results = assistant.search("quantum computing")
response = assistant.process_query(
"Explain recent advances in quantum computing"
)
```
## Development Tools
### Database Management
```python
# Create custom collection with indexes
core.create_collection_with_schema(
"research_papers",
indexes=[
[("paper_id", 1)],
[("title", "text")]
]
)
# Execute query
results = core.execute_query(
"research_papers",
{"title": {"$regex": "neural networks", "$options": "i"}}
)
# Bulk operations
core.bulk_insert(
"research_papers",
documents=[...]
)
```
### Template Management
```python
# Register template
template_id = core.register_template(
"research_template",
template_data={...},
metadata={"version": "1.0"}
)
# Get template
template = core.get_template("research_template")
# List templates
templates = core.list_templates()
```
## Best Practices
1. **Database Management**
- Use descriptive collection names
- Implement proper MongoDB indexes
- Regular maintenance of collections
- Back up important data
2. **Agent Creation**
- Start with templates
- Version control configurations
- Document custom settings
- Test before deployment
3. **Error Handling**
- Implement proper exception handling
- Log errors and warnings
- Include recovery strategies
- Monitor agent performance
## Contributing
1. Fork the repository
2. Create a feature branch
3. Follow PEP 8 style guide
4. Add tests for new features
5. Update documentation
6. Submit pull request
## License
MIT License - see LICENSE file for details
## Author
Leo Borcherding
## Links
- GitHub: https://github.com/Leoleojames1/agentCores
- Documentation: https://agentcore.readthedocs.io/
- Issues: https://github.com/Leoleojames1/agentCores/issues
## Version
0.1.27 (2024-12-11)
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"description": "# agentCores\r\n<p align=\"center\">\r\n <img src=\"https://raw.githubusercontent.com/Leoleojames1/agentCores/main/src/agentCores/data/agentCoresLogoFix.png\" alt=\"agentCores logo\" width=\"450\"/>\r\n</p>\r\n\r\n## Overview\r\n\r\nagentCores is a powerful MongoDB-based system for creating, managing, and deploying AI agents. It provides a comprehensive framework for handling complex agent configurations, making it easier to work with multiple AI models and agent types.\r\n\r\n### What agentCores Does\r\n\r\n1. **Agent Configuration Management**: \r\n - Create, store, and manage AI agent configurations in MongoDB\r\n - Version control and unique identifiers for each agent\r\n - Template-based agent creation with extensive customization\r\n\r\n2. **Database Integration**:\r\n - MongoDB collections for agent data, conversations, and knowledge\r\n - Customizable collection structures and configurations\r\n - Built-in database management and optimization\r\n\r\n3. **Model and Prompt Management**:\r\n - Support for multiple AI models (language, vision, speech)\r\n - Structured prompt templates and version control\r\n - Integration with Ollama and other model providers\r\n\r\n4. **Research and Development Tools**:\r\n - Built-in research assistant capabilities\r\n - Paper and code repository integration\r\n - Knowledge base management\r\n\r\n5. **Command-line Interface**:\r\n - Interactive agent management\r\n - Real-time chat capabilities\r\n - Database administration tools\r\n\r\n## Installation\r\n\r\n```bash\r\npip install agentCores\r\n```\r\n\r\n### Dependencies\r\n- Python 3.8+\r\n- MongoDB\r\n- ollama (optional, for LLM functionality)\r\n- duckduckgo_search (optional, for research capabilities)\r\n\r\n## MongoDB Structure\r\n\r\nagentCores uses a single MongoDB database named `agentCores` with organized collections:\r\n\r\n```\r\nMongoDB Database: agentCores/\r\n\u251c\u2500\u2500 System Collections:\r\n\u2502 \u251c\u2500\u2500 agent_matrix # Main agent configurations\r\n\u2502 \u251c\u2500\u2500 documentation # System documentation\r\n\u2502 \u251c\u2500\u2500 templates # Agent templates\r\n\u2502 \u2514\u2500\u2500 template_files # Template resources\r\n\u251c\u2500\u2500 Agent-Specific Collections:\r\n\u2502 \u251c\u2500\u2500 conversations_{agent_id} # Chat history\r\n\u2502 \u251c\u2500\u2500 knowledge_{agent_id} # Knowledge base\r\n\u2502 \u251c\u2500\u2500 embeddings_{agent_id} # Vector embeddings\r\n\u2502 \u251c\u2500\u2500 research_{agent_id} # Research data\r\n\u2502 \u2514\u2500\u2500 design_patterns_{agent_id} # Agent patterns\r\n\u2514\u2500\u2500 Shared Collections:\r\n \u251c\u2500\u2500 global_knowledge # Shared knowledge base\r\n \u251c\u2500\u2500 model_configs # Model configurations \r\n \u2514\u2500\u2500 prompt_templates # Prompt templates\r\n```\r\n\r\n## Quick Start\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom agentCores import agentCores\r\n\r\n# Initialize with default MongoDB connection\r\ncore = agentCores()\r\n\r\n# Or specify custom MongoDB connection\r\ncore = agentCores(connection_uri=\"mongodb://localhost:27017/\")\r\n\r\n# Create a simple agent\r\nagent = core.mintAgent(\r\n agent_id=\"my_assistant\",\r\n model_config={\r\n \"largeLanguageModel\": {\r\n \"names\": [\"llama2\"],\r\n \"instances\": None,\r\n \"concurrency\": [],\r\n \"parrallelModels\": None\r\n }\r\n },\r\n prompt_config={\r\n \"userInput\": \"You are a helpful assistant\",\r\n \"agent\": {\r\n \"llmSystem\": \"Provide clear and concise answers\",\r\n \"llmBooster\": \"Use examples when helpful\"\r\n }\r\n }\r\n)\r\n\r\n# Start chatting\r\ncore.chat_with_agent(\"my_assistant\")\r\n```\r\n\r\n### Research Assistant\r\n\r\n```python\r\nfrom agentCores import researchAssistant\r\n\r\n# Initialize research assistant with MongoDB connection\r\nassistant = researchAssistant(\r\n agent_id=\"research_agent\", \r\n connection_uri=\"mongodb://localhost:27017/\"\r\n)\r\n\r\n# Process a research query\r\nresponse = assistant.process_query(\"What is quantum computing?\")\r\n\r\n# Start interactive research session\r\nassistant.start_chat()\r\n```\r\n\r\n## Core Features\r\n\r\n### Agent Configuration Structure\r\n\r\n```python\r\n{\r\n \"agentCore\": {\r\n \"identifyers\": {\r\n \"agent_id\": str, # Unique identifier\r\n \"uid\": str, # Generated hash\r\n \"version\": int, # Version number\r\n \"creationDate\": float, # Creation timestamp\r\n \"cpuNoiseHex\": str # Hardware identifier\r\n },\r\n \"models\": {\r\n \"largeLanguageModel\": {\r\n \"names\": list, # Model names\r\n \"instances\": None, # Instance count\r\n \"concurrency\": list, # Concurrency settings\r\n \"parrallelModels\": None,\r\n \"model_config_template\": dict\r\n },\r\n \"largeLanguageAndVisionAssistant\": dict,\r\n \"yoloVision\": dict,\r\n \"speechRecognitionSTT\": dict,\r\n \"voiceGenerationTTS\": dict,\r\n \"embedding\": dict\r\n },\r\n \"prompts\": {\r\n \"userInput\": str,\r\n \"agent\": {\r\n \"llmSystem\": str,\r\n \"llmBooster\": str,\r\n \"visionSystem\": str,\r\n \"visionBooster\": str\r\n }\r\n },\r\n \"modalityFlags\": {\r\n \"TTS_FLAG\": bool, # Text-to-speech\r\n \"STT_FLAG\": bool, # Speech-to-text\r\n \"CHUNK_AUDIO_FLAG\": bool,\r\n \"AUTO_SPEECH_FLAG\": bool,\r\n \"LLAVA_FLAG\": bool,\r\n \"SCREEN_SHOT_FLAG\": bool,\r\n \"SPLICE_VIDEO_FLAG\": bool,\r\n \"AUTO_COMMANDS_FLAG\": bool,\r\n \"CLEAR_MEMORY_FLAG\": bool,\r\n \"ACTIVE_AGENT_FLAG\": bool\r\n },\r\n \"evolutionarySettings\": {\r\n \"mutation\": float,\r\n \"pain\": float,\r\n \"hunger\": float,\r\n \"fasting\": bool,\r\n \"rationalizationFactor\": float\r\n }\r\n }\r\n}\r\n```\r\n\r\n### Command-line Interface\r\n\r\nStart the interface:\r\n```bash\r\npython -m agentCores\r\n```\r\n\r\nAvailable commands:\r\n```\r\n/help Show available commands\r\n/agentCores List all agent cores\r\n/showAgent <agent_id> Show agent configuration\r\n/createAgent <template_id> <new_agent_id> Create new agent\r\n/createCustomAgent Interactive agent creation\r\n/createCollection <name> Create MongoDB collection\r\n/linkCollection <agent_id> <collection_name> Link collection to agent\r\n/storeAgent <file_path> Import agent from JSON\r\n/exportAgent <agent_id> Export agent to JSON\r\n/deleteAgent <agent_id> Delete an agent\r\n/resetAgent <agent_id> Reset agent to base template\r\n/chat <agent_id> Start chat session\r\n/knowledge <agent_id> View knowledge base\r\n/conversations <agent_id> View conversation history\r\n```\r\n\r\n## Advanced Usage\r\n\r\n### Custom Model Configuration\r\n\r\n```python\r\nmodel_config = {\r\n \"largeLanguageModel\": {\r\n \"instances\": 2,\r\n \"names\": [\"llama2\", \"codellama\"],\r\n \"concurrency\": [\"parallel\", \"sequential\"],\r\n \"parrallelModels\": True,\r\n \"model_config_template\": {\r\n \"temperature\": 0.7,\r\n \"context_window\": 4096,\r\n \"streaming\": True\r\n }\r\n }\r\n}\r\n\r\nagent = core.mintAgent(\r\n agent_id=\"advanced_agent\",\r\n model_config=model_config\r\n)\r\n```\r\n\r\n### Custom Collection Configuration\r\n\r\n```python\r\n# Create collection with indexes\r\ncore.create_collection_with_schema(\r\n \"research_papers\",\r\n indexes=[\r\n [(\"paper_id\", 1)],\r\n [(\"title\", \"text\")]\r\n ]\r\n)\r\n\r\n# Link collection to agent\r\ncore.linkDatabase(\r\n \"research_agent\",\r\n \"research_papers\"\r\n)\r\n```\r\n\r\n### Research Integration\r\n\r\n```python\r\nfrom agentCores import researchAssistant\r\n\r\n# Initialize with custom configuration\r\nassistant = researchAssistant(\r\n agent_id=\"research_agent\",\r\n connection_uri=\"mongodb://localhost:27017/\"\r\n)\r\n\r\n# Create research collection\r\nassistant.core.create_collection_with_schema(\r\n \"papers\",\r\n indexes=[\r\n [(\"paper_id\", 1)],\r\n [(\"title\", \"text\")]\r\n ]\r\n)\r\n\r\n# Process research queries\r\nresults = assistant.search(\"quantum computing\")\r\nresponse = assistant.process_query(\r\n \"Explain recent advances in quantum computing\"\r\n)\r\n```\r\n\r\n## Development Tools\r\n\r\n### Database Management\r\n\r\n```python\r\n# Create custom collection with indexes\r\ncore.create_collection_with_schema(\r\n \"research_papers\",\r\n indexes=[\r\n [(\"paper_id\", 1)],\r\n [(\"title\", \"text\")]\r\n ]\r\n)\r\n\r\n# Execute query\r\nresults = core.execute_query(\r\n \"research_papers\",\r\n {\"title\": {\"$regex\": \"neural networks\", \"$options\": \"i\"}}\r\n)\r\n\r\n# Bulk operations\r\ncore.bulk_insert(\r\n \"research_papers\",\r\n documents=[...]\r\n)\r\n```\r\n\r\n### Template Management\r\n\r\n```python\r\n# Register template\r\ntemplate_id = core.register_template(\r\n \"research_template\",\r\n template_data={...},\r\n metadata={\"version\": \"1.0\"}\r\n)\r\n\r\n# Get template\r\ntemplate = core.get_template(\"research_template\")\r\n\r\n# List templates\r\ntemplates = core.list_templates()\r\n```\r\n\r\n## Best Practices\r\n\r\n1. **Database Management**\r\n - Use descriptive collection names\r\n - Implement proper MongoDB indexes\r\n - Regular maintenance of collections\r\n - Back up important data\r\n\r\n2. **Agent Creation**\r\n - Start with templates\r\n - Version control configurations\r\n - Document custom settings\r\n - Test before deployment\r\n\r\n3. **Error Handling**\r\n - Implement proper exception handling\r\n - Log errors and warnings\r\n - Include recovery strategies\r\n - Monitor agent performance\r\n\r\n## Contributing\r\n\r\n1. Fork the repository\r\n2. Create a feature branch\r\n3. Follow PEP 8 style guide\r\n4. Add tests for new features\r\n5. Update documentation\r\n6. Submit pull request\r\n\r\n## License\r\n\r\nMIT License - see LICENSE file for details\r\n\r\n## Author\r\n\r\nLeo Borcherding\r\n\r\n## Links\r\n\r\n- GitHub: https://github.com/Leoleojames1/agentCores\r\n- Documentation: https://agentcore.readthedocs.io/\r\n- Issues: https://github.com/Leoleojames1/agentCores/issues\r\n\r\n## Version\r\n\r\n0.1.27 (2024-12-11)\r\n",
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