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agentCore beta v0.1.16
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Leoleojames1 committed Dec 12, 2024
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# agentCores
<p align="center">
<img src="agentCoresLogoFix.png" alt="agentCores logo" width="450"/>
<img src="src/agentCores/data/agentCoresLogoFix.png" alt="agentCores logo" width="450"/>
</p>

# agentCores: Advanced AI Agent Management System
Expand Down Expand Up @@ -69,7 +69,108 @@ agentCores provides a solid foundation for building sophisticated AI agent syste
pip install agentCore
```

# core methods
# quick start

# command-line interface

To access the command-line interface, run:

```shellscript
python -m agentCores
```
This will start the agentCores Management Interface where you can manage your agents using various commands.

Start by using the /help command:
```cmd
Enter commands to manage agent cores. Type '/help' for options.
> /help
Commands:
/agentCores - List all agent cores.
/showAgent <agent_id> - Show agents with the specified ID.
/createAgent <template_id> <new_agent_id> - Mint a new agent.
/createCustomAgent - Interactive custom agent creation.
/createDatabase <name> <path> - Create a new database.
/linkDatabase <agent_id> <name> <path> - Link database to agent.
/storeAgent <file_path> - Store agentCore from json path.
/exportAgent <agent_id> - Export agentCore to json.
/deleteAgent <uid> - Delete an agent by UID.
/resetAgent <uid> - Reset an agent to the base template.
/chat <agent_id> - Start a chat session with an agent."
/exit - Exit the interface."
```

Now to get the available agentCores:
```
> /agentCores
ID: default_agent, UID: ff22a0c1, Version: 1
ID: promptBase, UID: 6f18aba0, Version: 1
ID: speedChatAgent, UID: f1a7092c, Version: 1
ID: ehartfordDolphin, UID: 18556c0c, Version: 1
ID: minecraft_agent, UID: 25389031, Version: 1
ID: general_navigator_agent, UID: d1f12a46, Version: 1
```

Now to see an agentCore use the following command
```cmd
> /showAgent general_navigator_agent
```

The agentCore will now be displayed:
```json
{
"agentCore": {
"agent_id": "general_navigator_agent",
"version": 1,
"uid": "d1f12a46",
"save_state_date": "2024-12-11",
"models": {
"large_language_model": null,
"embedding_model": null,
"language_and_vision_model": null,
"yolo_model": null,
"whisper_model": null,
"voice_model": null
},
"prompts": {
"user_input_prompt": "",
"agentPrompts": {
"llmSystemPrompt": "You are a helpful llm assistant, designated with with fulling the user's request, the user is communicating with speech recognition and is sending their screenshot data to the vision model for decomposition. Receive this destription and Instruct the user and help them fullfill their request by collecting the vision data and responding. ",
"llmBoosterPrompt": "Here is the output from the vision model describing the user screenshot data along with the users speech data. Please reformat this data, and formulate a fullfillment for the user request in a conversational speech manner which will be processes by the text to speech model for output. ",
"visionSystemPrompt": "You are an image recognition assistant, the user is sending you a request and an image please fullfill the request. ",
"visionBoosterPrompt": "Given the provided screenshot, please provide a list of objects in the image with the attributes that you can recognize. "
}
},
"commandFlags": {
"TTS_FLAG": false,
"STT_FLAG": true,
"CHUNK_FLAG": false,
"AUTO_SPEECH_FLAG": false,
"LLAVA_FLAG": true,
"SPLICE_FLAG": false,
"SCREEN_SHOT_FLAG": false,
"LATEX_FLAG": false,
"CMD_RUN_FLAG": false,
"AGENT_FLAG": true,
"MEMORY_CLEAR_FLAG": false
},
"conversation": {
"save_name": "defaultConversation",
"load_name": "defaultConversation"
}
}
}
```

Now to export an agentCore to json execute the following:
```cmd
> /exportAgent general_navigator_agent
agentCore saved to general_navigator_agent_core.json
```

# core development methods

## __init__
```python
Expand Down Expand Up @@ -319,105 +420,6 @@ example usage:
core.commandInterface()
```

# Command-line Interface

To access the command-line interface, run:

```shellscript
python -m agentCores
```
This will start the agentCores Management Interface where you can manage your agents using various commands.

Start by using the /help command:
```cmd
Enter commands to manage agent cores. Type '/help' for options.
> /help
Commands:
/agentCores - List all agent cores.
/showAgent <agent_id> - Show agents with the specified ID.
/createAgent <template_id> <new_agent_id> - Mint a new agent.
/createCustomAgent - Interactive custom agent creation.
/createDatabase <name> <path> - Create a new database.
/linkDatabase <agent_id> <name> <path> - Link database to agent.
/storeAgent <file_path> - Store agentCore from json path.
/exportAgent <agent_id> - Export agentCore to json.
/deleteAgent <uid> - Delete an agent by UID.
/resetAgent <uid> - Reset an agent to the base template.
/chat <agent_id> - Start a chat session with an agent."
/exit - Exit the interface."
```

Now to get the available agentCores:
```
> /agentCores
ID: default_agent, UID: ff22a0c1, Version: 1
ID: promptBase, UID: 6f18aba0, Version: 1
ID: speedChatAgent, UID: f1a7092c, Version: 1
ID: ehartfordDolphin, UID: 18556c0c, Version: 1
ID: minecraft_agent, UID: 25389031, Version: 1
ID: general_navigator_agent, UID: d1f12a46, Version: 1
```

Now to see an agentCore use the following command
```cmd
> /showAgent general_navigator_agent
```

The agentCore will now be displayed:
```json
{
"agentCore": {
"agent_id": "general_navigator_agent",
"version": 1,
"uid": "d1f12a46",
"save_state_date": "2024-12-11",
"models": {
"large_language_model": null,
"embedding_model": null,
"language_and_vision_model": null,
"yolo_model": null,
"whisper_model": null,
"voice_model": null
},
"prompts": {
"user_input_prompt": "",
"agentPrompts": {
"llmSystemPrompt": "You are a helpful llm assistant, designated with with fulling the user's request, the user is communicating with speech recognition and is sending their screenshot data to the vision model for decomposition. Receive this destription and Instruct the user and help them fullfill their request by collecting the vision data and responding. ",
"llmBoosterPrompt": "Here is the output from the vision model describing the user screenshot data along with the users speech data. Please reformat this data, and formulate a fullfillment for the user request in a conversational speech manner which will be processes by the text to speech model for output. ",
"visionSystemPrompt": "You are an image recognition assistant, the user is sending you a request and an image please fullfill the request. ",
"visionBoosterPrompt": "Given the provided screenshot, please provide a list of objects in the image with the attributes that you can recognize. "
}
},
"commandFlags": {
"TTS_FLAG": false,
"STT_FLAG": true,
"CHUNK_FLAG": false,
"AUTO_SPEECH_FLAG": false,
"LLAVA_FLAG": true,
"SPLICE_FLAG": false,
"SCREEN_SHOT_FLAG": false,
"LATEX_FLAG": false,
"CMD_RUN_FLAG": false,
"AGENT_FLAG": true,
"MEMORY_CLEAR_FLAG": false
},
"conversation": {
"save_name": "defaultConversation",
"load_name": "defaultConversation"
}
}
}
```

Now to export an agentCore to json execute the following:
```cmd
> /exportAgent general_navigator_agent
agentCore saved to general_navigator_agent_core.json
```

### Additional Database Management

```bash
Expand Down

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