| Name | versionHQ JSON |
| Version |
1.1.5
JSON |
| download |
| home_page | None |
| Summary | LLM orchestration frameworks for model-agnostic AI agents that handle complex outbound workflows |
| upload_time | 2024-12-21 19:23:10 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | MIT License Copyright (c) 2024 Version IO Sdn. Bhd. 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 |
orchestration framework
orchestration
ai agent
multi-agent system
rag
agent
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
regex
requests
python-dotenv
pydantic
werkzeug
typing
json-repair
litellm
openai
composio
composio-langchain
composio-openai
pre-commit
gunicorn
setuptools
wheel
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Overview
 
An LLM orchestration frameworks for multi-agent systems with RAG to autopilot outbound workflows.
Agents are model agnostic.
Messaging workflows are created at individual level, and will be deployed on third-party services via `Composio`.
**Visit:**
- Marketing: [Landing page](https://home.versi0n.io)
- Client app: [Production](https://versi0n.io/)
- Backend: [Orchestration + multi-agent RAG system](https://github.com/versionHQ/multi-agent-system)
- Test CI: [Test client app (React)](https://github.com/versionHQ/test-client-app)
## Mindmap
LLM-powered `agent`s and `team`s use `tool`s and their own knowledge to complete the `task` given by the client or the system.
<p align="center">
<img src="https://res.cloudinary.com/dfeirxlea/image/upload/v1733556715/pj_m_home/urwte15at3h0dr8mdlyo.png" alt="mindmap" width="1000">
</p>
<hr />
## Table of Content
<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
- [Key Features](#key-features)
- [Technologies Used](#technologies-used)
- [Project Structure](#project-structure)
- [Setup](#setup)
- [Usage](#usage)
- [Installing as a Package Module (Alpha)](#installing-as-a-package-module-alpha)
- [Contributing & Customizing](#contributing--customizing)
- [Customizing AI Agents](#customizing-ai-agents)
- [Modifying RAG Functionality](#modifying-rag-functionality)
- [Package Management with uv](#package-management-with-uv)
- [Pre-Commit Hooks](#pre-commit-hooks)
- [Trouble Shooting](#trouble-shooting)
- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)
<!-- END doctoc generated TOC please keep comment here to allow auto update -->
<hr />
## Key Features
A mulit-agent systems with Rag that tailors messaging workflow, predicts its performance, and deploys it on third-party tools.
The `agent` is model agnostic. The default model is set Chat GTP 4o. We ask the client their preference and switch it accordingly using llm variable stored in the `BaseAgent` class.
Multiple `agents` can form a `team` to complete complex tasks together.
**1. Analysis**
- Professional `agents` handle the analysis `tasks` on each client, customer, and product.
**2. Messaging Workflow Creation**
- Several `teams` receive the analysis and design initial messaging workflow with several layers.
- Ask the client for their inputs
- Deploy the workflow on the third party tools using `composio`.
**3. Autopiloting**
- Responsible `agents` or `teams` autopilot executing and refining the messaging workflow.
## Technologies Used
**Schema, Database, Data Validation**
- [Pydantic](https://docs.pydantic.dev/latest/): Data validation and serialization library for Python
- [Pydantic_core](https://pypi.org/project/pydantic-core/): Core func packages for Pydantic
- [Chroma DB](https://docs.trychroma.com/): Vector database for storing and querying usage data
- [SQLite](https://www.sqlite.org/docs.html): C-language library to implements a small SQL database engine
- [Upstage](https://console.upstage.ai/docs/getting-started/overview): Document processer for ML tasks. (Use `Document Parser API` to extract data from documents)
**LLM-curation**
- OpenAI GPT-4: Advanced language model for analysis and recommendations
- [LiteLLM](https://docs.litellm.ai/docs/providers): Curation platform to access LLMs
**Tools**
- [Composio](https://composio.dev/): Conect RAG agents with external tools, Apps, and APIs to perform actions and receive triggers. We use [tools](https://composio.dev/tools) and [RAG tools](https://app.composio.dev/app/ragtool) from Composio toolset.
**Deployment**
- Python: Primary programming language. We use 3.12 in this project
- [uv](https://docs.astral.sh/uv/): Python package installer and resolver
- [pre-commit](https://pre-commit.com/): Manage and maintain pre-commit hooks
- [setuptools](https://pypi.org/project/setuptools/): Build python modules
## Project Structure
```
.
src/
└── versionHQ/ # Orchestration frameworks on Pydantic
│ ├── agent/
│ └── llm/
│ └── task/
│ └── team/
│ └── tool/
│ └── clients/ # Classes to store the client related information
│ └── cli/ # CLI commands
│ └── ...
│ │
│ ├── db/ # Database files
│ ├── chroma.sqlite3
│ └── ...
│
└──tests/
└── cli/
└── team/
└── ...
│
└── uploads/ # Uploaded files for the project
```
## Setup
1. Install the `uv` package manager:
```
brew install uv
```
2. Install dependencies:
```
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt -v
```
* In case of AssertionError/module mismatch, run Python version control using `.pyenv`
```
pyenv install 3.13.1
pyenv global 3.13.1 (optional: `pyenv global system` to get back to the system default ver.)
uv python pin 3.13.1
```
3. Set up environment variables:
Create a `.env` file in the project root and add the following:
```
OPENAI_API_KEY=your-openai-api-key
LITELLM_API_KEY=your-litellm-api-key
UPSTAGE_API_KEY=your-upstage-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_CLI_KEY=your-composio-cli-key
```
## Usage
1. Add features.
2. Test the features using the `tests` directory.
- Add a file to the `tests` directory.
- Run a test.
```
uv run <your file name>
```
* All the `.py` files' names in the `tests` have to be ended with `_test.py`.
3. Run a React demo app: [React demo app](https://github.com/versionHQ/test-client-app) to check it on the client endpoint.
```
npm i
npm start
```
The frontend will be available at `http://localhost:3000`.
4. `production` is available at `https://versi0n.io`. Currently, we are running beta.
## Installing as a Package Module (Alpha)
1. Open another terminal, set your repository as root, and run
```
uv pip install git+https://github.com/versionHQ/multi-agent-system.git#egg=versionhq
```
2. You can use the `versionhq` module in your Python app.
```
from versionhq.agent.model import Agent
agent = Agent(llm="your-llm"...)
```
## Contributing & Customizing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/your-amazing-feature`)
3. Pull the latest version of source code from the main branch (`git pull origin main`) *Address conflicts if any.
4. Commit your changes (`git add .` / `git commit -m 'Add your-amazing-feature'`)
5. Push to the branch (`git push origin feature/your-amazing-feature`)
6. Open a pull request
0. Flag with `#! REFINEME` for any improvements and `#! FIXME` for any errors.
### Customizing AI Agents
To add an agent, use `sample` directory to add new `project`. You can define an agent with a specific role, goal, and set of tools.
Your new agent needs to follow the `Agent` model defined in the `verionhq.agent.model.py`.
You can also add any fields and functions to the `Agent` model **universally** by modifying `verionhq.agent.model.py`.
### Modifying RAG Functionality
The RAG system uses Chroma DB to store and query past campaign dataset. To update the knowledge base:
1. Add new files to the `uploads/` directory. (This will not be pushed to Github.)
2. Modify the `tools.py` file to update the ingestion process if necessary.
3. Run the ingestion process to update the Chroma DB.
### Package Management with uv
- Add a package: `uv add <package>`
- Remove a package: `uv remove <package>`
- Run a command in the virtual environment: `uv run <command>`
* After updating dependencies, update `requirements.txt` accordingly or run `uv pip freeze > requirements.txt`
### Pre-Commit Hooks
1. Install pre-commit hooks:
```
uv run pre-commit install
```
2. Run pre-commit checks manually:
```
uv run pre-commit run --all-files
```
Pre-commit hooks help maintain code quality by running checks for formatting, linting, and other issues before each commit.
* To skip pre-commit hooks (NOT RECOMMENDED)
```
git commit --no-verify -m "your-commit-message"
```
## Trouble Shooting
Common issues and solutions:
- API key errors: Ensure all API keys in the `.env` file are correct and up to date. Make sure to add `load_dotenv()` on the top of the python file to apply the latest environment values.
- Database connection issues: Check if the Chroma DB is properly initialized and accessible.
- Memory errors: If processing large contracts, you may need to increase the available memory for the Python process.
- Issues related to dependencies:`rm -rf .venv uv.lock`, `uv cache clean` and run `uv run pip install -r requirements.txt -v`.
- Issues related to the AI agents or RAG system: Check the `output.log` file for detailed error messages and stack traces.
- Issues related to `Python quit unexpectedly`: Check [this stackoverflow article](https://stackoverflow.com/questions/59888499/macos-catalina-python-quit-unexpectedly-error).
## Frequently Asked Questions (FAQ)
**Q. Where can I see if the agent is working?**
> A. You can find a frontend app [here](https://versi0n.io) with real-world outbound use cases.
> You can also test features [here](https://github.com/versionHQ/test-client-app) using React app.
**Q. How do you analyze the customer?**
> A. We employ soft clustering for each customer.
> <img width="200" src="https://res.cloudinary.com/dfeirxlea/image/upload/v1732732628/pj_m_agents/ito937s5d5x0so8isvw6.png">
**Q. When should I use a team vs an agent?**
> A. In essence, use a team for intricate, evolving projects, and agents for quick, straightforward tasks.
> Use a team when:
> **Complex tasks**: You need to complete multiple, interconnected tasks that require sequential or hierarchical processing.
> **Iterative refinement**: You want to iteratively improve upon the output through multiple rounds of feedback and revision.
> Use an agent when:
> **Simple tasks**: You have a straightforward, one-off task that doesn't require significant complexity or iteration.
> **Human input**: You need to provide initial input or guidance to the agent, or you expect to review and refine the output.
<--- Remaining tasks --->
- llm handling - agent
- more llms integration
- simpler prompting
- broader knowledge
- utils - log
- utils - time
- end to end client app test
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"description": "# Overview\n\n \n\n\nAn LLM orchestration frameworks for multi-agent systems with RAG to autopilot outbound workflows.\n\nAgents are model agnostic.\n\nMessaging workflows are created at individual level, and will be deployed on third-party services via `Composio`.\n\n\n**Visit:**\n\n- Marketing: [Landing page](https://home.versi0n.io)\n- Client app: [Production](https://versi0n.io/)\n- Backend: [Orchestration + multi-agent RAG system](https://github.com/versionHQ/multi-agent-system)\n- Test CI: [Test client app (React)](https://github.com/versionHQ/test-client-app)\n\n\n## Mindmap\n\nLLM-powered `agent`s and `team`s use `tool`s and their own knowledge to complete the `task` given by the client or the system.\n\n<p align=\"center\">\n <img src=\"https://res.cloudinary.com/dfeirxlea/image/upload/v1733556715/pj_m_home/urwte15at3h0dr8mdlyo.png\" alt=\"mindmap\" width=\"1000\">\n</p>\n\n<hr />\n\n## Table of Content\n<!-- START doctoc generated TOC please keep comment here to allow auto update -->\n<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->\n\n- [Key Features](#key-features)\n- [Technologies Used](#technologies-used)\n- [Project Structure](#project-structure)\n- [Setup](#setup)\n- [Usage](#usage)\n- [Installing as a Package Module (Alpha)](#installing-as-a-package-module-alpha)\n- [Contributing & Customizing](#contributing--customizing)\n - [Customizing AI Agents](#customizing-ai-agents)\n - [Modifying RAG Functionality](#modifying-rag-functionality)\n - [Package Management with uv](#package-management-with-uv)\n - [Pre-Commit Hooks](#pre-commit-hooks)\n- [Trouble Shooting](#trouble-shooting)\n- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)\n\n<!-- END doctoc generated TOC please keep comment here to allow auto update -->\n\n<hr />\n\n## Key Features\n\nA mulit-agent systems with Rag that tailors messaging workflow, predicts its performance, and deploys it on third-party tools.\n\nThe `agent` is model agnostic. The default model is set Chat GTP 4o. We ask the client their preference and switch it accordingly using llm variable stored in the `BaseAgent` class.\n\nMultiple `agents` can form a `team` to complete complex tasks together.\n\n**1. Analysis**\n- Professional `agents` handle the analysis `tasks` on each client, customer, and product. \n\n**2. Messaging Workflow Creation**\n- Several `teams` receive the analysis and design initial messaging workflow with several layers.\n- Ask the client for their inputs\n- Deploy the workflow on the third party tools using `composio`.\n\n**3. Autopiloting**\n- Responsible `agents` or `teams` autopilot executing and refining the messaging workflow.\n\n\n## Technologies Used\n**Schema, Database, Data Validation**\n - [Pydantic](https://docs.pydantic.dev/latest/): Data validation and serialization library for Python\n - [Pydantic_core](https://pypi.org/project/pydantic-core/): Core func packages for Pydantic\n - [Chroma DB](https://docs.trychroma.com/): Vector database for storing and querying usage data\n - [SQLite](https://www.sqlite.org/docs.html): C-language library to implements a small SQL database engine\n - [Upstage](https://console.upstage.ai/docs/getting-started/overview): Document processer for ML tasks. (Use `Document Parser API` to extract data from documents)\n\n**LLM-curation**\n - OpenAI GPT-4: Advanced language model for analysis and recommendations\n - [LiteLLM](https://docs.litellm.ai/docs/providers): Curation platform to access LLMs\n\n**Tools**\n - [Composio](https://composio.dev/): Conect RAG agents with external tools, Apps, and APIs to perform actions and receive triggers. We use [tools](https://composio.dev/tools) and [RAG tools](https://app.composio.dev/app/ragtool) from Composio toolset.\n\n**Deployment**\n - Python: Primary programming language. We use 3.12 in this project\n - [uv](https://docs.astral.sh/uv/): Python package installer and resolver\n - [pre-commit](https://pre-commit.com/): Manage and maintain pre-commit hooks\n - [setuptools](https://pypi.org/project/setuptools/): Build python modules\n\n\n## Project Structure\n\n```\n.\nsrc/\n\u2514\u2500\u2500 versionHQ/ # Orchestration frameworks on Pydantic\n\u2502 \u251c\u2500\u2500 agent/\n\u2502 \u2514\u2500\u2500 llm/\n\u2502 \u2514\u2500\u2500 task/\n\u2502 \u2514\u2500\u2500 team/\n\u2502 \u2514\u2500\u2500 tool/\n\u2502 \u2514\u2500\u2500 clients/ # Classes to store the client related information\n\u2502 \u2514\u2500\u2500 cli/ # CLI commands\n\u2502 \u2514\u2500\u2500 ...\n\u2502 \u2502\n\u2502 \u251c\u2500\u2500 db/ # Database files\n\u2502 \u251c\u2500\u2500 chroma.sqlite3\n\u2502 \u2514\u2500\u2500 ...\n\u2502\n\u2514\u2500\u2500tests/\n \u2514\u2500\u2500 cli/ \n \u2514\u2500\u2500 team/\n \u2514\u2500\u2500 ...\n \u2502 \n \u2514\u2500\u2500 uploads/ # Uploaded files for the project\n\n```\n\n## Setup\n\n1. Install the `uv` package manager:\n ```\n brew install uv\n ```\n\n2. Install dependencies:\n ```\n uv venv\n source .venv/bin/activate\n\n uv pip install -r requirements.txt -v\n ```\n\n* In case of AssertionError/module mismatch, run Python version control using `.pyenv`\n ```\n pyenv install 3.13.1\n pyenv global 3.13.1 (optional: `pyenv global system` to get back to the system default ver.)\n uv python pin 3.13.1\n ```\n\n3. Set up environment variables:\n Create a `.env` file in the project root and add the following:\n ```\n OPENAI_API_KEY=your-openai-api-key\n LITELLM_API_KEY=your-litellm-api-key\n UPSTAGE_API_KEY=your-upstage-api-key\n COMPOSIO_API_KEY=your-composio-api-key\n COMPOSIO_CLI_KEY=your-composio-cli-key\n ```\n\n## Usage\n\n1. Add features.\n\n2. Test the features using the `tests` directory.\n\n- Add a file to the `tests` directory.\n- Run a test.\n```\nuv run <your file name>\n```\n* All the `.py` files' names in the `tests` have to be ended with `_test.py`.\n\n\n3. Run a React demo app: [React demo app](https://github.com/versionHQ/test-client-app) to check it on the client endpoint.\n ```\n npm i\n npm start\n ```\n The frontend will be available at `http://localhost:3000`.\n\n4. `production` is available at `https://versi0n.io`. Currently, we are running beta.\n\n\n## Installing as a Package Module (Alpha)\n\n1. Open another terminal, set your repository as root, and run\n```\nuv pip install git+https://github.com/versionHQ/multi-agent-system.git#egg=versionhq\n```\n\n2. You can use the `versionhq` module in your Python app.\n```\nfrom versionhq.agent.model import Agent\nagent = Agent(llm=\"your-llm\"...)\n```\n\n## Contributing & Customizing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/your-amazing-feature`)\n3. Pull the latest version of source code from the main branch (`git pull origin main`) *Address conflicts if any.\n4. Commit your changes (`git add .` / `git commit -m 'Add your-amazing-feature'`)\n5. Push to the branch (`git push origin feature/your-amazing-feature`)\n6. Open a pull request\n\n0. Flag with `#! REFINEME` for any improvements and `#! FIXME` for any errors.\n\n\n### Customizing AI Agents\n\nTo add an agent, use `sample` directory to add new `project`. You can define an agent with a specific role, goal, and set of tools.\n\nYour new agent needs to follow the `Agent` model defined in the `verionhq.agent.model.py`.\n\nYou can also add any fields and functions to the `Agent` model **universally** by modifying `verionhq.agent.model.py`.\n\n\n### Modifying RAG Functionality\n\nThe RAG system uses Chroma DB to store and query past campaign dataset. To update the knowledge base:\n\n1. Add new files to the `uploads/` directory. (This will not be pushed to Github.)\n2. Modify the `tools.py` file to update the ingestion process if necessary.\n3. Run the ingestion process to update the Chroma DB.\n\n\n### Package Management with uv\n\n- Add a package: `uv add <package>`\n- Remove a package: `uv remove <package>`\n- Run a command in the virtual environment: `uv run <command>`\n\n* After updating dependencies, update `requirements.txt` accordingly or run `uv pip freeze > requirements.txt`\n\n\n### Pre-Commit Hooks\n\n1. Install pre-commit hooks:\n ```\n uv run pre-commit install\n ```\n\n2. Run pre-commit checks manually:\n ```\n uv run pre-commit run --all-files\n ```\n\nPre-commit hooks help maintain code quality by running checks for formatting, linting, and other issues before each commit.\n\n* To skip pre-commit hooks (NOT RECOMMENDED)\n ```\n git commit --no-verify -m \"your-commit-message\"\n ```\n\n## Trouble Shooting\n\nCommon issues and solutions:\n- API key errors: Ensure all API keys in the `.env` file are correct and up to date. Make sure to add `load_dotenv()` on the top of the python file to apply the latest environment values.\n- Database connection issues: Check if the Chroma DB is properly initialized and accessible.\n- Memory errors: If processing large contracts, you may need to increase the available memory for the Python process.\n- Issues related to dependencies:`rm -rf .venv uv.lock`, `uv cache clean` and run `uv run pip install -r requirements.txt -v`.\n- Issues related to the AI agents or RAG system: Check the `output.log` file for detailed error messages and stack traces.\n- Issues related to `Python quit unexpectedly`: Check [this stackoverflow article](https://stackoverflow.com/questions/59888499/macos-catalina-python-quit-unexpectedly-error).\n\n\n## Frequently Asked Questions (FAQ)\n**Q. Where can I see if the agent is working?**\n\n> A. You can find a frontend app [here](https://versi0n.io) with real-world outbound use cases.\n> You can also test features [here](https://github.com/versionHQ/test-client-app) using React app.\n\n**Q. How do you analyze the customer?**\n\n> A. We employ soft clustering for each customer.\n> <img width=\"200\" src=\"https://res.cloudinary.com/dfeirxlea/image/upload/v1732732628/pj_m_agents/ito937s5d5x0so8isvw6.png\">\n\n\n**Q. When should I use a team vs an agent?**\n\n> A. In essence, use a team for intricate, evolving projects, and agents for quick, straightforward tasks.\n\n> Use a team when:\n\n> **Complex tasks**: You need to complete multiple, interconnected tasks that require sequential or hierarchical processing.\n\n> **Iterative refinement**: You want to iteratively improve upon the output through multiple rounds of feedback and revision.\n\n> Use an agent when:\n\n> **Simple tasks**: You have a straightforward, one-off task that doesn't require significant complexity or iteration.\n\n> **Human input**: You need to provide initial input or guidance to the agent, or you expect to review and refine the output.\n\n\n\n<--- Remaining tasks --->\n\n- llm handling - agent\n- more llms integration\n- simpler prompting\n- broader knowledge\n\n- utils - log\n- utils - time\n\n- end to end client app test\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2024 Version IO Sdn. Bhd. 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. ",
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