https://github.com/norton120/lucy

autonomous enabled workflow/perpetual agent for multi-tenant, multi-user applications
https://github.com/norton120/lucy

Last synced: about 1 year ago
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autonomous enabled workflow/perpetual agent for multi-tenant, multi-user applications

• Host: GitHub
• URL: https://github.com/norton120/lucy
• Owner: norton120
• License: mit
• Created: 2024-02-01T23:18:24.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-03T17:25:05.000Z (over 2 years ago)
• Last Synced: 2025-02-08T21:48:31.052Z (over 1 year ago)
• Language: Python
• Size: 1.09 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## Lucy

![Lucy](good_morning.gif)

_Applied design of the  MemGPT memory management strategy_

> [!IMPORTANT]

> Read all the README.md files in Lucy/lucy. This is becoming a full port of MemGPT to a production-able library that is backend-agnositc.

> We want light, modular and decoupled agent rendering using the brilliant paged memory design from MemGPT, but in a commerically viable implementation.

> Autogen may or may not be the multi-agent orchestrator of choice, we'll see when we get there.

### Why

We have a collection of use cases that need this. So Lucy is the abstraction where we figure this out together.

### Minimum Viable Capabilities

- **OSS Models Only**

  Must support VLLM/Ollama service. We can use Together.ai for testing initially with an eye on VLLM-OpenAI spec compatability

- **Perpetual chat with context recall**

  if you tell a Lucy agent that "I fucking love pickles" and later ask the agent or order me a sandwich, the bot should say "extra pickles, right?"

- **Agency via Python functions**

  a clean factory for providing agents with functions they can invoke and iterate with

- **Multi: User, Tenant, Agent**

  reflect almost every SaaS application on Earth - orgs/teams/companies have many users. Agents can differentiate between different users, different teams, and the relationship between the two.

  _note_: not multiplexing conversations. More than one human in the same conversation requires a completely different kind of model training that we haven't seen yet.

- **Task Solving**

  Agents can be given work to do and/or things to accomplish with the user, like getting status on a project or booking a flight.

- **grown-ass scalability**

  can be deployed in a container, scaled horizontally, load balanced, replicated across zones etc.

### Development

since Lucy is designed to drop into an application framework with orgs, venues, and users, you need frameworks to test it in.

- [fastapi_test](fastapi_test)

- `#TODO:` django_test

each is a basic instance of that framework where we can run a suite of Lucy tests in an agnostic way.