agential 0.0.1

A flexible agent library.

Agential

Features

Our primary goal is to provide easy-to-use and clean functions for natural language processing tasks, enabling developers to build robust and efficient applications with ease.

• Easy-to-Use Interface: Provides intuitive and user-friendly functions for rapid prototyping and development.

• Clean Functions: Offers clean and well-structured functions, promoting readability and maintainability of code.

• Modularized Implementations: Includes modularized implementations of popular LLM-based agents and agent-related methods, allowing users to leverage cutting-edge innovations from the literature.

Getting Started

question = 'Who was once considered the best kick boxer in the world, however he has been involved in a number of controversies relating to his "unsportsmanlike conducts" in the sport and crimes of violence outside of the ring?'

llm = ChatOpenAI(openai_api_key="YOUR_API_KEY")
agent = ReActAgent(llm=llm)
out = agent.generate(question=question)

Here's what the output would look like!

Thought : I need to search for the best kick boxer in the world, and then find any controversies or crimes they have been involved in.
Action : Search[best kick boxer in the world]
Observation : (Result 1/1) Ramon Dekkers is considered by many to be the best kickboxer in the world.
Thought : It mentions "unsportsmanlike conducts" and crimes of violence. I need to find more information about Ramon Dekkers.
Action : Lookup[crimes]
Observation: (Result 1/1) Dekkers was involved in a number of controversies relating to his "unsportsmanlike conducts" in the sport and crimes of violence outside of the ring.
Thought: Ramon Dekkers has been involved in controversies and crimes. I need to find more information about them.
...

Project Organization

---

├── data
│   ├── external                   <- Data from third party sources.
│   ├── interim                    <- Intermediate data that has been transformed.
│   ├── processed                  <- The final, canonical data sets for modeling.
│   └── raw                        <- The original, immutable data dump.
│
├── agential                       <- Source code for this project.
│   ├── cog   
│   │   ├── agent                  <- Model/agent-related modules.
│   │   │   
│   │   ├── eval                   <- Agent core modules.
│   │   │   
│   │   ├── functional                  
│   │   │
│   │   ├── modules           
│   │   │   ├── memory             <- Memory-related modules.
│   │   │   ├── plan               <- Planning-related modules.
│   │   │   ├── reflect            <- Reflecting-related modules.
│   │   │   └── score              <- Scoring-related modules.
│   │   │
│   │   ├── persona             
│   │   │
│   │   └── prompts             
│   │
│   └── utils                      <- Utility methods.
│       
├── docs                           <- An mkdocs project.
│
├── models                         <- Trained and serialized models, model predictions,
│                                          or model summaries.
│       
├── notebooks                      <- Jupyter notebooks. Naming convention is a number 
│                                    (for ordering), the creator's initials, and a short `-` delimited │ description, e.g. `1.0-jqp-initial-data-exploration`.
│  
│
├── references                     <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                        <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures                    <- Generated graphics and figures to be used in reporting.
│
└── tests                          <- Tests.

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Contributing

If you want to contribute, please check the contributing.md for guidelines!