llm-team 0.1.0
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Requires Python: >=3.10

# open-llm-swe

Note: This README is outdated - Much of the improvements is now focused on the team and conversation aspect.

This project explores various concepts to enhance the performance of Large Language Model (LLM) agents in producing code for non-trivial codebases. We have identified several key challenges that LLMs face when tasked with software development.

# Data Model

[memaid](docs/data_model.mermaid)

```mermaid
classDiagram
class LLMTool

    class LLMAgent {
        +List[LLMTool] tools
    }

    class LLMMessage {
        
    }

    class ConversationParticipant {
        <<enumeration>>
        Human
        System
        LLMAgent
    }

    class ConversationMessage {
        +ConversationParticipant sender
        +ConversationParticipant recipient
        +String content
    }

    class Conversation {
        +List[ConversationParticipant] participants
        +List[ConversationMessage] conversation_history
    }

    class Team {
        +List[LLMAgent] agents
        +LLMAgent teamManager
        +Workspace workspace
    }

    class Project {
        +List[Team] teams
        +String description
        +Workspace workspace
    }

    LLMAgent "0..*" --> "0..*" LLMTool : has
    ConversationMessage --> "2" ConversationParticipant : has
    Conversation "1" --> "2..*" ConversationParticipant : has
    Conversation "1" --> "0..*" ConversationMessage : has
    Team "1" --> "1..*" LLMAgent : has
    Team "1" --> "1" LLMAgent : has teamManager
    Project "1" --> "1..*" Team : has
    ConversationParticipant <|-- LLMAgent
    ConversationParticipant <|-- Human
    ConversationParticipant <|-- System

```


## Identified Challenges

### 1. Limitations in Handling Large Codebases

LLMs are constrained by their context window, which limits the amount of information they can process at once. This presents significant challenges when dealing with large, complex codebases:

- **Limited context window**: LLMs can only reason within a finite amount of input text, making it difficult to understand extensive codebases in their entirety.
- **Computational and financial costs**: Even if we could feed an entire codebase into the context, it would incur substantial computational and financial expenses.
- **Inefficient information retrieval**: Basic retrieval-augmented generation (RAG) might not be sufficient for accurately finding and utilizing relevant code snippets. Using embeddings to retrieve fixed-size chunks might be too simplistic due to the limitations in embedding matching accuracy.

To mitigate these issues, we need to develop tools that allow LLMs to efficiently navigate large codebases, similar to how human developers use abstractions to work within their limited working memory. For instance:

- Providing only the required information to the LLM based on the task at hand (e.g., directory structure for deciding where to place a utility subroutine, or specific code sections for debugging).
- Creating sophisticated retrieval mechanisms that can accurately locate and present relevant code snippets to the LLM.

### 2. Inherent Limitations of LLM Behavior

Several aspects of LLM behavior can impede their effectiveness in software development tasks:

1. **Overreliance on initial outputs**: LLMs tend to provide quick initial responses, which may not always be the most accurate or well-thought-out solutions.

2. **Reluctance to provide negative or empty answers**: LLMs might struggle to admit when they don't have enough information or when no action is necessary.

3. **Difficulty in maintaining long-term context**: LLMs may struggle to keep track of information across multiple interactions or code sections, leading to inconsistencies in larger projects.

4. **Lack of meta-cognitive abilities**: LLMs might have difficulty assessing their own understanding and capabilities in relation to the specific codebase they're working on.

5. **Uncertainty quantification**: LLMs may struggle to accurately express their level of certainty about their understanding or proposals, potentially leading to overconfident or unreliable outputs.


## Project Overview

Our approach divides the code generation process into two main phases:

1. Requirements Gathering
2. Code Generation

We incorporate several advanced concepts to improve the overall performance and reliability of the system.

### Requirements Gathering Process

<!-- ![Requirements Gathering Process](docs/code-generation-process.mermaid) -->

```mermaid
graph TD
A[User Input] --> B[PM Agent: Requirements Gathering]
B -->|Clarification Needed| C[User Feedback]
C --> B
B -->|Requirements Clear| D{Approach Selection}
D -->|TDD Approach| E[QA Agent: Test Case Generation]
D -->|Prototype Approach| F[Prototype Generator]
E & F --> G[User Review]
G -->|Approved| H[Code Generator Agent]
G -->|Revisions Needed| B
H --> I[Code Reviewer Agent]
I --> J[User Final Review]
J -->|Approved| K[Integration with Codebase]
J -->|Revisions Needed| H
```

The requirements gathering phase involves:

- PM Agent for clarifying and refining user requirements
- Approach selection between Test-Driven Development (TDD) and Prototype-First development
- User review and feedback loops

### Advanced Code Generation System

<!-- ![Advanced Code Generation System](docs/advanced-code-generation-system.mermaid) -->

```mermaid
graph TD
A[Code Generation Request] --> B[Orchestrator Agent]
B --> C[Context Retrieval System]
C --> D[Hierarchical Retriever]
C --> E[Adaptive Retriever]
D & E --> F[Context Synthesizer]
F --> G[Exploration Agent]
G --> H{Confidence Threshold Met?}
H -->|No| I[Additional Exploration]
I --> G
H -->|Yes| J[Code Generation Agent]
J --> K[Uncertainty Quantifier]
K --> L[Proposal Evaluator]
L -->|Meets Criteria| M[Code Integration Agent]
L -->|Doesn't Meet Criteria| N[Refinement Loop]
N --> J
M --> O[Final Output]

    subgraph Modular Specialized Agents
    P[Syntax Specialist]
    Q[Logic Flow Specialist]
    R[Optimization Specialist]
    S[Security Specialist]
    end

    J -.-> P & Q & R & S

    subgraph External Resources
    T[Project Knowledge Base]
    U[Coding Standards]
    V[External APIs/Libraries]
    end

    T & U & V -.-> B & C & G & J & L
```

The code generation phase incorporates several concepts:

1. **Hierarchical Retrieval**: Efficiently manages large codebases by fetching context at different levels of abstraction.

2. **Iterative Exploration**: Ensures thorough understanding of the codebase before code generation begins.

3. **Confidence Threshold**: Prevents premature code generation by ensuring sufficient understanding of the task and context.

4. **Explicit Uncertainty Quantification**: Helps identify areas that may need human attention or further refinement.

5. **Unknown State Handling**: Improves the system's ability to acknowledge and work with incomplete information.

6. **Modular Architecture**: Utilizes specialized agents for different aspects of code generation, such as syntax, logic flow, optimization, and security.

7. **Adaptive Retrieval**: Improves relevance of retrieved information over time through learning.

8. **Context Synthesis**: Combines and summarizes retrieved information for efficient use by other components.

9. **Proposal Evaluation**: Assesses generated code against project requirements and standards.

10. **Refinement Loop**: Allows for iterative improvement of generated code based on evaluation results.

## Getting Started

The project uses [poetry](https://python-poetry.org/) as the python package manager and builder. It uses NPM as the package manager for JS.

Only Anthropic is supported as the backing LLM API for now, although support for the other LLM APIs can be added relatively easily if there is demand for it.


### LLM Sonder

src is the python API behind the agent, conversation and team creation. 

Installing packages:

```bash
poetry install
```

Creating a basic agent to play around with

```bash
poetry run python -m src.agents.basic_agent
```

Creating an LLM team

```bash
poetry run python -m src.agents.team_creator_agent
```

### Agent Dashboard

agent_dashboard currently houses the Web UI for the project.

This is comprised of the following

1. AgentTrace - The monitoring dashboard for generated messages 
2. Conversations - Creating conversations and messages using the web UI.

Installing packages:

```bash
cd agent_dashboard/frontend
npm install
```

Running the backend:

```bash
poetry run python -m agent_dashboard.backend.server
```

Running the frontend:

```bash
cd agent_dashboard/frontend
npm run dev
```




## Contributing

### Conventions

1. All serializable objects expose `to_dict` and `from_dict` methods
2. The serialized object contain a `class_name` key to identify the original object


## License

(Include license information here)

## Contact

Feel free to contact me at zhuweiji1997@gmail.com or raise an issue on the github repo!