Metadata-Version: 2.4
Name: lllm-core
Version: 0.0.1.2
Summary: Low-Level Language Model framework for building LLM agentic systems.
Author: Junyan Cheng
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<div align="center">
  <img src="https://raw.githubusercontent.com/ChengJunyan1/lllm/main/assets/LLLM-logo.png" alt="LLLM Logo" width="600"/>
  <br>
  <br>
  <h1>Low-Level Language Model (LLLM) </h1>
</div>

LLLM is a lightweight framework designed to facilitate the rapid prototyping of advanced agentic systems. 
Prioritizing minimalism, modularity, and type safety, it is specifically optimized for research in program synthesis and neuro-symbolic AI. 
While these fields require deep architectural customization, researchers often face the burden of managing low-level complexities such as exception handling, output parsing, and API error management. 
LLLM bridges this gap by offering necessary abstractions that balance high-level encapsulation with the simplicity required for flexible experimentation.
It also tries to make the code plain, compact, easy-to-understand, with less unnecessary indirection, thus easy for customization for different projects' needs, to allow researchers to focus on the core research questions.



## Features

- **Modular Architecture**: Core abstractions, providers, tools, and memory are decoupled.
- **Type Safety**: Built on Pydantic for robust data validation and strict typing.
- **Provider Interface**: First-class OpenAI support with an extensible interface for adding more providers as needed.
- **Neuro-Symbolic Design**: Advanced prompt management with structured output, exception handling, and interrupt logic.
- **Jupyter Sandbox**: Secure code execution environment for program synthesis.

## Installation

```bash
pip install lllm-core
```

## Quick Start

### Basic Chat

```python
from lllm import AgentBase, Prompt, register_prompt

# Define a prompt
simple_prompt = Prompt(
    path="simple_chat",
    prompt="You are a helpful assistant. User says: {user_input}"
)
register_prompt(simple_prompt)

# Define an Agent
class SimpleAgent(AgentBase):
    agent_type = "simple"
    agent_group = ["assistant"]
    
    def call(self, task: str, **kwargs):
        dialog = self.agents["assistant"].init_dialog({"user_input": task})
        response, dialog, _ = self.agents["assistant"].call(dialog)
        return response.content

# Configure and Run
config = {
    "name": "simple_chat_agent",
    "log_dir": "./logs",
    "log_type": "localfile",
    "provider": "openai",           # or any provider registered via lllm.providers
    "auto_discover": True,          # set False to skip automatic prompt/proxy discovery
    "agent_configs": {
        "assistant": {
            "model_name": "gpt-4o-mini",
            "system_prompt_path": "simple_chat",
            "temperature": 0.7,
        }
    }
}

agent = SimpleAgent(config, ckpt_dir="./ckpt")
print(agent("Hello!"))
```

`provider` selects a registered backend (default `openai`), while `auto_discover` controls whether LLLM scans the paths listed in `lllm.toml` for prompts and proxies each time you spin up an agent or proxy.

## Documentation

See https://lllm.one/ for detailed documentation.

## Examples

Check `examples/` for more usage scenarios:
- `examples/basic_chat.py`
- `examples/tool_use.py`
- `examples/proxy_catalog.py`
- `examples/jupyter_sandbox_smoke.py`

### Proxies & Tools

Built-in proxies (financial data, search, etc.) register automatically when their modules are imported. If you plan to call `Proxy()` directly, either:

1. Set up an `lllm.toml` with a `[proxies]` section so discovery imports your proxy folders on startup, or
2. Call `load_builtin_proxies()` to import the packaged modules, or manually import the proxies you care about (e.g., `from lllm.proxies.builtin import exa_proxy`).

This mirrors how prompts are auto-registered via `[prompts]` in `lllm.toml`.

Once proxies are loaded you can check what is available by calling `Proxy().available()`.

### Auto-Discovery Config

A starter `lllm.toml.example` lives in the repo root. Copy it next to your project entry point and edit the folder paths:

```bash
cp lllm.toml.example lllm.toml
```

The sample configuration points to `examples/autodiscovery/prompts/` and `examples/autodiscovery/proxies/`, giving you a working prompt (`examples/hello_world`) and proxy (`examples/sample`) to experiment with immediately.

## Testing & Offline Mocks

- Run the full suite (for framework developers): `pytest`.
- For an end-to-end agent/tool flow without real OpenAI requests, see `tests/integration/test_tool_use_mock_openai.py`. It uses the scripted client defined in `tests/helpers/mock_openai.py`, mirroring what a VCR fixture would capture.
- Want template smoke tests? `tests/integration/test_cli_template.py` runs `python -m lllm.cli create --name demo --template init_template` inside a temp directory.
- When you want parity with real OpenAI traffic, capture responses into JSON (see `tests/integration/recordings/sample_tool_call.json`) and point `load_recorded_completions` at your file. `tests/integration/test_tool_use_recording.py` shows how to replay those recordings without network access.
- Optional future work: keep capturing real-provider recordings as APIs evolve, and consider running `examples/jupyter_sandbox_smoke.py` in CI to validate notebook tooling automatically.

## Testing

Run tests with pytest:

```bash
pytest tests/
```

## Experimental Features

- **Computer Use Agent (CUA)** – `lllm.tools.cua` offers browser automation via Playwright and the OpenAI Computer Use API. It is still evolving and may change without notice.
- **Responses API Routing** – opt into OpenAI’s Responses API by setting `api_type = "response"` per agent. This enables native web search/computer-use tools but currently targets OpenAI only.

## Work in Progress

- **Additional Providers** – Anthropic, Gemini, and other backends are planned but not yet implemented in `lllm.providers`.
- **Streaming Hooks** – provider-agnostic streaming and incremental parsing are on the roadmap.
- **Discovery UX** – improving the auto-discovery loop (reloading prompts/proxies without restarting) is tracked for an upcoming release.
