https://github.com/dreadnode/rigging
Lightweight LLM Interaction Framework
https://github.com/dreadnode/rigging
agents litellm llms pydantic
Last synced: about 2 months ago
JSON representation
Lightweight LLM Interaction Framework
- Host: GitHub
- URL: https://github.com/dreadnode/rigging
- Owner: dreadnode
- License: mit
- Created: 2024-01-29T20:15:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-05T20:03:31.000Z (about 2 months ago)
- Last Synced: 2025-05-06T18:49:06.202Z (about 2 months ago)
- Topics: agents, litellm, llms, pydantic
- Language: Python
- Homepage: https://docs.dreadnode.io/open-source/rigging
- Size: 3.84 MB
- Stars: 264
- Watchers: 15
- Forks: 18
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
Awesome Lists containing this project
- awesome-langchain - Rigging
README
Flexible LLM library for code and agents
Rigging is a lightweight LLM framework to make using language models in production code as simple and effective as possible. Here are the highlights:
- **Structured Pydantic models** can be used interchangably with unstructured text output.
- LiteLLM as the default generator giving you **instant access to a huge array of models**.
- Define prompts as python functions with **type hints and docstrings**.
- Simple **tool use**, even for models which don't support them at the API.
- Store different models and configs as **simple connection strings** just like databases.
- Integrated tracing support with [Logfire](https://logfire.pydantic.dev/docs/).
- Chat templating, forking, continuations, generation parameter overloads, stripping segments, etc.
- Async batching and fast iterations for **large scale generation**.
- Metadata, callbacks, and data format conversions.
- Modern python with type hints, async support, pydantic validation, serialization, etc.
```py
import rigging as rg
@rg.prompt(generator_id="gpt-4")
async def get_authors(count: int = 3) -> list[str]:
"""Provide famous authors."""
print(await get_authors())
# ['William Shakespeare', 'J.K. Rowling', 'Jane Austen']
```
Rigging is built by [**dreadnode**](https://dreadnode.io) where we use it daily.
## Installation
We publish every version to Pypi:
```bash
pip install rigging
```
If you want to build from source:
```bash
cd rigging/
poetry install
```
## Supported LLMs
Rigging will run just about any language model:
- Any model from [**LiteLLM**](https://litellm.vercel.app/docs/providers)
- Any model from [**vLLM**](https://docs.vllm.ai/en/latest/models/supported_models.html)
- Any model from [**transformers**](https://huggingface.co/docs/transformers/)
### API Keys
Pass the `api_key` in an generator id or use standard environment variables.
```py
rg.get_generator("gpt-4-turbo,api_key=...")
```
```bash
export OPENAI_API_KEY=...
export MISTRAL_API_KEY=...
export ANTHROPIC_API_KEY=...
...
```
Check out [the docs](https://docs.dreadnode.io/open-source/rigging/topics/generators#api-keys) for more.
## Getting Started
**Check out the guide [in the docs](https://docs.dreadnode.io/open-source/rigging/intro#getting-started)**
1. **Get a generator** using a connection string.
2. Build a **chat** or **completion** pipeline
3. **Run** the pipeline and get the output.
```py
import rigging as rg
import asyncio
async def main():
# 1 - Get a generator
generator = rg.get_generator("claude-3-sonnet-20240229")
# 2 - Build a chat pipeline
pipeline = generator.chat(
[
{"role": "system", "content": "Talk like a pirate."},
{"role": "user", "content": "Say hello!"},
]
)
# 3 - Run the pipeline
chat = await pipeline.run()
print(chat.conversation)
# Run the main function
asyncio.run(main())
# [system]: Talk like a pirate.
# [user]: Say hello!
# [assistant]: Ahoy, matey! Here be the salty sea dog ready to trade greetings wit' ye. Arrr!
```
Want more?
- Use [structured pydantic parsing](https://docs.dreadnode.io/open-source/rigging/topics/chats-and-messages#parsed-parts)
- Check out [raw completions](https://docs.dreadnode.io/open-source/rigging/topics/completions/)
- Give the LLM [access to tools](https://docs.dreadnode.io/open-source/rigging/topics/tools/)
- Track behavior with [tracing](https://docs.dreadnode.io/open-source/rigging/topics/tracing/)
- Play with [generation params](https://docs.dreadnode.io/open-source/rigging/topics/generators/#overload-generation-params)
- Use [callbacks in the pipeline](https://docs.dreadnode.io/open-source/rigging/topics/callbacks-and-mapping/)
- Scale up with [iterating and batching](https://docs.dreadnode.io/open-source/rigging/topics/iterating-and-batching/)
- Save your work with [serialization](https://docs.dreadnode.io/open-source/rigging/topics/serialization/)
## Examples
- Basic interactive chat: [**chat.py**](examples/chat.py)
- Jupyter code interpreter: [**jupyter.py**](examples/jupyter.py)
- OverTheWire Bandit Agent: [**bandit.py**](examples/bandit.py)
- Damn Vulnerable Restaurant Agent: [**dvra.py**](examples/dvra.py)
- RAG Pipeline: [**rag.py**](examples/rag.py) (from [kyleavery](https://github.com/kyleavery/))
- Integrating dreadnode-owned [robopages](https://github.com/dreadnode/robopages-cli) as a tool server (basic nmap scan example): [**rigging_example.py**](https://github.com/dreadnode/robopages-cli/blob/main/examples/rigging_example.py)
## Documentation
**[docs.dreadnode.io](https://docs.dreadnode.io/open-source/rigging/intro)** has everything you need.
## Star History
[](https://star-history.com/#dreadnode/rigging&Date)