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@ -55,6 +55,8 @@ pip install "pyautogen[blendsearch]"
Find more options in [Installation](https://microsoft.github.io/autogen/docs/Installation).
<!-- Each of the [`notebook examples`](https://github.com/microsoft/autogen/tree/main/notebook) may require a specific option to be installed. -->
For LLM inference configurations, check the [FAQ](https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints).
## Quickstart
* Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human.
@ -70,6 +72,8 @@ user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stoc
The figure below shows an example conversation flow with AutoGen.
![Agent Chat Example](https://github.com/microsoft/autogen/blob/main/website/static/img/chat_example.png)
Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat) for this feature.
* Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
```python
# perform tuning
@ -86,6 +90,8 @@ config, analysis = autogen.Completion.tune(
response = autogen.Completion.create(context=test_instance, **config)
```
Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-Inference) for this feature.
## Documentation
You can find a detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).