Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/OpenInterpreter/open-interpreter

A natural language interface for computers
https://github.com/OpenInterpreter/open-interpreter

chatgpt gpt-4 interpreter javascript nodejs python

Last synced: 24 days ago
JSON representation

A natural language interface for computers

Awesome Lists containing this project

README 

        
● Open Interpreter





    

        Discord

    JA doc

    ZH doc

     ES doc

    UK doc

    IN doc

    License

    


    
Get early access to the desktop app‎ ‎ |‎ ‎ Documentation









local_explorer











```shell

pip install open-interpreter

```



> Not working? Read our [setup guide](https://docs.openinterpreter.com/getting-started/setup).



```shell

interpreter

```







**Open Interpreter** lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running `$ interpreter` after installing.



This provides a natural-language interface to your computer's general-purpose capabilities:



- Create and edit photos, videos, PDFs, etc.

- Control a Chrome browser to perform research

- Plot, clean, and analyze large datasets

- ...etc.



**⚠️ Note: You'll be asked to approve code before it's run.**







## Demo



https://github.com/OpenInterpreter/open-interpreter/assets/63927363/37152071-680d-4423-9af3-64836a6f7b60



#### An interactive demo is also available on Google Colab:



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WKmRXZgsErej2xUriKzxrEAXdxMSgWbb?usp=sharing)



#### Along with an example voice interface, inspired by _Her_:



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NojYGHDgxH6Y1G1oxThEBBb2AtyODBIK)



## Quick Start



```shell

pip install open-interpreter

```



### Terminal



After installation, simply run `interpreter`:



```shell

interpreter

```



### Python



```python

from interpreter import interpreter



interpreter.chat("Plot AAPL and META's normalized stock prices") # Executes a single command

interpreter.chat() # Starts an interactive chat

```



### GitHub Codespaces



Press the `,` key on this repository's GitHub page to create a codespace. After a moment, you'll receive a cloud virtual machine environment pre-installed with open-interpreter. You can then start interacting with it directly and freely confirm its execution of system commands without worrying about damaging the system.



## Comparison to ChatGPT's Code Interpreter



OpenAI's release of [Code Interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter) with GPT-4 presents a fantastic opportunity to accomplish real-world tasks with ChatGPT.



However, OpenAI's service is hosted, closed-source, and heavily restricted:



- No internet access.

- [Limited set of pre-installed packages](https://wfhbrian.com/mastering-chatgpts-code-interpreter-list-of-python-packages/).

- 100 MB maximum upload, 120.0 second runtime limit.

- State is cleared (along with any generated files or links) when the environment dies.



---



Open Interpreter overcomes these limitations by running in your local environment. It has full access to the internet, isn't restricted by time or file size, and can utilize any package or library.



This combines the power of GPT-4's Code Interpreter with the flexibility of your local development environment.



## Commands



**Update:** The Generator Update (0.1.5) introduced streaming:



```python

message = "What operating system are we on?"



for chunk in interpreter.chat(message, display=False, stream=True):

  print(chunk)

```



### Interactive Chat



To start an interactive chat in your terminal, either run `interpreter` from the command line:



```shell

interpreter

```



Or `interpreter.chat()` from a .py file:



```python

interpreter.chat()

```



**You can also stream each chunk:**



```python

message = "What operating system are we on?"



for chunk in interpreter.chat(message, display=False, stream=True):

  print(chunk)

```



### Programmatic Chat



For more precise control, you can pass messages directly to `.chat(message)`:



```python

interpreter.chat("Add subtitles to all videos in /videos.")



# ... Streams output to your terminal, completes task ...



interpreter.chat("These look great but can you make the subtitles bigger?")



# ...

```



### Start a New Chat



In Python, Open Interpreter remembers conversation history. If you want to start fresh, you can reset it:



```python

interpreter.messages = []

```



### Save and Restore Chats



`interpreter.chat()` returns a List of messages, which can be used to resume a conversation with `interpreter.messages = messages`:



```python

messages = interpreter.chat("My name is Killian.") # Save messages to 'messages'

interpreter.messages = [] # Reset interpreter ("Killian" will be forgotten)



interpreter.messages = messages # Resume chat from 'messages' ("Killian" will be remembered)

```



### Customize System Message



You can inspect and configure Open Interpreter's system message to extend its functionality, modify permissions, or give it more context.



```python

interpreter.system_message += """

Run shell commands with -y so the user doesn't have to confirm them.

"""

print(interpreter.system_message)

```



### Change your Language Model



Open Interpreter uses [LiteLLM](https://docs.litellm.ai/docs/providers/) to connect to hosted language models.



You can change the model by setting the model parameter:



```shell

interpreter --model gpt-3.5-turbo

interpreter --model claude-2

interpreter --model command-nightly

```



In Python, set the model on the object:



```python

interpreter.llm.model = "gpt-3.5-turbo"

```



[Find the appropriate "model" string for your language model here.](https://docs.litellm.ai/docs/providers/)



### Running Open Interpreter locally



#### Terminal



Open Interpreter can use OpenAI-compatible server to run models locally. (LM Studio, jan.ai, ollama etc)



Simply run `interpreter` with the api_base URL of your inference server (for LM studio it is `http://localhost:1234/v1` by default):



```shell

interpreter --api_base "http://localhost:1234/v1" --api_key "fake_key"

```



Alternatively you can use Llamafile without installing any third party software just by running



```shell

interpreter --local

```



for a more detailed guide check out [this video by Mike Bird](https://www.youtube.com/watch?v=CEs51hGWuGU?si=cN7f6QhfT4edfG5H)



**How to run LM Studio in the background.**



1. Download [https://lmstudio.ai/](https://lmstudio.ai/) then start it.

2. Select a model then click **↓ Download**.

3. Click the **↔️** button on the left (below 💬).

4. Select your model at the top, then click **Start Server**.



Once the server is running, you can begin your conversation with Open Interpreter.



> **Note:** Local mode sets your `context_window` to 3000, and your `max_tokens` to 1000. If your model has different requirements, set these parameters manually (see below).



#### Python



Our Python package gives you more control over each setting. To replicate and connect to LM Studio, use these settings:



```python

from interpreter import interpreter



interpreter.offline = True # Disables online features like Open Procedures

interpreter.llm.model = "openai/x" # Tells OI to send messages in OpenAI's format

interpreter.llm.api_key = "fake_key" # LiteLLM, which we use to talk to LM Studio, requires this

interpreter.llm.api_base = "http://localhost:1234/v1" # Point this at any OpenAI compatible server



interpreter.chat()

```



#### Context Window, Max Tokens



You can modify the `max_tokens` and `context_window` (in tokens) of locally running models.



For local mode, smaller context windows will use less RAM, so we recommend trying a much shorter window (~1000) if it's failing / if it's slow. Make sure `max_tokens` is less than `context_window`.



```shell

interpreter --local --max_tokens 1000 --context_window 3000

```



### Verbose mode



To help you inspect Open Interpreter we have a `--verbose` mode for debugging.



You can activate verbose mode by using its flag (`interpreter --verbose`), or mid-chat:



```shell

$ interpreter

...

> %verbose true <- Turns on verbose mode



> %verbose false <- Turns off verbose mode

```



### Interactive Mode Commands



In the interactive mode, you can use the below commands to enhance your experience. Here's a list of available commands:



**Available Commands:**



- `%verbose [true/false]`: Toggle verbose mode. Without arguments or with `true` it

  enters verbose mode. With `false` it exits verbose mode.

- `%reset`: Resets the current session's conversation.

- `%undo`: Removes the previous user message and the AI's response from the message history.

- `%tokens [prompt]`: (_Experimental_) Calculate the tokens that will be sent with the next prompt as context and estimate their cost. Optionally calculate the tokens and estimated cost of a `prompt` if one is provided. Relies on [LiteLLM's `cost_per_token()` method](https://docs.litellm.ai/docs/completion/token_usage#2-cost_per_token) for estimated costs.

- `%help`: Show the help message.



### Configuration / Profiles



Open Interpreter allows you to set default behaviors using `yaml` files.



This provides a flexible way to configure the interpreter without changing command-line arguments every time.



Run the following command to open the profiles directory:



```

interpreter --profiles

```



You can add `yaml` files there. The default profile is named `default.yaml`.



#### Multiple Profiles



Open Interpreter supports multiple `yaml` files, allowing you to easily switch between configurations:



```

interpreter --profile my_profile.yaml

```



## Sample FastAPI Server



The generator update enables Open Interpreter to be controlled via HTTP REST endpoints:



```python

# server.py



from fastapi import FastAPI

from fastapi.responses import StreamingResponse

from interpreter import interpreter



app = FastAPI()



@app.get("/chat")

def chat_endpoint(message: str):

    def event_stream():

        for result in interpreter.chat(message, stream=True):

            yield f"data: {result}\n\n"



    return StreamingResponse(event_stream(), media_type="text/event-stream")



@app.get("/history")

def history_endpoint():

    return interpreter.messages

```



```shell

pip install fastapi uvicorn

uvicorn server:app --reload

```



You can also start a server identical to the one above by simply running `interpreter.server()`.



## Android



The step-by-step guide for installing Open Interpreter on your Android device can be found in the [open-interpreter-termux repo](https://github.com/MikeBirdTech/open-interpreter-termux).



## Safety Notice



Since generated code is executed in your local environment, it can interact with your files and system settings, potentially leading to unexpected outcomes like data loss or security risks.



**⚠️ Open Interpreter will ask for user confirmation before executing code.**



You can run `interpreter -y` or set `interpreter.auto_run = True` to bypass this confirmation, in which case:



- Be cautious when requesting commands that modify files or system settings.

- Watch Open Interpreter like a self-driving car, and be prepared to end the process by closing your terminal.

- Consider running Open Interpreter in a restricted environment like Google Colab or Replit. These environments are more isolated, reducing the risks of executing arbitrary code.



There is **experimental** support for a [safe mode](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/SAFE_MODE.md) to help mitigate some risks.



## How Does it Work?



Open Interpreter equips a [function-calling language model](https://platform.openai.com/docs/guides/gpt/function-calling) with an `exec()` function, which accepts a `language` (like "Python" or "JavaScript") and `code` to run.



We then stream the model's messages, code, and your system's outputs to the terminal as Markdown.



# Access Documentation Offline



The full [documentation](https://docs.openinterpreter.com/) is accessible on-the-go without the need for an internet connection.



[Node](https://nodejs.org/en) is a pre-requisite:



- Version 18.17.0 or any later 18.x.x version.

- Version 20.3.0 or any later 20.x.x version.

- Any version starting from 21.0.0 onwards, with no upper limit specified.



Install [Mintlify](https://mintlify.com/):



```bash

npm i -g mintlify@latest

```



Change into the docs directory and run the appropriate command:



```bash

# Assuming you're at the project's root directory

cd ./docs



# Run the documentation server

mintlify dev

```



A new browser window should open. The documentation will be available at [http://localhost:3000](http://localhost:3000) as long as the documentation server is running.



# Contributing



Thank you for your interest in contributing! We welcome involvement from the community.



Please see our [contributing guidelines](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/CONTRIBUTING.md) for more details on how to get involved.



# Roadmap



Visit [our roadmap](https://github.com/OpenInterpreter/open-interpreter/blob/main/docs/ROADMAP.md) to preview the future of Open Interpreter.



**Note**: This software is not affiliated with OpenAI.



![thumbnail-ncu](https://github.com/OpenInterpreter/open-interpreter/assets/63927363/1b19a5db-b486-41fd-a7a1-fe2028031686)



> Having access to a junior programmer working at the speed of your fingertips ... can make new workflows effortless and efficient, as well as open the benefits of programming to new audiences.

>

> — _OpenAI's Code Interpreter Release_