NonGNU-devel ELPA - gptel

gptel is a simple Large Language Model chat client, with support for multiple models/backends.

gptel supports

- The services ChatGPT, Azure, Gemini, Anthropic AI, Anyscale, Together.ai,
  Perplexity, Anyscale, OpenRouter, Groq and Kagi (FastGPT & Summarizer)
- Local models via Ollama, Llama.cpp, Llamafiles or GPT4All

 Additionally, any LLM service (local or remote) that provides an
 OpenAI-compatible API is supported.

Features:
- It’s async and fast, streams responses.
- Interact with LLMs from anywhere in Emacs (any buffer, shell, minibuffer,
  wherever)
- LLM responses are in Markdown or Org markup.
- Supports conversations and multiple independent sessions.
- Save chats as regular Markdown/Org/Text files and resume them later.
- You can go back and edit your previous prompts or LLM responses when
  continuing a conversation.  These will be fed back to the model.

Requirements for ChatGPT, Azure, Gemini or Kagi:

- You need an appropriate API key.  Set the variable `gptel-api-key' to the
  key or to a function of no arguments that returns the key.  (It tries to
  use `auth-source' by default)

  ChatGPT is configured out of the box.  For the other sources:

- For Azure: define a gptel-backend with `gptel-make-azure', which see.
- For Gemini: define a gptel-backend with `gptel-make-gemini', which see.
- For Anthropic (Claude): define a gptel-backend with `gptel-make-anthropic',
  which see
- For Together.ai, Anyscale, Perplexity, Groq and OpenRouter: define a
  gptel-backend with `gptel-make-openai', which see.
- For Kagi: define a gptel-backend with `gptel-make-kagi', which see.

For local models using Ollama, Llama.cpp or GPT4All:

- The model has to be running on an accessible address (or localhost)
- Define a gptel-backend with `gptel-make-ollama' or `gptel-make-gpt4all',
  which see.
- Llama.cpp or Llamafiles: Define a gptel-backend with `gptel-make-openai',

Consult the package README for examples and more help with configuring
backends.

Usage:

gptel can be used in any buffer or in a dedicated chat buffer.  The
interaction model is simple: Type in a query and the response will be
inserted below.  You can continue the conversation by typing below the
response.

To use this in any buffer:

- Call `gptel-send' to send the text up to the cursor.  Select a region to
  send only the region.

- You can select previous prompts and responses to continue the conversation.

- Call `gptel-send' with a prefix argument to access a menu where you can set
  your backend, model and other parameters, or to redirect the
  prompt/response.

To use this in a dedicated buffer:

- M-x gptel: Start a chat session
- C-u M-x gptel: Start another session or multiple independent chat sessions

- In the chat session: Press `C-c RET' (`gptel-send') to send your prompt.
  Use a prefix argument (`C-u C-c RET') to access a menu.  In this menu you
  can set chat parameters like the system directives, active backend or
  model, or choose to redirect the input or output elsewhere (such as to the
  kill ring).

- You can save this buffer to a file.  When opening this file, turn on
  `gptel-mode' before editing it to restore the conversation state and
  continue chatting.

gptel in Org mode:

gptel offers a few extra conveniences in Org mode.
- You can limit the conversation context to an Org heading with
  `gptel-org-set-topic'.
  
- You can have branching conversations in Org mode, where each hierarchical
  outline path through the document is a separate conversation branch.
  See the variable `gptel-org-branching-context'.
  
- You can declare the gptel model, backend, temperature, system message and
  other parameters as Org properties with the command
  `gptel-org-set-properties'.  gptel queries under the corresponding heading
  will always use these settings, allowing you to create mostly reproducible
  LLM chat notebooks.

Finally, gptel offers a general purpose API for writing LLM ineractions
that suit your workflow, see `gptel-request'.