About

fish-ai adds AI functionality to Fish. It's awesome! I built it to make my life easier, and I hope it will make yours easier too. Here is the complete sales pitch:

• It can turn a comment into a shell command and vice versa, which means less time spent reading manpages, googling and copy-pasting from Stack Overflow. Great when working with git, kubectl, curl and other tools with loads of parameters and switches.
• Did you make a typo? It can also fix a broken command (similarly to thefuck).
• Not sure what to type next or just lazy? Let the LLM autocomplete your commands with a built in fuzzy finder.
• Everything is done using two (configurable) keyboard shortcuts, no mouse needed!
• It can be hooked up to the LLM of your choice (even a self-hosted one!).
• The whole thing is open source, hopefully somewhat easy to read and around 2000 lines of code, which means that you can audit the code yourself in an afternoon.
• Install and update with ease using fisher.
• Tested on both macOS and the most common Linux distributions.
• Does not interfere with fzf.fish, tide or any of the other plugins you're already using!
• Does not wrap your shell, install telemetry or force you to switch to a proprietary terminal emulator.

This plugin was originally based on Tom Dörr's fish.codex repository. Without Tom, this repository would not exist!

If you like it, please add a ⭐.

Bug fixes are welcome! I consider this project largely feature complete. Before opening a PR for a feature request, consider opening an issue where you explain what you want to add and why, and we can talk about it first.

🎥 Demo

👨‍🔧 How to install

Install fish-ai

Make sure git and either uv, or a supported version of Python along with pip and venv is installed. Then grab the plugin using fisher:

fisher install realiserad/fish-ai

Create a configuration

Create a configuration file $XDG_CONFIG_HOME/fish-ai.ini (use ~/.config/fish-ai.ini if $XDG_CONFIG_HOME is not set) where you specify which LLM fish-ai should talk to. If you're not sure, use GitHub Models.

Anthropic

To use Anthropic:

[anthropic]
provider = anthropic
api_key = <your API key>
model = claude-sonnet-4-6

Azure OpenAI

To use Azure OpenAI:

[fish-ai]
configuration = azure

[azure]
provider = azure
server = https://<your instance>.openai.azure.com
model = <your deployment name>
api_key = <your API key>

Bedrock

AWS Bedrock provides LLMs hosted by AWS. They can be accessed either through the Mantle gateway or the Converse API.

If no api_key is configured, a short-term token is automatically generated from your AWS credentials. You can also specify an api_key directly if you prefer to use a Bedrock API key.

Use aws_profile to select a named profile from your AWS configuration. If omitted, the default credential chain is used.

Available model IDs are listed in the Bedrock documentation.

Converse API

To use the Converse API:

[fish-ai]
configuration = aws-converse

[aws-converse]
provider = bedrock
bedrock_api = converse
model = anthropic.claude-haiku-4-5-20251001-v1:0
aws_region = us-east-1
aws_profile = default

It requires the bedrock:InvokeModel permission.

Mantle gateway

To use the Mantle gateway:

[fish-ai]
configuration = aws-mantle

[aws-mantle]
provider = bedrock
model = anthropic.claude-haiku-4-5
aws_region = us-east-1
aws_profile = default

It requires the bedrock-mantle:CreateInference permission.

Cohere

To use Cohere:

[cohere]
provider = cohere
api_key = <your API key>
model = command-a-03-2025

DeepSeek

To use DeepSeek:

[deepseek]
provider = deepseek
api_key = <your API key>
model = deepseek-chat

GitHub Models

To use GitHub Models:

[fish-ai]
configuration = github

[github]
provider = self-hosted
server = https://models.github.ai/inference
api_key = <paste GitHub PAT here>
model = gpt-4o-mini

You can create a personal access token (PAT) here. The PAT does not require any permissions.

Google

To use Gemini from Google:

[google]
provider = google
api_key = <your API key>
model = gemini-3.1-pro-preview

Groq

To use Groq:

[groq]
provider = groq
api_key = <your API key>

OpenAI

To use OpenAI:

[fish-ai]
configuration = openai

[openai]
provider = openai
model = gpt-4o
api_key = <your API key>
organization = <your organization>

OpenRouter

To use OpenRouter:

[fish-ai]
configuration = openrouter

[openrouter]
provider = self-hosted
server = https://openrouter.ai/api/v1
model = google/gemini-3-flash-preview
api_key = <your API key>
extra_body = {"reasoning": {"effort": "minimal", "exclude": true}}

Self-hosted

To use a self-hosted LLM (behind an OpenAI-compatible API):

[fish-ai]
configuration = self-hosted

[self-hosted]
provider = self-hosted
server = https://<your server>:<port>/v1
model = <your model>
api_key = <your API key>

If you are self-hosting, my recommendation is to use Ollama with Llama 3.3 70B. An out of the box configuration running on localhost could then look something like this:

[fish-ai]
configuration = local-llama

[local-llama]
provider = self-hosted
model = llama3.3
server = http://localhost:11434/v1

Available models are listed here.

Put the API key on your keyring

Instead of putting the API key in the configuration file, you can let fish-ai load it from your keyring. To save a new API key or transfer an existing API key to your keyring, run fish_ai_put_api_key.

🙉 How to use

Transform comments into commands and vice versa

Type a comment (anything starting with #), and press Ctrl + P to turn it into shell command! Note that if your comment is very brief or vague, the LLM may decide to improve the comment instead of providing a shell command. You then need to press Ctrl + P again.

You can also run it in reverse. Type a command and press Ctrl + P to turn it into a comment explaining what the command does.

Autocomplete commands

Begin typing your command or comment and press Ctrl + Space to display a list of completions in fzf (it is bundled with the plugin, no need to install it separately).

To refine the results, type some instructions and press Ctrl + P inside fzf.

Suggest fixes

If a command fails, you can immediately press Ctrl + Space at the command prompt to let fish-ai suggest a fix!

🤸 Additional options

You can tweak the behaviour of fish-ai by putting additional options in your fish-ai.ini configuration file.

Change the default key bindings

By default, fish-ai binds to Ctrl + P and Ctrl + Space. You may want to change this if there is interference with any existing key bindings on your system.

To change the key bindings, set keymap_1 (defaults to Ctrl + P) and keymap_2 (defaults to Ctrl + Space) to the key binding escape sequence of the key binding you want to use.

To get the correct key binding escape sequence, use fish_key_reader.

For example, if you have the following output from fish_key_reader:

$ fish_key_reader
Press a key:
bind ctrl-p 'do something'
$ fish_key_reader
Press a key:
bind ctrl-space 'do something'

Then put the following in your configuration file:

[fish-ai]
keymap_1 = 'ctrl-p'
keymap_2 = 'ctrl-space'

Restart the shell for the changes to take effect.

Explain in a different language

To explain shell commands in a different language, set the language option to the name of the language. For example:

[fish-ai]
language = Swedish

This will only work well if the LLM you are using has been trained on a dataset with the chosen language.

Number of completions

To change the number of completions suggested by the LLM when pressing Ctrl + Space, set the completions option. The default value is 5.

Here is an example of how you can increase the number of completions to 10:

[fish-ai]
completions = 10

To change the number of refined completions suggested by the LLM when pressing Ctrl + P in fzf, set the refined_completions option. The default value is 3.

[fish-ai]
refined_completions = 5

Personalise completions using commandline history

You can personalise completions suggested by the LLM by sending an excerpt of your commandline history.

To enable it, specify the maximum number of commands from the history to send to the LLM using the history_size option. The default value is 0 (do not send any commandline history).

[fish-ai]
history_size = 5

If you enable this option, consider the use of sponge to automatically remove broken commands from your commandline history.

Preview pipes

To send the output of a pipe to the LLM when completing a command, use the preview_pipe option.

[fish-ai]
preview_pipe = True

This will send the output of the longest consecutive pipe after the last unterminated parenthesis before the cursor. For example, if you autocomplete az vm list | jq, the output from az vm list will be sent to the LLM.

This behaviour is disabled by default, as it may slow down the completion process and lead to commands being executed twice.

Configure the progress indicator

You can change the progress indicator (the default is ⏳) shown when the plugin is waiting for a response from the LLM.

To change the default, set the progress_indicator option to zero or more characters.

[fish-ai]
progress_indicator = wait...

Use custom headers

You can send custom HTTP headers using the headers option. Specify one or more headers using comma-separated Key: Value pairs. For example:

[fish-ai]
headers = Header-1: value1, Header-2: value2

🎭 Switch between contexts

You can switch between different sections in the configuration using the fish_ai_switch_context command.

🐾 Data privacy

When using the plugin, fish-ai submits the name of your OS and the commandline buffer to the LLM.

When you codify or complete a command, it also sends the contents of any files you mention (as long as the file is readable), and when you explain or complete a command, the output from <command> --help is provided to the LLM for reference.

fish-ai can also send an excerpt of your commandline history when completing a command. This is disabled by default.

Finally, to fix the previous command, the previous commandline buffer, along with any terminal output and the corresponding exit code is sent to the LLM.

If you are concerned with data privacy, you should use a self-hosted LLM. When hosted locally, no data ever leaves your machine.

Redaction of sensitive information

The plugin attempts to redact sensitive information from the prompt before submitting it to the LLM. Sensitive information is replaced by the <REDACTED> placeholder.

The following information is redacted:

• Passwords and API keys supplied as commandline arguments
• PEM-encoded private keys stored in files
• Bearer tokens, provided to e.g. cURL

If you trust the LLM provider (e.g. because you are hosting locally) you can disable redaction using the redact = False option.