parrot.nvim 🦜

This is parrot.nvim, the ultimate stochastic parrot to support your text editing inside Neovim.

Features • Demo • Getting Started • Commands • Configuration • Roadmap • FAQ

[!NOTE]
For best stability, I recommend using version 1.8.0 or earlier. Future releases may include new features and enhancements that could affect your experience.

Features

parrot.nvim offers a seamless out-of-the-box experience, providing tight integration of current LLM APIs into your Neovim workflows, with a focus solely on text generation. The selected core features include on-demand text completion and editing, as well as chat-like sessions within native Neovim buffers. While this project is still under development, a substantial part of the code is based on an early fork of the brilliant work by Tibor Schmidt's gp.nvim.

• Persistent conversations stored as markdown files within Neovim's standard path or a user-defined location
• Custom hooks for inline text editing based on user instructions and chats with predefined system prompts
• Unified provider system supporting any OpenAI-compatible API:
  • OpenAI API
  • Anthropic API
  • Google Gemini API
  • xAI API
  • Local and offline serving via ollama
  • Any custom OpenAI-compatible endpoint with configurable functions; also supports Perplexity.ai API, Mistral API, Groq API, DeepSeek API, GitHub Models, and NVIDIA API
• Flexible API credential management from various sources:
  • Environment variables
  • Bash commands
  • Password manager CLIs (lazy evaluation)
• Repository-specific instructions via .parrot.md file using the PrtContext command
• No autocompletion and no hidden requests in the background to analyze your files

Demo

Seamlessly switch between providers and models.

switch_provider_model.mp4

---

Trigger code completions based on comments.

implement_hook.mp4

---

Let the parrot fix your bugs.

fix_bugs_hook.mp4

---

Rewrite a visual selection with `PrtRewrite`.

rewrite.mp4

---

Append code with the visual selection as context with `PrtAppend`.

append.mp4

---

Add comments to a function with `PrtPrepend`.

prepend.mp4

---

Retry your latest rewrite, append or prepend with `PrtRetry`.

retry.mp4

Getting Started

Dependencies

This plugin requires the latest version of Neovim and relies on a carefully selected set of established plugins.

• neovim 0.10+
• plenary
• ripgrep (optional)
• fzf (optional, requires ripgrep)
• telescope (optional)

Installation

lazy.nvim

{
  "frankroeder/parrot.nvim",
  dependencies = { "ibhagwan/fzf-lua", "nvim-lua/plenary.nvim" },
  opts = {}
}

Packer

require("packer").startup(function()
  use({
    "frankroeder/parrot.nvim",
    requires = { 'ibhagwan/fzf-lua', 'nvim-lua/plenary.nvim'},
    config = function()
      require("parrot").setup()
    end,
  })
end)

Neovim native package

git clone --depth=1 https://github.com/frankroeder/parrot.nvim.git \
  "${XDG_DATA_HOME:-$HOME/.local/share}"/nvim/site/pack/parrot/start/parrot.nvim

Setup

The minimal requirement is to at least set up one provider, such as the one provided below or one from the provider configuration examples.

{
  "frankroeder/parrot.nvim",
  dependencies = { 'ibhagwan/fzf-lua', 'nvim-lua/plenary.nvim' },
  -- optionally include "folke/noice.nvim" or "rcarriga/nvim-notify" for beautiful notifications
  config = function()
    require("parrot").setup {
      -- Providers must be explicitly set up to make them available.
      providers = {
        openai = {
          name = "openai",
          api_key = os.getenv "OPENAI_API_KEY",
          endpoint = "https://api.openai.com/v1/chat/completions",
          params = {
            chat = { temperature = 1.1, top_p = 1 },
            command = { temperature = 1.1, top_p = 1 },
          },
          topic = {
            model = "gpt-4.1-nano",
            params = { max_completion_tokens = 64 },
          },
          models ={
            "gpt-4o",
            "o4-mini",
            "gpt-4.1-nano",
          }
        },
      },
    }
  end,
}

Commands

Below are the available commands that can be configured as keybindings. These commands are included in the default setup. Additional useful commands are implemented through hooks (see below).

General

Command	Description
PrtChatNew <target>	Open a new chat
PrtChatToggle <target>	Toggle chat (open last chat or new one)
PrtChatPaste <target>	Paste visual selection into the latest chat
PrtInfo	Print plugin config
PrtContext <target>	Edits the local context file
PrtChatFinder	Fuzzy search chat files using fzf
PrtChatDelete	Delete the current chat file
PrtChatRespond	Trigger chat respond (in chat file)
PrtStop	Interrupt ongoing respond
PrtProvider <provider>	Switch the provider (empty arg triggers fzf)
PrtModel <model>	Switch the model (empty arg triggers fzf)
PrtStatus	Prints current provider and model selection
PrtCmd <optional prompt>	Directly generate executable Neovim commands (requires explicit Return to execute)
Interactive
PrtRewrite <optional prompt>	Rewrites the visual selection based on a provided prompt (direct input, input dialog or from collection)
PrtEdit	Like PrtRewrite but you can change the last prompt
PrtAppend <optional prompt>	Append text to the visual selection based on a provided prompt (direct input, input dialog or from collection)
PrtPrepend <optional prompt>	Prepend text to the visual selection based on a provided prompt (direct input, input dialog or from collection)
PrtNew	Prompt the model to respond in a new window
PrtEnew	Prompt the model to respond in a new buffer
PrtVnew	Prompt the model to respond in a vsplit
PrtTabnew	Prompt the model to respond in a new tab
PrtRetry	Repeats the last rewrite/append/prepend
Example Hooks
PrtImplement	Takes the visual selection as prompt to generate code
PrtAsk	Ask the model a question

With <target>, we indicate the command to open the chat within one of the following target locations (defaults to toggle_target):

• popup: open a popup window which can be configured via the options provided below
• split: open the chat in a horizontal split
• vsplit: open the chat in a vertical split
• tabnew: open the chat in a new tab

All chat commands (PrtChatNew, PrtChatToggle) and custom hooks support the visual selection to appear in the chat when triggered. Interactive commands require the user to make use of the template placeholders to consider a visual selection within an API request.

Configuration

Options

{
    -- The provider definitions include endpoints, API keys, default parameters,
    -- and topic model arguments for chat summarization. You can use any name
    -- for your providers and configure them with custom functions.
    providers = {
      openai = {
        name = "openai",
        endpoint = "https://api.openai.com/v1/chat/completions",
        -- endpoint to query the available models online
        model_endpoint = "https://api.openai.com/v1/models",
        api_key = os.getenv("OPENAI_API_KEY"),
        -- OPTIONAL: Alternative methods to retrieve API key
        -- Using GPG for decryption:
        -- api_key = { "gpg", "--decrypt", vim.fn.expand("$HOME") .. "/my_api_key.txt.gpg" },
        -- Using macOS Keychain:
        -- api_key = { "/usr/bin/security", "find-generic-password", "-s my-api-key", "-w" },
        --- default model parameters used for chat and interactive commands
        params = {
          chat = { temperature = 1.1, top_p = 1 },
          command = { temperature = 1.1, top_p = 1 },
        },
        -- topic model parameters to summarize chats
        topic = {
          model = "gpt-4.1-nano",
          params = { max_completion_tokens = 64 },
        },
        --  a selection of models that parrot can remember across sessions
        --  NOTE: This will be handled more intelligently in a future version
        models = {
          "gpt-4.1",
          "o4-mini",
          "gpt-4.1-mini",
          "gpt-4.1-nano",
        },
      },
      ...
    }

    -- default system prompts used for the chat sessions and the command routines
    system_prompt = {
      chat = ...,
      command = ...
    },

    -- the prefix used for all commands
    cmd_prefix = "Prt",

    -- optional parameters for curl
    curl_params = {},

    -- The directory to store persisted state information like the
    -- current provider and the selected models
    state_dir = vim.fn.stdpath("data"):gsub("/$", "") .. "/parrot/persisted",

    -- The directory to store the chats (searched with PrtChatFinder)
    chat_dir = vim.fn.stdpath("data"):gsub("/$", "") .. "/parrot/chats",

    -- Chat user prompt prefix
    chat_user_prefix = "🗨:",

    -- llm prompt prefix
    llm_prefix = "🦜:",

    -- Explicitly confirm deletion of a chat file
    chat_confirm_delete = true,

    -- Local chat buffer shortcuts
    chat_shortcut_respond = { modes = { "n", "i", "v", "x" }, shortcut = "<C-g><C-g>" },
    chat_shortcut_delete = { modes = { "n", "i", "v", "x" }, shortcut = "<C-g>d" },
    chat_shortcut_stop = { modes = { "n", "i", "v", "x" }, shortcut = "<C-g>s" },
    chat_shortcut_new = { modes = { "n", "i", "v", "x" }, shortcut = "<C-g>c" },

    -- Option to move the cursor to the end of the file after finished respond
    chat_free_cursor = false,

     -- use prompt buftype for chats (:h prompt-buffer)
    chat_prompt_buf_type = false,

    -- Default target for  PrtChatToggle, PrtChatNew, PrtContext and the chats opened from the ChatFinder
    -- values: popup / split / vsplit / tabnew
    toggle_target = "vsplit",

    -- The interactive user input appearing when can be "native" for
    -- vim.ui.input or "buffer" to query the input within a native nvim buffer
    -- (see video demonstrations below)
    user_input_ui = "native",

    -- Popup window layout
    -- border: "single", "double", "rounded", "solid", "shadow", "none"
    style_popup_border = "single",

    -- margins are number of characters or lines
    style_popup_margin_bottom = 8,
    style_popup_margin_left = 1,
    style_popup_margin_right = 2,
    style_popup_margin_top = 2,
    style_popup_max_width = 160

    -- Prompt used for interactive LLM calls like PrtRewrite where {{llm}} is
    -- a placeholder for the llm name
    command_prompt_prefix_template = "🤖 {{llm}} ~ ",

    -- auto select command response (easier chaining of commands)
    -- if false it also frees up the buffer cursor for further editing elsewhere
    command_auto_select_response = true,

    -- Time in hours until the model cache is refreshed
    -- Set to 0 to deactive model caching
    model_cache_expiry_hours = 48,

    -- fzf_lua options for PrtModel and PrtChatFinder when plugin is installed
    fzf_lua_opts = {
        ["--ansi"] = true,
        ["--sort"] = "",
        ["--info"] = "inline",
        ["--layout"] = "reverse",
        ["--preview-window"] = "nohidden:right:75%",
    },

    -- Enables the query spinner animation 
    enable_spinner = true,
    -- Type of spinner animation to display while loading
    -- Available options: "dots", "line", "star", "bouncing_bar", "bouncing_ball"
    spinner_type = "star",
    -- Show hints for context added through completion with @file, @buffer or @directory
    show_context_hints = true
}

Demonstrations

With user_input_ui = "native", use vim.ui.input as slim input interface.

native_input.mp4

With user_input_ui = "buffer", your input is simply a buffer. All of the content is passed to the API when closed.

buffer_input.mp4

The spinner is a useful indicator for providers that take longer to respond.

spinner.mp4

Key Bindings

This plugin provides the following default key mappings:

Keymap	Description
<C-g>c	Opens a new chat via PrtChatNew
<C-g><C-g>	Trigger the API to generate a response via PrtChatRespond
<C-g>s	Stop the current text generation via PrtStop
<C-g>d	Delete the current chat file via PrtChatDelete

Provider Configuration Examples

The unified provider system allows you to configure any OpenAI-compatible API provider. Below are examples for popular providers:

Anthropic Claude

providers = {
  anthropic = {
    name = "anthropic",
    endpoint = "https://api.anthropic.com/v1/messages",
    model_endpoint = "https://api.anthropic.com/v1/models",
    api_key = utils.get_api_key("anthropic-api-key", "ANTHROPIC_API_KEY"),
    params = {
      chat = { max_tokens = 4096 },
      command = { max_tokens = 4096 },
    },
    topic = {
      model = "claude-3-5-haiku-latest",
      params = { max_tokens = 32 },
    },
    headers = function(self)
      return {
        ["Content-Type"] = "application/json",
        ["x-api-key"] = self.api_key,
        ["anthropic-version"] = "2023-06-01",
      }
    end,
    models = {
      "claude-sonnet-4-20250514",
      "claude-3-7-sonnet-20250219",
      "claude-3-5-sonnet-20241022",
      "claude-3-5-haiku-20241022",
    },
    preprocess_payload = function(payload)
      for _, message in ipairs(payload.messages) do
        message.content = message.content:gsub("^%s*(.-)%s*$", "%1")
      end
      if payload.messages[1] and payload.messages[1].role == "system" then
        -- remove the first message that serves as the system prompt as anthropic
        -- expects the system prompt to be part of the API call body and not the messages
        payload.system = payload.messages[1].content
        table.remove(payload.messages, 1)
      end
      return payload
    end,
  },
}

Google Gemini

providers = {
  gemini = {
    name = "gemini",
    endpoint = function(self)
      return "https://generativelanguage.googleapis.com/v1beta/models/"
        .. self._model
        .. ":streamGenerateContent?alt=sse"
    end,
    model_endpoint = function(self)
      return { "https://generativelanguage.googleapis.com/v1beta/models?key=" .. self.api_key }
    end,
    api_key = os.getenv "GEMINI_API_KEY",
    params = {
      chat = { temperature = 1.1, topP = 1, topK = 10, maxOutputTokens = 8192 },
      command = { temperature = 0.8, topP = 1, topK = 10, maxOutputTokens = 8192 },
    },
    topic = {
      model = "gemini-1.5-flash",
      params = { maxOutputTokens = 64 },
    },
    headers = function(self)
      return {
        ["Content-Type"] = "application/json",
        ["x-goog-api-key"] = self.api_key,
      }
    end,
    models = {
      "gemini-2.5-flash-preview-05-20",
      "gemini-2.5-pro-preview-05-06",
      "gemini-1.5-pro-latest",
      "gemini-1.5-flash-latest",
      "gemini-2.5-pro-exp-03-25",
      "gemini-2.0-flash-lite",
      "gemini-2.0-flash-thinking-exp",
      "gemma-3-27b-it",
    },
    preprocess_payload = function(payload)
      local contents = {}
      local system_instruction = nil
      for _, message in ipairs(payload.messages) do
        if message.role == "system" then
          system_instruction = { parts = { { text = message.content } } }
        else
          local role = message.role == "assistant" and "model" or "user"
          table.insert(
            contents,
            { role = role, parts = { { text = message.content:gsub("^%s*(.-)%s*$", "%1") } } }
          )
        end
      end
      local gemini_payload = {
        contents = contents,
        generationConfig = {
          temperature = payload.temperature,
          topP = payload.topP or payload.top_p,
          maxOutputTokens = payload.max_tokens or payload.maxOutputTokens,
        },
      }
      if system_instruction then
        gemini_payload.systemInstruction = system_instruction
      end
      return gemini_payload
    end,
    process_stdout = function(response)
      if not response or response == "" then
        return nil
      end
      local success, decoded = pcall(vim.json.decode, response)
      if
        success
        and decoded.candidates
        and decoded.candidates[1]
        and decoded.candidates[1].content
        and decoded.candidates[1].content.parts
        and decoded.candidates[1].content.parts[1]
      then
        return decoded.candidates[1].content.parts[1].text
      end
      return nil
    end,
  },
}

xAI

providers = {
  xai = {
    name = "xai",
    endpoint = "https://api.x.ai/v1/chat/completions",
    model_endpoint = "https://api.x.ai/v1/language-models",
    api_key = os.getenv "XAI_API_KEY",
    params = {
      chat = { temperature = 1.1, top_p = 1 },
      command = { temperature = 1.1, top_p = 1 },
    },
    topic = {
      model = "grok-3-mini-beta",
      params = { max_completion_tokens = 64 },
    },
    models = {
      "grok-3-beta",
      "grok-3-mini-beta",
    },
  },
}

Ollama

providers = {
  ollama = {
    name = "ollama",
    endpoint = "http://localhost:11434/api/chat",
    api_key = "", -- not required for local Ollama
    params = {
      chat = { temperature = 1.5, top_p = 1, num_ctx = 8192, min_p = 0.05 },
      command = { temperature = 1.5, top_p = 1, num_ctx = 8192, min_p = 0.05 },
    },
    topic_prompt = [[
    Summarize the chat above and only provide a short headline of 2 to 3
    words without any opening phrase like "Sure, here is the summary",
    "Sure! Here's a shortheadline summarizing the chat" or anything similar.
    ]],
    topic = {
      model = "llama3.2",
      params = { max_tokens = 32 },
    },
    headers = {
      ["Content-Type"] = "application/json",
    },
    models = {
      "codestral",
      "llama3.2",
      "gemma3",
    },
    resolve_api_key = function()
      return true
    end,
    process_stdout = function(response)
      if response:match "message" and response:match "content" then
        local ok, data = pcall(vim.json.decode, response)
        if ok and data.message and data.message.content then
          return data.message.content
        end
      end
    end,
    get_available_models = function(self)
      local url = self.endpoint:gsub("chat", "")
      local logger = require "parrot.logger"
      local job = Job:new({
        command = "curl",
        args = { "-H", "Content-Type: application/json", url .. "tags" },
      }):sync()
      local parsed_response = require("parrot.utils").parse_raw_response(job)
      self:process_onexit(parsed_response)
      if parsed_response == "" then
        logger.debug("Ollama server not running on " .. endpoint_api)
        return {}
      end

      local success, parsed_data = pcall(vim.json.decode, parsed_response)
      if not success then
        logger.error("Ollama - Error parsing JSON: " .. vim.inspect(parsed_data))
        return {}
      end

      if not parsed_data.models then
        logger.error "Ollama - No models found. Please use 'ollama pull' to download one."
        return {}
      end

      local names = {}
      for _, model in ipairs(parsed_data.models) do
        table.insert(names, model.name)
      end

      return names
    end,
  },
}

Adding a new command

Ask a single-turn question and receive the answer in a popup window

require("parrot").setup {
    -- ...
    hooks = {
      Ask = function(parrot, params)
        local template = [[
          In light of your existing knowledge base, please generate a response that
          is succinct and directly addresses the question posed. Prioritize accuracy
          and relevance in your answer, drawing upon the most recent information
          available to you. Aim to deliver your response in a concise manner,
          focusing on the essence of the inquiry.
          Question: {{command}}
        ]]
        local model_obj = parrot.get_model("command")
        parrot.logger.info("Asking model: " .. model_obj.name)
        parrot.Prompt(params, parrot.ui.Target.popup, model_obj, "🤖 Ask ~ ", template)
      end,
    }
    -- ...
}

Start a chat with a predefined chat prompt to check your spelling.

require("parrot").setup {
    -- ...
    hooks = {
      SpellCheck = function(prt, params)
        local chat_prompt = [[
          Your task is to take the text provided and rewrite it into a clear,
          grammatically correct version while preserving the original meaning
          as closely as possible. Correct any spelling mistakes, punctuation
          errors, verb tense issues, word choice problems, and other
          grammatical mistakes.
        ]]
        prt.ChatNew(params, chat_prompt)
      end,
    }
    -- ...
}

Refer to my personal lazy.nvim setup or those of other users for further hooks and key bindings.

Prompt Collection

If you're repeatedly typing the same prompts into the input fields when using PrtRewrite, PrtAppend, or PrtPrepend, a more lightweight alternative to user commands (also known as hooks) is to define prompts as follows:

require("parrot").setup {
    -- ...
    prompts = {
        ["Spell"] = "I want you to proofread the provided text and fix the errors." -- e.g., :'<,'>PrtRewrite Spell
        ["Comment"] = "Provide a comment that explains what the snippet is doing." -- e.g., :'<,'>PrtPrepend Comment
        ["Complete"] = "Continue the implementation of the provided snippet in the file {{filename}}." -- e.g., :'<,'>PrtAppend Complete
    }
    -- ...
}

They will appear as arguments for the aforementioned interactive commands and can also be used with the template placeholders.

Template Placeholders

Users can utilize the following placeholders in their hook and system templates to inject additional context:

Placeholder	Content
{{selection}}	Current visual selection
{{filetype}}	Filetype of the current buffer
{{filepath}}	Full path of the current file
{{filecontent}}	Full content of the current buffer
{{multifilecontent}}	Full content of all open buffers

Below is an example of how to use these placeholders in a completion hook, which receives the full file context and the selected code snippet as input.

require("parrot").setup {
    -- ...
    hooks = {
      CompleteFullContext = function(prt, params)
        local template = [[
        I have the following code from {{filename}}:

        ```{{filetype}}
        {{filecontent}}
        ```

        Please look at the following section specifically:
        ```{{filetype}}
        {{selection}}
        ```

        Please finish the code above carefully and logically.
        Respond just with the snippet of code that should be inserted.
        ]]
        local model_obj = prt.get_model("command")
        prt.Prompt(params, prt.ui.Target.append, model_obj, nil, template)
      end,
    }
    -- ...
}

The placeholders {{filetype}} and {{filecontent}} can also be used in the chat_prompt when creating custom hooks calling prt.ChatNew(params, chat_prompt) to directly inject the whole file content.

require("parrot").setup {
    -- ...
      CodeConsultant = function(prt, params)
        local chat_prompt = [[
          Your task is to analyze the provided {{filetype}} code and suggest
          improvements to optimize its performance. Identify areas where the
          code can be made more efficient, faster, or less resource-intensive.
          Provide specific suggestions for optimization, along with explanations
          of how these changes can enhance the code's performance. The optimized
          code should maintain the same functionality as the original code while
          demonstrating improved efficiency.

          Here is the code
          ```{{filetype}}
          {{filecontent}}
          ```
        ]]
        prt.ChatNew(params, chat_prompt)
      end,
    }
    -- ...
}

Completion

Instead of using the template placeholders, parrot.nvim supports inline completion via nvim-cmp and blink.cmp for additional contexts:

• @buffer:foo.txt - Includes the content of the open buffer foo.txt
• @file:test.lua - Includes the content of the file test.lua
• @directory:src/ - Includes all file contents from the directory src/

Hint: The option show_context_hints allows you to transparently see notifications about the actual file contents considered by the request. The completion keywords (e.g., @file) need to be placed on a new line!

Setup nvim-cmp

To enable parrot.nvim completions, add the source to your nvim-cmp configuration:

...
sources = cmp.config.sources({
  { name = "parrot" },
}),
...

Setup blink.cmp

For blink.cmp you need to add "parrot" to the default sources and configure the provider the following way:

...
parrot = {
    module = "parrot.completion.blink",
    name = "parrot",
    score_offset = 20,
    opts = {
        show_hidden_files = false,
        max_items = 50,
    }
},
...

Statusline Support

Knowing the current chat or command model can be shown using your favorite statusline plugin. Below, we provide an example for lualine:

  -- define function and formatting of the information
  local function parrot_status()
    local status_info = require("parrot.config").get_status_info()
    local status = ""
    if status_info.is_chat then
      status = status_info.prov.chat.name
    else
      status = status_info.prov.command.name
    end
    return string.format("%s(%s)", status, status_info.model)
  end

  -- add to lueline section
  require('lualine').setup {
    sections = {
      lualine_a = { parrot_status }
  }

Adding a custom provider

If the default provider is unavailable, you may define as many additonal custom providers to suit your needs. This allows you to customize various aspects such as endpoints, available models, default parameters, headers, and functions for processing the LLM responses. Please note that configuring providers in this manner is intended for advanced users. I encourage you to open an issue or a discussion if you require assistance or have suggestions for improving provider support.

  providers = {
    my_custom_provider = {
      name = "my_custom_provider",
      api_key = os.getenv("MY_API_KEY"),
      endpoint = "https://api.example.com/v1/chat/completions",
      model = { "model-1", "model-2" },
      -- Custom headers function
      headers = function(api_key)
        return {
          ["Content-Type"] = "application/json",
          ["Authorization"] = "Bearer " .. api_key,
          ["X-Custom-Header"] = "custom-value",
        }
      end,
      -- Custom payload preprocessing
      preprocess_payload = function(payload)
        -- Modify payload for your API format
        return payload
      end,
      -- Custom response processing
      process_stdout = function(response)
        -- Parse streaming response from your API
        local success, decoded = pcall(vim.json.decode, response)
        if success and decoded.content then
          return decoded.content
        end
      end,
    },
  }

Bonus

Access parrot.nvim directly from your terminal:

command nvim -c "PrtChatNew"

Also works by piping content directly into the chat:

ls -l | command nvim - -c "normal ggVGy" -c ":PrtChatNew" -c "normal p"

Roadmap

• Add status line integration/ notifications for summary of tokens used or money spent
• Improve the documentation
• Create a tutorial video
• Reduce overall code complexity and improve robustness

FAQ

• I am encountering errors related to the state.

  If the state is corrupted, simply delete the file ~/.local/share/nvim/parrot/persisted/state.json.

• The completion feature is not functioning, and I am receiving errors.

  Ensure that you have an adequate amount of API credits and examine the log file ~/.local/state/nvim/parrot.nvim.log for any errors.

• I have discovered a bug, have a feature suggestion, or possess a general idea to enhance this project.

  Everyone is invited to contribute to this project! If you have any suggestions, ideas, or bug reports, please feel free to submit an issue.

Related Projects

• parrot.nvim is a fork of an earlier version of robitx/gp.nvim, branching off the commit 607f94d361f36b8eabb148d95993604fdd74d901 in January 2024. Since then, a significant portion of the original code has been removed or rewritten, and this effort will continue until parrot.nvim evolves into its own independent version. The original MIT license has been retained and will be maintained.
• huynle/ogpt.nvim
• The idea for PrtCmd was inspired by exit.nvim.

Star History