
emigo
Agentic AI Development in Emacs
Stars: 91

Emigo is an AI-powered development tool for Emacs that integrates large language models to interact with projects, read files, write code, execute commands, and more. It acts as an agentic AI assistant, leveraging tool use to enhance development workflows within Emacs. Emigo is actively developed, offering features like agentic tool use, Emacs integration, flexible LLM support, and context-aware interactions. Users can install Emigo with Python dependencies and configure it within Emacs for seamless integration. The tool's core strength lies in its agentic tool use, where the AI analyzes requests, selects appropriate tools, executes actions, and provides feedback, enabling users to accomplish complex tasks efficiently.
README:
Emigo brings AI-powered development to Emacs, integrating large language models directly into your workflow. Inspired by the capabilities of tools like Aider and Cline, and building upon the foundation of Aidermacs, Emigo acts as an agentic AI assistant. It leverages tool use to interact with your project, read files, write code, execute commands, and more, all within Emacs.
Emigo is under active development. Expect frequent updates, potential breaking changes, and evolving features. Contributions and feedback are highly welcome!
- Agentic Tool Use: Emigo doesn't just generate text; it uses tools to interact with your environment based on the LLM's reasoning.
- Emacs Integration: Designed to feel native within Emacs, leveraging familiar interfaces and workflows.
- Flexible LLM Support: Connects to various LLM providers through LiteLLM, allowing you to choose the model that best suits your needs.
- Context-Aware Interactions: Manages chat history and project context for coherent sessions.
-
Prerequisites:
- Emacs 28 or higher.
- Python 3.x.
-
Install Python Dependencies:
pip install -r requirements.txt
-
Install with straight.el: Add to your Emacs config:
(use-package emigo :straight (:host github :repo "MatthewZMD/emigo") :config (emigo-enable) ;; Starts the background process automatically :custom ;; Example using OpenRouter with Claude 3.7 Sonnet (emigo-model "openrouter/anthropic/claude-3.7-sonnet") (emigo-base-url "https://openrouter.ai/api/v1") ;; Securely load your API key (replace with your preferred method) (emigo-api-key (emigo-read-file-content "~/.config/openrouter/key.txt"))) ;; With the current limitations, I do not recommend using a thinking model ;; by default to save tokens ;; --- Other LLM Examples (adjust model, base_url, api_key) --- ;; Anthropic: ;; (setq emigo-model "claude-3-5-sonnet-20240620") ;; (setq emigo-base-url nil) ; Uses default Anthropic endpoint ;; (setq emigo-api-key (getenv "ANTHROPIC_API_KEY")) ;; OpenAI: ;; (setq emigo-model "gpt-4o") ;; (setq emigo-base-url nil) ; Uses default OpenAI endpoint ;; (setq emigo-api-key (getenv "OPENAI_API_KEY")) ; Or use emigo-read-file-content
-
Start Emigo: Navigate to your project directory (or any directory you want to work in) and run
M-x emigo
. - Enter Prompt: You'll be prompted for your request in the minibuffer.
- Interact: Emigo will open a dedicated buffer. The AI will respond, potentially using tools. You might be asked for approval for certain actions (like running commands or writing files).
-
Add Files to Context: Mention files in your prompt using the
@
symbol (e.g.,Refactor the function in @src/utils.py
). Emigo will automatically add mentioned files to the context if they exist within the project. -
Manage Context:
-
C-c C-l
(emigo-list-context-files
): List files currently included in the chat context. -
C-c C-f
(emigo-remove-file-from-context
): Remove a file from the context.
-
Emigo manages sessions based on the directory where you invoke M-x emigo
. If invoked within a Git repository, the repository root is typically used as the session path. Use C-u M-x emigo
to force the session path to be the current default-directory
.
The core of Emigo's power lies in its agentic tool use. Instead of just providing code suggestions, the LLM analyzes your request and decides which actions (tools) are necessary to accomplish the task.
- LLM Reasoning: Based on your prompt and the current context, the LLM determines the next step.
-
Tool Selection: It chooses an appropriate tool, such as
read_file
,write_to_file
,replace_in_file
,execute_command
,list_files
,list_repomap
, orask_followup_question
. - Tool Execution: Emigo executes the chosen tool, potentially asking for your approval for sensitive operations.
- Result Feedback: The result of the tool execution (e.g., file content, command output, error message) is fed back into the conversation history.
- Iteration: The LLM uses this new information to decide the next step, continuing the cycle until the task is complete or requires further input.
This iterative process allows Emigo to tackle more complex tasks that involve multiple steps and interactions with your project files and system. The LLM uses an XML format to specify the tool and its parameters.
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