Llmling

LLMling is a Python library that provides a configurable LLM task processing framework, supporting multiple context processors and LLM provider integrations.

Developer tools Artificial intelligence chatbots #LLM framework #Task configuration #Python tool .Python

rating : 2.5 points

downloads : 33

update time : 2025-04-29

What is the MCP server?

The MCP server is a tool focused on model context management. It allows users to define the working environment and behavior of models through configuration files. It helps developers quickly set up customized model application scenarios, supporting multiple processors, context types, and task templates.

How to use the MCP server?

The MCP server defines the behavior of models through a configuration file. Users can configure components such as processors, contexts, and task templates, and run these configurations to complete specific tasks. For example, you can use it to generate code review suggestions or perform automated analysis.

Applicable scenarios

The MCP server is suitable for scenarios that require highly customized model workflows, such as code review, natural language processing, and data analysis.

Main features

Context managementSupports multiple context types (such as file paths, text content, command-line output) for flexible configuration of model inputs.

Task templatesPreset task templates simplify the configuration of complex tasks and support dynamic loading of processors and contexts.

Multi-model supportCompatible with multiple LLM providers, allowing users to choose different models to meet different needs.

Visual configurationEasily define model behavior and parameters through an intuitive configuration file format.

Advantages and limitations

Advantages

Powerful context management capabilities

Flexible task template configuration

Support for multiple models and providers

Easy to expand and customize

Limitations

High requirements for configuration files

May require a certain programming foundation

Dependent on external APIs (such as OpenAI)

How to use

Install the MCP server

First, ensure that the Python environment is installed, and install the MCP server via pip.

Create a configuration file

Edit the `config.yaml` file to define model contexts and task templates.

Run the server

Start the MCP server with the configuration file to begin processing model tasks.

Usage examples

Code reviewUse the MCP server for code review to automatically detect code quality issues.

Natural language processingUse the MCP server to generate natural language summaries.

Frequently Asked Questions

How to install the MCP server?

How to verify if the configuration file is correct?

Which models does the MCP server support?

Related resources

Official documentation

Detailed user manual and API documentation

GitHub code repository

Source code and contribution guidelines

Example configuration file

Reference configuration file example

🚀 Root Level: Configuration

This configuration file is used to set up various parameters for a system, including global settings, context processors, LLM providers, and more.

🚀 Quick Start

The following is the complete configuration content:

version: "1.0"
global_settings:  # Global settings
  timeout: 30  # Timeout in seconds
  max_retries: 3  # Maximum number of retries
  temperature: 0.7  # Temperature parameter

context_processors:  # Context processors (dictionary: string to processor configuration)
  processor1:
    type: function  # Processor type: function
    import_path: "utils.clean_text"  # Import path

  processor2:
    type: template  # Processor type: template
    template: "{{ content }}\n---"  # Template content

llm_providers:  # LLM providers (dictionary: string to LLMProvider)
  gpt4:
    model: openai/gpt-4  # Model name
    temperature: 0.7  # Temperature parameter

provider_groups:  # Provider groups (dictionary: string to list of strings)
  fast_models:
    - gpt4  # Added provider 1
    - claude2  # Added provider 2

contexts:  # Contexts (dictionary: string to Context (PathContext | TextContext | CLIContext))
  guidelines:
    type: path  # Context type: path context
    path: "./guide.md"  # File path
    description: "Guidelines"  # Description
    processors:  # List of processing steps
      - name: processor1  # Processor name
        keyword_args: {key: "value"}  # Keyword arguments

  prompt:
    type: text  # Context type: text context
    content: "System prompt"  # Text content
    description: "Basic prompt"  # Description

  git_diff:
    type: cli  # Context type: CLI context
    command: "git diff"  # Command
    description: "Changes"  # Description

context_groups:  # Context groups (dictionary: string to list of strings)
  review_contexts:
    - guidelines  # Added context 1
    - prompt  # Added context 2

task_templates:  # Task templates (dictionary: string to TaskTemplate)
  code_review:
    provider: gpt4  # Provider or group name
    context: review_contexts  # Context or group name
    settings:  # Task settings
      temperature: 0.5  # Temperature parameter