LangGPT

Provides a Markdown-based template system that organizes prompts into discrete sections (Profile, Rules, Workflow, Initialization) using a Role Template pattern. The framework enforces a hierarchical structure similar to object-oriented programming, where each role definition includes metadata (author, version, language), capability descriptions, behavioral constraints, and execution workflows. This enables prompts to be authored, versioned, and maintained as reusable code artifacts rather than ad-hoc text.

Designs prompts in a provider-agnostic format that can be executed across GPT-4, Claude, Gemini, Qwen, Doubao, and other LLMs without modification. The framework abstracts away provider-specific syntax and API differences, allowing a single Role Template to be deployed to multiple LLM backends. This is achieved through standardized section definitions (Profile, Rules, Workflow) that map to universal LLM instruction patterns rather than provider-specific prompt formats.

Enables composition of multiple Role Templates into prompt chains where the output of one prompt becomes the input to the next, creating multi-step reasoning or processing pipelines. Prompt chains are orchestrated sequences of prompts that work together to solve complex problems by breaking them into smaller, manageable steps. This allows complex tasks to be decomposed into reusable prompt components that can be chained together in different combinations.

Implements SOM (Self-Organizing Map) prompting patterns integrated with SAM (Specialized Agent Model) concepts, enabling prompts to organize and structure information hierarchically. SOM prompting allows prompts to define how information should be organized and processed, while SAM integration enables specialization of agents for specific tasks. This pattern enables complex information organization and agent specialization within the prompt structure itself.

Enables definition of multiple roles that can interact and collaborate within a single prompt or prompt chain, creating multi-agent scenarios where different roles have different perspectives, capabilities, or responsibilities. Multi-role collaboration patterns allow roles to be composed together to solve problems that require multiple specialized perspectives or capabilities. This enables complex collaborative reasoning where different roles contribute their expertise to reach conclusions.

Provides comprehensive documentation of prompt design principles, common patterns, and anti-patterns that guide effective prompt engineering within the LangGPT framework. This includes guidance on structuring prompts, avoiding common pitfalls, and applying proven patterns for different use cases. The documentation serves as a knowledge base that helps users apply the framework effectively and avoid common mistakes.

Provides pre-built example prompts and templates for common use cases including content generation, code generation, fitness planning, and other domains. These examples serve as starting points for users to understand how to apply the LangGPT framework to their specific problems, reducing the learning curve and enabling faster prompt development. Examples demonstrate best practices and patterns in action.

Supports variable placeholders within prompts that can be dynamically substituted at runtime, enabling parameterized prompt generation without manual text editing. Variables are defined using a syntax that integrates with the Role Template structure, allowing prompts to accept user input, context data, or system parameters. This enables the same prompt template to be reused across different inputs and contexts by simply changing variable values rather than rewriting the entire prompt.

Defines a Workflow section within Role Templates that specifies the sequence of steps an LLM should follow when executing a prompt. The workflow section acts as an execution plan, breaking down complex tasks into ordered steps with clear input/output expectations for each step. This enables multi-step reasoning patterns (similar to chain-of-thought) to be encoded directly in the prompt structure, making complex reasoning processes explicit and reproducible across different LLM invocations.

Provides a Rules section within Role Templates that explicitly defines behavioral constraints, operating principles, and guardrails that the LLM must follow when assuming a role. Rules are structured as a list of constraints that guide LLM behavior without requiring complex prompt engineering tricks. This enables consistent behavior enforcement across different LLM providers and invocations by making constraints explicit and machine-readable within the template structure.

Defines a Profile section that serves as a 'resume' for a role, containing essential metadata (author, version, language, description) and explicit capability declarations organized by skill category. The Profile section acts as machine-readable documentation of what a role can do, enabling role discovery, versioning, and capability-based selection. This allows prompts to be catalogued and selected based on declared capabilities rather than requiring manual inspection of prompt text.

Provides an Initialization section within Role Templates that specifies the initial context, system state, or setup information that should be provided to the LLM before executing the main prompt. The Initialization section enables prompts to be self-contained by encoding all necessary context and setup within the template itself, rather than requiring external context management. This allows prompts to be portable and executable in different environments without additional setup steps.

Supports conditional statements and branching logic within prompts, enabling different execution paths based on input conditions or LLM state. Conditional logic allows prompts to adapt their behavior dynamically, executing different Rules, Workflows, or outputs based on specified conditions. This enables single prompt templates to handle multiple scenarios without requiring separate prompt definitions for each case.

Defines a Commands feature that enables prompts to specify explicit commands or actions that the LLM should recognize and execute. Commands are structured directives that the LLM can interpret and act upon, enabling prompt-driven control of LLM behavior without requiring complex natural language instructions. This allows prompts to define a command vocabulary that the LLM should understand and respond to consistently.

Supports a Reminders feature that enables prompts to include periodic reinforcement of key instructions or constraints throughout the execution. Reminders are structured directives that reinforce important rules or behaviors at strategic points in the prompt execution, helping ensure the LLM maintains focus on critical requirements. This enables multi-part prompts to reinforce key instructions without requiring constant repetition of the same text.