MadcowD/ell

Features

• Prompt Programming Libraries - Encapsulates prompts as callable Python functions with automatic versioning, logging, and multimodal support.
• Prompt - Transforms a Python function into a language model program using its docstring and return value to define system and user messages.
• AI Model Clients - Provides a client resolution mechanism that returns an OpenAI client and fallback flag for a given model name.
• Prompt Chaining - Implements prompt chaining by composing multiple language model programs into sequential sub-tasks with output passing.
• Model Provider Plugins - Provides a plugin interface for registering third-party inference services and custom model providers.
• Multimodal Prompt Embedding - Integrates images, audio, and external documents into prompts via file paths or encoded data using rich type coercion.
• Provider Abstraction Layers - Decouples model selection from client instantiation with a registry for multiple provider backends.
• Multimodal Prompting - Accepts and returns images, audio, and text natively within message objects for multimodal model interactions.
• Versioned Prompt Variants - Tracks every change to a prompt function automatically with generated commit messages and full history.
• Prompt Programming Libraries - Defines a prompt as a callable Python function that combines system instructions, user messages, and model parameters into a reusable subroutine.
• Function-Based - Ships a mechanism to chain several language model calls into a single function for multi-step reasoning.
• Reasoning Pipelines - Chains several language model calls into a single function for multi-step reasoning or test-time compute.
• Prompt Version Trackers - Automatically tracks every change to a prompt function with generated commit messages and full history.
• Prompt Versioning Engines - Captures prompt versions by combining static code analysis with runtime invocation snapshots, generating commit messages automatically.
• Language Model Programming Libraries - Treats prompts as callable Python functions, transforming a Python function into a language model program.
• Model Client Fallback Resolutions - Returns an OpenAI client and a fallback flag for a given model name, decoupling model selection from client instantiation.
• Multimodal Type Coercions - Accepts and returns images, audio, and other media alongside text by coercing them into a unified message object format.
• Prompt and Agent Versioning - Tracks prompt iterations and analyzes performance shifts over time via a local dashboard.
• Temporary Model Registry Overrides - Maintains a global model configuration map that can be temporarily swapped within a context manager for testing or routing.
• Global Default Configurations - Sets global defaults for model parameters, storage, logging, and versioning behavior in a single initialization call.
• Local Interaction Stores - Persists prompt versions, invocations, and metadata to a local filesystem store for offline browsing and comparison.
• Multimodal Output Selectors - Allows returning either plain text or structured message objects with tool calls and multimodal content from a single function.
• Invocation Logging - Captures and stores the full input and output of every language model call for later analysis, fine-tuning, or dataset creation.
• Prompt Output Comparison Dashboards - Provides a local dashboard to visualize, compare, and track prompt outputs across different versions and runs.
• Prompt Version Dashboards - Launches a web-based UI to visualize, compare, and debug prompt versions and their outputs over time.

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