Welcome to the Eleanor Framework
The EleanorAI Framework is a Python-based monorepo designed to support my research needs. It integrates all capabilities around a standardized resource management model, which organizes namespaces, agents, memory collections, and users. Capabilities are implemented across several logical layers:
- Application Layer: Provides a CLI, an inversion of control (IoC) configuration management system, secrets management, and caching. It also includes low-level ORM and schema evolution capabilities and manages connectivity to external relational, vector, and graph databases, alongside monkeypatch management & control.
- LLM Layer: Includes LLM-powered map-reduce functionality for text summarization and compression, prompt chain management, and interoperability between different prompt chain models. It features a canonical representation of instruction templates, enabling quick support for new models by converting their instruction templates to and from the frameworkâs internal standard. It also includes quantization tools.
- Service Layer: Supports agent chat and various agent conative functions, such as memory creation, recall, and integration. It also facilitates agent persona evolution, allowing personas to evolve based on their accumulated memories.
- API Layer: Implements an OpenAI-compatible endpoint to maximize client compatibility. The API layer also allows remote clients to orchestrate service execution workflows over HTTPS.
- Job Execution Layer: Manages time-based recurring operations within the framework, such as agent memory integration and dynamic persona generation, both of which are computationally intensive.
See Capabilities for a detailed list of features and services provided by the EleanorAI Framework.