alexzhang13/rlm

Features

• Code Execution Engines - Provides a dedicated engine for managing the lifecycle and execution of code snippets generated by language models.
• LLM Orchestrators - Orchestrates the workflow between various language models and external tools, including interactive IPython shells.
• Model Backend Registrations - Configuring a list of distinct language models to execute specific tasks within a single automated workflow.
• Provider-Agnostic Model Interfaces - Standardizes connections to various language model backends using a unified, provider-agnostic API and configuration.
• Recursive Language Models - Implements recursive execution to explore and process large input contexts that exceed standard token limits.
• Recursive Task Decomposers - A tool for handling near-infinite input lengths by programmatically decomposing prompts and executing recursive model calls.
• Reinforcement Learning Training Pipelines - Provides a reinforcement learning training harness that records execution trajectories to teach models recursive task decomposition.
• Code Execution Environments - Runs model-generated code in isolated sandboxes or containers to safely perform computations and data processing.
• Sandboxed Code Execution Environments - Executes generated code within a sandboxed REPL environment to manipulate data and maintain state across operations.
• Code Sandboxing Environments - Provides isolated environments using containers and virtual machines to securely execute untrusted model-generated code.
• Execution Backends - Implements pluggable execution backends that switch between local processes, containers, and cloud virtual machines based on security needs.
• Reasoning Chain Visualizers - Records and renders execution trajectories to visualize how models decompose and solve complex tasks.
• Code Execution Environments - Uses a programmable REPL environment to read, transform, and partition large context variables via executed code.
• Long Context Processing - Divides massive input contexts into smaller subsets and maps recursive calls across them for processing.
• Tool-Use Integrations - Allows injecting external functions and data as callable tools for the language model to utilize during execution.
• Execution State Persistence - Stores prompts and intermediate results as variables in a REPL to allow data modification across multiple turns.
• Container Execution - Provides a secure, reproducible environment with a private filesystem by running code within isolated containers.
• Custom Container Images - Uses custom container images to define specific language versions and pre-installed libraries for reproducible code execution.
• Code Execution Sandboxes - Provides low-overhead execution for trusted tasks by running code within the host process using a restricted namespace.
• Execution Sandboxes - Runs untrusted code in ephemeral cloud virtual machines to secure the host system.
• REPL Workspace Management - Maintains a persistent REPL workspace where variables and intermediate results survive across multiple model interaction turns.
• Process Isolation - Ensures full namespace isolation by running generated code in separate subprocess kernels.
• Subprocess-Based Isolation - Runs generated code in separate subprocesses to ensure namespace isolation and strictly enforce execution timeouts.
• Custom Execution Environment Definitions - Allows the definition of custom sandbox templates via Dockerfiles to support specialized language versions and dependencies.
• Agent Trajectory Logs - Records prompts, generated code, and execution results into structured files to analyze model task decomposition.
• Trajectory Visualizers - Renders recorded execution trajectories into a visual interface to inspect the flow of code and model interactions.

Star history