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byLLM Release Notes#

This document provides a summary of new features, improvements, and bug fixes in each version of byLLM (formerly MTLLM). For details on changes that might require updates to your existing code, please refer to the Breaking Changes page.

byllm 0.4.17 (Unreleased)#

byllm 0.4.16 (Latest Release)#

  • MTIR-Powered Schema Generation: MTRuntime now uses compile-time MTIR info for generating JSON schemas with semantic descriptions. Tool and return type schemas include semstrings extracted at compile time, providing richer context for LLM calls.
  • Python Library Fallback Mode: When MTIR is unavailable (e.g., using byLLM as a Python library without Jac compilation), the runtime gracefully falls back to introspection-based schema generation, maintaining backward compatibility.
  • Schema Generation Fixes: Fixed several issues in schema generation that were exposed when MTIR data became correctly available:
  • Union Type Null Safety: Fixed NoneType has no attribute 'type_info' errors when processing Union types like str | None.
  • Dataclass Inherited Fields: Schema now correctly includes inherited fields from base classes (e.g., Dog | Cat union now includes name, age from Pet base class).
  • Required Fields Validation: Fixed OpenAI schema validation error ("Extra required key supplied") by only listing fields that actually exist in properties.
  • Function Schema Fallback: Dynamically created tools (like finish_tool) now work correctly even without MTIR info by falling back to function introspection.

  • Internal: Explicitly declared all postinit fields across the codebase.

  • Internal refactors: Removed orphaned files, etc.

byllm 0.4.15#

  • Direct HTTP model calls: Added support for calling custom LLM endpoints via direct HTTP (http_client in model config).

byllm 0.4.14#

  • Max Iterations for ReAct (max_react_iterations): Added a configurable limit for ReAct tool-calling loops via by llm(max_react_iterations=3) to prevent overly long or endless reasoning cycles. When the limit is reached, the model stops calling tools and returns a final answer based on the information gathered so far.

byllm 0.4.13#

byllm 0.4.12#

byllm 0.4.9#

  • LLM-Powered Graph Traversal (visit by): Introduced visit [-->] by llm() syntax enabling walkers to make intelligent traversal decisions. The LLM analyzes the semantic context of available nodes and selects which ones to visit based on the walker's purpose, bringing AI-powered decision making to graph navigation.

byllm 0.4.8#

  • Streaming with ReAct Tool Calling: Implemented real-time streaming support for ReAct method when using tools. After tool execution completes, the LLM now streams the final synthesized answer token-by-token, providing the best of both worlds: structured tool calling with streaming responses.

byllm 0.4.7#

  • Custom Model Declaration: Custom model interfaces can be defined by using the BaseLLM class that can be imported form byllm.lib. A guide for using this feature is added to documentation.

byllm 0.4.6#

  • byLLM In-Memory Images: byLLM Image class now accepts in-memory and path-like inputs (bytes/bytearray/memoryview, BytesIO/file-like, PIL.Image, Path), plus data/gs/http(s) URLs; auto-detects MIME (incl. WEBP), preserves URLs, and reads streams.

byllm 0.4.5#

  • byLLM Lazy Loading: Refactored byLLM to support lazy loading by moving all exports to byllm.lib module. Users should now import from byllm.lib in Python (e.g., from byllm.lib import Model, by) and use import from byllm.lib { Model } in Jac code. This improves startup performance and reduces unnecessary module loading.
  • NonGPT Fallback for byLLM: Implemented automatic fallback when byLLM is not installed. When code attempts to import byllm, the system will provide mock implementations that return random values using the NonGPT.random_value_for_type() utility.

byllm 0.4.4#

  • is Keyword for Semstrings: Added support for using is as an alternative to = in semantic string declarations (e.g., sem MyObject.value is "A value stored in MyObject").
  • byLLM Plugin Interface Improved: Enhanced the byLLM plugin interface with get_mtir function hook interface and refactored the by decorator to use the plugin system, improving integration and extensibility.

byllm 0.4.3#

  • byLLM Enhancements:
  • Fixed bug with Enums without values not being properly included in prompts (e.g., enum Tell { YES, NO } now works correctly).

byllm 0.4.2#

  • byLLM transition: MTLLM has been transitioned to byLLM and PyPi package is renamed to byllm. Github actions are changed to push byllm PyPi. Alongside an mtllm PyPi will be pushed which installs latest byllm and produces a deprecation warning when imported as mtllm.
  • byLLM Feature Methods as Tools: byLLM now supports adding methods of classes as tools for the llm using such as tools=[ToolHolder.tool]

byllm 0.4.1#

  • byLLM transition: MTLLM has been transitioned to byLLM and PyPi package is renamed to byllm. Github actions are changed to push byllm PyPi. Alongside an mtllm PyPi will be pushed which installs latest byllm and produces a deprecation warning when imported as mtllm.

mtllm 0.4.0#

  • Removed LLM Override: function_call() by llm() has been removed as it was introduce ambiguity in the grammer with LALR(1) shift/reduce error. This feature will be reintroduced in a future release with a different syntax.

mtllm 0.3.8#

  • Semantic Strings: Introduced sem strings to attach natural language descriptions to code elements like functions, classes, and parameters. These semantic annotations can be used by Large Language Models (LLMs) to enable intelligent, AI-powered code generation and execution. (mtllm)
  • LLM Function Overriding: Introduced the ability to override any regular function with an LLM-powered implementation at runtime using the function_call() by llm() syntax. This allows for dynamic, on-the-fly replacement of function behavior with generative models. (mtllm)

Version 0.8.0#