Toolnexus: a vendor-neutral tool-calling layer for LLMs, byte-identical across 5 languages
Toolnexus is a small, vendor-neutral library that provides a unified tool-calling interface for LLMs, ported byte-identically across JavaScript, Python, Go, Java, and C#. It treats MCP servers, agent skills, custom functions, HTTP endpoints, shell/file tools, and remote A2A agents as the same callable, emitting schemas in OpenAI, Anthropic, and Gemini formats. The library includes a client with built-in parallel and chained tool-calling loops and supports human-in-the-loop suspend/resume.
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Toolnexus: vendor-neutral tool-calling library for LLMs in 5 languages
Toolnexus is a small, vendor-neutral tool-calling library and client loop that provides a unified Tool interface over six sources: MCP servers, agent skills, custom functions, HTTP endpoints, built-in shell/file tools, and remote A2A agents. It handles the entire tool-calling loop (parallel/chained calls, streaming, hooks, retries, memory, metrics) and supports human-in-the-loop via suspend/resume. The library is ported identically across JavaScript, Python, Go, Java, and C#, solving the problem of fragmented tool integration for developers building LLM agents.
Declarative, sandboxed language for tool orchestration with LLMs
Skillscript is a small, declarative language for defining fixed procedures that local agents execute consistently, avoiding LLM drift and token waste. It lets users write and version agent behaviors instead of relying on model guesses each time. Solves the problem of unreliable, costly agent task execution for developers building local AI agents.
Community compares local LLMs for agentic workflows using tool-eval-bench
A GitHub user published an interactive comparison report evaluating local LLMs for agentic workflows, using the tool-eval-bench benchmark (84 scenarios, 16 categories, 8 trials). The report targets single DGX Spark or other 96-128GB rigs and covers multi-turn tool orchestration, function calling, and autonomous planning as exercised by Hermes Agent.
AgentMaker: a new Python framework for building LLM agents and multi-agent systems
AgentMaker is a general-purpose Python framework for building LLM agents and multi-agent systems, featuring tools, memory, RAG, context engineering, guardrails, human-in-the-loop, and observability. It is released under MIT license on GitHub and PyPI.
Enola: engineering intelligence layer for AI coding agents
Enola is an open-source engineering intelligence layer that helps AI coding agents understand existing codebases. It answers questions about change impact, dependency reachability, safe module deletion, refactoring priorities, and architecture drift. The tool uses LLMs to analyze code context and provide insights that reduce mistakes from both humans and AI agents.
