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.
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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.
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.
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.
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.
NeatContext: lightweight desktop app to give LLMs domain knowledge for oncall incident handling
NeatContext is a desktop application that lets LLMs access domain knowledge to handle oncall incidents more accurately. It solves the problem of SRE agents lacking domain-specific context, enabling better incident response without heavy infrastructure.