tool calling

AgentPort is a secure tool-calling layer for AI agents, with 50+ integrations, per-tool approval policies, encrypted credentials, and detailed call logs. It connects through MCP or CLI and is designed to let agents use external services without exposing secrets.

An embeddable AI agent library written in C with tool calling, memory, sessions, voice, streaming, structured output, and support for both cloud and local models.

Self-hosted, OpenAI-compatible AI gateway for private RAG, natural-language data access, and tool-calling agents. It connects files, databases, vector stores, APIs, and MCP tools through one backend with auth, observability, and governance features.

Go SDK for building AI-backed apps and endpoints with streaming, tool calling, structured output, and React frontend compatibility.

An open 45M-parameter agentic LLM for tiny devices, built for tool calling, device use, and structured extraction. It ships as a 14 MB binary and is positioned for phones, wearables, smart home devices, robots, and other low-RAM hardware.

A public guide to principles for building reliable LLM applications and production-ready agents. The page lays out 12 factors, with discussion of tool calls, context windows, control flow, human handoff, and state handling.

Open-source runtime guardrails for AI agents that intercept unsafe tool calls before they execute, aiming to block prompt-injection-driven exfiltration and other policy violations.

A minimal Rust crate for building agentic workflows in applications, with ticket-based orchestration, tools, knowledge storage, schema validation, retries, budget policies, and multi-provider support.

A text-first, hands-on guide to agentic AI for practicing engineers, with runnable local-first code samples and a module-based structure covering LLM basics, tool calling, RAG, memory, multi-agent patterns, evaluation, safety, and enterprise topics.

A Mojo port of the Pi AI Agent Toolkit with a set of runnable examples for chat, coding agents, tool calling, web research, and long-running agent loops.

A technical guide that shows how to build a small CLI-based LLM agent for microservices using tool discovery, model calls, conversation history, and a REPL loop.

A Prompt One blog post about compiled workflow agents, design-time tool selection, and why runtime agents should not choose tools dynamically.