Agent framework where the agent is a single file
A lightweight agent framework that defines the entire agent logic in one file. It uses LLMs to power autonomous decision-making and tool use, simplifying development for builders who want minimal boilerplate.
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Looped Agent Framework: single-file config agent framework
A framework for building and deploying single-purpose AI agents using a single YAML config file. It uses LLMs as the agent's reasoning core, with all permissions defined at config time for security, and runs natively in Docker for easy deployment. Aimed at developers who want to quickly create secure, portable 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.
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.
Local-first coding agent for long autonomous runs
Grinta is a local-first coding agent designed for long autonomous runs. It uses LLMs to autonomously plan and execute coding tasks, solving the problem of needing constant human supervision for extended development sessions.
Article explains how LLMs use tools and iterate to complete tasks
A technical article titled 'The Agent Loop: How AI Learns to Think, Act, and Get Things Done' describes how LLMs use tools, make decisions, learn from results, and iterate until tasks are complete. The piece provides a conceptual overview of agentic AI workflows.