一个从零实现 AI Agent 的开源学习项目——每一步都有源码,每一章都能运行,渐进式理解智能体。Build an AI Agent from Scratch — Learn AI Agents step by step with runnable source code.
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一个从零实现 AI Agent 的开源学习项目——每一步都有源码,每一章都能运行,渐进式理解智能体。Build an AI Agent from Scratch — Learn AI Agents step by step with runnable source code.
agents ai-agent llm-agents agentic-ai python-agents agent-tutorial agent-from-scratch python-agentic-ai
CXS is a deterministic continuity standard for long-range, multi-session, and long-chat LLM reasoning. It enables GPT, Claude, Grok, Gemini, and local models to share state through a human-readable Continuity Passport with strict-mode rules, drift detection/repair, and full chain-of-custody. Validated across 250+ continuity tests.
machine-learning ai deep-learning drift drift-detection large-language-models llm llm-training reasoning-engine memory-architecture ai-continuity continuity-theory python-agents ai-continuity-prompt-engineering continuity-ai continuity-architecture
Local-first LLM operating-system hub for BACH, Rinnsal, gardener, SQLite agent memory, MCP servers, skills, and multi-agent orchestration
open-source automation sqlite mcp multi-agent ai-agents local-first agent-framework llm local-ai llm-agents llm-memory llm-os text-based-os model-context-protocol mcp-server claude-code python-agents agent-os sqlite-memory
Python toolkit for parallel Claude and LLM agent orchestration with local state: consensus voting, stigmergy, and boss-worker workflows
python consensus orchestration multi-agent parallel-processing multi-agent-systems swarm-intelligence ai-agents stigmergy local-first agent-framework llm anthropic llm-agents llm-orchestration claude-code claude-agents python-agents parallel-llm consensus-voting
This repository showcases a variety of AI agent implementations, ranging from chat applications and RAG (Retrieval-Augmented Generation) pipelines to specialized MCP (Model Context Protocol) servers.
an example project demonstrating how to build, define, and integrate custom skills with the Google ADK (Agent Development Kit) 2.0 framework. The repository features a step-by-step implementation of a Python-based agent (skills_app/agent.py) that programmatically invokes external skill scripts (such as a lucky color lookup) to handle user prompts.
Top-tier contribution fork for smolagents: secure coded-agent patterns, MCP interoperability, and maintainer-friendly contributor guidance.
From zero-shot prompts to multi-agent loops — the complete progression of AI engineering in 2026. 7 levels, runnable Python code, real patterns used in production systems.
tutorial multi-agent correction ai-agents llm prompt-engineering chain-of-thought react-agent python-agents loop-agents loop-engineering llm-tutorial-2026
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