#llm-agents

Production-ready AI Business OS — 46 autonomous AI Employees, Graph RAG (Neo4j), multi-channel, BYOK across 17 LLM providers plus custom/local endpoints (Ollama), automatic fallback. 8 libs (Python+Java). Self-hosted, MIT licensed, guardrailed autonomy for regulated industries.

SDK, skills, and examples for Inkbox: give AI agents a persistent identity with email, phone number, and internet address

TypeScript harness for building personal AI agents with durable memory — agents that live across your channels (CLI, Teams, iMessage, mail, voice), use real tools, and stay coherent across turns. The runtime behind @ouro.bot.

Run agents you don't babysit, and trust the result. awf runs agentic workflows with independent gates that check every step and resumes after crashes.

An agentic software factory that closes the loop

A streaming action runtime for AI agents, model serving, and multimodal APIs

milk — a CLI/TUI that routes prompts between agents: a primary agent and a configurable escalation agent, with session-aware state management and real-time streaming. Escalation happens automatically (via routing rules) or manually, preserving context across the handoff between agents.

Long-horizon AI harness you actually own: one static Go binary, one persona, any OpenAI-compat LLM (Ollama, Gemini, Claude, ChatGPT), MCP tools, chat via Telegram/Discord/Slack. Outbound-only — no dashboard, no config UI, no open ports, ever. Hardened so small local models finish tool calls.

Token-minimal system views for AI agents. Translate Kubernetes and live state into compact Agent Native Format with Go tooling and governed execution primitives.

turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.

Local-first checkpoints and crystals for long-running AI coding agent sessions.

Small MIT-licensed runtime for bounded LLM traversal, inspired by FPF and focused on practical agent behavior.

productivity-focused sandboxing for Linux

Local memory + orchestration layer for custom LLM agents: SCOPE-style tactical/strategic memory over execution traces, Qdrant-backed, polyglot subprocess tools, agent-as-tool composition.

Sovereign single-binary knowledge mesh for coding agents (MCP): index, search and serve your notes + code as context. Fair-code (no reselling).

Read live tmux panes as deterministic JSON for LLM coding agents — capture, diff, and Claude/Codex/Copilot status. Single Go binary, no network egress.

2D game engine for Go. Every game you build is agent-playable out of the box: headless for CI bots, MCP for any AI agent, autopilot on screen.

AtFlows is a local observability tool for LLM applications. Point your SDK at it, see your costs, tokens, and latency in real-time.