You already have Claude Code, Codex, and Gemini CLI.
Sage lets them work together — or swap between them — without writing Python.
One bash script. No framework. No lock-in.

Quick Start • Why sage • Moat demos • Usage • Commands

Every competing orchestrator — claude-flow, ruflo, mux, emdash — optimizes for a single vendor. If Claude Code changes a flag or disappears, your workflows break. Sage is the vendor-neutral layer.

Three moats, in priority order:

1. Neutrality — 8 runtimes (Claude Code, Codex, Gemini CLI, Cline, Kiro, Ollama, llama.cpp, Bash) + any ACP agent. Same command surface. Swap backends with one flag.
2. Zero-dependency — one bash script. curl | bash and it works. No venv, no nvm, no node_modules. Ships in CI, airgapped boxes, ephemeral containers.
3. Unix-native — JSON out, stdin in, everything pipes. Behaves like git / kubectl / jq, not like a chat UI.

Agents are processes. Messages are files. The terminal is your IDE.

Sage is not a coding assistant and does not compete with Claude Code / Aider / Cline on code quality. It's the glue that lets you use them together. See docs/POSITIONING.md for the decision rubric.

# Homebrew (macOS + Linux)
brew tap youwangd/sage && brew install sage

# curl one-liner
curl -fsSL https://raw.githubusercontent.com/youwangd/SageCLI/main/install.sh | bash

# Manual
git clone https://github.com/youwangd/SageCLI.git
cd SageCLI && ln -s $(pwd)/sage ~/bin/sage && sage init

Requires: bash 4.0+, jq 1.6+, tmux 3.0+ · Optional runtimes: Claude Code · Gemini CLI · Codex · Cline · any ACP agent.

sage create worker --runtime claude-code
sage send worker "Build a Python CLI that converts CSV to JSON"

sage peek worker          # live tool calls
sage tasks worker         # task status
sage result <task-id>     # structured output

sage start is optional — send and call auto-start agents. Messages can be inline or @file:

sage send worker "Quick task"
sage send worker @prompt.md
sage send worker @~/tasks/big-project.md

See docs/USAGE.md for the 10 core workflows: parallel multi-runtime audit, headless CI, MCP/skills, guardrails, task templates, plan orchestrator, live monitoring, tracing.

Three things sage does that nobody else in this space does today. Each links to a full narrative.

Primary AI vendor goes down? Workflow auto-routes to the next healthy runtime. One flag. No config change.

sage send reviewer-primary "Review src/main.py" \
  --fallback reviewer-gemini \
  --fallback reviewer-local
# ⚠  primary 'reviewer-primary' runtime unreachable → failing over to 'reviewer-local'
# ✓  task t-1776985392-26333 → reviewer-local

Pre-flight health check — if the primary's binary is unreachable or the daemon isn't responding, sage tries each fallback in order. Every competitor (claude-flow, ruflo, mux, emdash) is single-vendor-locked. Full narrative + drill: docs/use-case-kill-switch.md.

Should you buy Claude Code seats, Gemini subscriptions, or run a local model? Run your actual tasks through each vendor's actual agent CLI:

sage bench run ./bench-tasks --agents claude-agent,gemini-agent,ollama-agent
sage bench report --format markdown

Real dogfood on this repo (5 tasks × 3 agents, CPU-only box, 2026-04-24):

For trivial orchestration tasks, a small local model on CPU beats a full coding agent CLI — the coding agent's scaffolding cost dominates wall-time for short prompts. Full analysis + honest caveats: docs/use-case-bench.md.

Three ollama agents running llama3.2:3b in parallel on a 16-core Xeon, 62 GB RAM, no GPU:

Set OLLAMA_NUM_PARALLEL=3 for real inference parallelism. See the r/LocalLLaMA write-up.

The acp runtime speaks JSON-RPC 2.0 over stdio and maintains persistent sessions — unlike the dedicated runtimes (one-shot per task), ACP enables true live steering. Adding a runtime is one file with two functions. See DEVELOPMENT.md.

sage CLI
  │
  ├─ sage create <name>    → ~/.sage/agents/<name>/{inbox,workspace,results}
  ├─ sage send <name> msg  → writes JSON to inbox/, auto-starts if needed
  │
  └─ runner.sh (per agent, in tmux window)
       ├─ polls inbox/ every 300ms
       ├─ sources runtimes/<runtime>.sh
       ├─ calls runtime_inject() per message
       ├─ streams events to tmux pane
       └─ writes task status + results mechanically

Everything is a file in ~/.sage/: inbox/workspace/results/steer.md per agent, plus runtimes/, tools/, tasks/, plans/, trace.jsonl.

53 commands in 12 domains. Full reference: docs/COMMANDS.md. Inline: sage help or sage <command> --help.

Highlights: create, send, call, tasks, plan, bench, task, mcp, skill, memory, trace, dashboard, doctor.

wc -l sage            # ~8,500 lines, one file
bats tests/           # 928 tests, 155 files
./sage init --force   # run from source
./sage create test --runtime bash

See DEVELOPMENT.md for architecture, runtime interface, and how to add new runtimes.

v1.0 → v1.4.0 shipped. Phases 0–21 complete: testing foundation, git worktrees, headless/CI, MCP + skills, inter-agent messaging, shared context, export/import, observability, guardrails, per-agent env, 8 runtimes, completions, swarm patterns, TUI dashboard, persistent sessions, token/cost tracking, file watcher, ACP Registry, vendor kill-switch, bench-as-code.

Remaining work is adoption, not features.

Full competitive intel, weekly monitoring, detailed specs: ROADMAP.md.

MIT — see LICENSE.

⚡ sage — Because the best agent framework is the one you can read in an afternoon.