Multi-agent orchestration & workflow engine.

Declarative YAML agent workflows. An LLM coordinator routes events through hub-and-spoke mailboxes with race-safe delivery. One YAML file, one Go binary. Runs on any provider goai supports.

Website · Docs · Blueprint · Architecture · YAML Reference · Examples

See it run

A real zenflow flow spec/v1/examples/full-featured.yaml --model google/gemini-3-flash-preview --workdir /tmp/full-feature-gemini --yolo --plan run. The --plan flag prints the DAG before execution; the coordinator narrates every step boundary; four agents (planner, coder, reviewer, deployer) call read / write / glob / grep / bash tools to plan, implement, review, and ship a feature; the deploy_staging sub-workflow (loaded via includes:) runs after the main DAG completes. The cast that produced this recording is pinned at demos/full-featured.cast - replay it locally with asciinema play demos/full-featured.cast.

Core features

• Declarative YAML agent workflows. Multi-agent workflows expressed in a small composable spec: steps, dependencies, parallel fan-out, conditions (CEL), loops (forEach, repeat-until via untilAgent/maxIterations), and includes for sub-workflow reuse.
• LLM coordinator with hub-and-spoke messaging. A coordinator agent narrates progress, forwards events between running steps, and finalizes the run. Peer agents never address each other directly.
• Race-safe Mailbox + Wake delivery. Every message is delivered through a per-agent mailbox with explicit drop reasons. No silent loss, no out-of-order delivery, no leaked goroutines.
• Multi-provider verified. Verified against Google gemini-3-pro-preview, AWS Bedrock (anthropic.claude-sonnet-4-6, minimax.minimax-m2.5), and Azure (DeepSeek-V3.2, claude-sonnet-4-6, gpt-5, gpt-5.3-codex) - any model goai supports works.
• Spec-first. Workflows validate against spec/v1/schema.json plus a Go validator with 40+ conformance fixtures BEFORE the first LLM call. Cycles, missing dependencies, unknown agents, malformed CEL - all rejected in milliseconds, not after a minute of model burn.
• Embed anywhere. CLI for one-shot runs (zenflow flow, zenflow goal, zenflow agent); Go library primitives (zenflow.New, Orchestrator.RunFlow) for embedding inside long-running services. Ships as a single static Go binary - no JVM, no Python interpreter, no Node runtime. go install, brew install, or curl | sh and you're running.

Install

The fastest path is the install script - it picks the right archive for your OS+arch from the latest GitHub Release, verifies the SHA-256 checksum, and drops zenflow into ~/.local/bin (or %LOCALAPPDATA%\Programs\zenflow on Windows).

# macOS / Linux
curl -fsSL https://zenflow.sh/install.sh | sh

# Windows (PowerShell)
iwr -useb https://zenflow.sh/install.ps1 | iex

Other options:

# Docker (linux/amd64 + linux/arm64 multi-arch image on GHCR)
docker pull ghcr.io/zendev-sh/zenflow:latest
docker run --rm \
  -e GEMINI_API_KEY \
  -e ZENFLOW_MODEL=google/gemini-2.0-flash \
  -v "$PWD":/wd -w /wd \
  ghcr.io/zendev-sh/zenflow:latest flow workflow.yaml

# Homebrew (macOS / Linux)
brew install zendev-sh/tap/zenflow

# Go install
go install github.com/zendev-sh/zenflow/cmd/zenflow@latest

# Manual download
# https://github.com/zendev-sh/zenflow/releases/latest

Requires Go 1.25+ when installing via go install or building from source. The Docker image runs as the distroless nonroot user and ships a static zenflow binary, no shell.

Quick start

Drop a workflow into a YAML file:

# debate.yaml
name: debate
agents:
  pro:    { description: "Argues IN FAVOR of the proposition." }
  con:    { description: "Argues AGAINST the proposition." }
  judge:  { description: "Impartial judge declaring a winner." }

steps:
  - id: team-pro
    agent: pro
    instructions: "Argue: 'AI assistants will replace junior dev roles within 5 years.'"

  - id: team-con
    agent: con
    instructions: "Argue against the same proposition."

  - id: verdict
    agent: judge
    instructions: "Declare a winner with reasoning."
    dependsOn: [team-pro, team-con]

Run it from the CLI:

export GEMINI_API_KEY=...
zenflow flow debate.yaml

For automated CI runs where you want to block shell access, add --sandbox:

zenflow flow debate.yaml --sandbox --model google/gemini-2.5-flash

--sandbox restricts tools to read, write, grep, and glob; bash is blocked even if --allow bash is also passed. See the CLI reference for the full permission flag set (--yolo, --allow, --deny, --strict).

Or embed in Go:

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/zendev-sh/goai/provider/google"
    "github.com/zendev-sh/zenflow"
)

func main() {
    wf, err := zenflow.LoadWorkflow("debate.yaml")
    if err != nil {
        log.Fatal(err)
    }

    llm := google.Chat("gemini-2.0-flash", google.WithAPIKey(os.Getenv("GEMINI_API_KEY")))
    orch := zenflow.New(
        zenflow.WithModel(llm),
        zenflow.WithCoordinator(zenflow.NewDefaultCoordRunner(llm)),
    )
    defer orch.Close()

    result, err := orch.RunFlow(context.Background(), wf)
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(result.Summary)
}

See examples/ for 19 runnable Go embeddings and spec/v1/examples/ for the matching YAML.

Three modes

zenflow exposes the same engine through three CLI verbs and one Go library surface:

The library form (zenflow.New(...).RunFlow(ctx, wf)) is the same engine; the CLI is a thin wrapper that resolves a provider from --model, wires the coordinator, and prints results.

Documentation

Compared to other multi-agent frameworks

zenflow takes a narrower position than CrewAI, AutoGen, and LangGraph: workflows are declarative YAML rather than Python control flow; messaging is mediated by a single coordinator instead of peer-to-peer; delivery is race-safe by construction via a mailbox + wake registry. See Compare for a side-by-side covering the tradeoffs each design makes.

Contributing

See CONTRIBUTING.md for dev setup, build/test commands, and the PR process.

AI contributors: two files cover different audiences and should not be confused.

• AGENTS.md (regenerated from CLAUDE.md by scripts/sync-agents-md.sh; pre-commit hook keeps them in sync) - instructions for AI agents editing the codebase. Code style, package layout, key rules, testing levels.
• SKILL.md - context for AI agents consuming zenflow as a tool. CLI verbs, env vars, YAML shape, NDJSON event schema, exit codes.

Community

• Code of Conduct - the standards we expect from all contributors and how to report violations.
• Security Policy - private vulnerability disclosure process; do not open a public issue for security reports.

License

Apache 2.0.