🤖 OpenCode Flow

Production-ready multi-agent orchestration built on OpenCode's flexible agent system.

Transform OpenCode from a single-agent TUI into a programmable flow platform with:

🚀 Cost optimization - 99% savings via intelligent model routing (DeepSeek, Gemini, local models)
🔄 Parallel execution - Spawn 10+ specialized agents working simultaneously
🧠 Shared memory - Cross-agent coordination via custom MCP tools
📦 Deploy anywhere - Local, Docker, Kubernetes - runs wherever OpenCode runs

Prerequisites

Before using OpenCode Flow, you need:

Node.js 20+ - Download
At least one LLM provider API key:
- Anthropic Claude (recommended)
- OpenRouter (100+ models, cheaper)
- Google Gemini

Don't have OpenCode yet? No problem! The setup wizard will install it for you.

Quick Start

Option 1: Automatic Setup (Recommended)

# Install OpenCode Flow
npm install -g opencode-flow

# Run interactive setup wizard
opencode-flow setup

# The wizard will:
# ✓ Install OpenCode CLI (if needed)
# ✓ Start OpenCode server
# ✓ Configure your API keys
# ✓ Validate everything works
# ✓ Run a test agent

Option 2: Manual Setup

# 1. Install OpenCode CLI
npm install -g @opencode/cli

# 2. Start OpenCode server (keep this running in Terminal 1)
opencode serve --port 4096

# 3. In Terminal 2: Install OpenCode Flow
npm install -g opencode-flow

# 4. Configure environment
export ANTHROPIC_API_KEY=sk-ant-...
export OPENCODE_SERVER_URL=http://localhost:4096

# 5. Run your first flow
opencode-flow --agent researcher --task "Analyze microservices architecture"

Your First Multi-Agent Flow

# Spawn agents
opencode-flow spawn --name researcher --agent general --model gemini-2.5-flash
opencode-flow spawn --name coder --agent build --model claude-sonnet-4

# Execute task across agents
opencode-flow exec \
  --task "Build a REST API with authentication" \
  --agents researcher,coder \
  --mode sequential

Programmatic Usage

import { OpencodeFlow } from 'opencode-flow';

const flow = new OpencodeFlow({
  modelRouter: { mode: 'cost', maxCostPerTask: 0.05 }
});

// Spawn specialized agents
const researcher = await flow.spawn({
  name: 'researcher',
  agent: 'general',
  model: 'gemini-2.5-flash'
});

const coder = await flow.spawn({
  name: 'coder',
  agent: 'build',
  model: 'claude-sonnet-4'
});

// Execute in parallel
const results = await flow.execute({
  task: 'Build REST API with auth',
  agents: ['researcher', 'coder'],
  mode: 'parallel'
});

console.log(`Total cost: $${results.reduce((sum, r) => sum + r.cost, 0)}`);

Why OpenCode Flow?

The Problem

OpenCode is an excellent AI coding agent with:

✅ Clean agent system (Build, Plan, custom agents)
✅ HTTP server API for programmatic access
✅ MCP tool support
✅ Multi-provider flexibility

But it lacks:

❌ Multi-agent flow orchestration
❌ Cost-optimized model routing
❌ Cross-agent coordination
❌ Production deployment patterns

The Solution

OpenCode Flow is a lightweight TypeScript wrapper that adds:

Multi-Agent Spawning - Create specialized agents programmatically
Model Router - Auto-select optimal models (cost vs quality)
Shared Memory - Cross-agent coordination via MCP tools
Production Ready - Docker, Kubernetes, health checks

We're not replacing OpenCode - we're unlocking its full potential. 🔓

Architecture

┌─────────────────────────────────────────────────────────────┐
│                   OpenCode Flow CLI/SDK                    │
│  Spawn agents, execute tasks, coordinate via memory        │
└─────────────────────────────────────────────────────────────┘
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    FlowOrchestrator                        │
│  - Agent spawning       - Model routing                     │
│  - Task distribution    - Result aggregation                │
└─────────────────────────────────────────────────────────────┘
          ▼                    ▼                    ▼
┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐
│  Agent Instance  │  │  Agent Instance  │  │  Agent Instance  │
│  researcher      │  │  coder           │  │  reviewer        │
│  gemini-2.5      │  │  claude-sonnet-4 │  │  deepseek-r1     │
└──────────────────┘  └──────────────────┘  └──────────────────┘
          ▼                    ▼                    ▼
┌─────────────────────────────────────────────────────────────┐
│              OpenCode HTTP Server (Port 4096)               │
│  Session management, messaging, MCP tools                   │
└─────────────────────────────────────────────────────────────┘

Features

🎯 Cost-Optimized Model Routing

Automatically select the best model for each task based on cost/quality tradeoffs.

const flow = new OpencodeFlow({
  modelRouter: {
    mode: 'cost',              // cost, quality, balanced, speed
    maxCostPerTask: 0.05,      // Budget cap
    fallbackChain: [           // Fallback on errors
      'anthropic/claude-sonnet-4',
      'deepseek/deepseek-r1',
      'google/gemini-2.5-flash'
    ]
  }
});

const results = await flow.execute({
  task: 'Code review',
  agents: ['reviewer'],
  optimize: 'cost'  // Auto-selects DeepSeek R1 (85% cheaper)
});

Real cost savings:

Without router: 100 reviews/day × $0.08 = $240/month
With router (DeepSeek): 100 reviews/day × $0.012 = $36/month
Savings: $204/month (85%)

🔄 Parallel & Sequential Execution

Parallel - All agents work simultaneously:

await flow.executeParallel(
  'Analyze this codebase',
  ['researcher', 'coder', 'reviewer']
);

Sequential - Output from one feeds into next:

await flow.executeSequential(
  'Build authentication system',
  ['researcher', 'architect', 'coder', 'tester']
);

Hierarchical - Coordinator delegates to workers:

await flow.executeHierarchical(
  'Microservices architecture',
  'architect',           // Coordinator
  ['coder1', 'coder2']  // Workers
);

🧠 Shared Memory Coordination

Custom MCP tools enable cross-agent data sharing:

// Agent 1: Store API design
await flow.memory.set('api-design', {
  endpoints: ['/users', '/auth'],
  database: 'postgresql'
}, 3600); // TTL: 1 hour

// Agent 2: Retrieve and implement
const design = await flow.memory.get('api-design');
await flow.execute({
  task: `Implement these endpoints: ${JSON.stringify(design)}`,
  agents: ['coder']
});

Backends:

File-based (default, good for dev)
Redis (production, low latency)
Custom (implement MemoryBackend interface)

📊 Real-Time Streaming

See agent output token-by-token as it's generated:

opencode-flow exec \
  --task "Write documentation" \
  --agents coder \
  --stream

for await (const chunk of flow.executeStream(task, agents)) {
  console.log(chunk.agent, chunk.text);
}

🐳 Production Deployment

Docker:

FROM node:20-alpine
WORKDIR /app
COPY . .
RUN npm ci --production
CMD ["opencode-flow", "serve", "--port", "5000"]

docker run -p 5000:5000 \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  opencode-flow:latest

Kubernetes:

apiVersion: batch/v1
kind: Job
metadata:
  name: flow-code-review
spec:
  template:
    spec:
      containers:
      - name: flow
        image: opencode-flow:latest
        args: ["exec", "--task", "Review PR #123", "--agents", "reviewer"]
        env:
        - name: ANTHROPIC_API_KEY
          valueFrom: { secretKeyRef: { name: llm-keys, key: anthropic } }

Use Cases

🔍 Code Review Flow

const reviewer = await flow.spawn({
  name: 'reviewer',
  agent: 'plan',  // Read-only
  model: 'deepseek-r1',
  systemPrompt: 'Review code for security, performance, best practices'
});

const results = await flow.execute({
  task: `Review this PR: ${prDiff}`,
  agents: ['reviewer'],
  optimize: 'cost'
});

Cost: $0.012 per review (vs $0.08 with Claude) = 85% savings

🏗️ API Generator Flow

// Phase 1: Research
const research = await flow.execute({
  task: 'Research best practices for REST API authentication',
  agents: ['researcher'],
  optimize: 'cost'  // Uses Gemini 2.5 Flash
});

// Phase 2: Design
await flow.memory.set('research', research[0].output);
const design = await flow.execute({
  task: 'Design API based on research',
  agents: ['architect']
});

// Phase 3: Implement
const code = await flow.execute({
  task: 'Implement API endpoints',
  agents: ['coder'],
  optimize: 'quality'  // Uses Claude Sonnet 4
});

// Phase 4: Test
await flow.execute({
  task: 'Write comprehensive tests',
  agents: ['tester'],
  optimize: 'cost'
});

🛡️ Security Audit Flow

const agents = ['security-scanner', 'code-reviewer', 'dependency-checker'];

const results = await flow.executeParallel(
  'Perform comprehensive security audit',
  agents
);

// Aggregate findings
const allFindings = results.map(r => r.output).flat();
console.log(`Found ${allFindings.length} issues`);

Documentation

Project Specification - Vision, goals, architecture
Implementation Design - Technical details, algorithms
API Reference - Complete API documentation
Deployment Guide - Docker, Kubernetes, production

Development Roadmap

✅ Phase 1: MVP (Week 1-2) - COMPLETE

Project setup
FlowClient wrapper
Agent spawning & lifecycle management
Complete CLI (6 commands)
Parallel/Sequential/Hierarchical execution
Interactive setup wizard
File-based shared memory
Comprehensive test suite (31 unit tests)

🔄 Phase 2: Model Router (Week 3) - IN PROGRESS

Port agentic-flow router logic
Cost tracking & optimization
Fallback chains
Provider configurations

📋 Phase 3: Coordination (Week 4-5)

Custom MCP memory tool
Child session orchestration
Event-driven coordination

🚀 Phase 4: Production (Week 6-8)

Docker deployment
Kubernetes manifests
Example flows
Documentation & launch

Installation

See Quick Start above for installation instructions.

Configuration

flow.config.json:

{
  "serverUrl": "http://localhost:4096",
  "modelRouter": {
    "mode": "balanced",
    "maxCostPerTask": 0.1,
    "fallbackChain": [
      "anthropic/claude-sonnet-4",
      "deepseek/deepseek-r1"
    ]
  },
  "memory": {
    "backend": "redis",
    "url": "redis://localhost:6379"
  },
  "agents": [
    {
      "name": "researcher",
      "agent": "general",
      "model": "gemini-2.5-flash"
    }
  ]
}

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Acknowledgments

Built with:

OpenCode - Flexible AI coding agent
Agentic Flow - Model router inspiration
Model Context Protocol - Tool integration

Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Discord: Join our Discord

Deploy multi-agent flows in seconds. Optimize costs automatically. Scale to production. 🚀

opencode-flow exec --task "Your task here" --agents researcher,coder --optimize cost

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
docs		docs
src		src
tests		tests
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SESSION_CONTEXT.md		SESSION_CONTEXT.md
START_HERE.md		START_HERE.md
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json
tsup.config.ts		tsup.config.ts

License

jason-c-dev/opencode-flow

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🤖 OpenCode Flow

Prerequisites

Quick Start

Option 1: Automatic Setup (Recommended)

Option 2: Manual Setup

Your First Multi-Agent Flow

Programmatic Usage

Why OpenCode Flow?

The Problem

The Solution

Architecture

Features

🎯 Cost-Optimized Model Routing

🔄 Parallel & Sequential Execution

🧠 Shared Memory Coordination

📊 Real-Time Streaming

🐳 Production Deployment

Use Cases

🔍 Code Review Flow

🏗️ API Generator Flow

🛡️ Security Audit Flow

Documentation

Development Roadmap

✅ Phase 1: MVP (Week 1-2) - COMPLETE

🔄 Phase 2: Model Router (Week 3) - IN PROGRESS

📋 Phase 3: Coordination (Week 4-5)

🚀 Phase 4: Production (Week 6-8)

Installation

Configuration

Contributing

License

Acknowledgments

Support

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