DEV Community: Anthony Max

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Anthony Max — Mon, 25 May 2026 08:24:34 +0000

Anthony Max

May 25

10 Open Source Tools Every Developer Should Know 🔥

#webdev #javascript #programming #opensource

105

Comments 4

5 min read

10 Open Source Tools Every Developer Should Know 🔥

Anthony Max — Mon, 25 May 2026 00:00:13 +0000

When studying a profession, we often encounter the fact that new knowledge gives us a feeling of the full picture, when everything becomes clearer.

As the industry develops, something new appears every day that gives more advantageous positions to those who know it. This was the case, for example, with the emergence of ChatGPT, knowledge of which gave people a huge advantage.

In this article, I tried to collect 10 projects, knowledge of which will help you become a great specialist.

Let's go!

1. 💎 Bifrost - Fastest enterprise AI gateway

To be honest, I can describe the project simply and quickly. If you want a powerful LLM Gateway for your project, and at the same time deploy it in a user-friendly interface without setting up a bunch of configs and other things, then this project is for you.

🌱 Star the Bifrost repository ☆

2. 👾 Godot - Free and open source 2D and 3D game engine

A truly legendary engine like Unity or Unreal Engine for games. If you are a game developer, you should know this project. From pet projects for the university to multi-million dollar games - it gives it all.

Godot Engine is a feature-packed, cross-platform game engine to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools, so that users can focus on making games without having to reinvent the wheel.

🌱 Star the Godot repository ☆

3. ▲ Turborepo - Build system optimized for JavaScript and TypeScript, written in Rust

Created by the same folks behind Vercel and Next.js, Turborepo is a high-speed build system that’s especially useful for monorepos.

Turborepo is a high-performance build system for JavaScript and TypeScript codebases, written in Rust.

🌱 Star the Godot repository ☆

4. 🐈‍⬛ Nest.js - A progressive Node.js framework for building efficient, scalable, and enterprise-grade server-side applications

If you’ve ever wished Express had a bit more structure, Nest.js is the upgrade you’ve been looking for. It brings TypeScript, OOP, and a modular architecture to Node.js development.

Nest is a framework for building efficient, scalable Node.js server-side applications. It uses modern JavaScript, is built with TypeScript (preserves compatibility with pure JavaScript) and combines elements of OOP (Object Oriented Programming), FP (Functional Programming), and FRP (Functional Reactive Programming).

🌱 Star the Nest.js repository ☆

5. ⚙️ GitMCP - free, open-source, remote MCP server for any GitHub project

The following project is a server implementing the MCP protocol that connects LLM directly to a GitHub repository.

GitMCP is a free, open-source, remote Model Context Protocol (MCP) server that transforms any GitHub project (repositories or GitHub pages) into a documentation hub.

🌱 Star the GitMCP repository ☆

6. 📁 Linux kernel - Linux kernel source tree

Now let's look at something truly colossal. The next project needs no introduction, but if anyone doesn't know, it's the kernel of the Linux operating system, which you may be using to view this article.

The Linux kernel is the core of any Linux operating system. It manages hardware,
system resources, and provides the fundamental services for all other software.

🌱 Star the Linux kernel repository ☆

7. 🗄️ Redis - Preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

If you need a fast response from your database, this system is 100% suitable.

non-relational NoSQL database management system

🌱 Star the repository ☆

8. 💼 Frappe HR - Open Source HR and Payroll Software

The next project is an open source business solution for HR management from Frappe. This company has been around for a long time and has a really big name in the industry, as you may have used (or are using) its ERPNext product if you work as a manager.

Frappe HR has everything you need to drive excellence within the company. It's a complete HRMS solution with over 13 different modules right from Employee Management, Onboarding, Leaves, to Payroll, Taxation, and more!

🌱 Star the Frappe HR repository

9. ⚙️ MLX - An array framework for Apple silicon

An excellent framework from Apple that will allow you to teach your MacBook, iMac and other devices to solve many necessary tasks for business and more.

MLX is designed by machine learning researchers for machine learning researchers. The framework is intended to be user-friendly, but still efficient to train and deploy models. The design of the framework itself is also conceptually simple.

🌱 Star the MLX repository

10. 👀 Mockoon - Easiest and quickest way to run mock APIs locally

Now, let's move on to creating an API. If you want to test your application route on a localhost, say, how information about products comes in, then you can do it thanks to this tool without deploying to dev or prod.

Mockoon is the easiest and quickest way to design and run mock APIs. No remote deployment, no account required, free and open-source.

🌱 Star the Mockoon repository

🖋️ Conclusion

In this article, I have reviewed only a small part of the volume of projects that are important and even necessary for every developer to know. It is clear that, well, let's say, there are hundreds of such projects today, and maybe more. But I hope you have discovered something new, and if not, at least remembered that there is.

Thank you very much for reading this article ❤️!

What other projects do you know that are not so popular, but also useful? It will be interesting to find out in the comments!

[Boost]

Anthony Max — Tue, 19 May 2026 23:28:11 +0000

Anthony Max

May 19

The Concept of Automatic Fallbacks And How Bifrost Implements It

#ai #webdev #programming #api

Comments 4

5 min read

The Concept of Automatic Fallbacks And How Bifrost Implements It

Anthony Max — Tue, 19 May 2026 22:49:23 +0000

The promise of AI is transformative. The reality is distributed, fragile, and increasingly complex. Production LLM applications need more than just a single provider. They need reliability by design.

If you've deployed ML workloads at scale, you know the pain: OpenAI goes down, your app goes down. Anthropic is overloaded, requests queue indefinitely.

This is where Bifrost automatic fallback mechanism comes in. It is a routing layer that transforms single points of failure into resilient, changing request chains.

💻 A small example of work

Traditional LLM integrations look like this:

// Traditional approach - brittle
const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello!" }]
});

What happens when OpenAI is down? Your entire application fails. No graceful degradation. No fallback. Just errors.

Even with try-catch, you're manually writing fallback logic:

let response;
try {
  response = await openai.chat.completions.create({...});
} catch (error) {
  console.log("OpenAI failed, trying Anthropic...");
  response = await anthropic.chat.completions.create({...});
}

This approach has real problems:

Boilerplate everywhere: Every API call needs fallback logic
Hard to maintain: Adding a third provider means refactoring all your code
Inconsistent behavior: Different services handle timeouts differently

⚙️ Declarative Resilience

Bifrost flips this model. Instead of embedding fallback logic in your application, you declare your resilience strategy once, at the Virtual Key level:

# Configure your Virtual Key with multiple providers
curl -X POST https://api.bifrost.example.com/virtual-keys \
  -H "Authorization: Bearer $TOKEN" \
  -d '{
    "name": "vk-prod-main",
    "provider_configs": [
      {
        "provider": "openai",
        "allowed_models": ["gpt-4o", "gpt-4o-mini"],
        "weight": 0.6
      },
      {
        "provider": "anthropic",
        "allowed_models": ["gpt-4o"],
        "weight": 0.4
      }
    ]
  }'

Now your application code is beautifully simple:

// With Bifrost - no fallback logic needed
const response = await fetch('http://localhost:8000/v1/chat/completions', {
  method: 'POST',
  headers: {
    'x-bf-vk': 'vk-prod-main',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: 'Hello!' }]
  })
});

Bifrost handles the rest automatically.

⚙️ How Automatic Fallbacks Work

Here's the magic: when you configure multiple providers on a Virtual Key, Bifrost creates an automatic fallback chain without any intervention from you.

The Anatomy of a Fallback Chain

Request: gpt-4o
        ↓
   [Load Balancer]
        ↓
   Route to: Anthropic (60% weight) ✓ Primary
        ↓
   ✗ Anthropic failed (timeout/error)
        ↓
   Fall back to: OpenAI (40% weight) ✓ Secondary
        ↓
   ✓ Success! Return response

Key behaviors:

Weighted Selection: Your primary request goes to the provider with the highest weight
Automatic Retry: If that provider fails, Bifrost automatically retries the next provider in line
Weight-Ordered Chain: Fallbacks are sorted by weight providers get priority
Transparent to Application: Your code never sees the fallback happen

A Real-World Example

Imagine this Virtual Key configuration:

{
  "provider_configs": [
    {
      "provider": "openai",
      "allowed_models": ["gpt-4o"],
      "weight": 0.15
    },
    {
      "provider": "anthropic",
      "allowed_models": ["gpt-4o"],  // via Model Catalog wildcard
      "weight": 0.05
    }
  ]
}

Request flow for a gpt-4o query:

Attempt 1: OpenAI (15% weight)
  → Rate limit exceeded

Attempt 2: Anthropic (5% weight)
  → ✓ SUCCESS - Response returned to user

Total latency: ~12 seconds
User sees: Normal response (not an error)

Without Bifrost, that same request would have failed at Attempt 1, never trying the fallbacks.

🔎 Preserving LLM Control

Automatic fallbacks are opt-in by default. If you already have fallback logic in your request, Bifrost respects it and doesn't add automatic chains:

// With explicit fallbacks - automatic chain is skipped
const response = await fetch('http://localhost:8000/v1/chat/completions', {
  method: 'POST',
  headers: { 'x-bf-vk': 'vk-prod-main' },
  body: JSON.stringify({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: 'Hello!' }],
    fallbacks: ['anthropic/claude-3-sonnet-20240229']  // ← Your custom chain
  })
});

This flexibility is crucial. Sometimes you need specific fallback behavior for compliance, cost, or performance reasons.

⚙️ Combining Automatic Fallbacks with Weighted Load Balancing

The real power emerges when you combine three Bifrost features:

1. Weighted Load Balancing

Distribute traffic proportionally across providers:

70% to cheap OpenAI (cost optimization)
5% to Anthropic (bleeding-edge features)

2. Automatic Fallbacks

If OpenAI fails → try Anthropic

3. API Key Restrictions

Ensure production workloads use production keys only:

{
  "provider_configs": [
    {
      "provider": "openai",
      "key_ids": ["key-prod-001"],  // Only production OpenAI key
      "allowed_models": ["gpt-4o"],
      "weight": 0.8
    }
  ]
}

Together, these features create a governance layer that makes your infrastructure simultaneously:

Resilient (automatic failover)
Cost-effective (weighted optimization)
Compliant (fine-grained access control)
Simple (no application-level boilerplate)

💻 Use Cases

Use Case 1

// Production VK - strict, resilient
{
  "name": "vk-prod",
  "provider_configs": [
    { "provider": "openai", "key_ids": ["key-prod"], "weight": 0.6 },
    { "provider": "anthropic", "key_ids": ["key-anthopic-prod"], "weight": 0.4 }
  ]
}

// Development VK - cheaper models
{
  "name": "vk-dev",
  "provider_configs": [
    { "provider": "openai", "key_ids": ["key-dev"], "allowed_models": ["gpt-4o-mini"], "weight": 1.0 }
  ]
}

Use Case 2

{
  "name": "vk-cost-optimized",
  "provider_configs": [
    // Primary: cheapest provider
    { "provider": "openai", "allowed_models": ["gpt-4o-mini"], "weight": 0.85 },
    { "provider": "anthropic", "allowed_models": ["claude-3-sonnet"], "weight": 0.05 }
  ]
}

Use Case 3

{
  "name": "vk-global",
  "provider_configs": [
    // Primary: lowest latency for your region
    { "provider": "anthopic", "key_ids": ["key-anthopic-eu"], "weight": 0.7 },
    // Fallback: global provider
    { "provider": "openai", "weight": 0.3 }
  ]
}

⚙️ Implementation Details You Should Know

How Bifrost Determines Fallback Order

Bifrost sorts fallback providers by weight, descending:

Weight 0.15 (OpenAI) → tried first  
Weight 0.05 (Anthropic) → tried last

This means your "best" provider (highest weight) is also your primary, and your fallbacks gracefully degrade through less-preferred options.

When Automatic Fallbacks DON'T Trigger

Automatic fallback chains are only created if:

✓ Your request has no existing fallbacks array
✓ You have multiple providers configured on the Virtual Key

If you've manually specified fallbacks, Bifrost respects your configuration and doesn't add automatic chains. This prevents surprising behavior for applications that have custom fallback strategies.

Model Validation Across Providers

Bifrost doesn't blindly route requests. It validates that the requested model is actually supported by the provider:

// ✓ This works - both Anthropic and OpenAI support gpt-4o
curl -H "x-bf-vk: vk-prod-main" \
  -d '{"model": "gpt-4o"}' \
  http://localhost:8000/v1/chat/completions

// ✗ This fails - only OpenAI supports gpt-4o-mini
curl -H "x-bf-vk: vk-prod-main" \
  -d '{"model": "gpt-4o-mini"}' \
  http://localhost:8000/v1/chat/completions
  # Error: Model not available on configured providers

The validation happens via Bifrost's Model Catalog, which syncs with each provider's actual supported models on startup and during updates.

🔎 Observability

With automatic fallbacks, visibility becomes critical. You need to know:

Which requests are using fallbacks?
Which providers are failing?
What's the fallback success rate?

Bifrost exposes this through:

Structured logs identifying which fallback was used
Metrics on fallback frequency and success rates
Distributed tracing showing the full request chain

{
  "timestamp": "2026-01-15",
  "request_id": "req-req1-123",
  "model": "gpt-4o",
  "primary_provider": "anthropic",
  "fallback_used": true,
  "fallback_provider": "openai",
  "total_latency_ms": 8420,
  "primary_latency_ms": 5000,
  "fallback_latency_ms": 3420
}

With this data, you can:

Detect outages early (spike in fallback usage)
Optimize weights (if fallbacks are used 50% of the time, reweight providers)
Cost allocation (track which provider actually fulfilled each request)

✅ Getting Started with Bifrost

To start building resilient AI applications:

Deploy or access Bifrost (self-hosted or managed service)
Add multiple providers to your infrastructure (OpenAI, Anthropic, etc.)
Create a Virtual Key with weighted provider configs
Replace your direct API calls with Bifrost-routed requests
Monitor and optimize based on fallback metrics

Your application code doesn't need to change. Just point it at the Bifrost endpoint instead of OpenAI directly.

🖋️ Conclusion

Building resilient AI systems has traditionally meant building resilience into your application code. Automatic fallbacks flip this model: resilience is declared once, at the infrastructure layer, and automatically enforced for all requests.

🔗 Resources:

Bifrost GitHub: https://github.com/maximhq/bifrost
Bifrost Docs: https://docs.getbifrost.ai
Bifrost CLI: npx -y @maximhq/bifrost-cli

💎 Star Bifrost ☆

[Boost]

Anthony Max — Thu, 09 Apr 2026 09:16:05 +0000

Cost tracking and rate limiting for teams

Anthony Max

Apr 2

Best Enterprise Claude Code Gateway🔥

#ai #webdev #programming #api

114

Comments 5

6 min read

[Boost]

Anthony Max — Sun, 05 Apr 2026 15:08:38 +0000

Cost tracking and rate limiting for teams

Anthony Max

Apr 2

Best Enterprise Claude Code Gateway🔥

#ai #webdev #programming #api

114

Comments 5

6 min read

[Boost]

Anthony Max — Fri, 03 Apr 2026 12:23:46 +0000

Cost tracking and rate limiting for teams

Anthony Max

Apr 2

Best Enterprise Claude Code Gateway🔥

#ai #webdev #programming #api

114

Comments 5

6 min read

Cool article

Anthony Max — Thu, 02 Apr 2026 15:30:37 +0000

Cost tracking and rate limiting for teams

Anthony Max

Apr 2

Best Enterprise Claude Code Gateway🔥

#ai #webdev #programming #api

114

Comments 5

6 min read

Best Enterprise Claude Code Gateway🔥

Anthony Max — Thu, 02 Apr 2026 14:56:03 +0000

Claude Code is powerful. But running it at scale in production requires more than just an API key. You need routing, control, visibility, cost tracking.

That's where Bifrost comes in.

Bifrost is a high-performance, Go-based gateway that transforms Claude Code from a developer tool into an enterprise-ready system. Combined with Bifrost CLI, it's the cleanest, fastest way to deploy Claude Code in production.

Let me show you why.

👀 Why You Need a Gateway for Claude Code

Claude Code works great locally. But in production, you face real problems:

No cost visibility — Who's using Claude Code? How much is it costing?
No access control — Everyone has access to everything
No rate limiting — A runaway workflow can spike costs $1,000+ in minutes
No audit trail — You can't prove compliance or debug failures
No model switching — Locked into one provider, one model
No failover — If one provider goes down, everything stops

Bifrost solves all of this.

⚙️ 1. Bifrost: Enterprise Control for Claude Code

What Bifrost Does

Bifrost is a lightweight, Go-based gateway that sits between Claude Code and your Claude API:

Claude Code → Bifrost Gateway → Claude API

💎 Star Bifrost ☆

Everything that makes Claude Code powerful stays intact. But now you get:

Control

Route Claude Code through a single gateway
Control which teams access what
Rate limit by user, team, or role
Enforce budgets and cost limits

Visibility

Track every Claude Code request
Know exactly what's costing money
Identify usage patterns
Audit all agent activity

Reliability

Automatic failover between providers
Load balance across API keys
Semantic caching
100% success rate at 5,000+ RPS

Security

Role-based access control (RBAC)
Secure key storage (OS keyring)
No plaintext credentials anywhere
Complete audit logs for compliance

Performance: 40x Faster Than Alternatives

Bifrost is written in Go, compiled into a single binary:

Gateway Overhead:     11 µs (vs 440 µs for another gateways)
Memory Usage:        -68% (compared to alternatives)
Queue Wait Time:    1.67 µs (vs 47 µs)
Success Rate @ 5k RPS: 100% (vs 89%)
Total Latency:     1.61 s (24% faster than others)

Why? Go's goroutines (lightweight concurrency), compiled binary (no runtime), and memory efficiency. It's the difference between adding milliseconds of overhead vs microseconds.

Easy Setup

# Start Bifrost gateway
npx -y @maximhq/bifrost -p 8000

# Opens http://localhost:8000
# Web UI for configuration

That's it. No complex setup. No Docker required (though available).

🔎 2. Bifrost CLI: Best CLI for Claude Code

Here's the problem with Claude Code in production:

# Every developer does this:
export ANTHROPIC_API_KEY="sk-..."
export CLAUDE_BASE_URL="https://api.anthropic.com"
claude

It's manual, error-prone, and doesn't scale.

Bifrost CLI solves this completely.

What Bifrost CLI Does

npx -y @maximhq/bifrost-cli -p 8000

That's all you need. The CLI:

✅ Detects your Bifrost gateway automatically

✅ Fetches available Claude models from Bifrost

✅ Configures API keys and base URLs automatically

✅ Installs Claude Code if needed

✅ Attaches MCP servers for tool access

✅ Stores credentials securely in OS keyring

✅ Launches Claude Code ready to work

Interactive Setup (30 seconds)

1. Base URL → http://localhost:8000
2. Virtual Key (optional) → your-key-or-skip
3. Choose Agent → Claude Code
4. Select Model → anthropic/claude-opus-4-5
5. Press Enter → Claude Code launches

Everything is configured. No config files. No manual setup.

Persistent Sessions & Model Switching

The CLI launches Claude Code in a tabbed terminal UI:

Ctrl+B — Open tab bar
n — New Claude Code session
m — Switch to different Claude model
x — Close current session
1-9 — Jump to tab

Want to switch from Claude 3.5 Sonnet to Claude Opus? Just press m and pick a different model. Everything reconfigures automatically.

Keyboard Shortcuts

Enter — Launch Claude Code
m — Change Claude model
h — Switch to different agent (Claude Code, Codex, Gemini)
d — Open Bifrost dashboard
r — Open documentation
q — Quit

Configuration Saved Automatically

{
  "base_url": "http://localhost:8000",
  "default_harness": "claude",
  "default_model": "anthropic/claude-opus-4-5-20250929"
}

Next time you run bifrost, your previous configuration is ready. Just press Enter.

⚙️ 3. Control: Role-Based Access

Different teams need different access levels:

roleToToolsMapping := map[string][]string{
    "engineering": {"filesystem", "database", "github-api"},
    "research":    {"web-search", "documents"},
    "finance":     {"reports", "cost-tracking"},
    "admin":       {"*"},  // All access
}

roleLimits := map[string]map[string]int{
    "engineering": {"database": 500},      // 500 requests/min
    "research":    {"web-search": 100},    // 100 searches/min
    "finance":     {"reports": 50},        // 50 reports/min
}

An engineer tries to run a database query — allowed. Finance tries to delete data — denied. Marketing tries to access source code — blocked.

Real example:

# Engineering user
curl -X POST http://localhost:8000/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet",
    "messages": [...],
    "user_role": "engineering"
  }'
# ✅ Success

# Finance user trying to access engineering tools (denied)
curl -X POST http://localhost:8000/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet",
    "messages": [...],
    "user_role": "finance"
  }'
# ❌ 403 Forbidden

Rate Limiting Prevents Cost Spikes

One AI workflow got stuck in a loop and made 1,000 requests in 5 minutes. Cost spike: $2,000.

With Bifrost rate limiting:

Request 1: ✅ OK (99/100 remaining)
Request 50: ✅ OK (50/100 remaining)
Request 100: ✅ OK (limit reached)
Request 101: ❌ Rate limited (retry after 45s)

The runaway workflow is caught immediately. Cost: ~$0.10 instead of $2,000.

4. 💻 Visibility: Cost Tracking & Audit Logs

Know Exactly What You're Spending

GET /v1/analytics/costs?team_id=team-engineering&period=month

{
  "total_cost": "$1,234.56",
  "budget": "$5,000.00",
  "remaining": "$3,765.44",
  "usage_by_user": [
    {"user_id": "engineer-1", "cost": "$456.78"},
    {"user_id": "engineer-2", "cost": "$234.56"}
  ]
}

Every Claude Code request is logged with:

Who ran it
What team they're on
How long it took
How much it cost
Whether it succeeded

Audit Everything

{
  "user_id": "engineer-001",
  "user_role": "engineering",
  "model": "claude-3-5-sonnet",
  "cost": "$0.45",
  "duration_ms": 1234,
  "success": true
}

For compliance audits: "Here's every Claude Code request from January." Done.

For debugging: "That model died at 2:15 PM." You have the exact request, model, input, output, and error.

📦 5. Reliability: Failover & Caching

Automatic Failover

If one API key hits rate limits or one provider goes down:

Claude Code request → Primary key fails → 
Automatically retry with secondary key → Success

No downtime. Transparent to Claude Code.

Semantic Caching

Claude Code asks: "Summarize this file"

First request: API call → $0.10
Second request (same file, different wording): Cached result → $0.00

Bifrost uses vector similarity to match requests semantically, not by exact string match.

💻 6. Before & After

Before Bifrost

❌ No cost visibility
❌ No access control
❌ One provider, one model
❌ No audit logs
❌ Runaway workflows = bill shock
❌ Every developer configures themselves
❌ No failover or redundancy

After Bifrost + Bifrost CLI

✅ One command: npx -y @maximhq/bifrost-cli
✅ Real-time cost tracking
✅ Role-based access control
✅ 50+ models from multiple providers
✅ Complete audit trail
✅ Rate limiting prevents cost spikes
✅ Automatic failover & load balancing
✅ Semantic caching
✅ MCP tools integrated automatically

📦 Quick Start: 5 Minutes to Production

Step 1: Start Bifrost Gateway

npx -y @maximhq/bifrost -p 8000
# Gateway at http://localhost:8000

Step 2: Configure Your Claude API Key

Open http://localhost:8000 and add your Anthropic API key. Done.

Step 3: Launch Bifrost CLI

In another terminal:

npx -y @maximhq/bifrost-cli
# Follow interactive setup
# Select: Claude Code → Claude model → Launch

Step 4: Start Coding

Claude Code launches with everything configured. All requests route through Bifrost. Cost tracking, rate limiting, and audit logs are active automatically.

Step 5: Monitor

Open http://localhost:8000 dashboard to see:

Real-time Claude Code usage
Cost breakdown by user and team
Rate limit status
Audit logs of all requests

Why Bifrost is the Best Enterprise Gateway for Claude Code

Performance — 40x less overhead than another gateways
Easy Setup — One command, easy configuration
Control — Role-based access, rate limiting, budgets
Visibility — Real-time cost tracking and audit logs
Reliability — Automatic failover, semantic caching, 100% uptime at scale
Security — Credentials in OS keyring, never plaintext
Flexibility — Use Claude Code, Codex or Opencode interchangeably
Open Source — Apache 2.0, full transparency

Whether you're a solo developer wanting to manage costs or an enterprise team needing governance and compliance, Bifrost is the cleanest, fastest, most reliable way to run Claude Code in production.

✅ Get Started

# Start Bifrost
npx -y @maximhq/bifrost

# In another terminal, launch CLI
npx -y @maximhq/bifrost-cli

# Select Claude Code and your preferred Claude model
# Start coding

No config files. Just Claude Code, powered by the best enterprise gateway available.