Codebuff: The Multi-Agent AI Assistant That Edits Codebases Through Natural Language

In the world of software development, programmers spend significant time handling repetitive coding tasks: fixing security vulnerabilities, refactoring code, adding new features. These tasks are necessary but consume valuable time that developers could otherwise dedicate to creative work. Codebuff addresses this exact pain point.

What is Codebuff?

Codebuff is an AI-powered programming assistant that allows developers to edit and manage codebases using natural language instructions. Unlike traditional single-model AI programming tools, Codebuff employs a multi-agent collaborative architecture that breaks down complex tasks and assigns them to specialized agents, delivering more accurate and reliable code modifications.

According to internal project evaluations, Codebuff achieves a 61% success rate across 175+ coding tasks, outperforming Claude Code’s 53% in the same assessments. This advantage has been validated through real-world scenario testing on multiple open-source projects.

Core Technical Architecture

Multi-Agent Collaboration System

Codebuff’s core strength lies in its multi-agent architecture. When you ask Codebuff to “add authentication to my API,” it coordinates multiple specialized agents working together:

File Explorer Agent scans the codebase to understand project structure and locate relevant files; Planner Agent creates modification plans, determining which files need changes and in what order; Implementation Agent performs actual code edits; Review Agent validates the correctness and completeness of changes.

This division of labor mimics how human development teams work, ensuring accuracy and contextual relevance of modifications.

Technology Stack

Codebuff is built on a modern technology stack: TypeScript as the primary programming language, Bun for package management and runtime environment, WebSocket for real-time client-server communication, and integration with multiple large language model providers (including Anthropic, OpenAI, Gemini) to handle different coding tasks.

Security Authentication Mechanism

Codebuff implements a secure authentication system using fingerprint-based device identification between CLI tools, backend services, and web applications.

Authentication Flow

When users first use the CLI tool, the system generates a unique device fingerprint consisting of hardware information plus 8 random bytes, ensuring device uniqueness. The CLI then sends an authentication request to the web application, generates an authentication code valid for 1 hour, and guides the user through the OAuth process.

The system checks fingerprint ownership, creates or updates session status. The CLI tool queries authentication status every 5 seconds until completion. This design ensures security while providing a smooth user experience.

Security Features

Codebuff’s authentication system includes multiple security measures: automatic expiration of authentication codes after 1 hour, guaranteed uniqueness of device fingerprints, detection and prevention of ownership conflicts, binding sessions to device fingerprints, and automatic fingerprint reset upon logout.

Three core database tables manage authentication: the fingerprint table stores device fingerprints and ownership signature hashes; the session table connects users to device fingerprints and manages expiration; the user table stores user account information.

Installation and Usage

CLI Tool Installation

Installing Codebuff CLI is straightforward:

npm install -g codebuff

After installation, navigate to your project directory and run:

codebuff

You can now use natural language instructions to tell Codebuff your requirements:

• “Fix the SQL injection vulnerability in user registration”
• “Add rate limiting to all API endpoints”
• “Refactor database connection code for better performance”

Codebuff automatically finds relevant files, makes necessary changes throughout the codebase, and runs tests to ensure no breaking changes are introduced.

Custom Agent Creation

Codebuff supports creating custom agents:

codebuff init-agents

You can write agent definition files to precisely control agent behavior. Implement workflows by specifying tools, spawnable sub-agents, and prompts. You can even use TypeScript generators for more programmatic control.

For example, here’s a git-committer agent that creates commits based on current git state:

export default {
  id: 'git-committer',
  displayName: 'Git Committer',
  model: 'openai/gpt-5-nano',
  toolNames: ['read_files', 'run_terminal_command', 'end_turn'],

  instructionsPrompt:
    'You create meaningful git commits by analyzing changes, reading relevant files for context, and crafting clear commit messages that explain the "why" behind changes.',

  async *handleSteps() {
    // Analyze what changed
    yield { tool: 'run_terminal_command', command: 'git diff' }
    yield { tool: 'run_terminal_command', command: 'git log --oneline -5' }

    // Stage files and create commit with good message
    yield 'STEP_ALL'
  },
}

This agent runs git diff and git log commands to analyze changes, then delegates to the LLM to generate meaningful commit messages and perform the commit operation.

SDK Integration

Beyond the CLI tool, Codebuff provides a complete SDK package that allows developers to integrate Codebuff’s capabilities directly into their applications:

npm install @codebuff/sdk

Basic SDK usage:

import { CodebuffClient } from '@codebuff/sdk'

// Initialize client
const client = new CodebuffClient({
  apiKey: 'your-api-key',
  cwd: '/path/to/your/project',
  onError: (error) => console.error('Codebuff error:', error.message),
})

// Execute coding task
const result = await client.run({
  agent: 'base', // Use base coding agent
  prompt: 'Add comprehensive error handling to all API endpoints',
  handleEvent: (event) => {
    console.log('Progress', event)
  },
})

Local Development Environment Setup

For developers wanting to contribute to the Codebuff project or run it locally, the project provides complete local development guidelines.

Prerequisites

First install the Bun package manager and runtime environment, plus direnv for environment variable management. Docker is also required to run the web server’s database.

Setup Steps

The setup process includes cloning the project repository, configuring Infisical for secret management, setting up direnv to automatically manage environment variables, installing dependencies, and finally starting development services.

The development environment requires running three services simultaneously: backend server, web server, and client. Each service runs in a separate terminal window.

Testing Methods

The project emphasizes testing importance and provides detailed testing guidelines. It recommends using spyOn() instead of mock.module() for mocking functions and methods, enabling clearer test isolation and avoiding global state interference.

Testing modes include normal test execution, watch mode testing, and running specific test files.

Project Advantages and Features

Deep Customization Capability

Codebuff allows creating complex agent workflows through TypeScript generators that mix AI generation with programmatic control. You can define custom agents that spawn sub-agents, implement conditional logic, and coordinate complex multi-step processes to adapt to specific use cases.

Multi-Model Support

Unlike tools locked to specific vendor models, Codebuff supports any model available on OpenRouter—from Claude and GPT to specialized models like Qwen and DeepSeek. You can switch models for different tasks without waiting for platform updates to use the latest versions.

Reusable Agent Ecosystem

You can compose and use any published agents to improve efficiency. Codebuff agents are becoming the new MCP (Model Context Protocol) standard!

Complete SDK Customization

Through the complete TypeScript SDK, you can build Codebuff’s capabilities directly into your applications. Create custom tools, integrate with CI/CD pipelines, build AI-powered development environments, or embed intelligent coding assistance into your products.

Development Best Practices

TypeScript Build State Management

The project uses bun run clean-ts command to clear all TypeScript build artifacts (.tsbuildinfo files and .next cache). This resolves infinite loop issues in the type checker caused by corrupted or stale build cache.

Error Handling and Debugging

The project provides debug.ts file for logging debug information, with error messages output to console and debug log files. WebSocket errors are caught and logged in both server and client code.

Security Considerations

The project uses environment variables to manage sensitive information (like API keys), uses secure WebSocket connections (WSS) in production environments, validates and sanitizes user input before processing, and restricts file operations to the project directory.

Project Goals and Vision

Codebuff aims to enhance developer productivity by reducing time and effort spent on common programming tasks. The system is designed to learn from user interactions and particularly focuses on empowering expert software engineers to work even more efficiently.

The tool handling system defines available tools in backend/src/tools.ts with implementations in npm-app/src/tool-handlers.ts. Available tools include: read_files, write_file, str_replace, run_terminal_command, code_search, browser_logs, spawn_agents, web_search, read_docs, run_file_change_hooks, and others.

The backend uses tool calls to request additional information or perform actions, while the client-side handles tool calls and sends results back to the server.

Agent System Architecture

Codebuff features a sophisticated agent system with multiple types:

LLM-based Agents: Traditional agents defined in backend/src/templates/ using prompts and LLM models.

Programmatic Agents: Custom agents using JavaScript/TypeScript generator functions in .agents/templates/.

Dynamic Agent Templates: User-defined agents in TypeScript files with handleSteps generator functions.

Agent templates define available tools, spawnable sub-agents, and execution behavior. Programmatic agents enable complex orchestration logic, conditional flows, and iterative refinement. Generator functions execute in a secure QuickJS sandbox for safety. Both types integrate seamlessly through the same tool execution system.

CLI Interface Features

The CLI interface includes several useful features: ESC key toggles the menu or stops AI responses, while CTRL+C exits the application entirely.

Package Management Standards

The project uses Bun for all package management operations. Commands should be run with bun instead of npm (e.g., bun install not npm install), and bun run should be used for script execution.

Referral System Implementation

The referral system requires special attention: referral codes must be applied through the npm-app CLI, not through the web interface.

The web onboarding flow shows instructions for entering codes in the CLI. Users must type their referral code in the Codebuff terminal after login. Auto-redemption during web login was removed to prevent abuse.

The handleReferralCode function in npm-app/src/client.ts handles CLI redemption, while the redeemReferralCode function in web/src/app/api/referrals/helpers.ts processes the actual credit granting.

OAuth Referral Code Preservation

NextAuth doesn’t preserve referral codes through OAuth flow because: NextAuth generates its own state parameter for CSRF/PKCE protection; custom state parameters are ignored/overwritten; OAuth callback URLs don’t always survive the round trip.

The solution uses a multi-layer approach implemented in SignInButton and ReferralRedirect components: using absolute callback URLs with referral codes for better NextAuth preservation; storing referral codes in localStorage before OAuth starts; and having the ReferralRedirect component on the home page catch missed referrals and redirect to the onboard page.

Environment Variable Management

The project uses Infisical for secret management. All secrets are injected at runtime.

To run any service locally, use the exec runner script from the root package.json, which wraps commands with infisical run --.

Example: bun run exec -- bun --cwd backend dev

Environment variables are defined and validated in packages/internal/src/env.ts. This module provides type-safe env objects for use throughout the monorepo.

Bun Wrapper Script

The .bin/bun script automatically wraps bun commands with Infisical when secrets are needed. It prevents nested Infisical calls by checking for the NEXT_PUBLIC_INFISICAL_UP environment variable, ensuring Infisical runs only once at the top level while nested bun commands inherit the environment variables.

Worktree Support: The wrapper automatically detects and loads .env.worktree files when present, allowing worktrees to override Infisical environment variables (like ports) for local development. This enables multiple worktrees to run simultaneously on different ports without conflicts.

The wrapper also loads environment variables in the correct precedence order: Infisical secrets are loaded first (if needed); .env.worktree is loaded second to override any conflicting variables; this ensures worktree-specific overrides (like custom ports) always take precedence over cached Infisical defaults.

The wrapper looks for .env.worktree in the project root directory, making it work consistently regardless of the current working directory when bun commands are executed.

Performance Optimizations: The wrapper uses the --silent flag with Infisical to reduce CLI output overhead and sets INFISICAL_DISABLE_UPDATE_CHECK=true to skip version checks for faster startup times.

Infisical Caching: The wrapper implements robust caching of environment variables in .infisical-cache with a 15-minute TTL (configurable via INFISICAL_CACHE_TTL). This reduces startup time from ~1.2s to ~0.16s (87% improvement). The cache uses infisical export which outputs secrets directly in KEY='value' format, ensuring ONLY Infisical-managed secrets are cached (no system environment variables). Multi-line secrets like RSA private keys are handled correctly using source command. Cache automatically invalidates when .infisical.json is modified or after TTL expires. Uses subshell execution to avoid changing the main shell’s working directory.

Session Validation: The wrapper detects expired Infisical sessions using infisical export with a robust 10-second timeout implementation that works cross-platform (macOS and Linux). Uses background processes with polling to prevent hanging on interactive prompts. Valid sessions output environment variables in KEY='value' format, while expired sessions either output interactive prompts or timeout. Provides clear error messages directing users to run infisical login.

Python Package Support

A Python package skeleton exists in the python-app directory. Currently a placeholder that suggests installing the npm version.

Project Templates

Codebuff provides starter templates for initializing new projects:

codebuff --create <template> [project-name]

Templates are maintained in the Codebuff community repository. Each directory corresponds to a template usable with the –create flag.

Testing Guidelines and Best Practices

The project emphasizes proper testing methodologies with specific guidelines:

Prefer specific imports over import * to make dependencies explicit. Exception: When mocking modules with many internal dependencies (like isomorphic-git), use import * to avoid listing every internal function.

Bun Testing Best Practices

Always use spyOn() instead of mock.module() for function and method mocking.

When mocking modules is required (for overriding constants instead of functions), use the wrapper functions found in @codebuff/common/testing/mock-modules.ts.

mockModule is a drop-in replacement for mock.module, but the module should be the absolute module path (e.g., @codebuff/common/db instead of ../db). Make sure to call clearMockedModules() in afterAll to restore the original module implementations.

Preferred approach:

// ✅ Good: Use spyOn for clear, explicit mocking
import { spyOn, beforeEach, afterEach } from 'bun:test'
import * as analytics from '../analytics'

beforeEach(() => {
  // Spy on module functions
  spyOn(analytics, 'trackEvent').mockImplementation(() => {})
  spyOn(analytics, 'initAnalytics').mockImplementation(() => {})

  // Spy on global functions like Date.now and setTimeout
  spyOn(Date, 'now').mockImplementation(() => 1234567890)
  spyOn(global, 'setTimeout').mockImplementation((callback, delay) => {
    // Custom timeout logic for tests
    return 123 as any
  })
})

afterEach(() => {
  // Restore all mocks
  mock.restore()
})

Real examples from the codebase:

// From main-prompt.test.ts - Mocking LLM APIs
spyOn(aisdk, 'promptAiSdk').mockImplementation(() =>
  Promise.resolve('Test response'),
)
spyOn(aisdk, 'promptAiSdkStream').mockImplementation(async function* () {
  yield 'Test response'
})

// From rage-detector.test.ts - Mocking Date
spyOn(Date, 'now').mockImplementation(() => currentTime)

// From run-agent-step-tools.test.ts - Mocking imported modules
spyOn(websocketAction, 'requestFiles').mockImplementation(
  async (ws: any, paths: string[]) => {
    const results: Record<string, string | null> = {}
    paths.forEach((p) => {
      if (p === 'src/auth.ts') {
        results[p] = 'export function authenticate() { return true; }'
      } else {
        results[p] = null
      }
    })
    return results
  },
)

Use mock.module() only for entire module replacement:

// ✅ Good: Use mock.module for replacing entire modules
mock.module('../util/logger', () => ({
  logger: {
    debug: () => {},
    error: () => {},
    info: () => {},
    warn: () => {},
  },
  withLoggerContext: async (context: any, fn: () => Promise<any>) => fn(),
}))

// ✅ Good: Mock entire module with multiple exports using anonymous function
mock.module('../services/api-client', () => ({
  fetchUserData: jest.fn().mockResolvedValue({ id: 1, name: 'Test User' }),
  updateUserProfile: jest.fn().mockResolvedValue({ success: true }),
  deleteUser: jest.fn().mockResolvedValue(true),
  ApiError: class MockApiError extends Error {
    constructor(
      message: string,
      public status: number,
    ) {
      super(message)
    }
  },
  API_ENDPOINTS: {
    USERS: '/api/users',
    PROFILES: '/api/profiles',
  },
}))

Benefits of spyOn:

• Easier to restore original functionality with mock.restore()
• Clearer test isolation
• Doesn’t interfere with global state (mock.module carries over from test file to test file, which is problematic and unintuitive)
• Simpler debugging when mocks fail

Test Setup Patterns

Extract duplicative mock state to beforeEach for cleaner tests:

// ✅ Good: Extract common mock objects to beforeEach
describe('My Tests', () => {
  let mockFileContext: ProjectFileContext
  let mockAgentTemplate: DynamicAgentTemplate

  beforeEach(() => {
    // Setup common mock data
    mockFileContext = {
      projectRoot: '/test',
      cwd: '/test',
      // ... other properties
    }

    mockAgentTemplate = {
      id: 'test-agent',
      version: '1.0.0',
      // ... other properties
    }
  })

  test('should work with mock data', () => {
    const agentTemplate = {
      'test-agent': {
        ...mockAgentTemplate,
        handleSteps: 'custom function',
      } as any, // Use type assertion when needed
    }

    const fileContext = {
      ...mockFileContext,
      agentTemplates: agentTemplate,
    }
    // ... test logic
  })
})

Benefits:

• Reduces code duplication across tests
• Makes tests more maintainable
• Ensures consistent mock data structure
• Easier to update mock data in one place

Constants and Configuration

Important constants are centralized in common/src/constants.ts:

• CREDITS_REFERRAL_BONUS: Credits awarded for successful referral
• Credit limits for different user types

Conclusion

Codebuff represents a new direction in AI-assisted programming—it doesn’t simply replace developers but enhances their capabilities through multi-agent collaboration. Through natural language interfaces, specialized agent division of labor, and flexible extensibility, Codebuff enables developers to focus more on creative work while delegating repetitive tasks to AI.

Whether through CLI tools for rapid completion of daily tasks or SDK integration to embed AI programming capabilities into applications, Codebuff provides a powerful and flexible solution. Its open-source nature means developers can customize and extend it according to their needs.

As artificial intelligence technology continues to develop, tools like Codebuff will increasingly integrate into software development workflows, redefining how developers interact with code. For developers looking to improve development efficiency, reduce repetitive work, and explore AI-assisted programming possibilities, Codebuff is undoubtedly a tool worth trying.