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VibeFlow

Ship code while you sleep.

A full AI engineering team — developer, architect, PM, QA, security, and DevOps — that shares context, remembers decisions, and ships code autonomously. This isn't autocomplete. It's an AI team that knows your codebase.

AI coding agent autonomously implementing features — writing code, creating git commits, and progressing tasks through a visual pipeline

AI agents are powerful.
Without structure, they're unreliable.

Every session starts from scratch. Agents forget what they learned, step on each other's work, and burn tokens re-discovering context that should have been persisted.

Context Loss

Agents start fresh every session. Architecture decisions, file conventions, and past failures are gone — forcing you to re-explain everything.

No Coordination

Multiple agents step on each other's work. No locking, no claiming, no awareness of what other sessions are doing.

No Work Queue

You manually tell agents what to do every time. There is no structured backlog, no priority ordering, and no autonomous task discovery.

Design Drift

Agents ignore your design system, invent their own conventions, and produce inconsistent code because specs live outside their context window.

No Audit Trail

No trace from requirement to implementation. You cannot see which agent changed what, when, or why — making debugging a guessing game.

Wasted Tokens

40-50% of tokens are spent on repeated context. Every session re-reads the same files, re-discovers the same patterns, and re-learns the same conventions.

The difference VibeFlow makes

Go from manual, session-by-session AI coding to fully autonomous, context-aware development with persistent memory.

Side-by-side comparison showing Vanilla Chat AI coding with high token waste and context loss versus VibeFlow with structured context, persistent memory, and efficient token usage
Agent Teams

Stop coding alone.
Build with an AI team.

VibeFlow gives you a full team of AI specialists — a developer, architect, product manager, QA engineer, security lead, and DevOps engineer — all sharing context and working together on your project.

Alex — Senior Software Engineer

Alex

Senior Software Engineer

Your workhorse. Give Alex a feature spec, a bug report, or a todo — and get back working code with tests, committed to a clean branch.

  • Implements features end-to-end
  • Follows your project conventions automatically
  • Commits with meaningful messages and clean diffs
Aria — Senior Product Manager

Aria

Senior Product Manager

Thinks about the why before the what. Creates PRDs, writes strategies, defines requirements, and ensures the team builds the right thing.

  • Drafts PRDs, strategy docs, and feature specs
  • Defines acceptance criteria and success metrics
  • Reviews output from a product perspective
Morgan — Senior Architect

Morgan

Senior Architect

Looks at the big picture. Designs systems, data models, API contracts, and catches design flaws before they become expensive refactors.

  • Designs system architecture and data models
  • Reviews code for scalability and maintainability
  • Documents architectural decisions (ADRs)
Quinn — Senior QA Lead

Quinn

Senior QA Lead

Thinks about everything that can go wrong — and writes tests to prove it won't. Ensures the code your team ships actually works.

  • Writes comprehensive test suites
  • Identifies edge cases and failure modes
  • Validates against acceptance criteria
Casey — Security Lead

Casey

Security Lead

Scans every change for security issues — injection vulnerabilities, auth gaps, data exposure risks, and compliance violations.

  • Identifies OWASP Top 10 vulnerabilities
  • Validates authentication and authorization flows
  • Ensures SOC 2, HIPAA, ISO 27001 compliance
Parker — DevOps Engineer

Parker

DevOps Engineer

Handles the last mile — getting code from a branch to production safely. Deployment configs, CI/CD pipelines, and operational monitoring.

  • Configures deployment pipelines and GitOps
  • Sets up monitoring, alerting, and logging
  • Orchestrates canary deployments and rollbacks

Precision context. Every level.

Not a giant context dump. VibeFlow organizes knowledge at four levels — project, feature, todo, and issue — so agents load only what's relevant to the task at hand. The result: faster execution, fewer tokens, zero hallucination from stale context.

L1

Project Context

Entire project
Architecture & stack decisions
Cross-feature conventions & patterns
Session history & commit log
Key files & directory map
L2

Feature Context

Per feature
Feature-specific gotchas & decisions
Implementation checklists
Component architecture & file map
Design docs & style guides
L3

Todo & Issue Context

Per work item
Attached PRDs & acceptance criteria
Wireframes & visual references
Linked documents & assets
Execution logs & progress history
L4

Git-Backed Truth

Every commit
Full commit attribution per agent
Line counts added & deleted
Traceable from requirement to code
Version-controlled & auditable

When Alex works on Todo #247, VibeFlow loads the project architecture, the parent feature's gotchas, and the todo's attached design doc — nothing more. Precision retrieval means fewer tokens, faster execution, and zero noise from unrelated context.

"It's like having a team that's been working on your project for a year, from day one. They know the codebase, they know the patterns, and they never forget a lesson learned."

Everything agents need to ship autonomously

Seven integrated capabilities that turn AI coding agents from powerful-but-chaotic into reliable, autonomous implementers.

Visual Project Management

A drag-and-drop swimlane board that gives humans full visibility into project status. Features flow through a defined lifecycle — from planning to done — with real-time status updates and progress tracking.

  • Nested hierarchy: Projects, Features, Todos, and Issues
  • Auto-refresh dashboard with real-time status updates
  • Status cascade — completing all todos marks features done
  • Progress tracking with completion percentages and counts
Visual kanban dashboard with swimlane columns showing project features flowing through planning, implementing, and done statuses

From plan to shipped code

Five steps from a feature request to a verified git commit. Humans plan, agents execute, VibeFlow orchestrates.

Autonomous agent lifecycle loop showing four phases: Initialize session and load context, Poll and claim work items, Execute implementation with git commits, and Finalize by updating context and cascading status
Step 1

Plan the work

Define features, write specs, attach design docs and context files in the visual dashboard.

Step 2

Agent initializes

Agent calls session_init, loads project context, design docs, and discovers the work queue.

Step 3

Autonomous execution

Agent polls for work, implements features, runs tests, and commits to git with tracked line counts.

Step 4

Context captured

Agent updates context files with what it learned — gotchas, decisions, and implementation notes.

Step 5

Review & repeat

Check the kanban, verify QA, approve or reject. Agent polls for the next item automatically.

Cut token costs by 45-65%

Structured context loading eliminates redundant token consumption. Agents spend tokens on implementation, not re-discovering your codebase.

Vanilla Vibe Coding
Tokens per task~50K
Context overhead40-50%
Work discoveryManual
Session continuityNone
Agent idle timeHigh
With VibeFlow
Tokens per task~25K
Context overheadStructured loading
Work discoveryAutonomous polling
Session continuityPersistent memory
Agent idle timeZero

Terminal UI for all your agents.

A terminal-native session manager that lets you launch, switch between, and supervise multiple AI coding agents from a single interface. Whether you're running Claude Code, OpenAI Codex, or Google Gemini — VibeFlow CLI keeps them all organized, isolated, and productive.

Open source on GitHub
VibeFlow CLI terminal interface showing multiple AI coding sessions with session list, live output preview, and keyboard shortcuts

Multi-Session Management

Launch multiple AI coding sessions in isolated tmux sessions. Switch between them with a single keypress — every session's provider, branch, and status visible at a glance.

Any AI Coding Agent

Built-in support for Claude Code, OpenAI Codex CLI, and Google Gemini CLI. Add any custom agent binary through YAML configuration.

Persona-Based Teams

Assign specialized personas — Developer, Architect, QA, Security, PM — to each session. Multiple personas work concurrently without conflict.

Git Worktree Isolation

Every session runs in its own git worktree on its own branch. No merge conflicts between concurrent agents. Automatic cleanup when sessions end.

Autonomous Execution

Connect to a VibeFlow server and agents become fully autonomous — polling tasks, implementing, committing, and reporting back while you supervise.

Single Go Binary

Zero external dependencies beyond tmux. Everything embedded — agent configs, provider definitions, full TUI. Works on macOS and Linux out of the box.

See It In Action

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