Mission Control for your AI team. Watch specialized agents collaborate in real-time, executing complex tasks with complete visibility and maximum control.
Single AI assistants give you one perspective. Commander.ai gives you a specialized team you can tune in real-time.
Unlike chatbots that force you to wait and guess, Commander.ai shows you exactly what's happening as your AI team works—and lets you engineer their behavior on the fly.
This isn't a chat interface with agents bolted on. It's Mission Control with a prompt engineering workshop built in.
Edit agent behavior in real-time without code changes
- 🔧 Visual Prompt Editor - Full CRUD for agent prompts via UI
- 🧪 Live LLM Testing - Test prompts with GPT-4o-mini before activating
- 📊 Performance Metrics - See tokens, time, and cost per test
- 🎯 Template Variables -
{query},{token_budget},{urgency},{tools_list} - 🔄 A/B Testing - Toggle between active/inactive prompts
- 🔍 Search & Filter - Find prompts by role, version, or content
- ⚙️ One-Click Access - Settings icon on every agent card
Impact: Frontend teams tune agents independently, 10x faster iteration, data-driven optimization.
Automate your AI team to work around the clock
- 📅 Flexible Scheduling - Cron expressions or simple intervals (every N minutes/hours/days)
- 🌍 Timezone Support - Full timezone selection for cron schedules
▶️ Run Now - Manual execution for testing or immediate needs- 📊 Execution History - Track past runs with metrics (tokens, duration, success rate)
- ⚡ Real-Time Updates - Scheduled tasks appear in Mission Control via WebSocket
- 🎛️ Complete Control - Enable/disable, edit, delete schedules on the fly
- 🔄 Persistent - APScheduler with PostgreSQL-backed job store survives restarts
- 🎯 Agent-Specific - Clock icon (⏰) on each agent card for schedule management
Use Cases:
- Daily health checks:
@alice check deprecated models(every day at 9am) - Regular updates:
@bob search latest AI news(every 4 hours) - Weekly reports:
@maya weekly reflection(Fridays at 5pm) - Automated maintenance:
@rex analyze performance metrics(daily)
Impact: Set it and forget it - your AI team works around the clock on schedules you define.
Intelligence layer for agent optimization
- 📈 Real-Time Leaderboards - See which agents excel at what tasks
- 🎯 Multi-Perspective Scoring - LLM self-assessment + user feedback + objective metrics
- 🎖️ Reward System - Bonuses/penalties for quality, efficiency, innovation
- 🧠 Intelligent Routing - Auto-route to best-performing agents
- 📊 Performance Charts - Score trends, category breakdown, task stats
- 💡 Routing Insights - Tooltips show agent strengths, model info, category performance
- 💰 Cost Efficient - ~$0.001/task for full intelligence layer
Impact: Data-driven agent selection, continuous improvement, optimized resource allocation.
Change LLMs on the fly - no restart needed
- 🔄 Live Switching - Change agent models without downtime
- 🌐 Multi-Provider - OpenAI (GPT-4o, GPT-4o-mini) + Anthropic (Claude Sonnet 4, Haiku 4)
- 💾 Database-Backed - Model configs persist with version tracking for rollback
- 🎨 UI Integration - Provider icons, model info in routing tooltips
- 🛡️ Safe Reload - Blocks changes if agent has active tasks, auto-rollback on failure
- 🧠 Per-Agent Config - Optimize each agent independently (speed vs quality)
Impact: Optimize agent performance and cost without backend deploys.
Real-time visibility into your AI team
- 🔴 Live Agent Activity - Watch tokens flow, LLM calls execute, nodes progress in real-time
- 📊 Complete Metrics - Tokens (green), LLM calls (purple), tool calls (yellow), duration (blue)
- 🎯 Execution Flow - Expandable timeline showing every step, decision, and tool use
- 🔍 Agent Graphs - Inline LangGraph visualization with zoom and pan
- 💬 Conversation Stream - Chronological command/response flow with expandable details
- 🎨 Light/Dark Theme - Full theme support with instant switching
- 🚀 True Parallelization - Multiple agents working simultaneously with separate metrics
One-click delegation for common tasks
- 📄 Alice: "List all documents", "Archive old files", "Check deprecated models"
- 🔍 Bob: "Latest AI news", "Market research", "Competitive analysis"
- 📊 Rex: "Analyze data", "Generate report", "Performance metrics"
- ⚖️ Sue: "Check compliance", "Regulatory review", "Risk assessment"
- 💡 Maya: "Reflect on progress", "Identify patterns", "Strategic insights"
- 🧪 Kai: "Verify solutions", "Test hypotheses", "Iterative refinement"
- 💬 Chat: "Answer questions", "General assistance", "Web search"
Maximum flexibility and control
- 🎯 Smart Routing - Single @mention = direct, multiple = @leo orchestrates
- 🖱️ Multi-Select - Hold ⌘/Shift to select multiple agents
- 🧹 Clear Completed - Remove finished tasks to keep conversation focused
- 🔍 Filter by Agent - See only tasks for specific agents
- ⚙️ Agent Settings - Per-agent prompt engineering, model switching, scheduling
- 📈 Performance Dashboard - View agent leaderboards, charts, routing insights
Mission Control: Real-time visibility into your AI team
Left Panel: AI Agents
- 7 specialized agents with real-time metrics
- Live token counts, LLM calls, tool usage
- Current processing node ("→ reasoning...")
- Active/queued task indicators
- System activity dashboard
Center Panel: Conversation Stream
- Chronological command/response flow
- Expandable metrics & execution flow
- Inline agent graph visualization with zoom
- Smooth, stable rendering (no animations to distract)
Right Panel: Quick Actions
- One-click pre-configured commands
- Organized by agent specialty
- Auto-fills command input
- Examples:
- 📄 Alice: "List all documents", "Archive old files"
- 🔍 Bob: "Latest AI news", "Market research"
- 📊 Rex: "Analyze data", "Generate report"
Agent tiles showing real-time token counts, LLM calls, and current processing node
Multiple agents working in parallel with complete execution visibility
Watch Your Agents Work:
@kai (Reflexion Specialist)
🟢 1 active
1,234 tok | 3 LLM | 2 tools | → reasoning
Every agent tile updates live as they:
- Consume tokens (green counter)
- Make LLM calls (purple counter)
- Use tools (yellow counter)
- Progress through workflow nodes (blue text)
Completed Task Tracking:
- "Done" counter in System Activity
- "Clear Completed" button (only shows when needed)
- Confirmation before clearing
- Keeps conversation focused on active work
Expandable metrics showing tokens, LLM calls, tool calls, duration, and step-by-step execution timeline
Single-Click Agent Selection:
- Click any agent tile → Auto-fills command input with
@agent - Instant delegation - just add your task and press Enter
- Example: Click @bob → type "latest AI news" → Send
Multi-Select with Modifier Keys:
- Hold ⌘ (Command) or Shift while clicking agents
- Build multi-agent commands effortlessly
- Example workflow:
1. Click @bob → "@bob " 2. Hold ⌘ + Click @alice → "@bob @alice " 3. Hold Shift + Click @kai → "@bob @alice @kai " 4. Add task: "research and document quantum computing" 5. Send → @leo orchestrates all three agents
Smart Command Routing:
- Single @mention → Direct to that agent
- Multiple @mentions → @leo orchestrates the team
- No @mention → Defaults to @leo
💡 Tip: Look for the hint "Hold ⌘ / Shift to select multiple agents" in the Agent Panel
Your conversational interface with live web search.
- GPT-4o-mini for natural conversations
- Automatic web search when you ask current questions
- Agentic tool execution loop
- Context-aware responses
Deep research with multi-source synthesis.
- Tavily web search + LLM analysis
- Automatic compliance flagging
- 24h cache for general queries, 1h for news
- Your investigative journalist
Keep your projects legally sound.
- GDPR, HIPAA, data protection review
- Regulatory compliance analysis
- Risk assessment and policy checks
- Your legal safeguard
Turn numbers into insights.
- Statistical analysis and visualization
- Pattern detection and trend analysis
- Matplotlib chart generation
- Your data scientist
Semantic document search and storage.
- PDF processing with OCR
- Web search → persistent storage
- Vector embeddings via Qdrant
- Collection management (create/delete/search)
- Your librarian with superpowers
Quality control through critique.
- Content review with severity ratings
- Issue identification (critical/important/minor)
- Generates improved versions
- Quality scoring (0-1.0)
- Your editor and QA team
Iterative reasoning through self-reflection.
- Up to 3 self-improvement cycles
- Shows reasoning evolution
- Self-critique and refinement
- Your deep thinker
@kai executing reflexive reasoning with full execution trace
The breakthrough: You're not stuck with pre-programmed agent behavior. Commander.ai v0.3.0 introduces live prompt engineering—edit how your agents think, test changes instantly, and optimize AI orchestration outcomes on the fly.
Access the Workshop:
- Hover over any agent card
- Click the ⚙️ Settings icon
- Enter the Prompt Management UI
Hover over any agent card to reveal the ⚙️ Settings icon. Click to access Prompt Management.
Inside the Prompt Engineer:
- 📋 Browse & Search
- View all prompts for the selected agent
- Search by keyword across descriptions and prompt text
- Filter by type (system, human, ai) and active status
- See creation/update timestamps
Prompt List Modal showing search, filters, and prompt cards with type badges, active status, and update timestamps.
- ✏️ Create & Edit
- Write new prompts with rich template variables
- Edit existing prompts while preserving version history
- Add dynamic variables:
{query},{token_budget},{urgency},{tools_list} - Toggle active/inactive for A/B testing
Prompt Editor with description, prompt text area, template variables section, and Test button for live LLM validation.
-
🧪 Test with Real LLM
- Click "Test" on any prompt
- Enter a test query
- See live GPT-4o-mini response with your custom prompt
- View performance metrics:
- Response time (ms)
- Token usage (prompt + completion)
- Total cost estimation
- Debug compiled messages (system + user prompts)
-
🔄 Iterate & Optimize
- See results instantly
- Compare prompt variations
- Measure impact on token efficiency
- Optimize for speed vs. quality
The prompt engineering interface follows an intuitive three-step process:
- Access - Hover over any agent → Click ⚙️ Settings
- Browse - Search/filter prompts → View details → Click Edit
- Edit & Test - Modify prompt → Add variables → Test with GPT-4o-mini → Save
Each step is designed for speed and clarity, making prompt optimization feel natural.
Before v0.3.0:
- Agent behavior was hardcoded
- Tuning required backend changes and redeployment
- No way to test prompt modifications
- One-size-fits-all approach
With v0.3.0 Prompt Engineering:
- ✅ Tune agents without touching code
- ✅ Test prompts with real LLM before activating
- ✅ See metrics: tokens, time, cost
- ✅ A/B test different approaches (toggle active/inactive)
- ✅ Dynamic variables adapt to task context
- ✅ Version history tracks all changes
Scenario 1: Reduce Token Usage
Problem: @bob uses too many tokens for simple queries
Solution:
1. Open @bob's prompts
2. Edit system prompt to be more concise
3. Test with "latest AI news"
4. Compare tokens: 1,234 → 856 (31% reduction!)
5. Activate optimized prompt
Scenario 2: Improve Response Quality
Problem: @maya's reflections lack depth
Solution:
1. Clone existing system prompt
2. Add: "Provide 3 specific examples for each issue"
3. Test with sample content
4. See richer, more actionable feedback
5. Switch to new prompt
Scenario 3: Task-Specific Behavior
Problem: Need @alice to prioritize speed over accuracy for demos
Solution:
1. Create new "demo_mode" prompt
2. Add variable: {mode} = "demo" | "production"
3. Adjust system instructions for speed
4. Test and activate for demos
5. Switch back to production mode after
You're not just using AI—you're engineering how AI thinks.
- Frontend teams can optimize agent behavior without backend deploys
- Prompt engineers can iterate 10x faster with live testing
- Product teams can A/B test different agent personalities
- Operations can tune for cost vs. performance in real-time
This is the workshop that turns Commander.ai from a tool into a platform.
# 1. Clone and configure
git clone https://github.com/iotlodge/commander.ai.git
cd commander.ai
cp .env.example .env
# Add your OPENAI_API_KEY and TAVILY_API_KEY to .env
# 2. Start infrastructure (PostgreSQL, Redis, Qdrant)
docker-compose up -d
# 3. Backend
uv sync # or: pip install -r requirements.txt
alembic upgrade head
python -m uvicorn backend.api.main:app --reload
# 4. Frontend (new terminal)
cd frontend && npm install && npm run dev
# 5. Open Mission Control
open http://localhost:3000That's it. You're in the command center.
# Quick questions with live web search
@chat what's the latest news about AI safety?
# Deep research
@bob research quantum computing breakthroughs in 2026
# Compliance review
@sue review this privacy policy for GDPR compliance
# Data analysis
@rex analyze sales trends from last quarter
# Document management
@alice search web for "climate change reports" into research_collection
# Quality assurance
@maya review this code for potential issues
# Complex problem solving
@kai solve: how can we reduce API latency by 50%?Click any Quick Action button to auto-fill commands:
- Alice: "List all documents" →
@alice list all documents in the system - Bob: "Latest AI news" →
@bob what's the latest news in AI? - Rex: "Generate report" →
@rex generate a detailed analytical report on
Edit the command, add context, hit Enter. Done.
Commander.ai learns and optimizes agent performance automatically.
The system tracks, evaluates, and routes tasks intelligently using:
- 🎯 Multi-Perspective Scoring - Objective metrics, LLM self-assessment, peer reviews, user feedback, category performance
- 🏆 Reward System - Gamification with bonuses/penalties based on quality, efficiency, speed, and innovation
- 🤖 Peer Evaluation - Agents (Kai + Maya) review each other's work for continuous improvement
- 🧠 Intelligent Routing - Auto-classify tasks and select best agent based on historical performance
- 📈 Real-Time Stats - Aggregated performance data drives routing decisions (~$0.001/task for full intelligence)
📘 Complete Performance System Guide →
Learn how to monitor, tune, and optimize the entire intelligence layer built across Phases 1-3.
Backend (Python 3.12+)
- LangGraph - Agent workflow orchestration
- FastAPI - High-performance async API
- PostgreSQL - Persistent storage with pgvector
- Redis - Hot memory layer (sessions)
- Qdrant - Vector database (semantic search)
- OpenAI + Anthropic - Multi-provider LLM support (GPT-4o, Claude Sonnet 4)
- Tavily - Web search API
Frontend (TypeScript)
- Next.js 14 - App Router with React Server Components
- Tailwind CSS - Utility-first styling
- shadcn/ui - Accessible component library
- Recharts - Performance visualization
- Zustand - Lightweight state management
- WebSocket - Real-time agent updates
┌─────────────┐
│ Redis │ ← Hot Layer (active conversations)
├─────────────┤
│ PostgreSQL │ ← Warm Layer (conversation history)
├─────────────┤
│ Qdrant │ ← Smart Layer (semantic search)
└─────────────┘
Every conversation persists. Every insight is searchable. Agents can recall past knowledge and build on previous work.
commander.ai/
├── backend/
│ ├── agents/
│ │ ├── base/ # Agent interface & registry
│ │ └── specialized/ # 8 specialist agents
│ │ ├── parent/ # @leo (Orchestrator)
│ │ ├── agent_a/ # @bob (Research)
│ │ ├── agent_b/ # @sue (Compliance)
│ │ ├── agent_c/ # @rex (Data Analysis)
│ │ ├── agent_d/ # @alice (Documents)
│ │ ├── agent_e/ # @maya (Reflection)
│ │ ├── agent_f/ # @kai (Reflexion)
│ │ └── agent_g/ # @chat (Chat Assistant)
│ ├── core/
│ │ └── prompt_engineer.py # 🧠 NEW - Dynamic prompt compilation & testing
│ ├── repositories/
│ │ └── prompt_repository.py # Database access for prompts
│ ├── models/
│ │ └── prompt_models.py # Pydantic schemas for prompts
│ ├── memory/ # Document store & embeddings
│ ├── tools/ # Web search, data analysis, PDF processing
│ └── api/
│ └── routes/
│ └── prompts.py # 🧠 NEW - REST API for prompt management
└── frontend/
├── components/
│ ├── mission-control/ # Three-panel UI
│ │ ├── agent-team-panel.tsx # Live agent metrics + ⚙️ Settings
│ │ ├── conversation-stream.tsx # Command/response flow
│ │ ├── quick-actions-panel.tsx # One-click commands
│ │ ├── inline-execution-flow.tsx # Metrics timeline
│ │ └── inline-agent-graph.tsx # Workflow visualization
│ └── prompt-management/ # 🧠 NEW - Prompt Engineering UI
│ ├── prompt-list-modal.tsx # Browse & search prompts
│ ├── prompt-editor-modal.tsx # Create/edit prompts
│ ├── prompt-test-modal.tsx # Live LLM testing
│ └── prompt-card.tsx # Individual prompt display
├── lib/
│ └── hooks/
│ └── use-prompts.ts # 🧠 NEW - Prompt CRUD operations
└── app/ # Next.js routes
Agent Tiles (Live Updates):
- Token consumption as agents work
- LLM call counts
- Tool usage (web search, data analysis, etc.)
- Current workflow node
- Active/queued task status
Metrics & Flow (Per Task):
Total Tokens: 1,234
├─ 800 prompt + 434 completion
LLM Calls: 3
Tool Calls: 2
Duration: 12.4s
Execution Flow (5 steps):
├─ 1. parse_input [210ms]
├─ 2. fetch_web [8.2s] 856 tokens
├─ 3. chunk_and_embed [2.1s] 362 tokens
├─ 4. store_chunks [890ms]
└─ 5. format_response [45ms]
System Activity Dashboard:
┌─────────┬─────────┬─────────┐
│ Active │ Queued │ Done │
│ 2 │ 1 │ 5 │
└─────────┴─────────┴─────────┘
[✓ Clear Completed (5)]
Complete visibility. No black boxes. See exactly what's happening.
Commander.ai is designed for extensibility. Add your own specialist in 5 steps:
mkdir -p backend/agents/specialized/agent_h
cd backend/agents/specialized/agent_h
touch __init__.py graph.py state.py nodes.py# state.py
from typing import TypedDict
class MyAgentState(TypedDict):
query: str
user_id: str
results: list[str]
error: str | None# nodes.py
async def process_query_node(state: MyAgentState) -> dict:
# Your logic here
return {**state, "results": ["processed"]}# graph.py
from langgraph.graph import StateGraph, END
from backend.agents.base.agent_interface import BaseAgent, AgentMetadata
class MyAgent(BaseAgent):
def __init__(self):
super().__init__(AgentMetadata(
id="agent_h",
nickname="vision", # @vision in UI
specialization="Image Analysis",
description="Analyzes images and extracts insights"
))
def create_graph(self) -> StateGraph:
graph = StateGraph(MyAgentState)
graph.add_node("process", process_query_node)
graph.set_entry_point("process")
graph.add_edge("process", END)
return graph# backend/agents/base/agent_registry.py
from backend.agents.specialized.agent_h.graph import MyAgent
_registry["agent_h"] = MyAgent()Done. Your agent appears in Mission Control with live metrics, Quick Actions integration, and full observability.
# Core (Required)
OPENAI_API_KEY=sk-... # GPT-4o-mini + embeddings
TAVILY_API_KEY=tvly-... # Web search
# Database (Auto-configured by docker-compose)
DATABASE_URL=postgresql+asyncpg://commander:changeme@localhost:5432/commander_ai
REDIS_URL=redis://localhost:6379/0
QDRANT_URL=http://localhost:6333
# Optional Tuning
WEB_CACHE_TTL_HOURS=24 # General content cache
WEB_CACHE_NEWS_TTL_HOURS=1 # News content cache
TAVILY_RATE_LIMIT_PER_MINUTE=60 # API rate limitdocker-compose up -dStarts PostgreSQL 16 (with pgvector), Redis 7, and Qdrant with health checks and auto-restart.
✅ v0.6.0 - NLP Command Scheduler (February 7, 2026) 🔥 MAJOR RELEASE
⏰ Automated Agent Task Execution:
- ✅ Schedule Creation - Visual UI with interval and cron support
- Clock icon (⏰) on each agent card for schedule management
- Interval schedules: Every N minutes/hours/days (5-minute minimum)
- Cron schedules: Full cron expressions with timezone support
- 50 schedules per user limit with rate limiting
- ✅ Real-Time Execution - Scheduled tasks appear in Mission Control
- WebSocket integration for live updates
- Tasks execute via existing agent pipeline
- Full metrics collection (tokens, duration, LLM calls)
- ✅ Execution Management - Complete control over schedules
- Enable/disable schedules with one click
- "Run Now" for manual execution
- Execution history viewer with performance metrics
- Edit schedules without recreation
- ✅ Backend Integration - Production-ready scheduler
- APScheduler with PostgreSQL-backed job store
- Persistent schedules survive restarts
- Automatic loading of enabled schedules on startup
- Full REST API with 9 endpoints
Use Cases:
- Daily health checks:
@alice check deprecated models(every day at 9am) - Regular updates:
@bob search latest AI news(every 4 hours) - Weekly reports:
@maya weekly reflection(Fridays at 5pm) - Automated maintenance:
@rex analyze performance metrics(daily)
Impact: Set it and forget it - your AI team works around the clock on schedules you define.
✅ v0.5.0 - Agent Performance System (February 6, 2026) 🔥 MAJOR RELEASE
🏆 Complete Intelligence Layer:
- ✅ Performance Analytics - Real-time leaderboards, charts, routing insights
- Multi-perspective scoring (LLM self-assessment, user feedback, objective metrics)
- Reward system with gamification (bonuses/penalties for quality, efficiency, innovation)
- Intelligent routing based on historical performance
- Real-time leaderboard with medals (🥇🥈🥉)
- Performance charts (score trends, category breakdown, task stats)
- Routing insights tooltips (agent strengths, model info, category performance)
- Test data generator (avoid $$$ API costs)
- ✅ Complete Performance Guide -
PERFORMANCE_SYSTEM_GUIDE.mdwith full architecture - ✅ 5 Database Tables - Scores, peer evaluations, node metrics, stats, templates
- ✅ 3 Backend Jobs - Stats aggregation, peer evaluation, performance tracking
- 📊 Cost: ~$0.001/task for full intelligence layer
✅ v0.4.0 - Dynamic LLM/Provider Switching (February 6, 2026)
💻 Multi-Provider Support:
- ✅ Live Model Switching - Change agent models on the fly (no restart needed)
- ✅ Multi-Provider - OpenAI (GPT-4o, GPT-4o-mini) + Anthropic (Claude Sonnet 4, Haiku 4)
- ✅ Database-Backed - Model configs persist, version tracking for rollback
- ✅ UI Integration - Provider icons, model info in routing tooltips
- ✅ Safe Reload - Blocks changes if agent has active tasks, auto-rollback on failure
✅ v0.3.0 - Live Prompt Engineering (February 5, 2026) 🔥 MAJOR RELEASE
🧠 Revolutionary New Feature:
- ✅ Live Prompt Engineering - Edit, test, and optimize agent behavior in real-time
- Full CRUD for agent prompts via UI
- Live LLM testing with GPT-4o-mini
- Performance metrics (tokens, time, cost)
- Template variables for dynamic context
- A/B testing with active/inactive toggles
- Search, filter, and version tracking
- ⚙️ Settings icon on every agent card
Core Features (Stable):
- ✅ Mission Control UI - Three-panel interface with real-time metrics
- ✅ 8 Specialized Agents - Leo (orchestrator), Chat, Research, Compliance, Data, Documents, Reflection, Reflexion
- ✅ Quick Actions Panel - One-click command delegation
- ✅ Live Agent Metrics - Token counts, LLM calls, tool usage, current node
- ✅ Execution Flow Tracking - Complete observability into every step
- ✅ Graph Visualization - Agent workflow diagrams with zoom controls
- ✅ Completed Task Management - Track and clear finished work
- ✅ Light/Dark Mode - Theme toggle with system preference detection
- ✅ Three-Tier Memory - Redis/PostgreSQL/Qdrant
- ✅ Web Search Cache - 24h general, 1h news TTL
- ✅ JWT Authentication - Production-ready security (94% test coverage)
- ✅ DocumentStore Singleton - Prevents connection pool exhaustion
- ✅ Agentic Tool Execution - Chat agent executes web searches automatically
What's New in v0.3.0:
- 🧠 PROMPT ENGINEERING WORKSHOP - The game-changer
- In-UI prompt editor with live testing
- Real GPT-4o-mini responses with metrics
- Template variables:
{query},{token_budget},{urgency} - Search across 10+ seeded prompts
- No backend deploy needed for tuning
- 🔧 PromptEngineer Service - Backend architecture for dynamic prompt compilation
- 📊 Prompt Testing API -
/api/prompts/testwith full metrics - 🎨 Nested Modal System - Browse → Edit → Test workflow
- 🔍 Prompt Search - Find and filter prompts by agent, type, keywords
Previous Releases:
- v0.2.0 (Feb 5, 2026) - Light/dark mode, agent status indicators, Leo orchestrator UI
- v0.1.0 (Feb 1, 2026) - Mission Control UI, 7 agents, real-time metrics
Roadmap:
- 📅 Prompt Marketplace - Share and discover optimized prompts
- 📅 Agent Integration - Auto-use PromptEngineer for all agent initialization
- 📅 Cost Analytics - Track prompt efficiency and ROI
- 📅 Vision Agent - Image analysis and generation
- 📅 CLI Interface - Terminal workflows for power users
- 📅 Code Execution - Sandboxed Python/JS agents
- 📅 Plugin System - Custom tools and integrations
- 📅 Enterprise SSO - SAML/OAuth integration
(Coming soon - walkthrough of Mission Control interface, agent delegation, and Quick Actions)
We're building the future of AI collaboration. Join us!
Ways to Contribute:
- 🐛 Report bugs or UX improvements
- 💡 Suggest new agent specializations
- 📝 Improve documentation
- 🧪 Add test coverage
- ⚡ Performance optimizations
- 🎨 UI/UX enhancements
See CONTRIBUTING.md for guidelines.
Apache License 2.0 - Commercial use, modification, distribution, and patent use allowed.
See LICENSE for full details.
- LangGraph - Agent orchestration framework
- LangChain - LLM integration layer
- shadcn/ui - Beautiful, accessible components
- Tavily - Fast, reliable web search API
- OpenAI - GPT-4o-mini powers the intelligence
Most AI tools hide what's happening. You ask, you wait, you hope.
Commander.ai shows you everything:
- Which agent is working
- What node they're on
- How many tokens they're using
- What tools they're calling
- How long it's taking
You're not just using AI. You're commanding it.
Try it. Watch @bob research while @alice stores results. See @maya catch issues before @kai refines the solution. Command, observe, control.
Questions? Ideas? Issues?
📧 Open an issue ⭐ Star the repo if this excites you 🔔 Watch for updates - we ship fast
Built by developers who believe AI should augment human capability, not replace it.
🚀 Status: v0.5.0 Production - Performance System Complete 📅 Last Updated: February 6, 2026
Three Major Releases in One Day 🎉
- v0.3.0: Live Prompt Engineering
- v0.4.0: Multi-Provider LLM Switching
- v0.5.0: Complete Performance & Intelligence System