A structured, machine-readable graph of your goals, priorities, and intentions — readable and writable by any AI tool.
{ "id": "goal_V1StGXR8", "title": "Launch AI Startup", "status": "active", "priority": { "level": "critical", "score": 95 }, "timeHorizon": "this_quarter", "children": 4, "progress": 0.35 }
You use five or more AI tools every day. Each one optimizes in isolation with zero awareness of your bigger picture. More tools should mean more leverage, but instead it means more chaos.
Each AI tool optimizes in isolation. Your coding agent doesn't know about your Friday pitch. Your writing assistant ignores your product roadmap. They all act like the only tool you use.
Memory tools store facts about you. But knowing you're a data scientist doesn't tell an agent you're launching a startup this month. Facts describe what is. Intent describes what you're trying to achieve.
You re-explain your priorities to every tool, every session. The cognitive overhead grows with each new AI tool you adopt. You're spending more time managing agents than benefiting from them.
GoalOS gives every AI tool a structured understanding of your goals, priorities, and intentions. Define your intent once. Every connected tool reads from the same source of truth.
Get started in under two minutes. Initialize your intent graph, define your goals, connect your AI tools, and watch them align to what actually matters.
Every connected tool now shares your context
Clean APIs, strong types, and zero magic. GoalOS works the way developers expect: import a library, call methods, get structured data back.
import { IntentGraph } from '@goalos/core'; // Create your personal intent graph const graph = IntentGraph.create('abtin', 'Q1 Goals'); // Add a root goal with full context const startup = graph.addGoal({ title: 'Launch AI Startup', status: 'active', priority: { level: 'critical', score: 95 }, timeHorizon: 'this_quarter', motivation: 'Build sovereign AI infrastructure', successCriteria: ['10 paying customers', 'Public launch'], }); // Add a sub-goal with dependency const landing = graph.addGoal({ title: 'Ship Landing Page', parentId: startup.id, status: 'active', priority: { level: 'high', score: 80 }, deadline: '2026-03-15T00:00:00Z', }); // Every AI tool now reads this context const priorities = graph.getTopPriorities(5); const progress = graph.getProgress(startup.id);
from goalos import IntentGraph, Goal, Priority # Create your personal intent graph graph = IntentGraph.create("abtin", "Q1 Goals") # Add a root goal with full context startup = graph.add_goal( title="Launch AI Startup", status="active", priority=Priority(level="critical", score=95), time_horizon="this_quarter", motivation="Build sovereign AI infrastructure", success_criteria=["10 paying customers", "Public launch"], ) # Add a sub-goal with dependency landing = graph.add_goal( title="Ship Landing Page", parent_id=startup.id, status="active", priority=Priority(level="high", score=80), deadline="2026-03-15T00:00:00Z", ) # Every AI tool now reads this context priorities = graph.get_top_priorities(5) progress = graph.get_progress(startup.id)
# Initialize a new intent graph $ goalos init --name "Q1 Goals" Created ~/.goalos/graph.json # Add your top-level goal $ goalos add "Launch AI Startup" \ --priority critical \ --horizon this_quarter \ --domain work Added goal_V1StGXR8: Launch AI Startup # Add sub-goals $ goalos add "Ship Landing Page" \ --parent goal_V1StGXR8 \ --priority high \ --deadline "2026-03-15" Added goal_Kq9mZpL2: Ship Landing Page # View your goal tree $ goalos tree Launch AI Startup [critical] ........... 35% Ship Landing Page [high] ............. active Write Manifesto [high] ............... active Ship MCP Server [medium] ............. planned # Start the MCP server for Claude Desktop $ goalos serve GoalOS MCP server running on stdio
First-class support for both Python and TypeScript. Pick your language, install the package, and start defining your intent graph.
from goalos import GoalManager manager = GoalManager() # Add a goal with full context goal = manager.add_goal( title="Ship v2.0", priority="critical", domain="engineering", success_criteria=["All tests pass", "Docs updated"] ) # Get top priorities across all goals priorities = manager.get_priorities(count=5)
import { IntentGraph } from '@goalos/core'; const graph = IntentGraph.create('my-project'); // Add a goal with full context const goal = graph.addGoal({ title: 'Ship v2.0', priority: { level: 'critical', score: 95 }, domain: 'engineering', successCriteria: ['All tests pass', 'Docs updated'], }); // Get top priorities across all goals const priorities = graph.getTopPriorities(5);
GoalOS integrates with ChatGPT, Claude, CrewAI, LangChain, and any tool that speaks MCP or OpenAPI. Define your intent once, use it everywhere.
Native integration with Claude Desktop and any MCP-compatible client. GoalOS exposes nine tools for the full goal lifecycle: read context, add goals, track progress, and more.
// claude_desktop_config.json { "mcpServers": { "goalos": { "command": "npx", "args": ["@anthropic/goalos-mcp"] } } }
Use GoalOS with ChatGPT Custom GPTs via the OpenAPI specification. Import the spec as a Custom GPT Action and ChatGPT can read and manage your intent graph directly.
# spec/openapi.yaml openapi: "3.1.0" info: title: "GoalOS API" version: "0.1.0" paths: /goals: get: # List all goals post: # Create a new goal /goals/{id}/priorities: get: # Get top priorities
Goal-aware CrewAI agents out of the box. The callback automatically feeds intent context into every crew task, so your agents align to what matters most.
from goalos import GoalManager from goalos.integrations.crewai import GoalOSCrewAICallback manager = GoalManager() callback = GoalOSCrewAICallback(manager) # Your CrewAI agents now read from # the shared intent graph automatically crew = Crew( agents=[researcher, writer], callbacks=[callback] )
Drop-in LangChain callback that injects goal context into chain runs. Every LLM call, tool use, and agent step is aware of what you are trying to achieve.
from goalos import GoalManager from goalos.integrations.langchain import GoalOSLangChainCallback manager = GoalManager() callback = GoalOSLangChainCallback(manager) # Attach to any LangChain chain or agent result = chain.invoke( {"input": "Plan next sprint"}, config={"callbacks": [callback]} )
Whether you're an individual power user or leading a team, GoalOS adapts to your workflow. Define intent at any scale.
Connect five or more AI tools through a single intent graph. Every tool you add makes every other tool more useful. Stop re-explaining your priorities and start compounding your leverage.
Build agents that understand user context from day one. Read the intent graph to know what matters, respect permissions, and ship AI that's aligned by design rather than by accident.
One intent graph connects your coding agent, writing assistant, and project manager. Your Friday pitch prep informs your Monday sprint. Context flows across every tool you touch.
Shared intent graphs for sprint goals, project milestones, and team alignment. Scoped permissions let each agent access only what it needs. Everyone works from the same source of truth.
Everything you need to integrate structured intent into your AI stack. TypeScript core, Python SDK, CLI, MCP server, and a spec that's extensible by design.
TypeScript core library with full type safety, schema validation, event system, dependency resolution, and merge strategies. The foundation everything else builds on.
Model Context Protocol server for Claude Desktop and any MCP-compatible client. Nine tools covering the complete lifecycle from reading context to completing goals.
Command-line tool for managing intent graphs. Initialize, add goals, set priorities, view tree structures, export data, and start the MCP server from your terminal.
Full Python SDK with Pydantic models, async support, and an API that mirrors the TypeScript library exactly. First-class support for the Python AI ecosystem.
Linked Data serialization for interoperability and semantic web compatibility. Intent graphs are portable, self-describing, and ready for any system that speaks JSON.
Granular agent permissions: read, write, complete, create sub-goals, and reprioritize. Scope access by goal, domain, or tree depth. Your intent, your rules.
Open source. Self-hosted. Your data stays yours. Define your intent once and let every AI tool work toward what actually matters.