Every organization runs on knowledge work—projects, processes, tasks. Most teams already structure this work in tools like Linear, HubSpot, Jira, GitHub, and internal systems.
At the same time, AI agents have become capable enough to execute a large portion of that work, especially well-scoped, structured tasks.
The problem isn’t model capability anymore.
The problem is the missing bridge.
Today, we’re excited to share that Peakflo 20X is now open source — a desktop app that connects your organized work directly to AI agents and turns your task list into an AI-powered workforce.
What Is Peakflo 20X?
Peakflo 20X is a self-improving agent orchestrator for knowledge work. It:
- Syncs tasks from your work management tools
- Assigns them to AI agents
- Streams execution live
- Keeps humans in the loop for critical decisions
- Improves over time through a skills system
20X runs entirely on your desktop, so your data never leaves your machine unless you choose to connect external tools. It does not require a cloud backend or ongoing subscription, and it is released under the MIT license so you can use, modify, and extend it freely.
Why We Built It
The engineering team at Peakflo built 20x internally because we kept copy-pasting Linear tickets into Claude, manually setting up branches, and babysitting agent output across terminals. Eventually, we just built the infrastructure to connect the two sides directly — and decided to open source it.
At Peakflo, we realized something important: The bottleneck to AI adoption isn’t model intelligence. It’s the lack of connective tissue between structured work and the agents that can execute it.
Teams already have well-organized task systems.
Agents are capable of execution.
But no one had built the infrastructure layer connecting the two in a reliable, observable, human-controlled way.
Peakflo 20X is that infrastructure layer.
How Peakflo 20X Works
1. Tasks Flow In
Tasks sync from tools like Linear, HubSpot, and Peakflo (with more integrations coming).
2. Agents Execute
You assign tasks to AI agents, such as:
- Claude Code
- Codex
- OpenCode
Agents read context, reason through the task, and execute step by step.
3. Live Monitoring
You can:
- Watch the output stream in real time
- Pause execution
- Approve risky actions
- Intervene anytime
4. Skills System
Create reusable instruction templates like:
- “Follow our code style guide.”
- “Run tests before committing.”
- “Use our compliance checklist.”
Skills track confidence over time and improve based on feedback.
5. Local-First Architecture
- SQLite database
- No external cloud storage
- Secure API key handling
- Full context isolation
You stay in control of your data.
The Architecture Is Task-Agnostic
We started with engineering workflows because agents are strongest there today. But the system itself doesn’t care what the task is. As AI improves in:
- Research
- Writing
- Analysis
- Finance operations
- Customer support
The same orchestration pipeline applies. That’s where things get interesting.
Below are two real-world use cases we’re already exploring.
Use Case 1: AI Support Agent
Support teams often spend hours every day handling repetitive tickets. These include status updates, clarification requests, bug reports, and configuration issues that follow predictable patterns but still require time and attention.
With Peakflo 20X, an AI Support Agent can:
- Pull new tickets directly from HubSpot
- Identify root causes using connected systems
- Resolve issues autonomously
- Flag high-risk issues for human approval
- Update ticket status automatically
Instead of a chatbot responding externally, this agent works inside your actual support workflow.
The impact:
- Faster first response time
- Reduced backlog
- Escalations only when necessary
Your team focuses on complex edge cases. The agent handles the repetitive layer. And because it runs locally, sensitive customer data doesn’t flow through additional SaaS tools.
Use Case 2: AI Back Office Agent
Back office teams often spend a significant amount of time dealing with manual bottlenecks across invoice processing, payment follow-ups, collections, claims, compliance checks, and reconciliations. Although these workflows are highly structured, they remain labor-intensive and require constant coordination and repetitive effort from the team.
With Peakflo 20X, AI agents can:
- Pull finance ad operations tasks and data automatically
- Process invoices against defined rules
- Draft payment reminders
- Update internal systems
- Trigger next-step workflows
- Maintain audit-friendly logs
Multiple agents can work in parallel. What used to take days of manual coordination can be completed in minutes, with approvals where needed.
The result:
- Faster turnaround
- Lower operational load
- Reduced human error
- Clear audit trails
Your back office becomes proactive instead of reactive.
Operations, finance, customer support, product, and growth teams can all use it to orchestrate structured work through AI agents. Any team that manages tasks in a system can plug into 20X and turn those tasks into executable workflows. If the work lives in your task manager, it can be orchestrated.
What’s Next
We’re just getting started.
Planned expansions include:
- Linux and Windows version coming soon
- More integrations (Jira, GitHub Issues, Notion, Asana)
- Agent templates
Because 20X is open source, the community can help shape where it goes.
Build Your AI Workforce
If you’ve been experimenting with AI tools but haven’t found a way to integrate them deeply into your operational workflow, 20X may be the missing piece.
It’s not another chatbot. It’s an execution layer for knowledge work.
👉 Explore the repo: https://github.com/peakflo/20x
The question isn’t whether AI can assist your team. The question is: What would you delegate first?
