Agents can do incredible things.
But they can't scale. Yet.
Your agent is Accomplishing...
through the same problem from scratch.
Full cost. Same latency. Same risk of failure.
Every time.
pflow turns that reasoning into workflows
your agent can reuse.
Plan once. Run forever.
Open source • Works with any local agent •
More example workflows coming soon
Demo recordings coming soon
Read the or view the source.
Drop the agent
Compiled workflows run as CLI commands. Pipe them, chain them, cron them. No orchestration overhead. Minimal LLM costs. Same reliable process every time.
Every tool call returns to the model. Every return costs tokens and time.
Traditional agents make round-trips to the model between every tool call—each one requiring inference. A 5-step workflow means 5+ inference passes, tokens accumulating at every step.
With pflow, workflows compile once. After that, data flows through validated nodes without returning to the model — 94% fewer tokens on a typical 4-step workflow
By writing explicit orchestration logic, Agents make fewer errors than when juggling multiple tool results in natural language. With pflow, just as with Programmatic Tool Calling, the model only needs to reason about the final result.
Five node types. Your agent composes the rest.
Your agent mixes deterministic execution with selective intelligence. MCP, HTTP, and Shell nodes cost zero orchestration tokens. LLM and Agent nodes are used only where reasoning adds real value — with the best model for each job.
So how does it work?
"Use pflow for workflow automation"$ pflow workflow discover "task"$ pflow registry discover "capability"$ pflow workflow-name$ pflow registry run node$ pflow instructions create$ pflow workflow save workflow.json$ pflow workflow-name param=valueEvery MCP you've been avoiding? Now you can use them.
Anthropic built Tool Search and Code Execution to tackle MCP's context cost. pflow shares the goal but differs on philosophy: LLMs perform best with clear, reusable blocks—not the freedom to generate anything. Structured workflows. Validated nodes. Others solve execution. pflow solves the lifecycle—persist, discover, reuse, compose.
Connect everything. pflow handles the complexity.
See how pflow solves: MCP Context Tax · Inference overhead · Context pollution · Safety and reliability

Your terminal. Your data. Your AI models. Your agents.
Open source and free forever. pflow workflows run locally with the AI providers you choose and get created by Agents that you already trust. No lock-in to OpenAI, Anthropic, or anyone else. Your workflow definitions and execution logs stay on your machine as JSON files. Install once, own forever.
Your agent solving the same problem again?
That's repeated reasoning that should have been saved as a workflow.
What if that workflow was plain markdown. But executable.
The AI orchestrates. It never sees your data.
pflow uses structure-only orchestration during creation of workflows. AI understands what to connect, not the data flowing through it. Your sensitive information stays in the runtime, never enters AI context.
Result: 10-20× token efficiency. Sensitive data stays out of AI context — relevant for regulated industries.
Use case: Let powerful cloud models create a workflow. Use local or compliance-verified models inside the workflow to read data.
{
"id": 123,
"name": "John Smith",
"email": "john@example.com",
"ssn": "███-██-████",
"dob": "1990-01-15",
"address": {...},
"payment_method": {...},
... 40 more fields
}
Your safety checks can't be skipped. Ever.
Traditional agents make individual tool calls, reasoning through each step every time. They might skip validation, modify the wrong data, or take different execution paths. This is why developers limit agents to read-only operations.
With pflow, agents operate with workflows as composite tools instead of executing individual steps. The entire workflow becomes a single, deterministic tool call—safety checks and guardrails are compiled in and can't be bypassed.
The result? You can automate write operations, deployments, and workflows you'd never trust to a traditional agent.
→ validate_input()
→ ask_confirmation()
→ execute_write()
✓ Success→ validate_input() → ask_confirmation() → execute_write() ⚠ Skipped safety
→ validate_input() → exec_different_path() ⚠ Different approach ⚠ or failure
→ validate_input()
→ ask_confirmation()
→ execute_write()
✓ Success→ validate_input()
→ ask_confirmation()
→ execute_write()
✓ Success→ validate_input()
→ ask_confirmation()
→ execute_write()
✓ SuccessTwo Ways to Use pflow
The open source CLI is the foundation — built for AI agents and developers. Cloud extends it with team collaboration and hosted execution.
Open Source CLI
Install in 60 seconds

“If this ain't a gem I don't fucking know what is.”— Opus 4.0
Managed Cloud
Everything in CLI, plus team features

FAQ
Common Questions
Common questions about pflow
What to expect
- A public roadmap shaped by your feedback
- New features and fixes every week

