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pflow is a workflow compiler for AI agents. Your agent reasons through a task once, pflow compiles it into a workflow, and that workflow runs instantly forever after — no re-planning, no repeated costs. Like shell scripts, but for AI operations.

The problem

AI agents re-reason through every task from scratch, even ones they’ve solved before:
  • Inconsistency: Agents take different paths or skip steps between runs — there’s no way to verify without watching
  • Cost: Each reasoning pass costs tokens — the same tokens, for the same logic, every time
  • Context bloat: Loading tool schemas (especially MCP servers) consumes tokens before any work begins

How pflow helps

pflow separates planning from execution:
  1. Your agent plans once - figures out what nodes to use and how to connect them
  2. pflow compiles the workflow - saves it as a reusable .pflow.md file
  3. Execution is instant - run the same workflow with different inputs, zero reasoning cost
Nodes are individual tools (call an LLM or API, read a file, or any MCP tool). Workflows chain them together. Save a workflow and your agent can discover it later, reuse it with different inputs, or chain it into a larger workflow.

Built for agents

The workflow format is markdown — headings, YAML, and code blocks. Agents already think in this structure, so they can write and iterate on workflows using patterns they already know. A side effect: open a .pflow.md file on GitHub and it reads like documentation. Render it in your IDE and it’s self-explanatory. The same file that executes as a workflow also serves as its own docs.

From idea to command

# Your agent builds and iterates on a .pflow.md workflow file
claude "Can you help me make a release workflow? Let's use pflow"

# You or your agent save it for easy reuse
pflow workflow save ./workflow.pflow.md --name generate-changelog

# Anyone can run it
pflow generate-changelog since_tag=v0.7.0

# Next time, your agent already knows how
claude "Generate a changelog for the next release"

Get started