Roadmap

Documentation Index

Fetch the complete documentation index at: https://docs.pflow.run/llms.txt

Use this file to discover all available pages before exploring further.

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Current status

Where pflow is today (v0.12.0):

• Standalone orchestration engine with compile-once batch optimization (~7x speedup for large parallel workflows)
• Markdown workflows — .pflow.md files that agents read and write naturally
• Node system — file, llm, http, shell, code (Python), claude-code, and MCP bridge
• Conditional branching — on-error, static routing, and data-driven routing via code nodes with branch convergence (?? coalesce operator)
• Nested workflows — saved or file-based workflows as nodes inside other workflows, with automatic input/output mapping
• Memoization cache — unchanged nodes serve cached results across re-runs, with --only, --no-cache, and per-node cache: false controls
• Execution reports — --report generates navigable markdown directories with rendered prompts, responses, token breakdowns, and cost data per node
• Dry-run execution plans — --dry-run previews cost, duration, and cache boundaries before execution
• Workflow visualization — pflow visualize generates Mermaid flowcharts of workflow topology
• Batch processing — process arrays through nodes, sequential or parallel, with per-item parameter overrides and error handling modes
• External file references — prompt: ./prompts/system.md keeps long content out of workflow files
• Workflow bundling — pflow save packages workflows with all file dependencies as self-contained folders
• Unified diagnostics — one error format across CLI text and JSON output, with structured suggestions and source file provenance
• Recursive sub-workflow validation — structural errors caught before any node executes
• Template variables — ${var} syntax with nested path access, automatic JSON parsing, and structured output schemas
• AI agent integration via CLI and MCP server
• Discovery — find nodes and workflows by describing what you need
• Unix-first piping — stdin/stdout, works with any Unix tool
• Skills publishing — save workflows as Claude Code skills, cross-platform
• Settings management — API keys, node filtering
• Unified model support — 100+ providers (OpenAI, Anthropic, Google, OpenRouter, Ollama, …) via LiteLLM
• Published on PyPI — uv tool install pflow-cli

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Now

Human-in-the-loop approval gates — pause workflow execution for human review before continuing. Workflows that create PRs, send messages, or modify infrastructure need a trust gate. Without this, workflows that take real-world actions aren’t trustworthy enough to run unattended.

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Next

Iteration speed and workflow quality

• Function-based code node syntax — write Python functions instead of top-level scripts, with automatic input/output wiring from type annotations
• Workflow export — export a workflow to standalone Python with zero pflow dependency. Build and iterate with structure, ship plain code.
• Workflow testing — mock nodes, assert outputs, pflow test. Modify a saved workflow and know it still works before re-publishing.
• Code and shell linting — catch syntax errors in code blocks during validation, not at runtime
• Batch limits — cap iteration count for development and cost control

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Later

Security and sandboxing

• Sandboxed execution runtime — isolated execution for shell and code nodes. Needed before running agent-generated workflows you haven’t reviewed.
• Structured output for Claude Code node — typed JSON responses from agentic coding tasks
• Export as MCP server packages — distribute workflows as standalone MCP servers that work without pflow installed

MCP ecosystem

• MCP gateway integration — route to remote MCP servers
• Dynamic MCP discovery — search and install MCP servers on demand instead of manual configuration
• OAuth for remote MCP servers — authenticate with HTTP-based MCP servers

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Vision

pflow is infrastructure, not a destination. It provides building blocks and a runtime — agents do the assembly. The better the building blocks get, the more capable the agents become. The longer-term direction:

• Code node dependency management — install packages on demand for code nodes
• TypeScript code node — for teams that think in TypeScript
• Reduce/fold for batch — aggregate batch results incrementally instead of collecting all at once
• Windows compatibility — run on Windows without WSL

pflow improves through a direct loop: agents build workflows, we find where they struggle, we fix it. Because the surface area is finite — node types, template syntax, error messages — each fix is targeted and compounds. A year from now pflow will be meaningfully better at helping agents build workflows, not because of some grand vision but because each friction point gets filed down one at a time.

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Get involved

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Built by a developer who got tired of watching agents re-think the same tasks.
Questions or ideas? Reach out — andreas@pflow.run