Dagpipe MCP server

DagPipe

The reliability layer that makes AI workflows safe to ship: crash recovery, schema validation, and cost routing

<!-- mcp-name: io.github.devilsfave/dagpipe -->

NeurIPS 2025 research analyzing 1,642 real-world multi-agent execution traces

found a 41–86.7% failure rate across 7 state-of-the-art open-source systems.

The root cause: cascading error propagation, where one failed node corrupts all downstream nodes.

DagPipe makes cascade failure structurally impossible.

Every node's output is independently validated and checkpointed before the next node executes. A failure at node 4 cannot corrupt nodes 1, 2, or 3. Delete nothing. Just re-run. DagPipe resumes exactly where it stopped, automatically.

Pipeline: research → outline → draft → edit → publish
                                  ↑
                            crashed here

Re-run → research ✓ (restored) → outline ✓ (restored) → draft (re-runs) → ...

Zero infrastructure. Zero subscription. Runs entirely on free-tier APIs.

Install

pip install dagpipe-core

Requirements: Python 3.12+ · pydantic >= 2.0 · pyyaml · A free Groq API key (no credit card)

Three Ways to Use DagPipe

For developers: install the library and build crash-proof LLM pipelines in Python:

pip install dagpipe-core

For non-coders: describe your workflow in plain English, receive production-ready crash-proof pipeline code as a downloadable zip. No coding required: 👉 Pipeline Generator on Apify ($0.05/run)

For AI agents and IDE users: connect directly via MCP. Use DagPipe from Claude Desktop, Cursor, Windsurf, or any MCP-compatible client without writing any code: 👉 DagPipe Generator MCP on Smithery

The generator outputs DagPipe pipelines, as every generated zip already has crash recovery, schema validation, and cost routing built in by default. No other LLM pipeline framework ships this.

Why DagPipe?

Shortened. The full README is on GitHub.