Framework Ports

This page turns adjacent-framework examples into a practical Hypergraph evaluation loop:

1. Pull real upstream examples into a local corpus

2. Prioritize the ones that best match Hypergraph's strengths

3. Rebuild them in a clean, Hypergraph-native style

4. Use the resulting ports to spot real documentation gaps and framework friction

The raw corpus lives under tmp/framework_corpus/. Rebuild it with:

uv run python scripts/build_framework_corpus.py

Why This Matters

This project is less about "winning a comparison" and more about pressure-testing Hypergraph against the workflows people already reach for:

• LangGraph / Mastra / Inngest for agentic loops, chats, and human review

• Hamilton / pipefunc / scikit-learn for explicit, data-heavy DAGs

• DBOS / Restate for durable orchestration and long-running workflows

Looking at those examples side-by-side clarifies where Hypergraph is already elegant and where the user experience still needs work.

Top Ports

These are the highest-priority examples I ported because they map directly onto current Hypergraph capabilities and reveal useful trade-offs.

Port	Upstream inspiration	Why it was prioritized	Hypergraph file
Agentic RAG	LangGraph adaptive RAG	Shows nested graph composition plus route-driven source selection without a state schema	examples/framework_ports/agentic_rag.py
Support Inbox	Mastra suspend/resume, Inngest support HITL, DBOS agent inbox, Restate chat durability	Exercises nested interrupts, pause propagation, and resume semantics in a realistic support workflow	examples/framework_ports/support_inbox.py
ML Model Selection	Hamilton modular ML example, scikit-learn preprocessing/training pipelines	Shows that Hypergraph can express modular ML workflows without turning the whole graph into framework-specific plumbing	examples/framework_ports/ml_model_selection.py
Document Batch Pipeline	DBOS document ingestion, Restate RAG ingestion, pipefunc mapped examples	Demonstrates the "write one document graph, then fan it out" pattern with mapped graph nodes	examples/framework_ports/document_batch_pipeline.py

What The Ports Suggest

Where Hypergraph feels strong

• Nested graphs are natural. The LangGraph-style and support-style ports become smaller once the repeated subflows are first-class graphs.

• Automatic wiring helps. The Hamilton- and scikit-learn-inspired pipelines stay readable because values move by name instead of through explicit edge boilerplate.

• Interrupt propagation is a good primitive. The support example shows that nested human-review steps can surface cleanly to the outer run.

• Mapped graph nodes are a real differentiator. The document and ML ports both benefit from the "think singular, scale later" model.

Where Hypergraph still feels thin

• Durability is more explicit than in DBOS, Restate, or Inngest. Hypergraph has checkpointing, but it is not yet as turnkey for event waits, inbox queries, or long-running service-style orchestration.

• The best map_over() patterns are under-documented. The capability is strong, but people coming from pipefunc or batch-oriented systems need more first-class examples.

• HITL examples should talk about persistence earlier. Interrupts are well-covered, but the durable "pause today, resume tomorrow" story should be easier to discover.

• Framework-comparison examples deserve a home in the docs. These ports make the trade-offs concrete in a way abstract comparison tables do not.

Suggested Next Ports

These upstream examples are promising next steps after the current set:

• DBOS reliable-refunds-langchain for a more explicit durable approval flow

• Restate chat-bot for longer-lived chat/session patterns

• Inngest code-assistant-rag for a tool-using research assistant

• Hamilton reverse_etl for a business-data pipeline with real IO boundaries

• pipefunc sensor-data-processing for a non-LLM batch-processing example

Verification

The committed ports are covered by focused tests in tests/test_framework_ports.py.