hi @Levitan-factory
Your ICC DSL is trying to hold two very different things in one file, and that is where the friction comes from:
- The workflow definition - structure, routing, references, output contracts (c1 to c2 to c3). This is code. It belongs in a StateGraph, or the Functional API, in git.
- The execution record - what actually ran, with which inputs and outputs, in what order and why. This is data. It belongs in a checkpointer for rerun, plus LangSmith traces for inspection.
Once you split them, both goals fall out of primitives LangGraph already ships, so you stop reinventing persistence and replay.
On where to keep it - it is not an either/or menu. YAML is fine for constraints and knobs but terrible for routing; the moment it grows conditionals it is a programming language with no debugger. Notebooks are fine for authoring, hostile to reproducibility (hidden state, out-of-order cells). Logs are neither inspectable at scale nor rerunnable. So: definition in code, constraints in config passed at invoke time, rerun state in a durable checkpointer, inspection trail in LangSmith. Your c1/c2/c3 map almost 1:1 onto nodes; %from is edges plus state channels; > openai.max is the model bound in that node; @file is a typed output channel. If you want something that reads like intent cells, the Functional API (one @task per cell) is the closest fit - each task result is checkpointed and restored on resume.
On the minimum state record - LangGraph has effectively answered this. A checkpointer saves a snapshot of graph state at each super-step, keyed by thread_id and checkpoint_id. The Checkpoint record is channel_values (the artifacts), channel_versions plus versions_seen (which node consumed which upstream version - this is your %from, recorded rather than declared, and it is literally how LangGraph decides what runs next), and next (pending nodes), with metadata step, parents, run_id. That maps straight onto your five wants: artifact produced is channel_values; upstream inputs used is channel_values plus versions_seen; why the next step ran is versions_seen plus next; you read it back via get_state_history and replay by invoking with a past checkpoint config.
Two things the checkpoint does not capture for free, and your DSL is right to make them explicit: which model ran and what constraints were active. Put those in state and in trace metadata/tags, so which model each cell used becomes a LangSmith query instead of a grep.
The critical caveat, which most homegrown DSLs get wrong: rerunnable is not the same as reproducible. Replaying a step re-executes it, it does not return a cached result - LLM calls fire again and can return different output. So resume is deterministic (completed steps are restored from the checkpoint), replay is a fresh answer, and the exact past artifact is reproduced by reading the checkpoint, not by re-running the model. Corollary: > openai.max is a moving target, max resolves to a different model next quarter, so pin the exact model id and params (temperature, seed, max_tokens). The checkpoint freezes your state; you must freeze the model contract.
So the genuine minimum record is: one checkpoint per step (channel_values, versions_seen, next, metadata) keyed by thread_id/checkpoint_id, plus the pinned model id and params and the active constraints per step. Keep the DSL if you like it, but make it a thin front-end that compiles to a graph, and let the checkpointer plus LangSmith be the actual trail - you get durable execution, time travel and queryable traces for free.
Docs: Checkpointers Checkpointers - Docs by LangChain - Replay/time travel Use time-travel - Docs by LangChain - Functional API determinism Functional API overview - Docs by LangChain - Metadata and tags Add metadata and tags to traces - Docs by LangChain