Vocion vs. LangGraph

LangGraph is an open-source library for building agent applications as graphs of nodes with durable state. It is the control-flow layer: you define the graph, it persists state through a checkpointer, and interrupt() pauses a run until you resume it with Command(resume=...) (https://docs.langchain.com/oss/python/langgraph/interrupts). What it deliberately does not include is the operating layer around a fleet of agents — a catalog of agent definitions under version control, a review queue a non-engineer can work through, per-action approval thresholds, or an audit row linking an output to the configuration that produced it. Vocion is an open-source agent workforce platform that supplies that operating layer: agents and workflows authored as YAML in git, applied to Postgres, and run by a workflow runner with approval gates (docs/entities/workflow.md, docs/workspace.md in vocion-core).

This page compares the two on the axes an operations read of a LangGraph prototype actually runs into: human-in-the-loop, registry, versioning, durability, and hosting. Several rows go to LangGraph, and they stay in — a comparison a LangGraph engineer would call unfair is worse than no comparison. Last verified 2026-09-08, against @vocion/core v2.37.0.

When LangGraph is the right answer

Plainly: when you want custom control flow in Python, you already have a LangChain stack, you are iterating on the graph shape daily, or you need the larger ecosystem — more integrations, more Stack Overflow answers, more engineers who already know the API. LangGraph's community and surface area are both larger than Vocion's, and that is not a close call. If the team is one or two engineers running one agent, adding an operating layer on top of that graph is very likely more process than the job needs.

Capability comparison table

Human-in-the-loop: interrupt() vs a review queue

Both are real, and neither is a workaround for the other. interrupt() pauses one graph run for one caller, waiting on Command(resume=...) in the same process or a resumed session — it is a per-run primitive, and the docs are explicit that production use needs a persistent checkpointer behind it. Vocion's approve and ask steps pause a workflow run inside a shared review queue that any authorized caller can list, filter, and act on over HTTP (GET /api/v1/reviews) — a multi-agent, multi-item work surface rather than a single paused function call. Neither the LangGraph docs nor Vocion's own API list a webhook or SSE channel for the queue: a client polls GET /api/v1/reviews for pending items and calls POST /api/v1/reviews/decide to act on one. If a caller needs to know the instant an item lands, that caller is polling, on either stack.

Where agent definitions live

In LangGraph, an agent is code: the graph, its nodes, and any prompts live wherever the Python or JS project puts them, versioned the way the rest of the codebase is versioned. In Vocion, an agent is a file (agents/<slug>.md, plus its supporting workflows/ and skills/) in a workspace repo, applied to the database with workspace:apply, which is what produces the workspace_version audit row. The practical difference: a Vocion agent change is reviewable in a PR by someone who does not read Python, and it is traceable after the fact from a specific output back to the exact workspace_sha that produced it (docs/workspace.md).

Temporal, on either stack

LangGraph's own durability is the checkpointer described above — state persists across a resume, but LangGraph does not run on Temporal itself. Vocion's workflow steps persist to Postgres directly (docs/guides/self-hosting.md calls this "in-process durable step runner"); Temporal is part of the Vocion stack for schedules and source syncs (docs/object-model.md), not (yet) for workflow step durability — Temporal-backed workflow durability is listed as forthcoming work in the self-hosting guide.

Keeping LangGraph and adding the operating layer

There is a documented path for bringing your own agent runtime into Vocion — see bring your own agent runtime. packages/agent-runtime exposes one HTTP contract (POST /invocations streaming SSE, GET /ping) that a compiled agent definition can be sent to, hostable anywhere including AWS Bedrock AgentCore (packages/agent-runtime/ README.md). Whether a LangGraph graph specifically can be dropped behind that contract with no changes is not documented — treat it as "you would wire the contract yourself," not as a supported integration today.

Licensing and hosting

LangGraph the library is MIT-licensed; LangGraph Platform is the vendor's hosted product, with deployment options documented at the link above. @vocion/core is MPL-2.0 plus a commercial license, is not published to npm, and is self-hosted — clone the repo and run it (README.md). Neither project has a hosted free tier documented as of this date; verify pricing directly before quoting it to a reader.

Which to pick

Pick LangGraph alone if you are one or two engineers, need custom control flow, want the larger ecosystem, or your only reviewers are engineers who are fine reading interrupt() state in a debugger. Pick Vocion if the people approving agent actions are not engineers, if you need an audit trail from an output back to the configuration that produced it, or if you want the agent roster itself under version control rather than embedded in application code. Pick both if you have an existing LangGraph investment and want a review queue and versioning layered on top of it — that path exists in principle (packages/agent-runtime) but is not a documented, supported integration today.

Related

• Workflows — the approve / ask step types referenced above.
• The review queue over HTTP — the full /api/v1/reviews surface.
• Bring your own agent runtime — the HTTP contract for hosting an existing agent inside Vocion.

Read the Vocion workflow entity reference or clone vocion-core and run a workflow with an approve step to see the review queue firsthand.