welt-io-langgraph

The LangGraph (Python) adapter for Welt's wire contract.

Install

uv add welt-io-langgraph

Usage

See examples/agent — the smallest complete agent built on this package (text streaming, tool use, file output, file input, and human-approval tools). The sections below explain the adapters it wires in.

Supported Versions

Welt

While both are 0.x, a welt-io-langgraph 0.Y release supports Welt v0.Y. From 1.0 on, a release supports any Welt release that shares its major version, and the minor versions move independently. Support is best effort either way, and other combinations come with no guarantee.

LangGraph

The badge at the top states the range this release installs against. Every push and pull request runs the suite at both ends of it: the declared floor, and the newest release CI has picked up. That is best effort rather than a guarantee — the floor is where the suite was last seen to pass, so a later release may raise it, and no ceiling is declared at all. langchain-core comes along as a dependency and carries no floor of its own, because LangGraph asks for a newer one than anything here needs.

The badge follows the current release. For the range an older release declared, read that release's own metadata on PyPI.

Something misbehaving inside that range is worth an issue.

API

The wire between Welt and the agent is JSON, specified by Welt's wire contract — plain LangGraph values do not fit it in either direction. Two functions adapt the inbound payload, two the outbound stream. The messages they read are LangChain's, which carry standard content blocks.

Inbound

decode_messages(messages)

Turns Welt's Converse-shaped messages — built from the Slack thread, file bytes base64-encoded — into role/content message dicts that feed the graph input ({"messages": decoded}) as-is:

Each file-carrying block gets the media type LangChain models expect in place of the Converse format token, and the base64 data stays base64 — standard content blocks need no decoding.

decode_interrupt_responses(responses)

Turns Welt's resume payload — a mapping of interrupt id to the answer a human chose and the widget it came from — into the mapping Command(resume=...) takes, answering every pending interrupt at once. A plain interrupt resumes with the answer as the value it was given:

agent.astream(
    Command(resume=decode_interrupt_responses(payload["interrupt_responses"])),
    config,
    stream_mode=["messages", "updates"],
)

The interrupt ids are LangGraph's own, as emitted by renderable_events; the config must point at the interrupted thread, which the host app stashes when an interrupt event goes by (see the example agent). Answers to a HumanInTheLoopMiddleware request are rejoined into the decisions it resumes from, so the host app calls this the same way either way.

What arrives is taken as correct

Welt builds the payload and checks its own output against the wire contract before releasing it, so these two functions do no field validation of their own. A payload that departs from the contract is a bug on the sending side rather than an input to guard against, and it surfaces as an ordinary error from whatever touches it first — a KeyError or a TypeError here, or a refusal from LangChain or Bedrock further on.

The one thing decode_messages refuses outright is a content block of a kind Welt never sends. A messages turn carries only text, image, document, and video blocks; a toolUse or toolResult block is not a malformed one of those but a forged conversation turn, and rebuilt into history it would let a caller that is not Welt put words the model treats as its own past tool calls and their results into the run. It raises ValueError. This is a trust-boundary check, not the field validation the contract otherwise saves you from.

Outbound

renderable_events(stream, files_from=...)

Reduces the (mode, payload) items of astream(..., stream_mode=["messages", "updates"]) — whose values Welt does not render — to the events Welt renders:

A run that stops for human input ends its stream with one interrupt event per pending interrupt — or per reviewed action, for a HumanInTheLoopMiddleware request; agents that do not use interrupts see no change.

A tool hands files to the model for either of two reasons — to have it read them, or to give them to the human — and only the agent knows which is which, so name the tools whose files belong in the thread:

async for event in renderable_events(stream, files_from={"create_sample_file"}):

A tool left out keeps its files to the model: one that reads a PDF for the model does not drop it into the thread as a side effect. A tool named there returns the file as a content block, which the model reads and Welt uploads:

return [
    {"type": "text", "text": f"Created {name}.csv."},
    {
        "type": "file",
        "name": name,
        "mime_type": "text/csv",
        "base64": b64encode(csv).decode("ascii"),
    },
]

A tool message carries the name of the tool that produced it, so nothing else has to be passed in. Uploaded names come from the block's own name plus its media type, the block's kind for the rest (image.png). That name is also the model's handle on the document — Converse rejects a request whose messages carry two documents under one name, so a tool that returns files has to keep their names apart across the run: the example appends a short uuid to each.

Each event carries only what Welt reads, and an event with nothing to render — a text chunk the model left empty, a file with no bytes — is not sent at all.

interrupt_reason(message, options=..., input=...)

Builds the structured reason Welt renders as a message with the specified widgets — choice buttons (options), a free-text field (input), or both. An option's value is any JSON value, and the pressed button answers with it as it was declared; with neither widget the message renders as itself and Welt's default Approve / Deny buttons answer it. The specs are the wire's own shapes, typed as OptionSpec and InputSpec, and omitted fields keep Welt's defaults:

answer = interrupt(
    interrupt_reason(
        "Deploy to prod?",
        [
            {"value": "Deploy", "style": "primary"},
            {"value": "Cancel"},
        ],
        input={"label": "Or type your answer"},
    )
)

Building the reason through this helper is what makes a typo an error. interrupt takes its value as Any, so a dict literal handed to it directly is checked by nothing, and Welt's reaction to a reason it cannot match is its default Approve / Deny buttons — no error, no log, just widgets you did not ask for. The typed parameters catch a misspelled key before the run; the checks inside catch it in runs where no type checker was involved. What they check is the shape, not the size: how many buttons one Slack block holds, and how long a button value may be, are Welt's to enforce.

Working with interrupts

Welt's Interrupts doc covers the Slack side: how each reason renders, who can answer, multiple questions, and expiry. On the LangGraph side:

• interrupt needs a checkpointer, even though the conversation history lives in Slack — pausing and resuming run through checkpoints. An in-memory checkpointer works on AgentCore Runtime, where each session keeps its own microVM.
• Start each conversation turn on a fresh thread. Welt sends the whole Slack thread every turn by default, so letting the checkpointer stack turns into its own history would double the conversation. Resume alone reuses the interrupted thread's config. (An agent that keeps its own history instead sets AGENT_MANAGES_HISTORY on the Welt side.)
• A plain interrupt value renders too. Any non-structured value — interrupt("Deploy to prod?") — becomes a question with Welt's default Approve / Deny buttons, whose answers arrive as True / False.
• Code before interrupt runs again on resume. LangGraph re-executes the interrupted node (or tool) from its start, so wrap whatever precedes an interrupt and must not run twice — side effects, or work that must match what the human approved — in a LangGraph task: a completed task is not re-executed on resume; its saved result is reused. The example agent's sample_draft_report shows the pattern.

Gating tools with HumanInTheLoopMiddleware

LangChain's HumanInTheLoopMiddleware pauses a tool before it runs, named in interrupt_on rather than written into the tool — which is what lets a tool the agent did not write, from a library or an MCP server, be gated at all. It works over Welt as-is:

create_agent(
    model=...,
    tools=[send_email],
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={
                "send_email": InterruptOnConfig(allowed_decisions=["approve", "reject"])
            }
        )
    ],
    checkpointer=InMemorySaver(),
)

The decisions an action allows become its widgets, and the answer comes back as the decision the widget stands for:

• A press is identified by the widget it came from. Welt says which widget produced each answer, so a press maps to the decision its button stands for and submitted text becomes the respond decision — a typed "approve" included, since what it means is read where meaning belongs: it reaches the model as the tool's answer.
• edit has no widget, rewriting an action's arguments being a form the wire has no shape for. An action allowing edit alongside others is asked about with the widgets for the rest; one allowing edit alone is passed through to Welt's fallback rendering, whose answers the middleware cannot resume from.
• One request becomes one question per action. The middleware bundles every gated call of a turn into a single interrupt and resumes from one decision per action; a Welt stop carries as many questions as it likes, so each action is asked about on its own — buttons per action, answered in any order, and Welt resumes the run once all of them are answered.
• Write the interrupt yourself when the question depends on the tool's own work. The middleware knows a call's name and arguments, nothing the tool computes, so showing something the tool produced — a draft, a diff, a dry run — needs interrupt inside the tool, as sample_draft_report does.

License

MIT