InferDAG

DAG powered inference marketplace. Connect models, run parallel workflows, and inspect every output and cost. One graph. Many minds.

[InferDAG @inferdag]

The InferDAG Workflow Marketplace is live.

Publish your workflows. Fork new ideas.
Run multi-model DAGs. Earn one-time creator fees in USDG on Robinhood mainnet.

Your graph can now become someone else’s starting point.

Explore → https://t.co/KpnDjTqhNa

[InferDAG @inferdag]

InferDAG turns a prompt into a multi-model workflow, automatically routes every node and combines independent perspectives into a final recommendation.
Build the graph. Share the edge.
https://t.co/BZl1bqs936

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Your workflow has a budget. InferDAG enforces it.

An over-budget quote is blocked before payment. Adjust your cap, review the exact node quote, then decide whether to approve it.

[InferDAG @inferdag]

Two reviews. One actionable result.

Run security and correctness checks in parallel, then merge their outputs into prioritized fixes. Inspect every node and its token usage in InferDAG.

https://t.co/X06XMet0Rk

[InferDAG @inferdag]

One graph. Different priorities.

InferDAG can choose a model for each node using cost, estimated task quality, and observed latency. Go balanced or set a priority per step and see why it chose that model.

https://t.co/X06XMet0Rk

[InferDAG @inferdag]

Don't stop at the final answer.

Inspect each branch's output, see how the merge combines findings, and follow the result into the next step. The execution graph makes the workflow easier to review.

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Set a limit before running your workflow.

InferDAG stops a node when its payment quote exceeds the remaining cap. Raise the limit and review the exact quote before approving.

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Smart Model Router is live on InferDAG.

Let each node choose its model based on cost, estimated task quality, or observed latency or balance all three.

See why each model was selected. Keep control of every payment and your spending cap.

Try “Auto-route all” → https://t.co/TLzcTfD4VT

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Add a node, choose its model, connect its dependencies, and turn a combined code review into a release checklist.
Build the graph around your task.

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Know the price before approving a model call.

Inspect the model, enter a prompt, and review its live USDG quote on Robinhood Chain.

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From brief to final draft, with every step visible.

Build an outline, draft the story, check the claims, and bring both branches into a final edit.
InferDAG connects the models and shows per-node usage.

[InferDAG @inferdag]

Connected Workflows are live now on mainnet

Turn any question into a coordinated multi-model workflow extract the brief, evaluate trade-offs, challenge assumptions and combine the findings into one recommendation.
Every model’s output stays visible and inspectable along the way.

[InferDAG @inferdag]

Pick models per node

Different steps can use different models.

Choose a model for security review, another for correctness, then combine their findings in one workflow.

See each node's output and token usage.

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InferDAG runs security and correctness checks in parallel, then passes both outputs to a node that prioritizes fixes.
Follow every step in the graph.

[InferDAG @inferdag]

A question becomes a workflow:

Extract the brief → evaluate trade-offs + challenge assumptions → write a recommendation.

Follow each node, inspect its output, and see how the final answer comes together.

[InferDAG @inferdag]

One model shouldn’t be responsible for every part of a complex task.
InferDAG uses a DAG to turn one request into a coordinated workflow: extract the brief → run trade-off analysis and assumption checks in parallel → converge into a final recommendation.
Every node can use a different model, with its own instructions, output, status and cost.
One graph. Many minds. Better results.

[InferDAG @inferdag]

A DAG is what turns InferDAG from a basic AI wrapper into a coordinated intelligence network.
Each model becomes a specialized node research, reasoning, coding, verification or writing. Independent nodes run in parallel, while dependent nodes automatically receive the outputs they need. Multiple perspectives can branch out, challenge each other and converge into one stronger result.

This makes every workflow faster, modular and transparent. You can inspect each node, choose the right model for every task, track its output and see exactly what each call costs.

Instead of asking one model to do everything, InferDAG lets many models work together without duplicated work or circular dependencies.
One graph. Many minds. Better outcomes.

https://t.co/KgH5AeGHwH

[InferDAG @inferdag]

Two models. Two perspectives. One combined review.

Run security and correctness checks in parallel, then pass both outputs to a final node that prioritizes fixes.

That’s a DAG doing useful work.

[InferDAG @inferdag]

Dex paid

[InferDAG @inferdag]

We have burned dev supply

https://t.co/CXXQyNtqLV