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Overview

The Inworld’s Unreal Runtime lets you iterate on prompts, models, custom nodes/edges parameters, and configs (LLM and TTS) without redeploying code. This guide covers the full workflow: graph creation, Portal setup, and experiment rollout.

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Experiments workflow summary

1

Create a Graph asset

Design your graph and enable remote config.
2

Register variants

Upload JSON configs for the baseline graph and its variants.
3

Start experiment

Define targeting rules and rollout percentages in the Portal.
4

Monitor & roll out

Track metrics, then promote the winning variant.

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Step 1 - Create a Graph asset

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1. Code your solution

In this example we developed a simple Blueprint that instantiates the graph, runs it, and prints the response on screen.

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2. Design your graph

The sample graph makes an LLM call: It includes a custom node that appends “PREFIX” (when its AddPrefix boolean is enabled) and inputs A and B as: 
PREFIX --- {A} --- {B}

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3. Enable remote config

Enable remote config inside the graph editor: Graph executions return results as:
  • AddPrefix is false: {Request text} --- {LLM response}
  • AddPrefix is true: PREFIX --- {Request text} --- {LLM response}
The baseline uses Ministral-8b-Latest with AddPrefix = false:

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Step 2 - Register variants

Registering a variant tells Portal which configuration can participate in Experiments. Start with the baseline, then add variants.

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1. Register the baseline variant

  • Register Graph:
  • Copy the Graph ID from the Graph Editor:
  • Enter or paste the Graph ID to the Portal:

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2. Create baseline variant

  • Export baseline JSON config from the Graph Editor:
  • Click on created Graph then Create Variant in the Portal:
  • Upload baseline JSON config to the Portal:

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3. Create GPT-5 variant

  • Export GPT-5 config from the Graph Editor:
  • Create and upload GPT-5 variant to the Portal:

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4. Create GPT-5 with AddPrefix variant

  • Export GPT-5 config from the Graph Editor:
  • Create and upload GPT-5 with prefix variant to the Portal

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Step 3 - Start an experiment

Open the Targeting & Rollout tab and set default variant: Changing the default targeting updates the configuration for:
  • All currently running graph instances (on their next execution)
  • All newly compiled graphs
Targeting changes may take time to propagate.

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Example results

Baseline execution: Switch targeting to GPT-5: Next execution result: Switch targeting to GPT-5 with prefix: Next execution result:

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Step 4 - Monitor & roll out

Monitor your experiment results and deploy the winner:
  • Watch metrics: Monitor dashboards, traces, and logs while the experiment runs
  • Gradual rollout: Increase the winning variant’s allocation gradually (50/50 → 70/30 → 90/10), then set it to 100% and retire old rules
  • Rollback: Roll back or tweak allocations if latency, errors, or business KPIs regress

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How Experiments work

When a request hits your graph, the runtime decides whether to use the local configuration or a remote variant from Experiments:
  1. Remote config must be enabled
  2. The graph ID must be registered in Experiments and have at least one active rule that returns a variant
If remote config is enabled, Experiments evaluates each request as follows:
  1. Local cache check: If the compiled variant for this user is cached, it executes immediately; otherwise Experiments is queried
  2. Variant fetch: Experiments evaluates your targeting rules, returns the selected variant, and falls back to the local configuration if no rule applies or the fetch fails
  3. Compile & cache: The runtime compiles the variant payload, caches it, and executes the graph with the new configuration

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Troubleshooting

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Failed to resolve struct flag

Error: LogInworldRuntime: Failed to resolve struct flag: ....... with targeting key: ....... with error: Flag not found If you see this error in the output log, it usually means one of the following:
  • Remote Config is enabled, but the graph was not registered in Portal
  • The Graph ID registered in Portal does not match the Graph ID in Unreal Engine

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Targeting changes not appearing

Issue: I changed targeting and don’t see changes on the next executions Sometimes targeting updates take time to propagate. Wait a few moments and retry—the new variant will be applied on the next graph execution once the change is fully propagated.

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Supported via Experiments

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Applied on next executions (no code redeploy)

  • Switch LLM/STT/TTS models or providers
  • Adjust node configuration (temperature, token limits, prompts)
  • Reorder/add/remove nodes while preserving the same inputs/outputs
  • Update processing logic (edges, preprocessing steps, data flow)
  • Modify custom node parameters
  • Modify custom edge conditions parameters

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Requires a code deployment

  • Deploying new custom nodes/edges
  • Changing processing functions for custom nodes/edges
  • Changing the graph’s input/output interface