Repository map first
List modules, plugins, gameplay systems, content roots, build targets, owners, and generated directories before selecting task evidence.
Kimi K3 1M context × Unreal repositories
Use Kimi K3's million-token context without flooding an Unreal task
Structure a large Unreal repository for Kimi K3's one-million-token context, with retrieval boundaries, task state, validation, and a tracked prototype handoff.
Direct answer
Kimi K3's official launch states a one-million-token context window and strong repository navigation. That capacity can hold more project history, but loading an entire Unreal repository is rarely the safest or cheapest first step. Start with a map, retrieve the smallest evidence set that can answer the task, and expand only when a named gap remains.
A million-token window is capacity, not a repository strategy
Large Unreal projects mix source, Blueprints, assets, generated data, plugins, build output, logs, documentation, and platform configuration. A reliable agent task needs an evidence graph and explicit state, not an indiscriminate archive dump.
Task-scoped retrieval
Pull only files, symbols, logs, screenshots, configuration, and history connected to the current acceptance checks.
Stable instruction layer
Keep engine version, coding rules, source-control policy, forbidden paths, test commands, and product boundaries separate from changing task evidence.
Context refresh rules
Replace stale evidence, summarize completed stages, record unresolved questions, and start a clean task when earlier reasoning creates contradictions.
A four-stage Unreal evaluation workflow
Define the exact change
Name the player-visible outcome, relevant system, engine and plugin versions, target platform, non-goals, and pass/fail checks.
Build the evidence graph
Connect source files, Blueprint owners, assets, logs, captures, tests, and documentation to each decision rather than relying on directory proximity.
Work in reversible stages
Plan, inspect, patch, compile, test, capture evidence, and checkpoint before expanding into another module or subsystem.
Freeze the handoff
Return changed files, assumptions, commands run, test results, unresolved risks, rollback point, and the next human decision.
Four bounded task prompts
Use these as task contracts, not as capability claims. Each one asks for observable evidence and a stopping condition.
Repository map prompt
Map modules, plugins, key systems, generated directories, build targets, and ownership for one feature; flag missing or conflicting project instructions.
Minimal evidence prompt
For one gameplay bug, name the smallest set of files, logs, screenshots, and tests required before proposing a fix, and explain why each item matters.
Context refresh prompt
Summarize completed stages, verified facts, rejected hypotheses, remaining questions, and safe next actions; exclude obsolete reasoning and unrelated files.
Native handoff prompt
Produce a file-level Unreal implementation and validation plan with Blueprint or C++ ownership, commands, performance checks, packaging matrix, and rollback point.
Concrete outputs to retain
Repository task map
A compact graph of modules, systems, owners, dependencies, generated paths, and evidence relevant to the task.
Context manifest
Every included file, log, image, instruction, and summary with freshness, purpose, sensitivity, and source.
Reversible change plan
Small stages with explicit validation, source-control boundaries, checkpoints, and stop conditions.
Prototype and native split
A browser-playable experience brief plus a separate Unreal implementation, build, performance, packaging, and review plan.
Best fit and human-review boundary
Best for
- Large Unreal repositories where one task crosses several modules
- Long-running investigations that need explicit stage summaries and recovery
- Teams trying to control context cost, privacy, and stale assumptions
Still needs human review
- A repository owner must approve sensitive context, tool permissions, write scope, and source-control actions
- Unreal engineers must compile, run automation, profile performance, package, and test on target hardware
- Asset, plugin, license, security, and platform constraints cannot be inferred from source context alone
Official evidence and adjacent K3 Unreal routes
Capability, availability, architecture, and pricing claims on this page are bounded to Moonshot AI's July 2026 launch post. Social comparisons are treated as demand signals, not verified results.
Kimi K3 1M context × Unreal repositories FAQ
How large is Kimi K3's context window?
Moonshot AI's official launch states that Kimi K3 has a one-million-token context window. Token capacity is not the same as reliable comprehension of every repository detail, and it does not eliminate retrieval, freshness, or instruction conflicts. Measure task outcomes with a controlled evidence set instead of assuming the largest possible prompt is automatically better.
Should I send my whole Unreal repository to Kimi K3?
Usually not as a first step. Exclude generated builds, caches, binaries, unrelated assets, secrets, personal data, and modules outside the task. Begin with a repository map and explicit question, then retrieve relevant source, Blueprint ownership, logs, screenshots, configuration, and tests. Expand only when a named evidence gap blocks progress.
How do Blueprints fit into a large-context workflow?
Blueprint behavior is not fully represented by adjacent C++ text. Provide authoritative exports, screenshots, asset references, ownership notes, runtime logs, and the engine version needed to understand the graph. A human must still open the project, inspect nodes and defaults, compile, test runtime behavior, and verify serialization and packaging.
Can million-token context prevent hallucinations?
No. More context can supply more evidence, but it can also contain stale code, generated files, conflicting instructions, duplicated APIs, and irrelevant logs. Use source labels, freshness dates, an evidence map, explicit uncertainty, retrieval limits, and verification commands. Treat any untested conclusion as a hypothesis regardless of context-window size.
What should be persisted between long agent sessions?
Persist the frozen brief, starting commit, project instructions, evidence manifest, decisions, commands run, results, rejected hypotheses, changed files, unresolved questions, and rollback point. Summarize completed stages rather than replaying every token. This makes recovery auditable and reduces the chance that old thinking history overrides the current task boundary.
Does SEELE AI receive the Unreal repository through this page?
No. The generation link carries only the visible game brief and attribution parameters to SEELE AI's generation page. It does not upload your repository, select Kimi K3, or grant tool access. The resulting browser-playable prototype is a separate design-validation artifact, not proof of native Unreal integration.
What is the best first task for a large Unreal repository?
Choose a narrow, player-visible issue with a known reproduction, a small ownership surface, and measurable acceptance checks—for example one interaction state or camera regression. Avoid starting with a project-wide refactor. A bounded task reveals whether retrieval, repository navigation, testing, and human-review practices work before the workflow is scaled.
Test the playable direction before native Unreal production
The prompt describes the complete game slice and does not select a model. This final route keeps the paid-download reminder and full attribution chain attached.