feat(rlm): Implement RLM Core Traits (LlmBackend, RlmEnvironment)
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enhancement
- Dominant language
- Rust
- Stars
- 4.5k
- Forks
- 603
- Avg merge
- 23h 32m
- Merged PRs (30d)
- 59
Description
Summary
Implement the foundational traits for Recursive Language Model (RLM) integration as specified in ADR-014.
Tasks
- Define
LlmBackendtrait with generate, embed, and model info methods - Define
RlmEnvironmenttrait with retrieve, decompose, synthesize, answer_query methods - Implement
RuvLtraBackendusing existing RuvLTRA model - Add token budget tracking
- Add KV cache integration
References
- ADR-014:
docs/adr/ADR-014-recursive-language-model-integration.md - DDD-001:
docs/ddd/DDD-001-recursive-language-model.md
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with docs/adr/ADR-014-recursive-language-model-integration.md and docs/ddd/DDD-001-recursive-language-model.md to understand the intended RLM design. Locate the existing RuvLTRA model and the integration entry points, then define the two traits, implement RuvLtraBackend, and add token-budget tracking and KV-cache integration. Done means the specified methods and integrations are implemented and covered by the project's applicable tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- rust
- Domain
- ai, backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100