Llama-Conductor Provides Router And Grounded Memory Harness
Llama-conductor is an open-source Python router and RAG harness that integrates llama-swap, llama.cpp, Qdrant, and popular frontends to provide deterministic memory and grounded reasoning. It implements Vodka (CTC context trimming and Total Recall), Mentats for Vault-based verification, and filesystem KB integration to reduce hallucinations, goldfish memory, and context bloat. Install via pip and run the router, then connect model runners, llama-swap, and Qdrant for Vault features.
Key Points
- 1Provides deterministic router and memory harness (Vodka, Mentats, Vault) integrating llama-swap and Qdrant
- 2Reduces hallucinations and context bloat by enforcing Vault grounding and trimming chat history (CTC)
- 3Enables low-resource devices to run 4B models reliably while maintaining verifiable provenance and TTL
Scoring Rationale
Practical, credible tooling with broad developer relevance; limited novelty beyond integration and workflow improvements, but highly usable.
Sources
Public references used for this report.
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