LHIC-The execution layer that gets safer and smarter with every run
Dual-path execution cuts LLM costs by running 80% of actions locally with zero latency.

LLM operators inside DuckDB SQL with WASM support and multi-provider swapping.
Data engineers and analysts using DuckDB for AI workflows
MindsDB · pgvector · LanceDB
In this new release, we've added a few major updates:
- Anthropic (Claude) & Multi-Provider Support: We now support OpenAI, Azure, Ollama, and Anthropic. You can define a model once with CREATE MODEL, then swap providers on the admin side without changing the downstream SQL queries.
- WASM Support: Flock now compiles and runs entirely inside DuckDB-WASM.
- LLM Metrics Tracking: We added end-to-end observability so you can track token usage, latency, and call counts for all LLM invocations directly within a query.
- Audio Transcription: We expanded our multimodal capabilities to include audio transcription, alongside our existing image support.
You can try it out via the community extension catalog: INSTALL flock FROM community;.
We'd love your feedback and contributions! Let us know what you think of the architecture or if you run into any edge cases.
Docs: https://dais-polymtl.github.io/flock/ Paper: https://dl.acm.org/doi/10.14778/3750601.3750685
Dual-path execution cuts LLM costs by running 80% of actions locally with zero latency.
LLMs as query operators the planner reasons about, not opaque UDF black boxes.
Consistent pseudonymization beats redaction when RAG embeddings must survive.
Transport-layer interception beats GPTCache with zero code changes required.
Semantic file maps look cool but require external LLM API keys despite local claims.
Separates LLM cognition from OS execution via discrete actions, not continuous control.