Nix
Context intelligence for AI coding agents.
Large language models are inherently myopic. When tasked with editing, refactoring, or architecting complex software, even state-of-the-art coding agents operate with fragmented sight — seeing single files or naive semantic search snippets while remaining blind to the broader system topology.
Nix is built to solve this fundamental context barrier. It acts as an autonomous intelligence layer between coding agents and software repositories, providing continuous, verified repository understanding beyond the immediate prompt.
Instead of treating code as plain text tokens, Nix treats repositories as living semantic graphs: linking Tree-sitter abstract syntax trees, cross-file type hierarchies, build dependencies, and runtime behavior into a coherent context substrate.
Tree-sitter AST symbol graphs
Vector embeddings compress code into fuzzy approximations. Nix replaces speculative text retrieval with deterministic Tree-sitter parse trees. By analyzing syntactic symbols across every file, Nix builds high-fidelity call graphs, type relationships, and interface boundaries.
resolve_references(symbol_graph) → CallPath[]
synthesize_context(target_file, prompt) → VerifiedContext
Ephemeral sandboxes & runtime traces
Static analysis is only half the puzzle. Nix equips autonomous coding agents with ephemeral, containerized work environments where code modifications can be built, executed, and benchmarked without risking host integrity. Agents verify that proposed edits actually compile and satisfy unit invariants before committing changes.
Currently in active development
Nix is being actively built, refined, and benchmarked internally at Nidonic Labs. We are collaborating with selected engineering teams and agent developers on private alpha trials to measure context recall accuracy, hallucination reduction, and agent task completion speed.