alexeyban/EKOS

EKOS is the open-source knowledge layer for AI agents: a compiler that turns source code, SQL,
documentation and Git history into evidence-backed knowledge that agents query over MCP. Project
site and documentation: ekos.dev. Install: ekos.dev/docs/getting-started/installation
(a setup wizard in your browser on Linux, Windows 11 and macOS — see Installation).
Unlike traditional enterprise systems that manage data, documents or metadata independently, EKOS treats the entire enterprise as a living knowledge system — a permanently evolving semantic model that can be trusted by both humans and AI.
Text-to-SQL benchmark: on BIRD Mini-Dev (release 1024, 302 hold-out questions over 11 databases), the same model, Claude Haiku 5.5, scores 53.3% execution accuracy on raw DDL, 71.5% on the knowledge EKOS compiles from each database, and 76.2% when it also checks its SQL against the data with EKOS's tools (+22.9 points, significant on paired questions), for about a quarter of a cent per question. Every question, the SQL it produced, tokens, cost and the scorer's parity with BIRD's official evaluator: BIRD Mini-Dev on EKOS.
First benchmark: on a real 2,022-file open-source repo (plausible/analytics), cold ingestion takes 34 seconds, and answering real questions from the compiled ledger costs 67-93% fewer tokens than raw grep-based search over the source — measured with a standard tokenizer (tiktoken), not a hand-rolled estimate, with the one case grep wins included rather than hidden. Full methodology, every command, and every raw output: The First Benchmark Number.
## About
EKOS is a compiler for enterprise knowledge, not a database or document store. It observes an
enterprise's existing systems — source code, Git history, SQL schemas, GitHub issues/PRs,
Confluence, local PDF/DOCX documents — without interpreting them, compiles
those observations through deterministic passes into a Canonical Knowledge Model, and stores the
result in an append-only ledger where every conclusion carries the evidence it was derived from. AI
agents (Claude Code among them) read that ledger through a read-only Model Context Protocol server
(ekos mcp serve, RFC 0013) — they never touch raw enterprise systems directly.
The project follows an RFC-first workflow (docs/rfcs/): every capability is designed in writing
before it's implemented, and the devlogs/devlog.md files are the running record of
what shipped, why, and what was learned building it. It is written in Rust (2024 edition) as a
Cargo workspace, and is licensed under the MIT License.
## The Problem
Modern enterprises contain enormous amounts of valuable knowledge distributed across disconnected systems: source code, databases, data warehouses, documentation, wikis, Git repositories, infrastructure-as-code, APIs, runtime logs, and monitoring systems. Every system contains only a partial description of reality. Documentation becomes outdated. Employees leave. Business logic remains hidden inside production code. AI assistants receive fragmented, inconsistent, and often contradictory information.
Enterprises continuously lose knowledge.
## The Insight
The enterprise already contains its own documentation — embedded inside source code, SQL, infrastructure definitions, APIs, logs, deployment history, schemas, and runtime behaviour. The problem is not missing information. The problem is the absence of a compiler capable of transforming enterprise reality into enterprise knowledge.