| openfde run --repo --agent claude|codex | 오케스트라 파견: 인용된 컨텍스트 팩을 가진 작업의 자신의 git worktree에 코딩 에이전트를 맡고 감사 트레일으로 작업하고, 종료 마커 ([T1]] / BLOCKED] / NEEDS_HUMAN]); run ready] 팬 아웃, run followup]는 검토 피드백과 동일한 세션을 계속합니다 | | openfde worktree list/open/remove | 태스크 워크트리가 생성된 런너: 브랜치, 베이스 앞의 커밋, 더러운 파일; 제거는 언머그드 작업 삭제를 거부합니다 | | [T7]|【T7】|일본 각 유형의 의미, 유형 당 인스턴스, orphans, schema 위반; --rdf] RDF / OWL 수출 (클래스, 관계, 개인, 입증 된 사실) | | openfde path | 2개의 엔티티티를 연결하는 가장 짧은 체인 | CFO가 재구성 워크플로우에 어떻게 의존하는지 |

OpenFDE: FDE를 위한 AI 작업 공간

한국어 | 日本語

더 빠른 AI 솔루션 100x. 인터뷰는 기억이되고, 메모리는 추적 가능한 토도가되고, 토도는 코딩 시약 작업이 됩니다. evals에 의해 문질러집니다.

OpenFDE는 앞으로 배포된 엔지니어를 위한 로컬 최초의 AI 작업 공간입니다.

| openfde run --repo --agent claude|codex | Orchestrated dispatch: spawn a coding agent in the task’s own git worktree with the cited context pack, stream its work into the audit trail, and move the task on from its exit marker (DONE / BLOCKED / NEEDS_HUMAN); run ready fans out, run followup continues the same session with review feedback | | openfde worktree list/open/remove | Task worktrees the runner created: branch, commits ahead of base, dirty files; removal refuses to drop unmerged work | | openfde ontology | The ontology as an artifact: what each type means, instances per type, orphans, schema violations; --rdf exports RDF/OWL (classes, relations, individuals, facts with provenance) | | openfde path | Shortest chain of cited facts connecting two entities — how the CFO relates to the reconciliation workflow |

OpenFDE: AI workspace for FDEs

English | 简体中文 | 日本語 | Español

Deliver AI solutions 100x faster. Interviews become memory, memory becomes traceable todos, todos become coding-agent work — gated by evals.

OpenFDE is a local-first AI workspace for forward deployed engineers. Think of it as an enterprise brain built the ontology way: the customer organization — its goals, workflows, decisions, constraints, data sources, people and pains — captured as a typed, cited, time-aware graph that both humans and coding agents read from and write back to. It compiles engagement material — interviews, chat logs, documents, PDFs, images — into an ontology-backed operational memory, and closes the loop between humans and coding agents: agents pull tasks and context bundles from the ledger, execute, and write findings back — while the customer’s leadership watches progress live, with every claim citing its source.

Why

An FDE’s working state lives in three fragile places:

• Knowledge lives in conversations. Who trusts which data source, why a decision was made, which constraint blocks a workflow — said once in a meeting, lost two weeks later.
• Tasks live in heads. The gap between “heard it in an interview” and “dispatched it to a coding agent” has no system, no context, no trail back to the source.
• Verification lives in feelings. Agent output gets accepted by vibes instead of evals.

OpenFDE turns all three into one system, starting with memory.

What OpenFDE does

• Ontology-backed operational memory. A fixed FDE domain ontology — goals, workflows, decisions, constraints, data sources, pain points — constrains extraction, so what enters the ledger is operational knowledge, not prose. A dot-line-plane lens (value planes → business flows → decision points) organizes it the way leadership thinks.