--- title: "Portable AI Decision Records: konspekt Contributed to FINOS Labs" description: konspekt is a proposed open standard for a portable, human-readable record of project decisions, open questions, artifacts, and provenance as work evolves across generative-AI tools. image: https://www.finos.org/hubfs/Community%20Blog/konspekt%20Contribution%20Image%20(1).png --- [![FINOS](https://www.finos.org/hs-fs/hubfs/sofin%20assets/SOFIN%20LOGOS/finos-logo.png?width=543&height=126&name=finos-logo.png "FINOS")](https://finos.org) - [Projects](https://www.finos.org/projects) - [Artificial Intelligence (AI)](https://ai.finos.org/) - [Common Domain Model (CDM)](https://www.finos.org/common-domain-model) - [FDC3](https://www.finos.org/fdc3) - [Fluxnova](https://fluxnova.finos.org) - [FINOS Common Cloud Controls](https://www.finos.org/common-cloud-controls-project) - [High Performance Computing (HPC)](https://hpc.finos.org) - [OSERA](https://osera.finos.org/) - [Open Source Readiness Initiative](https://www.finos.org/open-source-readiness) - 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To address this gap, **konspekt** has been contributed to FINOS Labs as an individual open-source contribution from maintainer Denis Urusov. konspekt is a proposed open standard for a portable, human-readable record of project decisions, open questions, artifacts, and provenance as work evolves across generative-AI tools. It gives engineering teams a durable, vendor-neutral audit trail that lives outside any single conversation, model, or platform. ![konspekt Contribution Image (1)](https://www.finos.org/hs-fs/hubfs/Community%20Blog/konspekt%20Contribution%20Image%20(1).png?width=500&height=280&name=konspekt%20Contribution%20Image%20(1).png) # **The Challenge: Scattered AI History and Unauditable Decisions** As teams increasingly do substantive work through generative-AI conversations, every session produces something worth keeping: decisions, reasoning, working definitions, and the artifacts themselves. Today, that state is scattered across chat transcripts and tied to its original platform. This creates two major systemic issues for financial institutions: **Context Fragmentation:** Knowledge does not cleanly move across tools. Reconstructing project context when switching tools, upgrading models, or onboarding team members is costly and error-prone. **Governance Gaps:** There is no independent, durable record of where a given conclusion came from or who approved it. For regulated financial institutions, where provenance and human accountability are strict requirements, that gap is a major governance problem. # **The Solution: A Portable, Human-Curated Record of Project State** **konspekt** defines project state as a compact, typed knowledge graph—using a few core node types (*Concept*, *Noteworthy*, *Artifact*, *Waypoint*) connected by a single edge table. Stored entirely as plain, human-readable files, the graph moves cleanly between AI platforms via simple copy-paste, storing every fact once so inventories are dynamically queried rather than manually maintained. Two key properties make konspekt tailored for regulated financial institutions: **Human-Curated Review Boundary:** While an AI model may propose graph entries, only a human can accept them into the permanent record. Every entity carries verbatim provenance linking back to its exact source text, creating an explicit audit trail. **Upstream Open Format:** As a zero-dependency open format, konspekt sits upstream of machine memory systems (such as Mem0 or Zep) rather than competing with them. A curated konspekt instance provides a clean, human-validated record that downstream memory engines can reliably consume. This approach aligns directly with FINOS initiatives focused on AI governance, provenance, and vendor independence for financial infrastructure. # **Key Features** - **Typed knowledge graph in plain files:** A small schema of typed nodes and a single edge table, stored as human-readable text that diffs and reviews like source code. - **Human-in-the-loop propose→accept:** A model may propose entries; only a person accepts them into the record, creating a clear, auditable review boundary. - **Human authority verbs:** A small vocabulary a person issues over the graph (pin, validate, refute, resolve, abandon, lift), covering the override moments an assistant must not decide on its own. - **Content-addressed provenance:** Every entry links to its verbatim source text, addressed by content hash, so any statement can be checked against exactly what it came from. - **Persona layers:** Opt-in role profiles that shape the operating instructions an assistant follows and the conformance checks applied to an instance, switched on per project with a single flag. The engineer role is the pilot persona; it records the commands the AI executes, keeping an auditable trail of the actions taken during a project. - **Open and vendor-neutral:** Portability is achieved by keeping project state as plain, human-readable files that move between generative-AI tools by copy-paste, with no service to run and no proprietary runtime. A zero-dependency scaffolder drops konspekt into any existing repository. # **Get Involved** - **Explore the Code:**[github.com/finos-labs/konspekt](https://github.com/finos-labs/konspekt). The standard lives in spec/, with a working instance that tracks konspekt's own development in .konspekt/instance/. - **Try It:** From a project root (Node 18+), run node setup/init.mjs --name "My Project" --goal "what the project is for" to scaffold konspekt into your repo; see setup/README.md. - **Read the Standard:** README.md and WHITEPAPER.md for the model and rationale; spec/data-model/ and spec/architecture/ for the details. - **Contribute:**[CONTRIBUTING.md](https://github.com/finos-labs/konspekt/blob/main/CONTRIBUTING.md) for the process. 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