lauras (formerly call-laura)

Structured document review grounded in Laura Serna Gaviria's Human–AI Co-Evolution research. An MCP (Model Context Protocol) server: any agent submits a plan or document, gets back structured findings across four lenses (free) or the full 15-agent expert team (review_team) — no opaque score, every finding cites the exact span of your text it's reacting to.

Renamed 2026-07-13: call-laura-core → lauras-core, laura-mcp → lauras-mcp, laura-api → lauras-api, laura-team → lauras-team. All four now publish together at v0.2.0.

Fully deterministic and local. No network call, no API key, no external dependency. The same document always produces the same review — every finding traces back to a plain keyword/pattern match you can read directly in laura-core/src/lenses/. This is a deliberate trade: real semantic understanding for full reproducibility, transparency, and zero cost. See "Why it's different" below for what that trade actually costs.

Co-designed by Laura Serna Gaviria (Emergent Interaction Lab), Simeon Kepp (RFI-IRFOS), and Claude. Part of RFI-IRFOS's open-core model.

Status

Live, 2026-07-13. Laura reviewed real sample output before this shipped, and confirmed the licensing terms below before the 2026-07-13 public rename/republish. lauras-core, lauras-mcp, lauras-team, and lauras-api are all published on crates.io at v0.2.0; lauras-api is also deployed at laura-api.fly.dev, serving /mcp (what the Smithery listing uses), /review, and /team. 50 unit tests, verified end-to-end against both the local stdio server and the live public URL.

An earlier version of this tool called an LLM (NVIDIA-hosted) per lens. That path is gone — not deferred, removed — after the NVIDIA account hit a persistent 403 Forbidden on every inference call despite /v1/models succeeding (an account-side entitlement gap, confirmed across 5 different models). Rather than wait on that, every lens was rewritten as deterministic keyword/pattern matching. This turned out to be a genuine improvement, not just a workaround: zero cost, zero external dependency, and every decision is now fully inspectable in source rather than living inside an LLM's judgment call.

Attribution & Sourcing — read this before trusting any output

This is the most important section in this README. Every one of the four lenses below carries a mandatory source field in its output, and it is exactly one of:

source	Meaning
laura-8layer-2025 / laura-uip-2025	Directly Laura Serna Gaviria's own published framework — see her OSF preprint, Human–AI Interaction Emergent Co-Evolution.
rfi-irfos-operationalization	A concept she names in her paper, but this project's own operational definition of how to measure/apply it — not verbatim hers.
rfi-irfos-addition	Not from her framework at all.

Only eight_layer and uip_check are directly hers — specifically, the category/rule names. The classification method (keyword matching) applying those names to your text is this project's own operationalization, not something her paper specifies an algorithm for. resonance uses the same general idea as her paper's CCET metric (compare passages, measure similarity) applied to a different question (cross-section agreement in a static document, not turn-to-turn stability in a live conversation) and a cruder mechanism (shared words, not shared meaning) — it is explicitly not CCET. ecocentric is entirely RFI-IRFOS's own addition and has no connection to her research; it ships in this tool because the team co-designing it chose to include it, not because it's grounded in her work.

This discipline is inherited directly from the production platform this project was extracted from (emergent-interaction-lab), which holds itself to the same standard for the same reason: a review tool bearing a named researcher's identity only has integrity if you can verify, per finding, whether it's really her work or this project's own judgment call.

Why it's different

• No opaque score. Every finding names a claim, quotes the exact evidence span from your input it's reacting to, and carries a severity. uip_check/ecocentric findings are only ever constructed from spans actually present in your text — there's no separate "generate then verify" step to get wrong, the finding is the matched sentence.

• Four independent lenses, not one blended verdict:

  Lens	What it checks	How	Source
  eight_layer	Classifies each section against Laura's 8-Layer taxonomy, flags entirely-absent layers	Keyword triggers per layer	laura-8layer-2025
  uip_check	Her UIP's four rules: stated constraints, unsupported absolute claims, unverified "done" claims, un-auditable conclusions	Sentence-level pattern matching	laura-uip-2025
  resonance	Flags low lexical overlap between sections that should plausibly agree (e.g. goals vs. success criteria)	Local term-frequency cosine similarity	rfi-irfos-operationalization
  ecocentric	Missing environmental/downstream/long-term/systemic consideration	Keyword-category presence check	rfi-irfos-addition

• Honest, explicit limitations. Every lens's attribution_note states plainly what keyword matching can't do (miss things phrased without trigger words, false-positive on words used in an unrelated sense, no real semantic understanding). This isn't hedging — it's the actual shape of the trade this version makes, said out loud rather than implied by confident-sounding output.

• Honest partial failure. A lens with no classifiable input (e.g. resonance on a single-section document) reports its own error field rather than a fabricated result — call_laura_core::review runs every lens independently.

Quick start

cargo install lauras-mcp
claude mcp add laura -s user -- lauras-mcp

No API key, no environment setup. Then, from any MCP-connected agent:

// tools/call, name: "review_plan"
{ "text": "# Goals\n...\n# Success Criteria\n..." }
// omit "lenses" to run all four; or request a subset, e.g. ["eight_layer","uip_check"]

// tools/call, name: "review_team"
{ "text": "We deploy with no rollback and store personal data without consent." }
// omit "agents" to run all 15; or request a subset, e.g. ["osint", "data_privacy"]

Hosted API

lauras-api is deployed to Fly.io. Three surfaces on the same server:

• POST /mcp — MCP JSON-RPC over HTTP, keyless, rate-limited only. This is what the Smithery listing points at, so any MCP-connected agent can use review_plan with zero setup.
• POST /review / POST /team — plain REST convenience endpoints, Authorization: Bearer <key> required, same JSON body/response shapes as the MCP tools. GET /health for a liveness check. 10 req/min per IP by default on all three — abuse/DoS hygiene, not cost protection (there's no external API cost per request, ever — both /review and /team are fully local computation).

Workspace layout

laura-core/   pure lens logic — package name "lauras-core" on crates.io,
              open-core, LGPL-3.0-or-later
laura-mcp/    stdio MCP server binary, "lauras-mcp" on crates.io (BSL-1.1)
laura-api/    Fly-hosted HTTP surface, "lauras-api" on crates.io, also deployed (BSL-1.1)
laura-team/   15-agent "SWAT team" module + Laura orchestrator, "lauras-team" on
              crates.io (BSL-1.1) — free 4-lens core lives in laura-core

Every directory keeps its original name; only the crates.io package identity changed in the 2026-07-13 rename (see each crate's own Cargo.toml for the name field).

License

lauras-core: LGPL-3.0-or-later. lauras-mcp/lauras-api/lauras-team: Business Source License 1.1 with a non-commercial/research use grant, commercial/production use requires a license from RFI-IRFOS. Confirmed with Laura Serna Gaviria 2026-07-13 — see the NOTE at the top of LICENSE-LGPL and LICENSE-BSL. Full terms in LICENSE.