Lexara

Rewrite educational text to a target grade level. Return multi-framework proof of what changed.

Lexara is developer infrastructure for edtech builders. One API call scores a passage, rewrites it toward a target US grade level using OpenAI (gpt-4o-mini), rescores after each pass, and returns input → output with per-framework before/after proof.

Measured against a 12-passage K-12 dataset (science, social studies, math, ELA, grades 2–12):

• 58% of passages hit the target grade within ±1 grade level (7/12 with OpenAI gpt-4o-mini)
• Average grade reduction: −8.9 levels per rewrite
• Works best for grade 4–12 targets. Passages targeting below grade 4 on academic-register text consistently fall short — see Accuracy & Limitations.

curl -s http://localhost:8000/v1/readability/rewrite \
  -H "Authorization: Bearer dev-local-key" \
  -H "Content-Type: application/json" \
  -d '{"text":"Photosynthesis is the biochemical process by which chlorophyll-containing organisms convert light energy into chemical energy.","target_grade":6,"max_passes":5}'

Not a readability score you stare at. A rewrite you can act on — with scored evidence before and after.

---

Why Lexara exists

Score-only is not enough

A score-only API tells you a worksheet reads at grade 11. It does not give you a grade-5 version. Your user still has to rewrite by hand, guess whether it worked, and hope one readability number reflects their district's expectations.

That is a diagnostic. Edtech products need an action: adjust the text, verify the result, retry if needed.

Why multi-framework scoring + rewrite together

No single readability formula is authoritative — districts, publishers, and literacy tools reference different frameworks (Flesch-Kincaid, Dale-Chall, Lexile-style, ATOS-style). Lexara:

1. Scores across frameworks before and after the rewrite
2. Rewrites toward your target grade, not a single opaque number
3. Returns proof — outcome.hit_target, frameworks_improved, and per-framework grades on both sides

Scoring is the verification layer for the rewrite. The product is the loop: rewrite → rescore → prove.

---

Quick start

python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env
lexara-api   # → http://localhost:8000/docs

Alpha note: Default LEXARA_LLM_PROVIDER=mock is for local dev and tests only. It uses word substitution, not an LLM — expect low hit_target on hard passages. External demos and alpha require openai. Run python examples/demo_showcase.py to see a mock warning banner when the API uses mock.

1. Rewrite (curl) — start here

curl -s http://localhost:8000/v1/readability/rewrite \
  -H "Authorization: Bearer dev-local-key" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Photosynthesis is the biochemical process by which chlorophyll-containing organisms convert light energy into chemical energy, subsequently producing glucose and releasing oxygen as a byproduct of cellular metabolism.",
    "target_grade": 6,
    "preserve_meaning": true,
    "max_passes": 5
  }' | python3 -m json.tool

Actual output (OpenAI gpt-4o-mini, measured):

input.estimated_grade_level:  18.3
output.estimated_grade_level:  7.4
outcome.hit_target:            true
output.text: "Photosynthesis helps plants make food. Plants have a green color
              called chlorophyll. This color captures light from the sun. Then,
              plants change this light into chemical energy to produce glucose,
              and they release oxygen as a result."

Look for: outcome.summary, outcome.hit_target, input.estimated_grade_level, output.estimated_grade_level, output.text.

2. Rewrite (Python SDK)

from lexara import LexaraClient, Tone

client = LexaraClient(api_key="dev-local-key")

result = client.readability.rewrite(
    "Photosynthesis is the biochemical process by which chlorophyll-containing "
    "organisms convert light energy into chemical energy.",
    target_grade=6,
    preserve_meaning=True,
    max_passes=5,
)

print(result.outcome.summary)
print(f"{result.input.estimated_grade_level} → {result.output.estimated_grade_level} (target {result.target.grade})")
print(f"Hit target: {result.hit_target} | Frameworks improved: {result.outcome.frameworks_improved}")
print(result.output.text)

3. Score only (when you don't need a rewrite yet)

scored = client.readability.score(
    passage,
    frameworks=["flesch_kincaid", "lexile"],
)
print(scored.aggregate.estimated_grade_level)

Use POST /v1/readability/score for diagnostics. Use rewrite when you need to act on the result.

---

Demo-ready examples

Three passages you can run in a customer call, Show HN thread, or live demo.

Mock provider: Demos against local lexara-api with default settings use mock. Rewrite quality is not production-representative. For customer-facing demos set LEXARA_LLM_PROVIDER=openai.

Demo A — Science passage: college → grade 6

Story: "Your curriculum import is too hard for middle school. One call fixes and proves it."

curl -s http://localhost:8000/v1/readability/rewrite \
  -H "Authorization: Bearer dev-local-key" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Photosynthesis is the biochemical process by which chlorophyll-containing organisms convert light energy into chemical energy, subsequently producing glucose and releasing oxygen as a byproduct of cellular metabolism.",
    "target_grade": 6,
    "max_passes": 5,
    "tolerance": 1.5
  }'

from lexara import LexaraClient

PASSAGE = (
    "Photosynthesis is the biochemical process by which chlorophyll-containing "
    "organisms convert light energy into chemical energy, subsequently producing "
    "glucose and releasing oxygen as a byproduct of cellular metabolism."
)

client = LexaraClient(api_key="dev-local-key")
r = client.readability.rewrite(PASSAGE, target_grade=6, max_passes=5, tolerance=1.5)
print(f"Before: grade {r.input.estimated_grade_level}  After: grade {r.output.estimated_grade_level}")
print(r.output.text)

Talk track: Show input.frameworks[] vs output.frameworks[] — four frameworks moved, not one proprietary number.

---

Demo B — Worksheet instructions: teacher tone, grade 4

Story: "Teachers paste LMS instructions. Lexara returns student-ready text with meaning preserved."

from lexara import LexaraClient, Tone

client = LexaraClient(api_key="dev-local-key")

result = client.readability.rewrite(
    "Students will subsequently utilize the provided manipulatives to demonstrate "
    "their comprehension of fractional equivalence, and they must obtain sufficient "
    "evidence before completing the assessment.",
    target_grade=4,
    tone=Tone.friendly,
    preserve_meaning=True,
    max_passes=4,
)

if result.hit_target:
    ship_to_lms(result.output.text)
else:
    print(result.outcome.summary, result.execution.warnings)

Run: python examples/sdk_rewrite_to_target.py

---

Demo C — Already at target: no LLM call

Story: "Lexara doesn't burn tokens when content is already right — it scores, verifies, and skips."

from lexara import LexaraClient

client = LexaraClient(api_key="dev-local-key")

result = client.readability.rewrite(
    "Photosynthesis lets plants make food from sunlight. Plants need sun and water to grow.",
    target_grade=5,
    tolerance=1.0,
)

assert result.execution.skipped is True
assert result.execution.passes_used == 0
assert result.hit_target is True
assert result.output.text == result.input.text
print(result.outcome.summary)
# → "Already at grade 5.1 (target 5, within ±1). No rewrite needed."

Full payload: examples/canonical/rewrite_already_at_target.json

Run all three: python examples/demo_showcase.py

---

API

Method	Path	Purpose
POST	/v1/readability/rewrite	Primary — rewrite to target grade + multi-framework proof
POST	/v1/readability/score	Diagnostics only — no rewrite
GET	/health	Liveness

Auth: Authorization: Bearer <key> or x-api-key: <key>

Rewrite response (read in this order)

Block	Meaning
input / output	Original vs rewritten text + per-framework grades
outcome	hit_target, grade from → to, frameworks_improved, summary
target	Goal grade, tolerance, grade band
execution	Passes used, provider, skipped, warnings

Shortcuts: rewritten_text, hit_target.

Canonical JSON: examples/canonical/

---

Python SDK

from lexara import LexaraClient, Tone, RewriteOptions

client = LexaraClient(api_key="...", timeout=120, max_retries=2)

# Hero workflow
result = client.readability.rewrite(
    text,
    target_grade=5,
    preserve_meaning=True,
    tone=Tone.friendly,
    max_passes=4,
)

# Or bundle options (all fields optional except target_grade on rewrite call)
opts = RewriteOptions(preserve_meaning=True, tone=Tone.friendly, max_passes=4)
result = client.readability.rewrite(text, target_grade=5, options=opts)

Parameter	Default	Meaning
target_grade	required	Desired US grade level (1–16)
preserve_meaning	True	Keep facts, numbers, names, steps
tone	neutral	friendly, formal, playful, academic, …
max_passes	3	Max rewrite → rescore iterations
frameworks	all	Frameworks used to judge progress
tolerance	1.0	Grade levels within target = hit

Examples: examples/sdk_rewrite_to_target.py · examples/sdk_rewrite_inspect_scores.py · examples/sdk_score_only.py

Exceptions: AuthenticationError · ValidationError · LexaraTimeoutError · LexaraConnectionError · LexaraAPIError

---

How rewrite works

Each POST /v1/readability/rewrite request:

1. Scores input across selected frameworks
2. Skips the LLM if already within tolerance of target_grade
3. Otherwise rewrite → rescore → retry up to max_passes
4. Returns the best attempt with full before/after snapshots

Provider failures become execution.warnings — the endpoint returns 200 with the best text so far. Check outcome.hit_target before shipping to users.

execution.degraded means no usable rewrite was produced (e.g. all provider calls failed). Warnings alone do not set degraded when a rewrite succeeded.

---

Frameworks & safe claims

Framework	ID	Confidence	What to tell customers
Flesch-Kincaid Grade	flesch_kincaid	exact	Standard formula; syllable counts use an estimated syllable heuristic
New Dale-Chall	dale_chall	estimated	Published formula; familiar-word list is approximate, not the licensed 3,000-word list
ATOS-style	atos_estimated	estimated	ATOS-style estimate — not an official Accelerated Reader level
Lexile-equivalent	lexile_estimated	estimated	Lexile-scale estimate — not an official MetaMetrics Lexile score

Legacy aliases: atos → atos_estimated, lexile → lexile_estimated.

Safe to say

• Rewrite toward a target US grade level in one API call
• Multi-framework before/after proof (input.frameworks → output.frameworks)
• Flesch-Kincaid uses the standard grade formula; other frameworks are clearly labeled estimated
• Developer-friendly SDK: client.readability.rewrite(text, target_grade=6)

Do not say

• Official Lexile, ATOS, or Dale-Chall certification
• Guaranteed rewrite success on every passage — always check outcome.hit_target
• Single "true" reading level — Lexara returns multiple frameworks intentionally

---

Accuracy & Limitations

Measured with OpenAI gpt-4o-mini against a 12-passage K-12 dataset (v2, May 2026):

• Below grade 4 on academic-register text: expect misses. A passage scoring 17.1 targeting grade 2 landed at 4.5 after five passes — still 2.5 levels off. The pipeline simplifies vocabulary and sentence structure, but readability formulas score short function words and sentence count, not whether the content is truly accessible to a 7-year-old.
• Borderline passages vary run to run. A grade-4 target passage hit 4.5 in one run and 7.0 in the next with the same prompt and model. Always check outcome.hit_target at call time; do not cache a one-time pass as a permanent quality signal.
• Extreme grade gaps (> 12 levels) reliably miss — this is expected. The economics stress-test passage (original grade 22.3 → target 5.0) landed at 9.8. A single rewrite loop cannot close a 17-level gap; this failure mode is intentional and documented in the dataset.
• Human review is recommended before shipping to students. outcome.hit_target confirms the scored grade landed in range — it does not verify that meaning was preserved, that the text is age-appropriate in tone, or that domain-specific terms were handled correctly. Treat the output as a draft, not a final.

---

Configuration

See .env.example.

Var	Default	Meaning
LEXARA_ENV	development	development or production
LEXARA_ALLOW_MOCK_PROVIDER	true	Set false with LEXARA_ENV=production to block mock
LEXARA_LLM_PROVIDER	mock	mock (dev/test) or openai (external alpha)
LEXARA_OPENAI_API_KEY	–	Required when provider is openai
LEXARA_OPENAI_MODEL	gpt-4o-mini	Chat model for rewrites
LEXARA_API_KEYS	dev-local-key	Valid API keys

Real provider setup:

pip install -e ".[openai]"
export LEXARA_LLM_PROVIDER=openai
export LEXARA_OPENAI_API_KEY=sk-...
lexara-api

---

Rewrite effectiveness evals

Measure rewrite quality before demos — not just formula correctness:

lexara-eval                              # table summary (mock)
lexara-eval --output eval/results.json   # save JSON for review

Dataset: src/lexara/eval/data/rewrite_effectiveness.json (grades 2–12, five passage types).

With OpenAI: lexara-eval --provider openai --output eval/results-openai.json

---

Homepage & API copy (suggested)

Headline: Rewrite text to your target grade. Prove it across four frameworks.

Subhead: Lexara is edtech API infrastructure — score, rewrite, rescore, and return before/after proof in one call. Not a number. A number you acted on.

API one-liner: POST /v1/readability/rewrite — rewrite toward a target grade; get input, output, and outcome.hit_target with Flesch-Kincaid, Dale-Chall, ATOS-style, and Lexile-equivalent verification.

Bullets for landing page:

• Score-only APIs diagnose. Lexara rewrites and proves the result.
• Multi-framework before/after — not one proprietary readability number.
• One SDK call: client.readability.rewrite(passage, target_grade=6)
• Skips the LLM when text is already at target.

---

Demo script (5 minutes)

Use this flow in a customer call or Show HN live demo:

1. Problem (30s) — Paste the Demo A science passage. "This reads at college level. Your middle-school product can't use it as-is."
2. Rewrite (60s) — Run curl or SDK. Show outcome.summary and grade drop in input → output.
3. Proof (60s) — Expand input.frameworks and output.frameworks. "We don't trust one formula — we show four, before and after."
4. Teacher workflow (60s) — Demo B worksheet instructions with tone=friendly. Show rewritten bullet-friendly text.
5. Efficiency (30s) — Demo C already-at-target. "No LLM call when content is already right."
6. Ship check (30s) — Point at outcome.hit_target, execution.warnings, and the safe-claims table. "You decide when to ship; Lexara gives you structured proof."

Runnable: python examples/demo_showcase.py

---

Alpha release checklist

Before any external demo or alpha customer:

• LEXARA_LLM_PROVIDER=openai and LEXARA_OPENAI_API_KEY set
• LEXARA_ENV=production and LEXARA_ALLOW_MOCK_PROVIDER=false (optional guard)
• lexara-eval --provider openai --output eval/results-openai.json — review hit_target rate
• Manual semantic review on Demo A/B/C (python examples/demo_showcase.py)
• pytest -m integration with live OpenAI key
• Safe-claims table reviewed in customer-facing materials
• Confirm execution.degraded vs execution.warnings behavior with integrators

---

Architecture

Layer	Role
api/	HTTP, auth, middleware
services/	Rewrite + scoring orchestration
scoring/	Pure readability formulas
rewriting/	LLM provider + rewrite/rescore pipeline
eval/	Rewrite effectiveness harness
client.py	Python SDK

---

Tests

pytest                                    # full suite (mock, no network)
pytest tests/test_eval_harness.py -v
pytest tests/test_client.py -v

# Live OpenAI — opt-in:
LEXARA_OPENAI_API_KEY=sk-... pytest -m integration -v