REPOGEO REPORT · LITE

BoundaryML/baml

Default branch canary · commit d2339a33 · scanned 6/27/2026, 12:06:57 AM

GitHub: 8,434 stars · 440 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface BoundaryML/baml, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening statement to emphasize type-safe structured LLM output

Why:

CURRENT

BAML is a simple prompting language for building reliable **AI workflows and agents**.

COPY-PASTE FIX

BAML is a declarative, type-safe schema language for building reliable **AI workflows and agents** that deliver consistent, structured output from LLMs across multiple programming languages.

mediumcomparison#2

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, titled 'BAML vs. [Competitor Names]' or 'Why BAML?', that explicitly highlights BAML's core differentiator (declarative, type-safe schema language for LLM functions) against common alternatives like Instructor, Guardrails AI, and LangChain.

lowtopics#3

Add more specific topics for structured LLM output and multi-language support

Why:

CURRENT

baml, boundaryml, guardrails, llm, llm-playground, playground, prompt, prompt-config, prompt-templates, structured-data, structured-generation, structured-output, vscode

COPY-PASTE FIX

baml, boundaryml, guardrails, llm, llm-playground, playground, prompt, prompt-config, prompt-templates, structured-data, structured-generation, structured-output, vscode, structured-llm-output, type-safe-ai, llm-orchestration, multi-language-llm

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface BoundaryML/baml

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Instructor

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Instructor · recommended 2×
LangChain · recommended 2×
Guardrails AI · recommended 2×
LlamaIndex · recommended 2×
Pydantic · recommended 1×

CATEGORY QUERY
How to achieve reliable, structured output from LLMs across multiple programming languages?
you: not recommended
AI recommended (in order):
1. Pydantic
2. Instructor
3. OpenAPI Specification
4. JSON Schema
5. datamodel-code-generator
6. openapi-generator
7. Swagger
8. json-schema-to-typescript
9. LangChain
10. Guardrails AI
11. TypeChat
12. Zod
13. LlamaIndex
14. jsonschema
15. everit-org/json-schema (everit-org/json-schema)
16. gojsonschema
AI recommended 16 alternatives but never named BoundaryML/baml. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What framework helps engineer LLM prompts for consistent, type-safe, and robust AI workflows?
you: not recommended
AI recommended (in order):
1. Pydantic-LLM
2. Instructor
3. LangChain
4. Guardrails AI
5. Marvin
6. LlamaIndex
AI recommended 6 alternatives but never named BoundaryML/baml. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of BoundaryML/baml?
pass
AI named BoundaryML/baml explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts BoundaryML/baml in production, what risks or prerequisites should they evaluate first?
pass
AI named BoundaryML/baml explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo BoundaryML/baml solve, and who is the primary audience?
pass
AI named BoundaryML/baml explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite