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@H2Shami H2Shami commented Oct 23, 2025

Hi! Wanted to add the helicone gateway to the repo.

Their models can be retrieved through an endpoint, so I thought for maintainability it would be good to have a script that updates the database from the endpoint, for the most accurate information.

If this is not preferred please let me know and I will have the script removed!

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H2Shami commented Dec 8, 2025

@rekram1-node

can we get a review on this? this PR is a prereq needed to add helicone support the opencode integration

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yes

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can you mkae this follow the pattern of other logo.svg files?

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mostly just using: currentColor instead of actual colors

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heyo! saw that the logos are generally monochromatic, so I just left the helicone logo's outline and used the currentColor for the stroke color

Copilot AI review requested due to automatic review settings December 8, 2025 21:04
@rekram1-node rekram1-node merged commit 6f58b8b into sst:dev Dec 8, 2025
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Pull request overview

This PR adds the Helicone AI gateway provider to the repository, including 85 model configurations and an automated generation script that fetches model data from Helicone's public API endpoint.

Key Changes:

  • Introduces a TypeScript generation script that dynamically creates model TOML files from Helicone's model registry API
  • Adds provider configuration and logo for Helicone
  • Generates 85 model TOML files covering various AI models (OpenAI, Anthropic, Google, xAI, Meta, DeepSeek, etc.)

Reviewed changes

Copilot reviewed 95 out of 96 changed files in this pull request and generated 56 comments.

Show a summary per file
File Description
providers/helicone/provider.toml Provider configuration defining Helicone gateway API endpoint and environment variables
providers/helicone/README.md Documentation explaining the generation script, commands, and data format conventions
providers/helicone/logo.svg Helicone brand logo in SVG format
providers/helicone/models/*.toml 85 model configuration files with pricing, capabilities, and limits (auto-generated)
packages/core/script/generate-helicone.ts TypeScript script to fetch and transform Helicone's model registry into TOML format
package.json Adds helicone:generate npm script to run the model generation

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[cost]
input = 0.3
output = 2.9000000000000004
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The output cost has excessive floating-point precision (2.9000000000000004) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 2.9.

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[cost]
input = 1.25
output = 10
cache_read = 0.12500000000000003
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The cache_read cost has excessive floating-point precision (0.12500000000000003) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.125.

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[cost]
input = 5
output = 25
cache_read = 0.5000000000000001
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The cache_read cost has excessive floating-point precision (0.5000000000000001) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.5.

Suggested change
cache_read = 0.5000000000000001
cache_read = 0.5

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[cost]
input = 3
output = 15
cache_read = 0.30000000000000004
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The cache_read cost has excessive floating-point precision (0.30000000000000004) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.3.

Suggested change
cache_read = 0.30000000000000004
cache_read = 0.3

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open_weights = false

[cost]
input = 0.19999999999999998
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The input cost has excessive floating-point precision (0.19999999999999998) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.2.

Copilot uses AI. Check for mistakes.
open_weights = false

[cost]
input = 0.19999999999999998
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The input cost has excessive floating-point precision (0.19999999999999998) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.2.

Copilot uses AI. Check for mistakes.
[cost]
input = 1.25
output = 10
cache_read = 0.12500000000000003
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Copilot AI Dec 8, 2025

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The cache_read cost has excessive floating-point precision (0.12500000000000003) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.125.

Copilot uses AI. Check for mistakes.

[cost]
input = 0.049999999999999996
output = 0.09999999999999999
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The output cost has excessive floating-point precision (0.09999999999999999) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.1.

Copilot uses AI. Check for mistakes.
[cost]
input = 1
output = 5
cache_read = 0.09999999999999999
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The input cost has excessive floating-point precision (0.09999999999999999) which results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.1.

Suggested change
cache_read = 0.09999999999999999
cache_read = 0.1

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open_weights = false

[cost]
input = 0.09999999999999999
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The input cost has excessive floating-point precision (0.09999999999999999) which likely results from JavaScript's floating-point arithmetic. The value should be rounded to a reasonable precision, e.g., 0.1.

Copilot uses AI. Check for mistakes.
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2 participants