Document Tuning Engines OpenAI-compatible configuration

[cerebrixos]

Summary

• Adds a small documentation example showing how to point this project’s existing OpenAI-compatible configuration at Tuning Engines.
• Keeps this project’s APIs and runtime behavior unchanged: no new dependency, adapter, or code path.
• Shows a path for teams to keep this framework in charge of app, agent, tool, workflow, or retrieval logic while routing model calls through a governed OpenAI-compatible endpoint.

Why this belongs here

Tuning Engines is an AI control plane for teams that want centralized model access, routing, tenant-scoped keys, policy/guardrail checks, audit logs, traces, approvals, and usage/cost visibility around existing AI applications.

This project already supports OpenAI-compatible endpoints. The docs change makes that existing capability discoverable for users who want governance and observability without rewriting their application around a separate SDK or changing this project’s runtime behavior.

Testing

• git diff --check

Summary by CodeRabbit

• Documentation
  • Expanded the "Using LLM Class Attributes" section with a new code example showing how to configure an LLM instance for Tuning Engines OpenAI-compatible endpoints. The example demonstrates configuration with model, API key, and base URL settings, and illustrates integration with an Agent.

[coderabbitai]

No actionable comments were generated in the recent review. 🎉

ℹ️ Recent review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: 250e10d4-089f-434f-a048-9162b94289db

📥 Commits

Reviewing files that changed from the base of the PR and between 9c11603 and 5dabd84.

📒 Files selected for processing (1)

• docs/en/learn/llm-connections.mdx

🚧 Files skipped from review as they are similar to previous changes (1)

• docs/en/learn/llm-connections.mdx

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📝 Walkthrough

Walkthrough

This PR adds a new documentation example demonstrating how to configure an LLM instance to connect to a Tuning Engines OpenAI-compatible endpoint. The example shows passing model, api_key, and base_url parameters to create the LLM, then wiring it into an Agent.

Changes

Tuning Engines OpenAI-compatible endpoint documentation

Layer / File(s)	Summary
Tuning Engines endpoint configuration example docs/en/learn/llm-connections.mdx	New code example added under the "Connecting to OpenAI-Compatible LLMs" section demonstrating LLM setup for Tuning Engines with model, API key, and custom base URL, then integrated into an Agent.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🐰 A doc update hops into view,
Tuning Engines shining bright and new,
Just eleven lines of learning light,
To guide the agents true and right! ✨

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and specifically describes the main change: adding documentation for Tuning Engines OpenAI-compatible configuration, which aligns directly with the PR's objective of documenting how to use the existing OpenAI-compatible configuration with Tuning Engines.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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[coderabbitai]

Actionable comments posted: 1

🧹 Nitpick comments (1)

docs/en/learn/llm-connections.mdx (1)

160-161: 💤 Low value

Consider a more neutral comment tone.

The comment lists specific features ("model routing, policy controls, audit logs...") which reads somewhat promotional. Compare with the Google example on line 171 which uses a simpler, more neutral description: # Example using Gemini's OpenAI-compatible API.

Consider simplifying to something like:

# Example using Tuning Engines OpenAI-compatible endpoint

This aligns better with the documentation style of other examples while still being descriptive.

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@docs/en/learn/llm-connections.mdx` around lines 160 - 161, Replace the
promotional comment line beginning with "# Governed OpenAI-compatible endpoint
for model routing, policy controls, audit logs, traces, approvals, and cost
visibility." with a shorter, more neutral comment such as "# Example using
Tuning Engines OpenAI-compatible endpoint" so it matches the tone of the other
examples (e.g., the Google/Gemini example) and keeps the description concise and
factual.

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Inline comments:
In `@docs/en/learn/llm-connections.mdx`:
- Around line 159-168: Update the Tuning Engines example in the LLM snippet to
use an actual supported model ID and neutral wording: replace
model="gpt-4o-mini" with a verified Tuning Engines model ID (or cite the exact
model/source in a short parenthetical), keep the LLM(...) and Agent(...) usage
but ensure base_url="https://api.tuningengines.com/v1" remains consistent with
an OpenAI-compatible gateway, and change the leading comment to a neutral
instructional sentence (e.g., "Example showing how to configure an
OpenAI-compatible gateway for model routing and policy controls") instead of
promotional language; ensure references to the LLM class, model parameter,
base_url, and Agent class are updated accordingly.

---

Nitpick comments:
In `@docs/en/learn/llm-connections.mdx`:
- Around line 160-161: Replace the promotional comment line beginning with "#
Governed OpenAI-compatible endpoint for model routing, policy controls, audit
logs, traces, approvals, and cost visibility." with a shorter, more neutral
comment such as "# Example using Tuning Engines OpenAI-compatible endpoint" so
it matches the tone of the other examples (e.g., the Google/Gemini example) and
keeps the description concise and factual.

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: 12745f3c-60bf-44fc-acc2-b47422c1ca6b

📥 Commits

Reviewing files that changed from the base of the PR and between 5cdc420 and 9c11603.

📒 Files selected for processing (1)

• docs/en/learn/llm-connections.mdx

[cerebrixos]

Thanks for the review. I pushed an update that addresses the wording/model note:

• changed the comment to a neutral Example using Tuning Engines OpenAI-compatible endpoint
• replaced the hardcoded gpt-4o-mini with your-model-alias and a note to use a model alias from the Tuning Engines catalog
• rebased the branch onto current main

Validation: git diff --check origin/main...HEAD passes.