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langchain - ✅(Solved) Fix docs: add Kalibr integration to third-party providers — execution path routing [2 pull requests, 2 comments, 2 participants]

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langchain-ai/langchain#36260•Fetched 2026-04-08 01:31:04
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Fix Action

Fixed

PR fix notes

PR #36236: docs: add Kalibr to third-party integrations — execution path routing for production reliability

Description (problem / solution / changelog)

Fixes #36260

What this adds

Documentation entry for Kalibr on LangChains integrations/providers page, with a working code example showing how to use Kalibr with router.as_langchain().

What is Kalibr

Kalibr is an execution path router for AI agents. It learns which model+parameter combinations succeed at specific tasks from live production signals, and routes each call to the optimal path in real time.

It is NOT:

  • Observability (LangSmith, Langfuse — they surface dashboards; Kalibr feeds signals into routing)
  • A model gateway (LiteLLM/OpenRouter route by cost/latency; Kalibr routes by task outcome success)
  • A proxy (model calls go directly to providers)

Classification: Execution path routing based on outcome signals.

Integration

Two lines of code, zero changes to existing chain definitions:

import kalibr  # first import
from kalibr import Router
router = Router(goal="task", paths=["gpt-4o", "claude-sonnet-4-20250514"])
llm = router.as_langchain()  # drop-in BaseChatModel

Benchmark during provider degradation events:

SetupTask Success Rate
Single ChatOpenAI16–36%
Kalibr-routed88–100%

Docs: https://kalibr.systems/docs | PyPI: https://pypi.org/project/kalibr/

Changed files

  • docs/docs/integrations/providers/kalibr.mdx (added, +112/-0)

PR #36262: docs: add Kalibr to third-party integrations — execution path routing for production reliability

Description (problem / solution / changelog)

Fixes #36260

What this adds

Documentation entry for Kalibr on LangChains integrations/providers page, with a working code example showing how to use Kalibr with router.as_langchain().

What is Kalibr

Kalibr is an execution path router for AI agents. It learns which model+parameter combinations succeed at specific tasks from live production signals, and routes each call to the optimal path in real time.

It is NOT:

  • Observability (LangSmith, Langfuse — they surface dashboards; Kalibr feeds signals into routing)
  • A model gateway (LiteLLM/OpenRouter route by cost/latency; Kalibr routes by task outcome success)
  • A proxy (model calls go directly to providers)

Classification: Execution path routing based on outcome signals.

Integration

Two lines of code, zero changes to existing chain definitions:

import kalibr  # first import
from kalibr import Router
router = Router(goal="task", paths=["gpt-4o", "claude-sonnet-4-20250514"])
llm = router.as_langchain()  # drop-in BaseChatModel

Benchmark during provider degradation events:

SetupTask Success Rate
Single ChatOpenAI16-36%
Kalibr-routed88-100%

Docs: https://kalibr.systems/docs | PyPI: https://pypi.org/project/kalibr/

Changed files

  • docs/docs/integrations/providers/kalibr.mdx (added, +112/-0)

Code Example

import kalibr  # first import
from kalibr import Router
router = Router(goal="task", paths=["gpt-4o", "claude-sonnet-4-20250514"])
llm = router.as_langchain()  # BaseChatModel drop-in
RAW_BUFFERClick to expand / collapse

Feature Request

Add documentation for Kalibr to the LangChain third-party integrations/providers page.

What is Kalibr

Kalibr is an execution path router for AI agents in production. It captures outcome signals from every LLM call, learns which model+parameter combinations succeed at specific tasks, and routes the next call to the optimal path in real time.

Classification: execution path routing based on outcome signals.

Not: observability (LangSmith/Langfuse), model gateway (LiteLLM/OpenRouter), or a proxy.

Integration

Kalibr provides router.as_langchain() — a drop-in BaseChatModel that works anywhere ChatOpenAI or ChatAnthropic is used:

import kalibr  # first import
from kalibr import Router
router = Router(goal="task", paths=["gpt-4o", "claude-sonnet-4-20250514"])
llm = router.as_langchain()  # BaseChatModel drop-in

Why it belongs here

LangChain users building production agents need reliability tooling beyond single-model hardcoding. Kalibr is the production routing layer — 2 lines of code, zero changes to existing chains.

Benchmark during provider degradation: 88-100% success vs 16-36% for hardcoded single-model.

extent analysis

Fix Plan

To add documentation for Kalibr to the LangChain third-party integrations/providers page, follow these steps:

  • Update the LangChain documentation to include a new section for Kalibr
  • Add a brief description of Kalibr and its functionality
  • Provide an example of how to use Kalibr with LangChain, including the necessary import statements and code snippets

Example code:

import kalibr
from kalibr import Router

# Create a Kalibr router
router = Router(goal="task", paths=["gpt-4o", "claude-sonnet-4-20250514"])

# Use the router as a drop-in replacement for ChatOpenAI or ChatAnthropic
llm = router.as_langchain()

Verification

To verify that the fix worked, check the LangChain documentation for the new Kalibr section and ensure that the example code is accurate and functional.

Extra Tips

  • Make sure to include links to the Kalibr PyPI page, documentation, and GitHub repository in the LangChain documentation for easy access to more information.
  • Consider adding a benchmark or comparison to the documentation to demonstrate the benefits of using Kalibr with LangChain.

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