Principle:Openai Evals LangChain Integration
| Knowledge Sources | |
|---|---|
| Domains | Evaluation, Framework Integration, LLM Orchestration |
| Last Updated | 2026-02-14 10:00 GMT |
Overview
An integration pattern that wraps external LLM orchestration framework pipelines into the evaluation system's completion function protocol, enabling fair side-by-side comparison of chain-based reasoning approaches with direct model calls.
Description
LangChain Integration addresses the challenge of evaluating compound reasoning systems alongside simple model calls within a unified framework. Modern LLM applications frequently use orchestration frameworks to decompose complex problems into multi-step chains -- for example, a math problem might be broken into sub-expressions, each solved independently, then combined. These chain-based approaches need to be evaluated using the same benchmarks and metrics as direct model calls to determine whether the added complexity provides genuine benefit.
The integration works by implementing the CompletionFn protocol, which is the standard interface that the evaluation framework uses to obtain model responses. Any class that implements this protocol can be used as a drop-in replacement for a direct model call. The LangChain integration wraps the framework's chain execution logic inside this protocol, handling two key translation tasks:
- Prompt conversion: The evaluation framework provides prompts in its own message format (system messages, user messages, assistant messages). The integration converts these into the format expected by the orchestration framework's chain input interface.
- Response extraction: The orchestration framework returns results in its own format (often a dictionary with multiple output fields). The integration extracts the relevant answer text and wraps it in the CompletionFn response format expected by the evaluation framework.
A concrete example is the LangChainMathChainCompletionFn, which wraps a LangChain math chain. This chain receives a mathematical word problem, decomposes it into arithmetic sub-steps using a language model, executes each sub-step, and returns the final numerical answer. By wrapping this chain as a CompletionFn, it can be evaluated on the same math benchmarks used for direct GPT-4 calls, enabling a fair performance comparison.
Usage
Apply LangChain integration in the following scenarios:
- Comparing chain-based reasoning against direct model calls on the same benchmark to quantify the benefit of decomposition.
- Evaluating multi-step pipelines (math chains, retrieval-augmented generation, tool-use chains) within the standard evaluation framework.
- Benchmarking new orchestration strategies by implementing them as chains and running them through existing evaluation suites.
The integration is registered as a CompletionFn in the evaluation configuration:
completion_fns:
langchain_math:
class: evals.completion_fns.langchain:LangChainMathChainCompletionFn
args:
model_name: gpt-4
This can then be invoked in an evaluation run:
oaieval langchain_math math_benchmark
Extensibility: The same pattern can be applied to any orchestration framework, not just LangChain. Any system that can be wrapped in the CompletionFn protocol can be integrated. This includes custom pipelines, agent frameworks, and retrieval-augmented generation systems.
Theoretical Basis
The theoretical foundation draws from the adapter pattern in software design and the principle of protocol-based polymorphism. By defining a narrow, well-specified interface (CompletionFn), the evaluation framework achieves substitutability -- any system that conforms to the protocol can be evaluated identically, regardless of its internal complexity.
The integration algorithm proceeds as follows:
1. Evaluation framework calls CompletionFn with a prompt:
- prompt: list of messages in eval format [{role, content}, ...]
2. Convert prompt to chain input format:
- Extract the user's question from the message list
- Format it as the chain's expected input (e.g., {"question": "..."})
3. Execute the chain:
- Initialize the orchestration framework chain (e.g., LLMMathChain)
- Run the chain with the converted input
- Chain internally performs multi-step reasoning and computation
4. Extract the result:
- Parse the chain's output dictionary for the answer field
- Convert the answer to a plain text string
5. Return the result in CompletionFn response format:
- Wrap the answer text in the standard response object
- Evaluation framework scores it identically to any direct model response
The key benefit of this approach is evaluation parity: a chain that internally makes five LLM calls and performs intermediate computation is scored by the exact same metric as a single direct model call. This ensures that comparisons are methodologically sound and not confounded by differences in evaluation methodology.