chore(llmobs/langchain): stop using in-process vcr

[Kyle-Verhoog]

I suspect that the in-process VCR is leading to flaky tests as:

• VCR patches all http connections including possibly those to the test agent; and
• I don't trust in-process VCR monkeypatching very much

Instead, we can continue to use VCR but out of process by proxying through the test agent which now supports a VCR feature.

Checklist

• PR author has checked that all the criteria below are met
• The PR description includes an overview of the change
• The PR description articulates the motivation for the change
• The change includes tests OR the PR description describes a testing strategy
• The PR description notes risks associated with the change, if any
• Newly-added code is easy to change
• The change follows the library release note guidelines
• The change includes or references documentation updates if necessary
• Backport labels are set (if applicable)

Reviewer Checklist

• Reviewer has checked that all the criteria below are met
• Title is accurate
• All changes are related to the pull request's stated goal
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Newly-added code is easy to change
• Release note makes sense to a user of the library
• If necessary, author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[github-actions]

CODEOWNERS have been resolved as:

tests/llmobs/llmobs_cassettes/openai/openai_chat_completions_post_e16811d2.yaml  @DataDog/ml-observability
tests/llmobs/llmobs_cassettes/openai/openai_completions_post_7b116e69.yaml  @DataDog/ml-observability
tests/llmobs/llmobs_cassettes/openai/openai_completions_post_8b80b4c0.yaml  @DataDog/ml-observability
tests/llmobs/llmobs_cassettes/openai/openai_completions_post_a23a180e.yaml  @DataDog/ml-observability
tests/llmobs/llmobs_cassettes/openai/openai_completions_post_b2bf069e.yaml  @DataDog/ml-observability
tests/contrib/langchain/conftest.py                                     @DataDog/ml-observability
tests/contrib/langchain/test_langchain.py                               @DataDog/ml-observability
tests/contrib/langchain/test_langchain_llmobs.py                        @DataDog/ml-observability

[github-actions]

Bootstrap import analysis

Comparison of import times between this PR and base.

Summary

The average import time from this PR is: 286 ± 6 ms.

The average import time from base is: 286 ± 6 ms.

The import time difference between this PR and base is: -0.8 ± 0.3 ms.

Import time breakdown

The following import paths have shrunk:

ddtrace.auto 1.883 ms (0.66%)

ddtrace.bootstrap.sitecustomize 1.211 ms (0.42%)

ddtrace.bootstrap.preload 1.211 ms (0.42%)

ddtrace.internal.remoteconfig.client 0.639 ms (0.22%)

ddtrace 0.672 ms (0.24%)

ddtrace.internal._unpatched 0.028 ms (0.01%)

json 0.028 ms (0.01%)

json.decoder 0.028 ms (0.01%)

re 0.028 ms (0.01%)

enum 0.028 ms (0.01%)

types 0.028 ms (0.01%)

[pr-commenter]

Benchmarks

Benchmark execution time: 2025-07-10 14:27:38

Comparing candidate commit a5a7065 in PR branch kylev/langchain-death-flaky with baseline commit b17d958 in branch main.

Found 0 performance improvements and 2 performance regressions! Performance is the same for 516 metrics, 2 unstable metrics.

scenario:iastaspects-lstrip_aspect

• 🟥 execution_time [+748.612ns; +835.323ns] or [+7.148%; +7.976%]

scenario:iastaspectsospath-ospathsplitext_aspect

• 🟥 execution_time [+668.518ns; +780.800ns] or [+14.584%; +17.034%]