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langchain-performance-tuning

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plugins/saas-packs/langchain-py-pack/skills/langchain-performance-tuning/SKILL.md

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# LangChain Performance Tuning

## Overview

An engineer calls `chain.batch(inputs_1000)` expecting 1000 parallel LLM calls. Actual behavior: `Runnable.batch` and `Runnable.abatch` in LangChain 1.0 default to `max_concurrency=1`, so the 1000 inputs run **sequentially with bookkeeping overhead** — sometimes slower than a plain `for` loop. This is pain-catalog entry P08. The fix is one line:

```python
# Before: serial, ~1000 * per_call_latency
await chain.abatch(inputs)

# After: 10x throughput at 10 providers' worth of concurrency
await chain.abatch(inputs, config={"max_concurrency": 10})
```

Other silent regressions in the same pain catalog: P48 (`invoke` inside `async def` blocks the FastAPI event loop), P22 (`InMemoryChatMessageHistory` loses every user's chat on restart), P62 (`RedisSemanticCache` at the default `score_threshold=0.95` returns under 5% hit rate), P59 (async retrievers leak connections on cancellation), P60 (`BackgroundTasks` fires *after* the response — wrong for per-token SSE), P01 (streaming token counts are only reliable on the `on_chat_model_end` event).
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