What are cost optimization techniques for LangChain?

LLM API costs are primarily driven by token usage. LangChain applications can apply several techniques at different layers to reduce costs without significantly degrading quality:

• Response caching — the single highest-impact technique for repetitive queries. InMemoryCache or RedisSemanticCache returns stored responses for identical or semantically similar prompts, paying zero tokens for cache hits.
• Model tiering — use cheaper models (GPT-4o-mini, Claude Haiku) for simple classification, routing, and extraction tasks; reserve expensive models (GPT-4o, Claude Sonnet) for complex reasoning. Implement this with RunnableBranch routing.
• Prompt compression — use LLMLingua (via langchain-community) to compress retrieved context by removing low-information tokens before injecting into the prompt.
• Token counting before calling — use llm.get_num_tokens(text) to check prompt size; truncate or summarise if it exceeds a budget:

llm = ChatOpenAI()
token_count = llm.get_num_tokens(prompt_text)
if token_count > 3000:
    # Summarise or truncate before proceeding
    ...

• Streaming — stream responses to clients early; use max_tokens to cap output length for use cases where truncation is acceptable.
• Batch processing — use .batch() with appropriate concurrency for offline workloads to maximise throughput per dollar.
• Avoid over-engineering with agents — a simple RAG chain is 10-50x cheaper per query than a multi-step agent. Only use agents when the task genuinely requires dynamic decision-making.

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