Stop Wasting $500/Day on API Calls (OpenAI + Python + LangChain + Redis)

Stop paying for the same LLM call twice. In this video, I build a semantic caching and token budget system in Python that cuts AI agent API costs by 60–80%. If your OpenAI bill exploded after moving your agent to production, this is the fix.

We use LangChain, Redis, and tiktoken to build two cost-control layers from scratch: a semantic cache that catches repeated and similar queries before they hit the API, and a token budget manager that enforces per-request and per-user spending limits. The full implementation is ~100 lines of Python you can drop into any existing LangChain agent.

🛠️ Tech stack:
— Python
— LangChain + langchain-redis
— Redis Stack (Docker)
— tiktoken
— OpenAI gpt-4o-mini

📂 Source code: https://github.com/ByteBuilderLabs/AI-Demos/blob/main/token_budget_agent/agent_cost_optimizer.py

🔗 Docs and resources:
— langchain-redis: https://python.langchain.com/docs/integrations/caches/redis_llm_caching/
— Redis Stack Docker: https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/docker/
— tiktoken: https://github.com/openai/tiktoken
— OpenAI pricing: https://openai.com/api/pricing/

👤 About ByteBuilder:
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#llm #aiagents #caching #tokens #langchain #redis #python #openai #APIcosts #agentops #bytebuilder

Видео Stop Wasting $500/Day on API Calls (OpenAI + Python + LangChain + Redis) канала ByteBuilder