Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching (opens in new tab)

(pypi.org)

12 pointssreenathmenon7mo ago6 comments

I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development.

It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%.

Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables

  from llmswap import LLMClient

  client = LLMClient(cache_enabled=True)
  response = client.query("Explain quantum computing")
  # Second identical query returns from cache instantly (free)

The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications.

Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development.

GitHub: https://github.com/sreenathmmenon/llmswap PyPI: https://pypi.org/project/llmswap/

6 comments

rav7mo ago

How is it "50-90%" savings? If a given application doesn't repeat its queries, surely there's nothing to save by caching the responses?

sreenathmenonOP7mo ago

Hey, Thanks for the great feedback! You're raising valid point.

Actually, this package started based on a hackathon project where I was burning the Anthropic API credits for our hackathon project which was RAG (internal documentation) + MCP.

There were question which were getting repeated several times. The 50% + comes from this experience. So, based on this, I was thinking of some of the use cases like this:

Multi-User Support/FAQ Systems: - How do I reset my password? - Reset password steps? - Forgot my password help - Password reset procedure

RAG based: - How to configure VM? - How to deploy? - How to create a network?

Educational/Training Apps Developer Testing scenarios, etc

You're absolutely right that apps with unique queries won't see these benefits - this won't help in - Personalized Content - Real-Time Data - User-Specific Queries - Creative Generation and other scenarios

I think I should clarify this in the docs. Thanks for the great feedback. This is my first opensource package and first conversation in hackernews. Great to interact and learn from all of you

wasabi9910117mo ago

How does this compare to decorating with @functions.cache?

sreenathmenonOP7mo ago

Hey, functools.cache is definitely simpler and would be sufficient for most basic cases. But I was thinking of multi-tenant and context aware scenario's - that's why went with different strategy.

0points7mo ago

I hate to be that guy, but your AI should have suggested you used one of the off-the-shelf in-memory key-value databases.

The most popular probably being redis.

sreenathmenonOP7mo ago

Fair point! Redis would be better for production. I went with in-memory for zero-config simplicity, but should add Redis as an option. Thanks!

j / k navigate · click thread line to collapse

Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching (opens in new tab)

(pypi.org)

12 pointssreenathmenon7mo ago6 comments

I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development.

It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%.

  from llmswap import LLMClient

  client = LLMClient(cache_enabled=True)
  response = client.query("Explain quantum computing")
  # Second identical query returns from cache instantly (free)

The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications.

Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development.

GitHub: https://github.com/sreenathmmenon/llmswap PyPI: https://pypi.org/project/llmswap/

6 comments

rav7mo ago

How is it "50-90%" savings? If a given application doesn't repeat its queries, surely there's nothing to save by caching the responses?

sreenathmenonOP7mo ago

Hey, Thanks for the great feedback! You're raising valid point.

Actually, this package started based on a hackathon project where I was burning the Anthropic API credits for our hackathon project which was RAG (internal documentation) + MCP.

There were question which were getting repeated several times. The 50% + comes from this experience. So, based on this, I was thinking of some of the use cases like this:

Multi-User Support/FAQ Systems: - How do I reset my password? - Reset password steps? - Forgot my password help - Password reset procedure

RAG based: - How to configure VM? - How to deploy? - How to create a network?

Educational/Training Apps Developer Testing scenarios, etc

I think I should clarify this in the docs. Thanks for the great feedback. This is my first opensource package and first conversation in hackernews. Great to interact and learn from all of you

wasabi9910117mo ago

How does this compare to decorating with @functions.cache?

sreenathmenonOP7mo ago

Hey, functools.cache is definitely simpler and would be sufficient for most basic cases. But I was thinking of multi-tenant and context aware scenario's - that's why went with different strategy.

0points7mo ago

I hate to be that guy, but your AI should have suggested you used one of the off-the-shelf in-memory key-value databases.

The most popular probably being redis.

sreenathmenonOP7mo ago

Fair point! Redis would be better for production. I went with in-memory for zero-config simplicity, but should add Redis as an option. Thanks!

j / k navigate · click thread line to collapse