If you’ve ever built an AI agent using Claude and watched your API bill quietly eat your budget, you already know the pain.
So What is Autocache.?
At the core, Autocache is a simple, smart middleman. It’s a proxy server that sits between your app and Anthropic’s API. Its only job? To quietly intercept your requests, inject the right cache-control headers, and turn on prompt caching.
The result is pretty wild: you can slash your API costs by up to 90% and make your agent respond up to 85% faster. Plus, because developers love knowing what’s going on under the hood, it drops a little “money saved” receipt right into the response headers so you can track your exact ROI in real-time.
The Real Problem: Why are we bleeding money on AI agents?
Here’s the dirty secret about building complex AI agents right now. Whether you’re using visual builders like n8n, Flowise, and Make.com, or coding it yourself with frameworks like LangChain and LlamaIndex, you are likely paying for the same data over and over again.
To make an AI agent actually useful, you have to give it a massive “brain.” That usually looks like:
- A huge, detailed system prompt telling it exactly how to behave and what its role is (easily 1,000 to 5,000+ tokens).
- A giant toolbox of instructions, where you define 10 or 20 different tools and APIs it can use to do its job (another 5,000 to 15,000+ tokens).
When your agent runs, it doesn’t just remember this stuff. Every single time it needs to answer a user’s question or take an action, it sends that entire, massive instruction manual back to Anthropic. Over and over. For every single turn of the conversation.
Anthropic actually built a brilliant fix for this called prompt caching. It basically tells the API, “Hey, remember that massive instruction manual I sent a second ago? Just use that from your memory, don’t charge me to read it all again.”
But here is the catch: almost none of the platforms we actually use to build agents support this feature out of the box. They don’t know how to send the right cache headers. So, even though the cost-saving feature exists at the API level, you can’t easily use it without ripping out your agent builder and writing messy, custom API calls. You’re stuck paying full price.
The Real-World Math
Let’s look at what this actually means for your wallet, using a standard 15,000-token agent:
- Without caching: Running that heavy agent costs you about $0.045 every single time it runs. If your agent is active and runs 100 times a day, that’s $4.50 a day. $135 a month. For a single agent.
- With Autocache: The very first run costs the same. But every single run after that? It drops to $0.0045. You just saved 90%. That $135/month bill instantly drops to under $15.
Why This is Important for Developers?
For developers, Autocache isn’t just about saving a few bucks. It’s the difference between an agent being a fun, expensive weekend prototype and a viable, scalable feature you can actually put into production.
When you’re building AI workflows, latency and cost are the two biggest bottlenecks to going live. Autocache bridges the gap between what Anthropic offers and what your agent builder supports. It lets you build smarter, faster, and way cheaper agents, without having to write a single line of custom caching logic or abandon your favorite low code platform.
You just point your agent at Autocache, and let it do the heavy lifting.
Features
Seamless Drop In Replacement:
You don’t need to refactor your client logic or overhaul your SDKs to start using this. Just swap out your base API URL to point to our endpoint, and the system acts as a transparent proxy. You instantly get automatic caching without having to touch your underlying application code.
Built In ROI Analytics & Metrics:
We expose detailed cost savings and break even analysis directly through the HTTP response headers. This allows you to programmatically intercept and track exactly how many compute cycles or tokens you are saving per request.
It makes it incredibly easy to pipe these metrics into your own dashboards to monitor usage patterns and justify infrastructure costs in real time.
Intelligent Cache Routing:
This isn’t just a naive, exact string match Redis cache. The system dynamically determines optimal cache breakpoints using multiple evaluation strategies.
By understanding the context and structure of the payloads, it maximizes cache hit rates and reduces latency without requiring you to manually define strict TTLs or invalidation rules for every single endpoint.
High Performance Streaming Support:
We built this to handle high throughput environments with minimal overhead. The proxy is fully optimized for both standard RESTful JSON responses and Server Sent Events (SSE). If you are working with streaming outputs, like real time LLM token generation the system handles the data flow seamlessly without introducing buffering delays or breaking the stream connection.
Highly Configurable Strategies:
Every application’s tolerance for stale data is different, so we give you full control over the cache behavior. You can fine-tune custom thresholds, define strict invalidation rules, and easily toggle between different caching strategies to fit your specific latency requirements and workload characteristics.
Container Native Deployment:
We designed this to fit naturally into your existing CI/CD and DevOps pipelines. It ships production ready with an optimized Dockerfile and a pre configured docker compose setup. Whether you are running it locally for testing or deploying it into a distributed Kubernetes cluster, you can spin up and scale the service in minutes.
Structured Observability & Logging:
Debugging a black box proxy layer is frustrating, so we made sure the telemetry is highly transparent. The system outputs comprehensive, fully structured JSON logs for all requests and responses.
This makes it trivial to ingest your traffic data directly into ELK, Datadog, Grafana, or whatever observability stack your team is already using.
Comparison to the Alternatives
Look, I’ve been looking at how to handle prompt caching without having to rewrite my entire stack, and honestly, the current landscape is a mess. Most of the tools out there either force you into a specific ecosystem, require a ton of annoying config, or just give you zero visibility into whether they’re actually saving you money.
That’s exactly why I like the Autocache approach. It’s not just another libraryit’s a zero config, transparent proxy. You just drop it in, it handles the cache breakpoints dynamically by analyzing tokens, and it pipes the actual ROI metrics right into your HTTP headers. It’s basically plug and play for visual workflows like n8n or Flowise.
Here is how I see it compared to what else is out there right now:
Comparison with LiteLLM
LiteLLM is a proxy too, so you can swap out your API URL and drop it into n8n just like Autocache. But the caching? It’s definitely not automatic. You have to sit there and write your own config rules, which gets old fast. It’s strictly rule based, so there’s no smart token analysis happening under the hood, and you get absolutely no ROI analytics to prove it’s working.
LangChain Smart Cache
If you are building your entire app exclusively inside LangChain, this is a decent library. It does the automatic caching thing well with a priority based system, and you get some internal stats. But that’s the dealbreaker for me, it’s just a library. I can’t run it as a standalone proxy to power my other no code tools or external agents. It only works if you stay inside the LangChain bubble.
Anthropic Cost Tracker
This is really just a Python library built for observability. Yeah, it gives you a nice dashboard for tracking costs, but as far as I can tell, it doesn’t do any of the actual automatic cache injection or intelligent routing I want. Plus, being Python-only means I can’t just drop it into my infrastructure as a universal proxy.
OpenRouter
OpenRouter is awesome for easily hitting different models, and it plugs right into Flowise. The problem is caching is totally out of your hands, it depends entirely on what the upstream provider supports. There’s no unified, intelligent caching layer of its own, zero token analysis, and no ROI tracking.
You just have to cross your fingers and hope the provider is optimizing things.
AWS Bedrock
Bedrock is heavy-duty. It has legit ML-based caching and it’s smart. But… you have to live in AWS. It’s a total walled garden. If you want to keep your stack flexible, or if you’re routing requests to external LLMs that aren’t inside the AWS ecosystem, Bedrock is useless as a generic drop-in solution.
License
Autocache is a free and open source project that is released under the MIT License.
