A local gateway that caches what your LLM already answered.

Relay is a small gateway that sits in front of LLM providers and adds the things a raw provider call doesn't give you: streaming, a cache that serves near-duplicate prompts without paying for them again, failover between providers, rate limiting, and a live page showing cost and latency.

The part I actually wanted to build is the semantic cache. It embeds the incoming prompt, searches for the nearest prompt it has already answered, and if that one is close enough it returns the stored response instead of calling a provider. The nearest-neighbor search runs on proxima, an HNSW index I wrote in C++. Relay is partly an excuse to run something real on top of it.

Everything runs locally. There's a mock provider so you can exercise the whole thing with no API keys and no spend, which is also how the numbers below were measured.

The dashboard above, captured mid load test. The numbers are explained further down.

Calling a provider directly is one function call. Putting that call in front of traffic is where the gaps show up. You pay for every request even when it's a near-duplicate of one you answered a minute ago. One provider having a bad five minutes takes you down with it. Nothing caps a runaway client. And you can't see what's slow or what it costs. Relay is the layer that handles those, the same way a service ends up behind a reverse proxy, except the concerns here are LLM-shaped (tokens, dollars, semantic similarity).

• An OpenAI-compatible POST /v1/chat/completions, streaming (SSE) or not.
• Three providers behind one interface: OpenAI, Anthropic, and a local mock.
• An exact cache (hash of the normalized prompt and params) checked first.
• A semantic cache on proxima checked second.
• Failover: if a provider errors or stalls before the first token, try the next.
• Token-bucket rate limiting per API key and route.
• A metrics page with hit rate, p50/p95 latency, tokens, and modeled cost saved.
• A load test that produces the numbers in this README.

A request flows through the gateway like this:

client ──POST /v1/chat/completions──▶ gateway
   auth + rate limit (token bucket per key/route)   ─▶ 429 if over
   normalize messages+params, sha256                ─▶ exact cache hit? serve it
   embed prompt (MiniLM, 384-d)                     ─▶ proxima nearest neighbor
       similarity ≥ threshold and same route?       ─▶ semantic hit? serve it
   miss ─▶ provider chain with failover, stream out, then store the result

The exact cache is just a hash lookup. Messages are trimmed and the params that change an answer (model, temperature, top_p, max_tokens) go into the hash, so an identical request comes straight back from SQLite without touching a provider.

The semantic cache is the interesting one. proxima is in-memory and, importantly, has no deletes and no in-place updates. So I don't treat it as the store. SQLite is the source of truth and holds everything real: the prompt, its embedding, the response, tokens, and timestamps. proxima is a rebuildable index over those embeddings, where each vector's label is the SQLite row id. To look something up I embed the prompt, ask proxima for the nearest label, load that row, and accept it only if the cosine similarity clears the route's threshold and the route matches. In cosine space proxima returns distance as 1 - similarity, so a 0.90 threshold means accepting a distance up to 0.10.

Eviction works around the no-delete limitation by rebuilding. When the cache is over its size cap I mark the least-recently-used rows dead in SQLite. The index keeps serving as-is until enough rows are stale, then I build a fresh index from the survivors and swap it in. Because the embeddings live in SQLite, a rebuild never has to call the embedding model again. Inserts and the swap are guarded by a single lock, since proxima is a single writer; searches don't take it.

Providers all implement one async stream() that yields text deltas plus a final usage chunk, so the gateway never sees a provider-specific type. The failover chain tries them in order. The catch worth calling out: once a provider has streamed a token to the client there's no clean way to switch, so failover only happens before the first token. After that, a mid-stream failure just ends there.

I ran the load test on this machine (Windows 11, Python 3.12) against the mock provider so the run is free and reproducible. The mock simulates latency: 120 ms to first token, then 8 ms per token for a 40-token reply, so a miss is roughly 450 ms of "provider" time. 4000 requests, 24 concurrent. The traffic mix is 45% exact repeats, 35% paraphrases, 20% genuinely novel prompts. Same workload run twice, once with the cache on and once with RELAY_CACHE_DISABLED=1.

A few honest notes on reading that table. The median drops a lot because most requests become a ~10 ms cache read instead of a ~450 ms provider call. The p95 barely moves, and that's expected: about a fifth of requests still miss and pay full provider latency, so the 95th-percentile request is usually a miss. The cache helps the typical request, not the worst one. Spend tracks the hit rate almost exactly, roughly a 76% reduction, which works out to about $1.33 saved per million tokens served at the mock's reference prices.

The dollar figures are modeled, not billed. With no real keys, cost is the measured token counts times a published per-token price table (in config.yaml), not an invoice. The token counts, latencies, and hit rate are all really measured.

On the 0.90 similarity threshold: I didn't pick it to look good. With this corpus, 31 of 48 paraphrases land above it against their canonical question, and none of the 4000 novel prompts collide. Lower it and the semantic hit rate goes up but you start serving stored answers to prompts that aren't really equivalent. That tradeoff is the whole game with a semantic cache, and 0.90 is a conservative spot for it.

pip install -r requirements.txt
python -m relay

proxima isn't in requirements.txt because it's not a PyPI package; it's my own engine, built and installed from its own repo. Check it's importable:

python -c "import proxima"

The first start downloads the MiniLM embedding model once (a few hundred MB) and caches it. After that it runs offline. With no keys set, the default route uses the mock provider, so you can hit it right away:

curl -N http://127.0.0.1:8000/v1/chat/completions \
  -H "content-type: application/json" \
  -d '{"model":"mock","messages":[{"role":"user","content":"hello"}],"stream":true}'

The dashboard is at http://127.0.0.1:8000/ and updates live over SSE, with hit-rate, latency, throughput, and cache-mix charts drawn in plain SVG. There's no chart library, so it works offline like the rest of the project. To use the real providers, put keys in a .env (see .env.example) and add them to the route's provider chain in config.yaml.

To reproduce the numbers, start the server and run:

python loadtest/bench.py --n 4000 --concurrency 24

then restart with RELAY_CACHE_DISABLED=1 and run it again. There's also a standard Locust file at loadtest/locustfile.py if you'd rather drive it that way.

Each file under tests/ runs on its own with plain Python, no pytest needed:

python tests/test_cache.py        # exact cache, normalization
python tests/test_semantic.py     # semantic hit, eviction by rebuild
python tests/test_failover.py     # failover and timeouts
python tests/test_ratelimit.py    # token bucket