cortex/CLAUDE.md

CLAUDE.md — cortex

Project overview

cortex is a Rust reverse-proxy that sits in front of multiple mistral.rs inference nodes and presents a unified OpenAI + Anthropic compatible API surface. It handles model routing, lifecycle management (load/unload/evict), request translation, and metrics collection.

Repository layout

cortex/
├── Cargo.toml              # workspace root
├── cortex.toml      # example gateway config
├── README.md
├── CLAUDE.md               # ← you are here
├── crates/
│   ├── cortex-core/            # shared types, config, envelopes
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── config.rs       # figment-based config structs
│   │       ├── node.rs         # NodeState, ModelStatus
│   │       ├── openai.rs       # OpenAI request/response types
│   │       ├── anthropic.rs    # Anthropic request/response types
│   │       ├── translate.rs    # OpenAI <-> Anthropic translation
│   │       └── metrics.rs      # RequestMetrics, histogram helpers
│   ├── cortex-gateway/         # the HTTP proxy server
│   │   └── src/
│   │       ├── lib.rs
│   │       ├── state.rs        # CortexState: Arc<RwLock<...>>
│   │       ├── router.rs       # model -> node routing logic
│   │       ├── proxy.rs        # streaming HTTP proxy to backends
│   │       ├── evictor.rs      # LRU/priority eviction logic
│   │       ├── poller.rs       # background task polling node status
│   │       ├── handlers.rs     # axum handlers (chat, completions, models, etc.)
│   │       └── metrics.rs      # prometheus exporter endpoint
│   ├── cortex-agent/           # per-node sidecar (future: defrag, restart)
│   │   └── src/
│   │       ├── lib.rs
│   │       └── agent.rs        # local node management
│   └── cortex-cli/             # CLI entrypoint
│       └── src/
│           └── main.rs
└── tests/                  # integration tests (future)

Key design decisions

mistral.rs HTTP API for model lifecycle

mistral.rs (v0.8+) supports dynamic model loading/unloading at runtime:

• POST /v1/models/unload {"model_id": "..."} — frees VRAM, preserves config
• POST /v1/models/reload {"model_id": "..."} — explicitly reload
• POST /v1/models/status {"model_id": "..."} — loaded/unloaded/reloading
• GET /v1/models — lists all models with status field
• Lazy loading: requests to unloaded models trigger automatic reload

The gateway does NOT manage systemd units for model swaps. It calls these HTTP endpoints directly. The only systemd interaction is for full-process restarts after VRAM fragmentation accumulates (defrag_after_cycles).

Streaming proxy

Chat completions are proxied as SSE streams. The gateway must:

1. Parse the inbound request to extract the model name
2. Route to the correct backend node
3. Stream the response back, capturing token timing for metrics
4. NOT buffer the full response — true streaming passthrough

Anthropic translation

When a request arrives at /v1/messages (Anthropic format), the gateway translates it to OpenAI format before proxying to mistral.rs, then translates the response back. This is stateless envelope transformation.

Eviction

The evictor runs as a background task. Before loading a model on a node where VRAM is tight:

1. Check if the model is already loaded elsewhere → route there instead
2. Find the LRU model on the target node (excluding pinned models)
3. Call /v1/models/unload on that model
4. The incoming request's lazy-load triggers the new model load

Metrics

Per-request: model, node, prompt_tokens, completion_tokens, total_tokens, tok_per_sec, time_to_first_token_ms, total_latency_ms. Exposed as Prometheus histograms/counters on a separate port.

Tech stack

• Rust 2024 edition — workspace with 4 crates
• Axum 0.8 — HTTP framework (same as mistral.rs itself)
• reqwest — HTTP client for proxying to backends
• figment — config loading (TOML + env vars)
• tokio — async runtime
• metrics + metrics-exporter-prometheus — observability
• tracing — structured logging

Build commands

cargo build --release           # build all crates
cargo run -p cortex-cli -- serve    # run the gateway
cargo test                      # run all tests
cargo clippy --workspace        # lint

CI

Gitea Actions runs on every push to any branch. All three checks must pass before merging:

cargo fmt --check --all                    # formatting
cargo clippy --workspace -- -D warnings   # lint (warnings are errors)
cargo test --workspace                     # tests

Run these locally before pushing. cargo fmt --all fixes formatting automatically. Clippy warnings must be resolved, not suppressed with #[allow(...)] unless there is a clear rationale.

Environment

• Targets Fedora 43 (systemd, SELinux enforcing)
• Nodes communicate over a private network (e.g. WireGuard mesh)
  • One or more GPU nodes running mistral.rs on port 8080
  • Optionally a metrics-only node (no GPU) for Prometheus/Grafana
• Each node runs mistralrs serve on port 8080
• Gateway listens on port 8000 (API) and 9100 (metrics)
• TLS terminated at gateway or via nginx; internal traffic is plaintext over WireGuard

Conventions

• Error handling: anyhow for binaries, thiserror for library crates
• No unwrap() in library code; expect() only with clear rationale
• All public types derive Debug, Clone, Serialize, Deserialize where sensible
• Config structs use figment with TOML as primary source, env vars as override
• Prefer Arc<RwLock<...>> for shared fleet state; minimize lock duration
• SSE streaming uses tokio_stream + eventsource-stream for parsing
• Log at info for request routing, debug for proxy details, warn for eviction and node health, error for proxy failures

mistral.rs API gotchas

These are sharp edges Claude Code will hit when implementing the proxy. Read before touching proxy.rs or handlers.rs.

Model name validation

mistral.rs validates that the model field in every request matches the model that was actually loaded. If the names don't match, the request is rejected outright. The special model name "default" bypasses this validation entirely.

Implication for cortex: The gateway must ensure the model field in the proxied request body matches what mistral.rs expects. Two strategies:

1. Passthrough — the client uses the exact HuggingFace model ID (e.g. Qwen/Qwen3-Coder-30B-A3B-Instruct) and cortex routes based on that. This is the simplest approach and should be the default.
2. Rewrite to "default" — if cortex introduces its own model aliases, it must rewrite the model field to "default" before proxying. This is a future feature, not phase 1.

Lazy loading latency

When a request hits an unloaded model, mistral.rs automatically reloads it before processing. This can take 10-60+ seconds for large models. The gateway must:

• Set a generous HTTP client timeout (already 300s in the scaffold).
• Mark the request as cold_start: true in metrics.
• Not retry or time out prematurely — the upstream is busy loading, not dead.

SSE stream format

mistral.rs streams use standard OpenAI SSE format:

data: {"id":"...","choices":[{"delta":{"content":"token"},...}]}\n\n
data: [DONE]\n\n

The proxy must forward these chunks verbatim. Do not attempt to parse or re-serialize each chunk — that adds latency and risks breaking the stream. Parse only for metrics extraction (token counts from the final usage object, timing from chunk arrival).

Multi-model mode

mistralrs serve can load multiple models when started with a selector config or multiple --text-model / --vision-model flags. The /v1/models response lists all of them with a status field. When sending requests, the model field must match one of the listed model IDs — "default" only works if you don't care which model handles it.

Unload preserves config

POST /v1/models/unload frees VRAM but keeps the model's config in memory. A subsequent request to that model (or explicit reload) will reload from disk/HF cache — not re-download. This is fast relative to initial download but still involves loading weights into VRAM.

Implementation plan

Each phase is a branch → PR. CI must pass (fmt, clippy, test) before merge. Phases are sequential — each builds on the previous.

Phase 1: Compile and proxy a basic request ✅

Completed. 6 integration tests in cortex-gateway/tests/proxy_basic.rs: chat completion proxy, health endpoint, list models, model not found, no healthy nodes, missing model field. Test helpers in tests/common/mod.rs provide spawn_mock_backend() and spawn_gateway() using axum as the mock mistral.rs backend.

Phase 2: Streaming SSE passthrough ✅

Completed. The existing Body::from_stream(bytes_stream()) proxy works for SSE out of the box. 2 integration tests in cortex-gateway/tests/streaming.rs:

• test_streaming_sse_passthrough — 5 chunks with 50ms delays, verifies incremental delivery (time spread between first and last chunk)
• test_streaming_done_terminator — verifies data: [DONE] is forwarded

Phase 3: Poller + live /v1/models ✅

Completed. Extracted poll_once() from poll_loop() for testability. 4 tests in cortex-gateway/tests/poller.rs:

• test_poller_discovers_models — 2 models (loaded + unloaded) discovered with correct status
• test_poller_updates_gateway_models_endpoint — /v1/models reflects polled state with node attribution
• test_poller_marks_unreachable_node_unhealthy — unreachable node flipped to unhealthy
• test_poller_removes_stale_models — model removed from upstream is pruned from state

Phase 4: Eviction ✅

Completed. Added last_accessed tracking in handlers (touch_model called after routing). 5 tests in cortex-gateway/tests/eviction.rs:

• test_evict_lru_model — older model evicted, unload call verified on mock
• test_eviction_skips_pinned_models — pinned model protected, newer model evicted instead
• test_eviction_nothing_to_evict — all models pinned, returns None
• test_eviction_increments_lifecycle_cycles — counter incremented after eviction
• test_last_accessed_updated_on_request — last_accessed set after proxied request

Router-triggered eviction (automatic eviction on VRAM pressure during request routing) deferred — requires per-model VRAM tracking which is not yet populated. The evict_lru_on_node function is callable and tested for when that integration is added.

Phase 5: Anthropic translation ✅

Completed. Non-streaming Anthropic round-trip implemented: handler buffers upstream OpenAI response, translates via openai_to_anthropic, returns Anthropic-format JSON. 5 tests in cortex-gateway/tests/anthropic.rs:

• test_anthropic_to_openai_round_trip — full request/response translation with stop_reason mapping ("stop" → "end_turn") and usage field names
• test_anthropic_with_system_prompt — system field translated to system message
• test_anthropic_with_content_blocks — array content blocks handled
• test_anthropic_model_not_found — 404 for unknown model
• test_anthropic_invalid_request — 400 for malformed request

Streaming Anthropic SSE translation (OpenAI SSE → Anthropic SSE event types) deferred as a follow-up.

Phase 6: Metrics instrumentation

Goal: Every proxied request emits Prometheus metrics. /metrics on port 9100 returns valid Prometheus text format.

Files to change:

• cortex-gateway/src/proxy.rs or cortex-gateway/src/handlers.rs — wrap each proxy call with timing instrumentation:
  • Instant::now() before the request, compute duration after
  • Parse usage from the response (non-streaming) or final chunk (streaming) for token counts
  • Emit: metrics::histogram!("cortex_request_duration_seconds", ...) with labels model and node
  • Emit: metrics::counter!("cortex_requests_total", ...)
  • Emit cold start, eviction, and error counters
• cortex-gateway/src/metrics.rs — already installs the exporter; verify the described metrics appear
• tests/ — test that after a proxied request, the /metrics endpoint contains the expected metric names

Done when: curl localhost:9100/metrics shows request counters and duration histograms after proxying a test request.

Phase 7 (lower priority): Agent sidecar

Goal: Per-node binary that handles VRAM defrag restarts and reports real VRAM usage via nvidia-smi.

This is deferred. The gateway handles the critical path (model lifecycle) entirely via the mistral.rs HTTP API. The agent adds operational polish: automatic process restart when lifecycle_cycles exceeds threshold, real VRAM reporting (vs. estimates), and potentially GPU temperature/power monitoring.

Defer until: Phases 1-6 are merged and running in production.