rob thijssen 8fa1d1962e
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feat(helexa-acp): anthropic-messages provider
Stage 6b. Third provider impl, completing the wire-format trio
(openai-chat, openai-responses, anthropic-messages). Lets a
helexa-acp endpoint configured with `wire_api = "anthropic-messages"`
drive Claude models — either against Anthropic directly or via
cortex's /v1/messages translation surface.

## Encoder (CompletionRequest → Anthropic body)

- System messages flatten to the top-level `system` field
  (concatenated with blank lines when there are multiple).
- User text → `{role:"user", content:"..."}`.
- User MultiPart (text + images) → `content` array with Anthropic's
  distinct image shape: `{type:"image", source:{type:"base64",
  media_type, data}}` — structurally different from OpenAI's
  `image_url` data URI.
- Assistant text → `{role:"assistant", content:"..."}`.
- Assistant tool_calls → `content` array with optional `{type:"text"}`
  block plus one `{type:"tool_use", id, name, input:<parsed json>}`
  per call. The internal arguments JSON string is parsed back to a
  Value before encoding (Anthropic requires the parsed form);
  malformed JSON falls back to a String input so the request body
  still serialises.
- Tool result → `{role:"user", content:[{type:"tool_result",
  tool_use_id, content}]}` per Anthropic's convention (no separate
  `tool` role).
- `max_tokens` is required by Anthropic; defaults to 8192 when the
  request doesn't specify.

## Decoder (Anthropic SSE → CompletionEvent)

Named SSE events:

- `message_start` → captures input_tokens from `usage` for the
  eventual UsageStats.
- `content_block_start` (type=text) → TextDelta (initial text, if any).
- `content_block_start` (type=tool_use) → ToolCallStart; if a
  pre-buffered `input` is present, also emits a single
  ToolCallArgsDelta.
- `content_block_start` (type=thinking, for extended-thinking
  models) → ReasoningDelta.
- `content_block_delta` (text_delta) → TextDelta.
- `content_block_delta` (input_json_delta) → ToolCallArgsDelta,
  correlated by block index.
- `content_block_delta` (thinking_delta) → ReasoningDelta.
- `message_delta` → Usage (final output_tokens) + Finish with
  stop_reason mapped: end_turn/stop_sequence → "stop", max_tokens
  → "length", tool_use → "tool_calls".
- `message_stop` → stream terminates.
- `ping` ignored (Anthropic's keep-alive).
- `error` → yields Err and ends the stream.

## Wiring

- Authentication: `x-api-key` + `anthropic-version: 2023-06-01`
  headers (not Bearer). Both ship when api_key is configured;
  servers that don't care (cortex) ignore them.
- `WireApi::AnthropicMessages` in build_provider now constructs
  the provider instead of erroring "reserved for future".
- `provider::mod.rs` registers the new module.

18 new unit tests: encoder (system collapse, multi-system concat,
default max_tokens, multipart with image, tool_use blocks, tool
results, malformed JSON arg fallback), decoder (text streaming,
tool_use lifecycle, max_tokens→length mapping, empty deltas, ping
events, error events, cancellation, malformed payload skip,
thinking blocks).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-31 14:01:59 +03:00

cortex

A Rust reverse-proxy and fleet management layer for multi-node GPU inference clusters. Cortex sits in front of one or more neuron daemons (each running candle-based inference on a local GPU host) and presents a unified OpenAI + Anthropic compatible API surface.

Problem

Running local LLMs across multiple GPU nodes (different VRAM tiers, different model affinities) requires a unified API surface that:

  • Presents a single /v1/models catalogue merging every model that can be served by any neuron in the fleet.
  • Routes requests to the correct node based on where a model is loaded (or can be loaded), handling cold-load and eviction transparently.
  • Manages model lifecycle — load on demand, unload cold models, pin critical ones — by calling each neuron's /models/{load,unload} API.
  • Translates between OpenAI and Anthropic request/response envelopes so every client speaks whichever dialect it prefers.
  • Captures per-request metrics (tokens, tok/s, TTFT, latency) and exposes them as Prometheus counters/histograms.

Architecture

┌──────────────┐  ┌──────────┐  ┌────────────┐  ┌────────────┐
│ Claude Code  │  │ Zed/IDE  │  │ Tidal / mm │  │ curl / etc │
└──────┬───────┘  └─────┬────┘  └──────┬─────┘  └──────┬─────┘
       │                │              │               │
       └────────────────┴──────┬───────┴───────────────┘
                               │
                    ┌──────────▼──────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │                     │
                    │  Router · Metrics   │
                    │  Evictor · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  :13131   │  │ :13131 │  │  :13131   │
            │  candle   │  │ candle │  │  candle   │
            └───────────┘  └────────┘  └───────────┘
                  private network (.internal)

Crates

Crate Purpose
cortex-core Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types
cortex-gateway Axum HTTP server: proxy, router, evictor, poller, metrics exporter
neuron Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API
cortex-cli CLI entrypoint (cortex serve, cortex status, etc.)

Node setup

Each GPU node runs neuron (listening on :13131). Neuron uses huggingface/candle for in-process inference — there is no external inference subprocess to manage.

Inside the daemon, every CUDA device gets one dedicated OS thread (named cuda-dev-N) that owns the device's CUDA context for the daemon's lifetime. Model loads, forward passes, KV-cache resets, NCCL collectives, VRAM queries, and unloads all route through that thread via a job channel; tensors never escape it alive. This pins context binding to a known thread, makes the CUDA Drop contract structurally safe, and isolates driver-error poisoning to one worker rather than the whole process. See CLAUDE.md for the design rationale and crates/neuron/src/harness/device_worker/ for the code.

The neuron RPM (helexa-neuron) ships a systemd unit:

dnf copr enable helexa/helexa
dnf install helexa-neuron
systemctl enable --now neuron

Gateway config

# /etc/cortex/cortex.toml
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"

[eviction]
strategy = "lru"        # lru | priority
defrag_after_cycles = 50

[[neurons]]
name = "beast"
endpoint = "http://beast.internal:13131"

[[neurons]]
name = "benjy"
endpoint = "http://benjy.internal:13131"

Model placement profiles live in models.toml — see models.example.toml.

Building

cargo build --release

CI

Every push triggers format, lint, and test checks. Ensure these pass locally before pushing:

cargo fmt --check --all                    # must be clean
cargo clippy --workspace -- -D warnings   # warnings are errors
cargo test --workspace                     # all tests must pass

Tagged releases (v*) additionally build SRPMs for both cortex and helexa-neuron and publish to COPR.

Running

# start the gateway
cortex serve --config /etc/cortex/cortex.toml

# check fleet status
cortex status

# list all models across nodes
curl http://localhost:31313/v1/models

License

GPL-3.0

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