rob thijssen e23d5011d0 feat(helexa-acp): scaffold ACP bridge with provider trait + OpenAI chat
Adds a new workspace crate `helexa-acp` (binary, Apache-2.0) — the
start of "the missing ACP binary" for multi-endpoint LLM setups
mixing public APIs, private LAN deployments, and various wire
formats. Today it speaks OpenAI /v1/chat/completions; the
Provider trait is the seam that lets OpenAI Responses, Anthropic
/v1/messages, and other wire formats slot in later without touching
the agent loop.

The crate is intentionally self-contained — no dependencies on the
other workspace crates (cortex-core, cortex-gateway, neuron) — so a
future migration to a dedicated GitHub repo is a Cargo.toml-only
change. All deps come from crates.io.

This commit lands:

  * `config.rs` — TOML config at $XDG_CONFIG_HOME/helexa-acp/config.toml
    with multi-endpoint support (each `[[endpoints]]` declares its
    name, base_url, wire_api, default_model, optional API key /
    api_key_env). Falls back to env-only single-endpoint config when
    no TOML exists (HELEXA_ACP_BASE_URL, HELEXA_ACP_MODEL, etc.). The
    `endpoint:model` selector syntax is validated and tested.

  * `provider/mod.rs` — `Provider` trait + provider-agnostic types
    (`CompletionRequest`, `CompletionEvent`, `Message`, `ToolCall`,
    `ToolSpec`, `Role`, `UsageStats`). Agent loop consumes these
    without knowing the wire format on the other side.

  * `provider/openai_chat.rs` — `OpenAIChatProvider` impl. Compatible
    with cortex, LM Studio, Ollama (compat mode), OpenRouter, OpenAI
    itself. Streams via reqwest + eventsource-stream + async-stream.
    Surfaces text deltas, reasoning deltas (for models that emit
    `reasoning_content`), tool-call lifecycle (start, args-delta,
    completion), usage, finish reason. Cancellation-token aware.

  * `main.rs` — tokio + stderr-only tracing-subscriber + Stdio
    transport. Builds a provider per configured endpoint at startup,
    surfacing config mistakes before the editor even initializes.
    Currently responds to `initialize`; everything else stubs to
    `not implemented yet` until the agent loop lands in the next
    commit.

12 unit tests pass — encoder shape, decoder shape (text-only,
tool-call progressive, cancellation, malformed-chunk recovery),
config parsing (multi-endpoint TOML, env fallback, validation).

The `#![allow(dead_code)]` on `provider/mod.rs` is temporary — the
agent loop in the next commit reads every field. It's noted in the
module-level docstring so the next reader knows.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-28 08:13:47 +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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