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113 lines (100 loc) · 3.77 KB
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[workspace]
members = ["crates/*"]
resolver = "2"
[workspace.package]
version = "0.51.0"
edition = "2021"
rust-version = "1.88"
license = "MIT OR Apache-2.0"
authors = ["Yutaka Nishimura"]
repository = "https://github.com/ynishi/algocline"
homepage = "https://github.com/ynishi/algocline"
documentation = "https://docs.rs/algocline"
description = "LLM amplification engine — MCP server with Lua scripting"
keywords = ["llm", "mcp", "lua", "reasoning", "ai"]
categories = ["development-tools", "science"]
[workspace.dependencies]
algocline-core = { path = "crates/algocline-core", version = "0.51.0" }
algocline-engine = { path = "crates/algocline-engine", version = "0.51.0" }
algocline-app = { path = "crates/algocline-app", version = "0.51.0" }
algocline-mcp = { path = "crates/algocline-mcp", version = "0.51.0" }
clap = { version = "4", features = ["derive"] }
serde = { version = "1", features = ["derive"] }
serde_json = "1"
# safetensors — the format candle already writes; taken directly so a
# checkpoint can carry a `__metadata__` header, which candle's own
# `save` does not write. Pinned to the version candle 0.11 links, since
# both write the same files.
safetensors = "0.8"
toml = { version = "0.8", features = ["preserve_order"] }
tempfile = "3"
thiserror = "2"
dotenvy = "0.15"
libc = "0.2"
tokio = { version = "1", features = ["macros", "rt-multi-thread", "sync", "time", "process", "net", "io-util", "signal"] }
tokio-util = { version = "0.7" }
tracing = "0.1"
async-trait = "0.1"
rand = "0.10"
rmcp = { version = "1.7", features = ["server", "transport-io"] }
# `alc_pack` writes a single archive rather than a directory: a migration
# artifact has to freeze the state it was taken from, and a directory keeps
# changing under whoever holds it. tar gives the atomic unit, sha2 makes the
# freeze checkable after the fact.
tar = "0.4"
flate2 = "1"
sha2 = "0.10"
[package]
name = "algocline"
version.workspace = true
edition.workspace = true
rust-version.workspace = true
description.workspace = true
license.workspace = true
authors.workspace = true
repository.workspace = true
homepage.workspace = true
keywords.workspace = true
categories.workspace = true
readme = "README.md"
# Repository tooling config, of no use to anyone building the crate.
exclude = [".gitignore", ".styluaignore"]
[[bin]]
name = "alc"
path = "src/main.rs"
[dependencies]
algocline-app = { workspace = true }
algocline-core = { workspace = true }
algocline-engine = { workspace = true }
algocline-mcp = { workspace = true }
clap = { workspace = true }
rmcp = { workspace = true }
serde_json = { workspace = true }
tokio = { workspace = true }
tracing = { workspace = true }
tracing-appender = "0.2"
tracing-subscriber = { version = "0.3", features = ["env-filter", "fmt"] }
anyhow = "1"
dirs = "6"
tempfile = "3"
[features]
# Opt-in neural-network bridge (alc.nn). Off by default so the standard MCP
# build does not link candle. Enable with `--features nn` to build/install an
# `alc` binary whose alc_run exposes alc.nn.
nn = ["algocline-engine/nn"]
# CUDA backend for the alc.nn bridge. Propagates through algocline-engine
# to algocline-nn (which flips candle-core/cuda + candle-nn/cuda +
# candle-transformers/cuda in lockstep; candle-nn gates its LayerNorm CUDA
# path behind the crate's own `cuda` feature, so the features must move
# together). Use `cargo install --path . --features nn-cuda` on a
# CUDA-capable host to install an `alc` binary whose
# alc.nn.preset.gpt2("...", { device = "cuda" }) actually resolves.
nn-cuda = ["nn", "algocline-engine/nn-cuda"]
[dev-dependencies]
insta = { version = "1", features = ["json", "redactions"] }
libc = { workspace = true }
regex = "1"
rmcp = { version = "1.7", features = ["client", "transport-child-process"] }
serde_json = { workspace = true }
tempfile = "3"
tokio = { workspace = true }