ad198a8501
This tries to short-circuit multiple round-trips to llm for reading code. It's a precursor to trying to context engineer tailored to specific tasks. In initial experiments, it's only marginally faster than regular mode, and burns more tokens.
47 lines
1.0 KiB
TOML
47 lines
1.0 KiB
TOML
[workspace]
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members = [
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"crates/g3-cli",
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"crates/g3-core",
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"crates/g3-planner",
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"crates/g3-providers",
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"crates/g3-config",
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"crates/g3-execution",
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"crates/g3-computer-control",
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"crates/g3-console"
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]
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resolver = "2"
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[workspace.dependencies]
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# Async runtime
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tokio = { version = "1.0", features = ["full"] }
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# HTTP client
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reqwest = { version = "0.11", features = ["json", "stream"] }
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# Serialization
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# CLI
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clap = { version = "4.0", features = ["derive"] }
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# Error handling
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anyhow = "1.0"
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thiserror = "1.0"
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# Logging
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tracing = "0.1"
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tracing-subscriber = "0.3"
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# Configuration
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config = "0.14"
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# Utilities
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uuid = { version = "1.0", features = ["v4"] }
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[package]
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name = "g3"
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version = "0.1.0"
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edition = "2021"
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authors = ["G3 Team"]
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description = "A general purpose AI agent that helps you complete tasks by writing code"
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license = "MIT"
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[dependencies]
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g3-cli = { path = "crates/g3-cli" }
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tokio = { workspace = true }
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anyhow = { workspace = true }
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