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https://github.com/outbackdingo/optimclaw.git
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[email protected]andClaude Opus 4.6
b59a0b9e42
Add CodeAct execution (Tier 1) using the Monty embedded Python interpreter, following the Recursive Language Model (RLM) pattern from arXiv:2512.24601. Key additions: - executor/scripting.rs: Monty integration with FunctionCall-based tool dispatch, catch_unwind panic safety, resource limits (30s, 64MB, 1M allocs) - LlmResponse::Code variant + ExecutionTier::Scripting - Context-as-variables (RLM 3.4): thread messages, goal, step_number, previous_results injected as Python variables — LLM context stays lean while code accesses data selectively - llm_query(prompt, context) (RLM 3.5): recursive subagent calls from within Python code — results stored as variables, not injected into parent's attention window (symbolic composition) - Compact output metadata between code steps instead of full stdout - MontyObject ↔ serde_json::Value bidirectional conversion - Updated architecture plan with RLM design principles 74 tests passing, zero clippy warnings. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
30 lines
882 B
TOML
30 lines
882 B
TOML
[package]
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name = "ironclaw_engine"
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version = "0.1.0"
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edition = "2024"
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rust-version = "1.92"
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description = "Unified thread-capability-CodeAct execution engine for IronClaw"
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authors = ["NEAR AI <[email protected]>"]
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license = "MIT OR Apache-2.0"
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homepage = "https://github.com/nearai/ironclaw"
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repository = "https://github.com/nearai/ironclaw"
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publish = false
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[package.metadata.dist]
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dist = false
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[dependencies]
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async-trait = "0.1"
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chrono = { version = "0.4", features = ["serde"] }
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monty = { git = "https://github.com/pydantic/monty.git", branch = "main" }
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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thiserror = "2"
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tokio = { version = "1", features = ["sync", "time", "macros", "rt"] }
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tracing = "0.1"
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uuid = { version = "1", features = ["v4", "serde"] }
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[dev-dependencies]
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pretty_assertions = "1"
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tokio = { version = "1", features = ["full", "test-util"] }
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