Malong LiuHe（码龙·六合工具）

LLM-Native Code Operations Toolkit — Code tooling reinvented for the LLM that has no hands, no eyes, and no memory.

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Malong LiuHe is a toolkit built for LLMs rather than humans. It ships two components:

• malong/ — an MCP toolset (44 tools): symbol read/write, indexing, impact analysis, reference tracing, call graphs, dead-code detection, code review, security scanning, atomic batch editing, test orchestration, pipeline verification — served to the LLM over MCP (JSON-RPC over stdio).
• malong-parse/ — a Rust parsing service: a tree-sitter-based multi-language symbol extraction engine (JavaScript/TypeScript/TSX/Python/Go/Rust/C/C++/Java/Bash), talking to the toolset over a Unix socket, with an LRU tree cache and batched parallel extraction.

Core Idea

Traditional tools (git, sed, IDE) assume a user with hands, eyes, and memory. An LLM has none of these. Malong LiuHe is redesigned around that fact:

Missing	Design Consequence
No hands	Operations must be atomic, undoable, retryable (edit_transaction with rollback and undo journal)
No eyes	Output must be structured and self-explanatory (JSON for direct consumption; errors carry suggestion/next_action)
No memory	Every call is self-contained (takes workspace_dir); optimistic concurrency with version anchors (read_symbol → write_symbol(base_version) conflict state machine)

Performance (Measured)

Metric	Value	Note
Small-file read P95	1ms	50 runs, warmed
Small-file write P95	7ms	30 runs, incl. post-write re-index
repo_map	98ms	down from tens of seconds (SQLite index + Rust parsing)
Memory	RSS 134MB / 26%	under a real docker --memory=512m cgroup limit
Concurrency	128 concurrent / 256 in-flight requests, zero OOM	32-way mixed read+write on one hot file, no tearing, DB integrity_check PASS
Throughput	~588 calls/s	single-threaded ceiling — 60–600× real LLM demand
Token savings	↓65.3%	same task 7673 → 2662 est. (primitives)
Call reduction	↓50.0%	6 calls (legacy) → 3 calls (primitives)
Tool-description compression	44 tools ≈ 1.33k tokens	tiered: core kept, low-freq ≤70 chars, verbose ≤230; detail flows via next_step hints
Index throughput	538 files / 7s	scoped reindex, measured
dry_run fidelity	47/47	golden-hash comparison across 3 languages, 100%
Concurrent writes to same symbol	16/16	exactly one winner + one conflict, zero silent overwrites

Language Support

Language	Symbol Extraction	Reference Extraction
JavaScript / TypeScript / TSX / JSX / MTS / CTS	✓	✓
Python	✓	✓
Go	✓	✓
Rust (impl blocks / trait / enum semantics)	✓	✓
C / C++ (incl. headers)	✓	✓
Java	✓	✓
Bash	✓	✓

Tool Overview

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