🚀 dsaEngine

dsaEngine is a high-performance, deterministic MCP (Model Context Protocol) server providing 142+ production-grade DSA algorithms for AI coding agents (Cursor, Windsurf, Claude Code, GitHub Copilot, and more).

⚡ Zero-Cost Algorithm Advisor

DSAEngine runs locally on your machine and integrates with your favorite coding agent via the Model Context Protocol:

• ✅ No API calls — 100% local, zero cost per use
• ✅ 142+ algorithms — Optimal implementations categorized & indexed
• ✅ Deterministic — No randomness, repeatable recommendations
• ✅ 7 agents — Cursor, Windsurf, Claude Code, GitHub Copilot, Kiro, Augment, Antigravity
• ✅ MCP compatible — JSON-RPC over stdio, zero hosting required

How It Works

You ask Cursor: "Find shortest path in weighted graph with negative edges"
        ↓
Cursor sees .cursorrules → "consult DSAEngine first"
        ↓
Cursor calls: dsa_classify("Find shortest path in weighted graph with negative edges")
        ↓
DSAEngine responds: "Bellman-Ford Algorithm, O(V·E), confidence: 0.95"
        ↓
Cursor calls: graphs.bellman_ford(edges, source)
        ↓
DSAEngine returns: Rust code + pseudocode + result
        ↓
Cursor writes optimized code

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1. Install DSAEngine

Linux / macOS (Bash)

curl -sSL https://raw.githubusercontent.com/sVm19/DSAengine/main/install.sh | bash

Windows (PowerShell)

irm https://raw.githubusercontent.com/sVm19/DSAengine/main/install.ps1 | iex

Note: Requires git and cargo installed if no prebuilt binary is available for your platform.

2. Setup Your Agent

• Cursor: Setup Guide
• Windsurf: Setup Guide
• Claude: Setup Guide

3. Start Coding

Ask your agent to write an algorithm — DSAEngine will ensure it's optimal.

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📖 The 142+ Algorithm Library

Organized into 13 categories:

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📖 The 12 Pages (Skill Categories)

You load specifically what you need by activating Cargo features (--features "arrays graphs"), keeping binaries microscopically tight.

Feature Flag	Skill Category	Examples
arrays	Arrays & Strings	Two-Sum, KMP Search, Sliding Window, Kadane's Algorithm
linked_lists	Linked Lists	Cycle Detection, Reverse, Merge Sorted, Intersection
stacks_queues	Stacks & Queues	Min Stack, Valid Parentheses, Next Greater, Histogram
trees_binary	Binary Trees	Traversals, LCA, Serialize, Invert, Max Path Sum
trees_advanced	Advanced Trees	Trie, Segment Tree, Fenwick Tree, AVL Tree, Red-Black
graphs	Graphs	Dijkstra, Bellman-Ford, BFS/DFS, MST (Kruskal/Prim)
dynamic_programming	Dynamic Programming	Coin Change, LCS, Edit Distance, Knapsack
greedy_algorithms	Greedy	Activity Selection, Huffman Coding, Jump Game
backtracking	Backtracking	N-Queens, Sudoku, Combinations, Permutations
sorting_searching	Sorting & Searching	Merge Sort, Quick Sort, Binary Search
dsa_fundamentals	Fundamentals	Big-O Analysis, Two Pointers, Sliding Window
advanced_topics	Advanced	LRU/LFU Cache, Union-Find, Bloom Filter, Trie Autocomplete

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🎯 Usage

As MCP Server (Recommended for Agents)

After setup, your agent automatically calls DSAEngine:

Cursor:
"Write a function to find all connected components in an undirected graph"

→ Cursor calls dsa_classify("connected components undirected graph")

DSAEngine responds:
{
  "algorithm": "Union-Find",
  "confidence": 0.98,
  "time_complexity": "O(α(n))",
  "space_complexity": "O(n)",
  "tool_name": "advanced_topics.union_find",
  "reason": "Optimal for connectivity queries with near-O(1) amortized complexity"
}

→ Cursor calls advanced_topics.union_find(...)

→ You get optimized code

As Rust Library

use dsaengine::skills::graphs::dijkstra::Dijkstra;

fn main() {
    let edges = vec![(0, 1, 4), (1, 2, 2), (0, 2, 5)];
    let (distances, paths) = Dijkstra::shortest_path(&edges, 0);
    println!("Shortest distances: {:?}", distances);
}

As HTTP API

# Run HTTP server (default: localhost:8000)
dsaengine

# Test:
curl -X POST http://localhost:8000/api/v1/graphs/dijkstra \
  -H "Content-Type: application/json" \
  -d '{...}'

# Swagger UI: http://localhost:8000/swagger-ui/

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🛠 Installation Methods

Method 1: Cargo (Easiest)

cargo install dsaengine

Requires: Rust 1.70+

Method 2: Pre-built Binaries

Download from GitHub Releases:

• dsaengine (macOS/Linux)
• dsaengine.exe (Windows)
• dsaengine-arm64 (macOS Apple Silicon)

Method 3: From Source

git clone https://github.com/yourusername/dsaengine
cd dsaengine
cargo build --release
# Binary: target/release/dsaengine

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🔧 Setup Guides

• Cursor Setup — Step-by-step for Cursor users
• Windsurf Setup — Step-by-step for Windsurf users
• Claude Code Setup — Step-by-step for Claude users

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📝 Commands

# Run MCP stdio server (for agents)
dsaengine --mcp

# Run HTTP web server (for API/testing)
dsaengine

# Generate rules files for agents
dsaengine --install

# Show help
dsaengine --help

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🧠 Use Cases

✅ Optimal Algorithm Selection

Ask Cursor/Windsurf to solve algorithmic problems — DSAEngine ensures optimal approach.

✅ Deterministic Recommendations

No randomness, no LLM hallucinations — pure keyword-based algorithm matching.

✅ O(n) → O(log n) Optimization

Catch brute-force approaches and suggest optimal alternatives automatically.

✅ Zero API Cost

Runs completely local — no external API calls, no rate limits.

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📊 Performance

Metric	Value
Binary Size	~25 MB (release build)
Startup Time	<100ms
Recommendation Latency	<5ms
Memory Usage	~50 MB at runtime
Algorithms Available	142+

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🤝 Contributing

DSAEngine welcomes contributions! See CONTRIBUTING.md for guidelines.