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DSA Solver Agent

DSA Solver Agent

GravityNexus

|
3 installs
| (0) | Free
An advanced agentic DSA problem-solving assistant for VS Code
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
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More Info

DSA Solver Agent

A constraint-aware, adversarial multi-agent loop that turns a problem statement into a reviewed, submission-ready solution. Every node has a distinct responsibility and the final answer must pass a verifier.

problem + constraints
       |--------------------|
          semantic analyzer
                  |
       |-----------------------|
 N code generators    M edge-case generators
       |-----------------------|
                  |
       parallel candidate validators
                  | fail
                 pass --------> repair + revalidation
                  |
             final solution

The edge-case node targets boundary values, duplicates, degenerate structures, overflow, counterexamples to tempting greedy logic, and worst-case performance. Strategy agents receive these cases before writing code.

Run

Requires Python 3.11+ and an OpenAI API key. Copy the example file and edit .env:

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -e .
Copy-Item .env.example .env
# Replace the placeholder in .env with: OPENAI_API_KEY=sk-...
# This project defaults to the US regional endpoint. To override it, add:
# OPENAI_BASE_URL=https://api.openai.com/v1
dsa-solve problem.txt --constraints constraints.txt --language cpp --output solution.json

.env is excluded from Git. Use --env-file path\to\another.env for another file.

Render the execution trace from the generated JSON as a PNG:

dsa-graph solution.json --output agent-graph.png

Tune fan-out with --agents 6 --edge-agents 4 --validators 2 --repairs 2. The normal successful path uses only three serial API waves; generators and validators run concurrently. The JSON contains the interpretation, generated edge cases, approach, proof, complexity, code, confidence, review, and execution trace.

The LLM interface is provider-independent and fakeable in tests. The system fails closed if no candidate passes verification: no LLM can guarantee correctness for literally every DSA problem, so confidence is checked instead of assumed.

For --language cpp, the final verifier enforces #include <bits/stdc++.h> as the only include and rejects Boost or other external libraries.

Run offline tests with python -m pytest after installing pytest.

dsa-solve problem.txt --constraints constraints.txt --language cpp --agents 6 --edge-agents 4 --validators 2 --repairs 2 --output solution.json

(Get-Content solution.json -Raw | ConvertFrom-Json).code | Set-Content solution.cpp

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