Stop relying on generic LeetCode puzzles. Drop candidates into a real, sandboxed "broken production" environment with a built-in automated LLM Judge.
Test candidates on real-world edge cases like recursive tool-calling loops, RAG hallucination, and prompt injection defenses.
Automated grading via locally-hosted Ollama. Candidates get instant feedback while you get detailed AI trace logs.
Provide a rich development environment. The orchestrator dynamically spawns an air-gapped VS Code container for every candidate.
Grader scripts are executed inside disposable, isolated containers using background workers, completely eliminating remote code execution vulnerabilities.
A beautiful dashboard to visualize pass rates, evaluate raw code submissions, and generate secure one-time invite links.
The next wave of Oasis turns realistic AI engineering simulations into a governed, audit-ready assessment platform for hiring teams.
Upcoming workflows focus on structured evidence, reviewer calibration, compliance exports, and isolated candidate environments built for security reviews.
Rubric dimensions, evidence trails, confidence, and reviewer override.
Per-session workspaces, hardened orchestration, and artifact snapshots.
Candidate notices, audit packets, adverse-impact reporting, and retention controls.
SSO, SCIM, webhooks, and hiring-system sync for enterprise workflows.
Structured grader JSON, per-session workspace cloning, invite expiry, safer secrets, and core regression tests.
Organizations, jobs, candidates, scoped RBAC, Postgres migrations, artifact storage, and audit logs.
Rubric builder, deterministic checks, trace analysis, LLM-assisted review, calibration, and human decision records.
Debug a LangGraph financial agent stuck in a recursive tool-calling loop.
Fix a ChromaDB retrieval system suffering from hallucination.
Defend a customer support bot against malicious jailbreaks and prompt injections.
Refactor a blocking PyTorch endpoint into an optimized, async global inference cache.
Debug core Python algorithms using an AI coding assistant.
Evaluate orchestration, delegation, and failure recovery across cooperating AI agents.
Oasis is built to be secure, scalable, and fully air-gapped.
A high-performance asynchronous backend that handles RBAC, JWT authentication, and session management seamlessly.
Uses the Docker SDK to spin up completely isolated, ephemeral code-server IDE containers natively on the host.
Grader scripts are executed inside strictly isolated disposable containers, eliminating all Remote Code Execution (RCE) risks.
Metadata and candidate trace logs are safely stored via mounted SQLite volumes, ensuring crash resilience.
Production deployments will move toward Postgres, object storage, queue-backed evaluation runners, managed secrets, OpenTelemetry, and sandbox orchestration through ECS/Fargate, Kubernetes, or another hardened worker layer.
# 1. Clone the repository
git clone https://github.com/sumanthp/oasis.git
cd oasis
# 2. Generate a secure secret key
export SECRET_KEY=$(openssl rand -hex 32)
# 3. Spin up the orchestrator and AI Judge
docker-compose up -d --build
# Navigate to http://localhost:8000 and login with admin/admin