Storage benchmark agent
Describe a storage workload in plain English. The agent generates configs for fio, vdbench, iozone, and iometer, runs all four in parallel, parses IOPS / throughput / latency / CPU, and produces a full side-by-side comparison report saved to disk.
- Natural language → fio + vdbench + iozone + iometer configs automatically
- All 4 tools run concurrently via ThreadPoolExecutor — no waiting
- Parses IOPS, throughput, P99 latency, and CPU from each tool
- Full comparison report saved as JSON + text on every run
- Dry-run mode — full simulation with no tools or API key needed
- GitHub Actions CI included — green badge on every push
- Rate-limit safe — 5s throttle keeps under Gemini free tier (15 RPM)
$ export DRY_RUN=true
$ python storage_benchmark_agent.py
REST API agent (LangGraph + CrewAI)
Two complete agent implementations — LangGraph and CrewAI — both targeting REST APIs from natural language prompts. Includes retry logic, exponential back-off on 5xx, Gemini free-tier throttling, and a side-by-side comparison runner.
- LangGraph agent: stateful graph with conditional retry edges and MemorySaver checkpointing
- CrewAI agent: 4-role crew — researcher, API caller, parser, writer
- 5s throttle between LLM calls — stays under Gemini's 15 RPM free tier
- Exponential back-off with jitter on 429 rate limit errors
- Side-by-side runner compares both frameworks on the same query
- Demo target: JSONPlaceholder — no auth, runs immediately
$ export GOOGLE_API_KEY="AIza..."
$ python rest_api_agents_starter.py
PrestoDB EKS scaling agent
Replaced a fragile Selenium-based browser automation workflow with an AI agent that autonomously scales a PrestoDB cluster on AWS EKS from 0 → 80 Kubernetes pods — supporting large-scale financial data query workloads at a financial institution.
- Replaces Selenium browser automation with goal-driven agent execution
- Scales PrestoDB from 0 → 80 EKS pods based on query load signals
- Monitors pod readiness and coordinator registration before declaring scale complete
- Fault-tolerant — retries failed pod schedules and handles node pressure
- Built for a financial institution handling large distributed SQL queries
Connectathon NFS Agent
Runs NFS Connectathon test suites — basic, general, special, and lock — across NFSv3, NFSv4, and NFSv4.1 or multiple mount points. Parses pass/fail/skip per suite per version, detects regressions, and produces a full comparison report.
- Covers all 4 suites: basic (b), general (g), special (s), lock (l)
- Compares across NFSv3, NFSv4, NFSv4.1 and multiple mount points
- Detects regressions and improvements vs baseline version
- Explains expected failures (e.g. NFSv4 root squash / setuid)
- Dry-run mode — full simulation, no NFS server needed
- Reports saved as JSON + text on every run
$ python connectathon_agent.py
OpenStack Tempest Agent
Runs OpenStack Tempest test groups — compute, network, storage, identity, image — across two environment configs (e.g. prod vs staging). Surfaces regressions, improvements, and exact failed test names with a full comparison report.
- Covers compute, network, storage, identity, image, object groups
- Compares two configs side by side — prod vs staging, A vs B
- Lists exact failing test names per group per config
- Detects new regressions and fixed tests between configs
$ python tempest_agent.py