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Nishant Tyagi Junior AI / GenAI Engineer | LLM Evaluation, RAG & Python Backends I build reliable LLM applications, agent evaluation systems, RAG pipelines, and Python backends. I focus on…
Nishant Tyagi
Junior AI / GenAI Engineer | LLM Evaluation, RAG & Python Backends
I build reliable LLM applications, agent evaluation systems, RAG pipelines, and Python backends. I focus on deterministic testing, safety guardrails, and production reliability—so agent behavior can be measured, gated in CI, and improved without silent regressions.
Featured Projects
AgentEval
CI for AI agents that turns production failures into minimized regression tests.
Local-first evaluation harness for multi-step LLM agents:
- YAML golden suites with correctness, hallucination, tool-call accuracy, latency, and cost
- Production Failure Memory (v0.3.0): redaction before persistence, deterministic clustering, sync/async replay, automatic failure minimization, human-approved golden export, recurrence and resurfacing detection
- GitHub Actions regression gates and local SQLite storage
- Zero-network demo path (no API key required for the flagship Failure Memory demo)
| | |
|---|---|
| Repository | https://github.com/nishanttyagi28/agenteval |
| PyPI | https://pypi.org/project/nishanttyagi-agenteval/ (nishanttyagi-agenteval · CLI/import: agenteval) |
| Release | https://github.com/nishanttyagi28/agenteval/releases/tag/v0.3.0 |
| Static demo | https://nishanttyagi28.github.io/agenteval/ |
| Dashboard | https://agenteval-6honbe24hradazngswxkrq.streamlit.app/ |
Agentic Data Analyst
Multi-agent Streamlit app for natural-language analysis of uploaded CSVs: quality gates, text-to-SQL (Groq Llama 3.3 70B), EDA/AutoML, forecasting, and ChromaDB RAG follow-ups.
Scheme Saathi
Government scheme eligibility navigator for Indian citizens. Rules-engine-first (deterministic), with optional local RAG (ChromaDB) and LangGraph + Groq for conversational guidance. FastAPI + Streamlit.
Contract Shield
Freelancer contract risk reviewer: deterministic rules plus Groq for plain-language explanations. FastAPI backend and Streamlit UI for PDF/DOCX/TXT uploads and risk reports.
Technical focus
Python · FastAPI · Streamlit · LangGraph · Groq · Llama 3.3 70B · ChromaDB · SQL · SQLite · GitHub Actions · pytest · Docker
Current flagship release
AgentEval v0.3.0 — Failure Memory, deterministic replay/minimization, and CI regression protection for production agent failures.
Install: pip install nishanttyagi-agenteval==0.3.0
Contact
- GitHub: nishanttyagi28
*Built with a focus on verifiable, working systems. Open to Junior AI Engineer, GenAI Engineer, LLM Developer, and Python Backend opportunities.*
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agenteval ★ PINNED
No description.
Python ★ 3 5d agoExplain → -
truthgraph
No description.
Python ★ 0 9h agoExplain → -
karmasakshi-protocol
Verified Effect Commit Protocol for AI agents — seal the intended effect, witness the actual outcome.
Python ★ 0 3d agoExplain → -
nishanttyagi28
No description.
★ 0 7d agoExplain → -
agentic-data-analyst
Multi-agent Streamlit app: text-to-SQL, auto ML, and RAG over uploaded CSVs
Python ★ 0 15d agoExplain → -
contract-shield
Freelancer Contract Risk Reviewer: deterministic rules engine + Groq-narrated plain-language risk reports (PDF/DOCX/TXT, FastAPI + Streamlit)
Python ★ 0 21d agoExplain → -
scheme-saathi
Govt scheme eligibility navigator for Indian citizens — rules-engine-first, zero-cost stack (Groq + local RAG + FastAPI + Streamlit)
Python ★ 0 21d agoExplain → -
hr-attrition-app
No description.
Python ★ 0 26d agoExplain → -
customer-churn-analytics
No description.
Python ★ 0 28d agoExplain → -
reddit-ai-scraper
No description.
Python ★ 0 29d agoExplain → -
snake-game
No description.
Python ★ 0 1mo agoExplain → -
hr-policy-chatbot-rag
No description.
Python ★ 0 1mo agoExplain → -
agentic-rag-langgraph
No description.
Jupyter Notebook ★ 0 1mo agoExplain → -
agentic-rag-pipeline
No description.
Jupyter Notebook ★ 0 1mo agoExplain → -
chromadb-rag-pipeline
No description.
Jupyter Notebook ★ 0 1mo agoExplain → -
semantic-chunking-rag
No description.
Jupyter Notebook ★ 0 1mo agoExplain → -
ai-ecommerce-sales-analysis
No description.
Python ★ 0 3mo agoExplain → -
customer-intelligence-app
No description.
Jupyter Notebook ★ 0 3mo agoExplain → -
job-market-intelligence-system
No description.
Jupyter Notebook ★ 0 3mo agoExplain → -
bank-marketing-sql-analysis
Customer segmentation and conversion rate analysis using MySQL on 45K+ bank marketing records.
★ 0 3mo agoExplain → -
credit-risk-prediction
No description.
Jupyter Notebook ★ 0 3mo agoExplain → -
resume-analyzer
No description.
Python ★ 0 3mo agoExplain → -
Red-Wine-EDA
No description.
Jupyter Notebook ★ 0 4mo agoExplain → -
-t20-worldcup-analytics-dashboard
e: t20-worldcup-analytics-dashboard Description: T20 World Cup data analytics project using Python, SQL and Power BI
Python ★ 0 4mo agoExplain → -
loan-approval-ml-project
End-to-end loan approval prediction using Python & ML
★ 0 4mo agoExplain → -
nifty50-analysis-project
No description.
Jupyter Notebook ★ 0 4mo agoExplain → -
ipl-data-analytics-dashboard
No description.
Jupyter Notebook ★ 0 4mo agoExplain → -
telecom-churn-monitoring
No description.
Jupyter Notebook ★ 0 5mo agoExplain → -
customer-churn-data-science-project
End-to-end Customer Churn Analysis using Python, Machine Learning & Power BI
Jupyter Notebook ★ 0 5mo agoExplain → -
hr-payroll-automation-system
An automated HR payroll system built in Excel with attendance-based salary calculation, statutory deductions (PF, ESI, PT), and dynamic salary slip generation using XLOOKUP and dropdown controls.
★ 0 6mo agoExplain → -
cosmetics-market-analysis
Web scraping and Power BI dashboard for cosmetics market analysis
Jupyter Notebook ★ 0 6mo agoExplain → -
job-market-skill-gap-analyzer
AI-powered system to analyze job market skill demand and gap using Python, NLP and Power BI.
Python ★ 0 6mo agoExplain → -
finance-projectttt
Power BI dashboard project analyzing budget vs actual spending using Python-cleaned Excel data.
Jupyter Notebook ★ 0 6mo agoExplain → -
sales-performance-dashboard
End-to-end Sales Performance Dashboard using real-world data. Data cleaned in Excel, analyzed with Python Pandas in Jupyter Notebook, aggregated using MySQL, and visualized in Power BI. Key insights include Total Sales 2.30M, Profit 286.40K, category-wise and region-wise performance. Tools used: Excel, Python, Pandas, MySQL, Power BI.
★ 0 6mo agoExplain →
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