Skip to content
View anmolg1997's full-sized avatar
🧠
Building Amazing Products
🧠
Building Amazing Products

Block or report anmolg1997

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
anmolg1997/README.md
Anmol Jaiswal, Applied AI Engineer

Multi-Agent AI Architect · Enterprise RAG Specialist · LLM Fine-Tuning & Serving

LinkedIn   Email   PyPI version   PyPI downloads   GitHub followers

Contributor to Google ADK · Pydantic AI · LiteLLM · llama.cpp · ag-ui
7+ years in applied AI  ·  author of a package in the official ADK integrations catalog

About

I build production AI systems that reason, plan, and execute autonomously: multi-agent orchestration, agent skills and harnesses for coding agents, enterprise RAG pipelines, LoRA fine-tuning at scale, and multi-adapter inference serving.

Focus area Working with
Multi-agent AI Google ADK A2A Protocol MCP Agent Orchestration
Agent skills & harnesses SKILL.md Claude Code Operator Skills Evidence Gates
Knowledge graphs & RAG Neo4j GraphRAG Hybrid Search Reranking Guardrails
LLM fine-tuning & serving LoRA / QLoRA Unsloth vLLM Multi-Adapter Inference
LLM observability Langfuse MLflow OpenSearch Domain Evaluation
Model building Transformers from scratch DeepSpeed SFT / DPO / RLHF

In industry

  • Building an enterprise decision-intelligence platform: multi-agent orchestration on Google ADK, NL2SQL over governed data, AG-UI streaming to a React frontend
  • Production hardening: durable sessions, tenant isolation, model benchmarking, incident postmortems

Now building

Project What it is
rhytm Multi-agent framework for proprietary data analysis with business intelligence
SLM-From-Scratch Ongoing: alignment utilities and GGUF / ONNX export paths for small language models

Tracking the frontier

Where agentic AI is heading in 2026, and where I'm already hands-on:

Frontier My work there
Agent skills: portable SKILL.md capabilities for coding agents
Harness engineering: the control plane around coding agents
  • harness-engineer-skill: audits any repo's harness, any convention
  • Runs the Fresh Session Test, enforces an evidence-before-done gate
Agent protocols: MCP A2A AG-UI AGENTS.md
Context engineering: sessions, memory and state over prompts
  • adk-database-memory: durable agent memory
  • Firestore session service patterns adopted upstream in Google ADK
Agent reliability: evals, failure taxonomies, observability

AI x markets

  • EquityOracle: self-improving equity recommender with multi-horizon predictions and paper trading
  • fincept-operator-skill: agent skill operating a live trading terminal, paper-only with human-approved live actions

The journey

Years Era Proof
2019–2021 Data science and classical ML Lead-Scoring · Telecom-Churn · Product-Recommender
2021–2023 NLP and deep learning Deep-Learning-Projects · Spark-GPU-Sentiment
2023–2025 GenAI foundations: RAG, fine-tuning, model building Enterprise-RAG · LLM-Finetuning-Toolkit · SLM-From-Scratch
2025–now Agentic AI: orchestration, skills, harnesses Multi-Agent-Framework · adk-database-memory · prepostmortem-skills
Open source
Google ADK Python

Google ADK Python PRs
  • First Firestore session service: transactional state, subcollection events, batch deletes
  • Patterns adopted in the official implementation, credited by a Google engineer
ADK Community

ADK Community PRs
  • FirestoreSessionService with 19 unit tests and in-memory mocks
  • Race-safe transactions, N+1 query elimination, async batch deletes
ag-ui Protocol

ag-ui Protocol PRs
  • ADK middleware upgraded to google-adk 2.0: +975/−48 LOC, closed 2 upstream issues
  • LangGraph adapter fixes: routes, fork config, stream message IDs
Pydantic AI

Pydantic AI PRs
  • Merged: retry-safe LLM-as-judge evals via constrained reason field (#5089)
  • History processor repairing orphaned tool calls behind provider 400s
llama.cpp

llama.cpp PRs
  • Type + integer-range validation in GGUFWriter.add_key_value
  • Catches overflow before it silently corrupts model metadata
LiteLLM

LiteLLM PRs
  • Merged: KeyError crash fix in Anthropic file-id discovery (#26228)
  • Deterministic aiohttp session disposal, fixing leaks (#32003)
adk-database-memory

adk-database-memory PRs
(pre·post)mortem

(pre·post)mortem PRs
  • 261 tagged real-world incidents, 12-class agentic-AI failure model
  • Shipped as agent skills for pre- and post-change risk review
Featured work
  • BPE tokenizer + composable Transformer: RoPE, GQA, SwiGLU
  • DeepSpeed training, SFT / DPO / RLHF alignment, GGUF / ONNX export

PyTorch DeepSpeed RLHF

  • Hybrid search (dense + BM25) with cross-encoder reranking
  • Guardrails, semantic caching, RAGAS evals, Langfuse observability

LlamaIndex OpenSearch Langfuse

  • Coordinator, planner, coder and reviewer agents on Google ADK
  • YAML-driven personas, A2A protocol

Google ADK A2A FastAPI

  • Live schema introspection, self-correction, few-shot learning
  • Multi-dialect: Postgres, Snowflake, SQLite

SQLGlot PostgreSQL Snowflake

  • Pluggable backends (Unsloth, TRL, Axolotl) driven by YAML recipes
  • MLflow tracking, vLLM serving

Unsloth vLLM MLflow

  • One base model, many LoRA adapters per request
  • OpenAI-compatible gateway, tenant routing, Prometheus metrics

vLLM LoRA Prometheus

  • Neo4j knowledge graph + RAG for document QA
  • Entity extraction, graph-traversal retrieval, hybrid grounding

Neo4j FastAPI React

  • Medical, legal, finance and code specialization via curriculum training
  • Domain benchmarks (MedQA, LegalBench), safety guardrails

MedQA LegalBench LoRA

Tech stack




Full tech breakdown
LLM providers      OpenAI · Anthropic · Google Gemini · Llama · Mistral
Agent frameworks   Google ADK · A2A Protocol · MCP · LangGraph · CrewAI
RAG stack          LlamaIndex · LangChain · Neo4j · OpenSearch
Vector DBs         Pinecone · Weaviate · Milvus · Qdrant · ChromaDB
Observability      Langfuse · MLflow · Weights & Biases · OpenTelemetry
Inference          vLLM · Multi-LoRA serving · TensorRT-LLM · ONNX Runtime
Fine-tuning        LoRA · QLoRA · DoRA · Unsloth · Axolotl · DeepSpeed · RLHF / DPO
Model building     PyTorch Transformers · BPE tokenizers · GGUF / ONNX export
Frontend           React · Vite · Next.js · TypeScript · TailwindCSS
Backend            FastAPI · Python · Node.js · GraphQL
Cloud              AWS (Bedrock, SageMaker) · GCP (Vertex AI) · Azure (OpenAI, AI Search)
Data & distributed PySpark · Spark NLP · Databricks · Streamlit · scikit-learn · XGBoost
Infrastructure     Docker · Kubernetes · Terraform · GitHub Actions
Activity Contribution activity graph

GitHub stats GitHub streak

Top languages

Building something complex? Let's talk.

LinkedIn   Email

Pinned Loading

  1. adk-database-memory adk-database-memory Public

    Persistent memory service for Google Agent Development Kit (ADK) agents. Async SQLAlchemy backend for SQLite, PostgreSQL, MySQL/MariaDB. Listed in the official ADK integrations catalog.

    Python 4

  2. Deep-Learning-Projects Deep-Learning-Projects Public

    A curated collection of deep learning projects — CNNs, RNNs, ResNets, YOLO, Neural Style Transfer, UNet segmentation, and more. Built from scratch with NumPy and TensorFlow.

    Jupyter Notebook

  3. Spark-GPU-Sentiment-Analyzer Spark-GPU-Sentiment-Analyzer Public

    Distributed sentiment analysis framework using PySpark, Spark NLP, and Hugging Face Transformers with GPU acceleration, quantization, and MLflow tracking.

    Jupyter Notebook

  4. Sentiment-Based-Product-Recommendation-System Sentiment-Based-Product-Recommendation-System Public

    Hybrid recommendation system that combines collaborative filtering with NLP-based sentiment analysis to surface the best products from 30k+ reviews.

    Jupyter Notebook

  5. Lead-Scoring Lead-Scoring Public

    Logistic regression model that assigns lead scores (0-100) to predict conversion likelihood, improving sales targeting from 30% to 80% conversion rate.

    Jupyter Notebook 1