Top 20 LangGraph Trending Projects & Assignments
- Codersarts
- Jun 15
- 7 min read
LangGraph is gaining significant traction for building robust, stateful AI agents and multi-agent systems. Its graph-based approach allows for complex workflows, cyclical processes, and better control over LLM interactions.
Here's a list of top 20 trending LangGraph projects or assignment ideas for AI Engineers and LLM Developers, drawing inspiration from current trends and real-world applications:
Core LangGraph Project Assignments
Intelligent SQL Agent
Overview: Translates user’s natural-language questions into executable SQL, runs queries, and returns results in human-readable form.
Core Components:
LangGraph for state management and error-handling loops
SQLAlchemy (Python) or Knex.js (Node) for DB access
Prompt templates for schema introspection and query refinement
Extensions:
Auto-suggest index optimizations based on query patterns
Integrate a visualization layer (e.g., Plotly) for charted results
Web Research Agent for Article Generation
Overview: Crawls and scrapes multiple web sources, synthesizes findings, and drafts structured articles (with citations).
Core Components:
Browser-automation (Playwright/Puppeteer) steps encoded as LangGraph nodes
RAG retrieval from a document store (e.g., Pinecone, FAISS)
LLM summarization + outline refinement agent loops
Extensions:
Automated fact-checking sub-agent that flags inconsistencies
Multi-language support via translation APIs
Agentic Financial Analyst
Overview: Ingests financial reports (PDF/HTML), extracts key metrics, and answers queries about market trends.
Core Components:
PDF parser (pdfplumber), OCR (Tesseract) for scanned docs
Numeric extraction + time-series preprocessing
Chart generation (matplotlib) via LangGraph tool calls
Extensions:
Real-time data feeds integration (Alpha Vantage, Yahoo Finance)
Technical indicator agent (e.g., moving averages, RSI)
Customer Support Bot
Overview: Stateful chatbot maintaining conversation context, retrieving knowledge base articles, and escalating to human operators when needed.
Core Components:
Vector-store retrieval for KB lookup
Context window management with memory nodes
Human-in-the-loop breakpoints for sensitive topics
Extensions:
Sentiment-analysis triggers to adjust agent tone
Auto-ticket creation in Zendesk/ServiceNow via API
Prompt Generation Agent
Overview: Dynamically crafts and refines prompts for downstream LLMs, based on user goals and past effectiveness metrics.
Core Components:
A/B testing harness to compare prompt variants
Feedback loop storing prompt → response quality scores
LangGraph logic for branching on performance thresholds
Extensions:
UI dashboard for visualizing prompt efficacy over time
Reinforcement-learning fine-tuning of prompt templates
Code Generation Agent using RAG and LangGraph
Overview: Automates “dev cycle” by generating code stubs, running tests, and iterating on failures until all test cases pass.
Core Components:
Test harness (pytest/Jest) invocation via tool nodes
RAG retrieval of relevant docs or StackOverflow snippets
Conditional branching on test success/failure
Extensions:
Integration with CI pipelines (GitHub Actions) for full automation
Auto-documentation agent that generates README sections
Crypto Analyst Agent
Overview: Monitors live crypto markets, assesses risk, and summarizes trading opportunities or alerts on anomalies.
Core Components:
Websocket or REST feeds from exchanges (Binance, Coinbase)
Indicator calculators (MACD, Bollinger Bands) as sub-agents
Notification hooks (Slack, Telegram)
Extensions:
Simulated backtesting agent to validate strategies
Portfolio-optimization recommendations
Multi-Agent Research for Enterprise Knowledge
Overview: Several specialized agents (retriever, verifier, synthesizer) collaborate to deliver accurate, up-to-date corporate research.
Core Components:
Document ingestion pipeline (PDF, HTML, slides)
Cross-verification agent comparing multiple sources
Final drafting agent that merges and formats insights
Extensions:
Compliance-check agent for regulated industries
Scheduled re-runs to keep research fresh
Medical AI Agent
Overview: Conversational assistant for basic symptom triage, appointment scheduling, and emergency detection.
Core Components:
Symptom-checker knowledge graph
Secure API integration with scheduling systems (FHIR/HL7)
Escalation flow for red-flag symptoms
Extensions:
HIPAA-compliant data handling
Telemedicine video handoff agent
Streamlit-LangGraph Chatbot for EDA
Overview: Interactive web UI where users upload datasets, ask questions, and get charts or stats on demand.
Core Components:
Streamlit frontend with file uploader and chat window
Pandas/NumPy introspection agents
Chart-generation tool calls (matplotlib via LangGraph)
Extensions:
Auto-report generator that exports PDFs of analysis
Support for big-data connectors (Spark, Dask)
Unit Test Generation Agent
Overview: Scans codebases, identifies functions/classes, and auto-generates corresponding unit tests.
Core Components:
AST parsing (ast in Python) to discover signatures
Test-template prompts tailored per language/framework
Validation loop running tests and refining failing cases
Extensions:
Coverage-analysis integration to highlight untested code
Mock-service generation for external dependencies
Real-Time Code Generation Copilot
Overview: Browser/IDE plugin that suggests code completions, function implementations, and documentation in real time.
Core Components:
WebSocket bridge between IDE and backend LangGraph service
Incremental context window management
Latency-optimized LLM calls with fallbacks
Extensions:
Local-inference option (Llama 3.2) for privacy
Customizable style guides (PEP8, Google style)
Property Management Copilot
Overview: Assists property managers with tasks like lease tracking, maintenance scheduling, and tenant communication.
Core Components:
Calendar-API integration (Google/Outlook) for scheduling
CRM-style database agent for tenant info
Workflow nodes for rent reminders and work orders
Extensions:
Auto-PDF generation for lease agreements
Chatbot interface for tenants
End-to-End Generative AI Chat App
Overview: Full-stack chat application combining LangGraph, Tavily front end, and GPT-4 back end for live retrieval and response.
Core Components:
Next.js or React Native front end
LangGraph orchestration of RAG + tool invocations
Session management with Redis or DynamoDB
Extensions:
Voice-enabled interface via Web Speech API
Multi-tenant support with per-user custom kernels
Low-Code Workflow Platform for Chatbots and RAG
Overview: Drag-and-drop UI to assemble LangGraph nodes into complex agent workflows without writing code.
Core Components:
Canvas editor (e.g., React Flow)
Node definitions for common tools (LLM call, DB query, API call)
Runtime engine to execute serialized graphs
Extensions:
Plugin marketplace for custom node types
Versioning and collaboration features
Production-Ready FastAPI LangGraph Agent
Overview: Boilerplate template combining FastAPI, Docker, Kubernetes manifests, and LangGraph for scalable AI services.
Core Components:
Auth (JWT/OAuth2) and rate-limiting middlewares
Health-check endpoints and Prometheus metrics
CI/CD templates (GitHub Actions, Helm charts)
Extensions:
Canary-deploy agent updates automatically
Blue/green deployment scripts
Multi-Actor Team Collaboration Platform
Overview: Multiple AI agents representing roles (analyst, editor, reviewer) collaborate on content creation or project planning.
Core Components:
Role-based agent personas with distinct prompt profiles
Shared memory graph for passing artifacts
Voting or consensus sub-agent to finalize outputs
Extensions:
Human moderator integration for oversight
Gamification layer to track agent performance
AI Code Review Loop
Overview: Orchestrates static analysis, linting, security scanning, and LLM-based feedback in a continuous loop.
Core Components:
Tool nodes for flake8, ESLint, Bandit, etc.
LangGraph decision nodes to categorize issues
Automated PR comments via GitHub API
Extensions:
Remediation code generation for certain classes of issues
Historical trend dashboards
Human-in-the-Loop AI Agent
Overview: Embeds checkpoints in agent workflows where humans can review, correct, or approve before proceeding.
Core Components:
UI review panels (React/Streamlit)
LangGraph pause/resume primitives
Audit-trail logging for compliance
Extensions:
Role-based access controls for different reviewers
SLA-based timeout fallbacks
Dynamic Multi-Step Research Pipeline
Overview: Automated pipeline that retrieves, verifies, refines, and packages research outputs in multiple iterative steps.
Core Components:
Source-selector agent choosing between web, PDF, or API inputs
Verifier sub-agent cross-checks against trusted datasets
Packager agent exporting to markdown, PDF, or slides
Extensions:
Scheduler to re-run and update research daily or weekly
Email digest integration for stakeholder updates
Bonus Ideas
Language Learning Conversation Partner Agent
What you’ll build: An interactive agent that simulates realistic conversations in a target language, corrects learner’s mistakes, and tracks vocabulary growth.
Tech stack: LangGraph, LLM fine-tuned on bilingual corpora, a UI with text/audio, spaced-repetition API for vocab.
Complexity: ★★★★☆
Learning outcomes:
Combining generation with error-correction sub-agents
Tracking long-term learning progress
Multimodal input/output (speech/text)
Extensions:
Pronunciation scoring via speech-analysis model
Leaderboard for learners in a classroom setting
Incident Response & Monitoring Agent
What you’ll build: A system that ingests logs/metrics, detects anomalies, initiates triage workflows, and notifies on-call staff with runbook suggestions.
Tech stack: LangGraph, Prometheus or ELK stack for data, anomaly-detection library, webhook integrations (PagerDuty/Slack).
Complexity: ★★★★☆
Learning outcomes:
Streaming-data processing in agents
Anomaly detection loops with self-correcting thresholds
Triage-playbook generation and automated notifications
Extensions:
Automated remediation scripts for known issue patterns
Dashboard for historical incident trends
Additional Ideas & Next Steps
Customizable RAG-Based LLM Chatbot: Extend any of the above with open-source LLMs (e.g., Llama 3.2) and vector DB embeddings (BGE) for on-prem or privacy-focused deployments.
Fine-Tuning & DPO Training Orchestrator: Use LangGraph to manage data pipelines, training loops, and evaluation metrics for supervised or preference-optimized fine-tuning.
Multimodal AI App with Vision-Language Models: Combine LangGraph with a vision-language model (e.g., BLIP or Vision LLM) and Gradio for an end-to-end multimodal assistant.
Key Learning Outcomes
These projects focus on:
Stateful Workflows: Managing context across multiple interactions
Multi-Agent Coordination: Building systems where multiple AI agents collaborate
Human-in-the-Loop Systems: Incorporating human oversight and feedback
Production-Ready Deployment: Building scalable, reliable AI systems
Integration Capabilities: Connecting with external APIs and databases
These assignments represent the cutting edge of AI development, focusing on building intelligent, stateful agents that can handle complex, real-world tasks while maintaining reliability and scalability.
Ready to Bring Your LangGraph Project to Life?
At Codersarts, we specialize in turning cutting-edge AI ideas into production-ready solutions. Whether you’re building a proof-of-concept, scaling a multi-agent system, or upskilling your team, we offer end-to-end support:
🚀 Our LangGraph Services
Consulting & Strategy
Architecture design for stateful, cyclical workflows
Tech-stack selection (LLMs, vector DBs, orchestration layers)
Project scoping, timelines & cost estimates
Custom Development & Integration
Agent pipelines (RAG, browser automation, tool-calls)
Multi-agent coordination and memory graph implementation
API & external-tool integration (databases, DevOps, IoT)
Prompt Engineering & Knowledge Retrieval
High-performance prompt templates and A/B testing
Vector-store design, indexing & embeddings
Retrieval-augmented generation with corrective loops
UI/UX & Frontend
Interactive dashboards (Streamlit, React/Next.js)
Chatbots, copilot plugins & low-code platforms
Data visualizations (Plotly, matplotlib)
Deployment, DevOps & Scalability
Docker, Kubernetes & CI/CD pipelines
Monitoring, logging & alerting (Prometheus, Grafana)
Security best practices and SLAs
Training, Workshops & Support
Hands-on workshops: LangGraph fundamentals to advanced multi-agent patterns
Code reviews, QA automation & performance tuning
Ongoing maintenance, upgrades and 24/7 support
Let’s Get Started
Book a Free Consultation: Tell us about your LangGraph idea → we’ll map out a clear roadmap and estimate.
Kick Off Your POC: Validate concepts quickly with a targeted proof-of-concept.
Scale to Production: From agent orchestration to full-stack deployment, we’ll be your partner every step.
Who Can Benefit from Our LangGraph Expertise
Our LangGraph services are tailored to a wide range of organizations and teams:
Startups & SMBs: Fast-track your AI roadmap with lean proofs-of-concept, MVPs, and scalable agent architectures—without large upfront investments.
Enterprises & Product Teams: Integrate stateful AI agents into your core applications, automate complex workflows, and deliver robust, production-grade solutions at scale.
Data Science & ML Engineering Teams: Accelerate your team’s productivity with expert prompt engineering, RAG pipelines, and multi-agent orchestration patterns.
DevOps & Infrastructure Teams: Leverage our turnkey FastAPI + LangGraph templates, CI/CD pipelines, and Kubernetes deployments to bring AI services into your existing DevOps workflows.
Research Labs & Academic Institutions: Build advanced multi-agent research pipelines, teaching tools, and reproducible experiment frameworks using LangGraph’s modular workflows.
Product Designers & UX Teams: Collaborate on intuitive chat interfaces, low-code platforms, and data visualization dashboards that make AI accessible to non-technical users.
Consulting Firms & System Integrators: Partner with us to upsell LangGraph expertise, co-deliver complex AI projects, and embed stateful agent capabilities into broader digital transformation initiatives.
Ready to Get Started?
Book a Free Consultation: Share your team’s needs and we’ll recommend the perfect LangGraph approach.
Pilot a Proof-of-Concept: Validate use cases quickly with a focused, time-boxed engagement.
Scale to Full Production: From agent pipelines to global deployment, we’ll support you at every stage.
📧 Email us at contact@codersarts.com
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Let’s build the next generation of intelligent, stateful AI agents together!
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