High DemandHands-on LabAgentic AI
LangGraph Workshop: Build Agentic AI Workflows (3h30 Hands-on)
LangGraph is the reference framework for building complex agentic workflows with LLMs. This workshop teaches you to create agents that use tools, manage memory, and orchestrate multi-step tasks.
Duration
3h30
Audience
Python Developers, Data Scientists, ML Engineers
Technologies
LangGraph, LangChain, OpenAI/Anthropic APIs
Format
On-site or Remote
Detailed program
0:00 – 0:25
Introduction: Agents vs Workflows
- What is an AI agent?
- Difference between chains and agents
- Why LangGraph? Comparison with AutoGPT, CrewAI, etc.
- LangGraph architecture: nodes, edges, state
0:25 – 1:25
Lab 1: Agent with tools, memory, and persistence
- Setup: installing LangGraph and dependencies
- Create a simple agent with LangChain
- Add tools (web search, calculator, external API)
- Implement conversational memory
- Persist state in a database (SQLite, Redis)
- Test agent on multi-turn tasks
1:35 – 2:20
Failure management and robustness
- Retry logic: retry in case of LLM or API error
- Fallback strategies: what to do if the agent fails?
- Timeouts and limits: avoid infinite loops
- Output validation: Pydantic schemas
- Error handling best practices
2:30 – 3:30
Observability & Deployment
- Logging and tracing with LangSmith
- Metrics: latency, costs, success rate
- Deploy a LangGraph agent in production
- Operational notes: token management, rate limits
Deliverables
- Template repository for your future LangGraph agents
- State diagram of your agentic workflow
- Deployment script (Docker + FastAPI)
- Observability playbook: logs, traces, metrics
Prerequisites
- Python (intermediate level)
- Basic knowledge of LLMs and APIs
- OpenAI or Anthropic account (API key)
Pricing
€990 excl. tax / person
€4,500 excl. tax / group (up to 8 participants)
Level
Intermediate to Advanced
Accessibility
Workshop accessible to people with disabilities. Contact our disability referent: accessibilite@ikasia.ai
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