High DemandHands-on LabAgentic AI
Agentic Workflows with LangGraph
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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