Production-grade tutorials for the infrastructure technologies that power modern distributed systems. Each guide covers core concepts, architecture, setup, usage examples, best practices, and FAQs.
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Official framework for multi-agent delegation, handoffs, input/output guardrails, and OpenTelemetry tracing.
Build multi-step agents in JS/TS with Zod schemas, streamText, tool calling loops, and Next.js integration.
Safely execute untrusted LLM-generated code. E2B Firecracker MicroVMs, WASM (Pyodide), and container runtimes.
Distributed event streaming platform. Topics, partitions, producers, consumers, KRaft mode, and real-time data pipelines.
Lightning-fast in-memory data structure server. Strings, Hashes, Lists, Sets, Sorted Sets, Pub/Sub, and caching patterns.
Stateful stream processing framework. DataStream API, event time, watermarks, checkpointing, and real-time analytics.
Vector databases for RAG pipelines and agent memory. Qdrant, Milvus, pgvector — embeddings, HNSW indexing, and metadata filtering.
Deploy and manage containerized AI agents at scale. Pods, services, deployments, HPA autoscaling, Helm charts, and GPU node pools.
The most advanced open-source database. JSONB for agent state, CTEs, window functions, advanced indexing, partitioning, and replication.
Batteries-included Python framework. ORM, REST APIs with DRF, admin panel, authentication, middleware, and production deployment.
Distributed task queue for async AI agent workloads. Workers, brokers, retry strategies, periodic scheduling, and Flower monitoring.
Amazon Web Services for AI agents. Bedrock for LLMs, Lambda for serverless tools, DynamoDB for state, SQS for messaging, and ECS for containers.
Google Cloud for AI agents. Vertex AI & Gemini for LLMs, Cloud Run for containers, Firestore for state, Pub/Sub for messaging.
Microsoft Azure for AI agents. Azure OpenAI for GPT, Functions for serverless, Cosmos DB for state, Service Bus for messaging.
Design and build complex workflows. Compare LangGraph, AutoGen, and CrewAI for state, loops, tool execution, and human-in-the-loop.
Serverless NoSQL compared. Learn AWS DynamoDB, GCP Firestore, and Azure Cosmos DB for session memory, state sharding, and TTL.
Anthropic open standard. Connect LLMs to local data resources, databases, custom prompts, and executable tool servers.
Standard protocols for agent message passing, target handoffs, task negotiation, and execution synchronization.
Type-safe production agent framework by Pydantic. Structured outputs, type validation, dependency injection, and contract reliability.
Event-driven agentic RAG and query orchestration. Multi-source routing, reranking, and dynamic context retrieval workflows.
Trace, monitor, and evaluate production agents. OpenTelemetry, Arize Phoenix debugging, and DeepEval CI/CD test automation.
Unit testing and mock testing for Python AI agents. Test prompts, tool calls, agent runs, and validate output structures using pytest.
Continuous Integration and Deployment for AI agents. GitHub Actions workflows, Docker containerization, and evaluation testing.
Self-hosted automation pipelines for Python AI agents. Jenkinsfiles, docker agent nodes, credential storage, and evaluation reporting.
Superfast Python package resolution and virtualenv creation using Astral's uv tool. Learn project locking, workspace dependencies, and offline caching.
