An open-source Digital Worker platform for reliable execution and continuous co-evolution.

A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline.

Stop overpaying to run your agents. Kalibr routes every request to lower-cost model and tool paths without degrading performance.

Universal autonomous agent framework with ReAct loop, multi-provider LLM routing, reasoning graph, and MCP integration, domain-agnostic for building specialized AI agents.

Open source software for machine learning production monitoring : maintain control over production models, detect bias, explain your results.

The open-source safety layer for AI agents — block unsafe tool calls, require approval, enforce budgets, audit, replay.

Production operations framework for AI-powered SaaS. The architectural patterns, failure modes, and operational playbooks that determine whether your AI systems scale profitably or fail expensively.

🚀 Build AI Agent Teams as Production-Ready APIs. Orchestrate CrewAI agents with FastAPI for enterprise-grade AI services. Leverage Groq's lightning-fast LLMs to deploy collaborative AI workflows at scale.

Production-grade architecture patterns, decision frameworks, and best practices for building reliable AI agents. Framework-agnostic reference for engineers.

Engineering patterns for taking AI agents to production — deployment, tools, memory, long-running work, human oversight, and observability at scale. From Microsoft Build 2026.

AI Engineering Bootcamp documentation: 48-week full-time program. Published curriculum, assessment standards, rubrics, and open competency evaluation for LLMs, agents, and production AI.

Production-ready agentic AI framework. High-performance, lightweight, simple. Built-in safety, memory, and 4 reasoning patterns. Ships to production fast.

Engineering deterministic, production-grade systems around non-deterministic LLMs — FSM, durable execution, retries, DAGs, agent runtimes, model routing, edge inference, RAG, memory, multi-agent orchestration, security, and observability. 14 runnable proof-of-concept phases.

40x faster AI inference: ONNX to TensorRT optimization with FP16/INT8 quantization, multi-GPU support, and deployment

Production-grade AgentOps control plane for safe AI agent execution. Dual-plane architecture: Rust governance engine + Python LLM runtime + Next.js dashboard. Deny-by-default policies, budget enforcement, approval gates & audit logging.

AxonFlow governance for OpenClaw agents — block dangerous tools, govern MCP access, and keep audit trails for production agent workflows

The Kubernetes of AI Agents: Production-grade orchestration for LLM-powered agents at scale. Multi-tenant, secure, observable. Built for real deployments, not demos.

Production approval-gate pattern for AI agents: draft, validate, approve, dispatch, audit. JSON schemas + n8n + email examples.

A complete, structured end-to-end roadmap to become a versatile Generalist AI Engineer from Python/math foundations through ML/DL, CV, NLP, LLMs/GenAI, MLOps, and 23+ real-world projects.

Production-grade AI inbox triage agent. Hardened with prompt-injection ▎ defenses, sender allowlisting, idempotent processing, and fail-closed secret ▎ handling. FastAPI · Gmail · LLM · Postgres.