Observability, gateways, model routing, cost controls, and distributed execution.
13 articles
A 2026 comparison of the most worthwhile AutoGPT alternatives — OpenHands, MetaGPT, Agency Swarm, CrewAI, AutoGen, BabyAGI — covering autonomy, controllability, production maturity, cost, and observability, and explaining why 'autonomous agents' are no longer mainstream in 2026, and how alternatives solve AutoGPT's core problems.
A practical 2026 comparison of the top AI agent frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Mastra, and Claude Agent SDK. Covers architecture philosophy, production maturity, multi-language support, observability, and team fit, with a selection decision tree.
A deep comparison of CrewAI and the leading multi-agent framework alternatives in 2026 — LangGraph, AutoGen, MetaGPT, OpenHands, Agency Swarm, and Microsoft Agent Framework — covering role abstractions, flow control, observability, production maturity, and enterprise features, with a decision tree by business complexity.
A deep dive into the OpenAI Agents SDK's core abstractions (Agent, Handoff, Guardrail, Tool, Tracing), covering multi-agent routing, MCP integration, production deployment, and comparisons with Swarm, LangGraph, and the Claude Agent SDK. Includes complete code examples and selection guidance.
Deep comparison of LangGraph (LangChain team's state-graph Agent orchestration framework) and AutoGen (Microsoft Research's conversation-driven multi-agent framework) across architecture abstraction, orchestration, control granularity, observability, and production maintainability. Includes a decision tree by task structure, team scale, and controllability requirements.
A systematic deep dive into model gateway core capabilities, LiteLLM self-hosted configuration, fallback chain design, task-aware routing, cost-aware routing, A/B testing routing, rate limiting and quotas, semantic caching, Prometheus monitoring, and production-grade deployment.
Build a production-grade observability stack for multi-step agents using OpenTelemetry: OpenLLMetry semantic conventions, hierarchical span correlation, token cost attribution, retrieval quality metrics, and layered alerting.
The biggest hidden cost in agent production is not token pricing but redundant calls and model mismatches. A quantifiable cost-control framework covering caching strategies, fallback chains, and routing rules.
A production walkthrough of LiteLLM based on official docs and its 51k-star repo: Virtual Keys, Guardrails, routing, and Postgres-backed cost governance.
Using Ray's distributed runtime to scale agent prototypes from a single machine to horizontally scalable cluster deployments.
Four LLM gateways compared, with production patterns for fallback, smart routing, cost observability, and scheduling.
A systematic guide to the three pillars of agent observability — distributed tracing, metrics monitoring, and automated evaluation — for building production-grade agent monitoring.
Based on real production experience, this guide explains how to build a closed loop of tracing, evaluation, and cost analytics for AI agents with Langfuse.