Articles tagged "comparison".
7 articles, published April 2026 to May 2026, 65 minutes of reading in total. Sorted newest first.
Filed under the comparison cluster of the blog. These articles also carry the tags agent memory, AutoGen, Claude SDK, CrewAI, Google ADK, human-in-the-loop, LangChain, Letta, LlamaIndex, Mem0, MemGPT, multi-agent, OpenAI SDK and RAG.
Agent SDK Comparison: ONTO vs Claude vs OpenAI vs Google ADK (2026)
An honest head-to-head of the four major agent SDKs — where each wins, where each loses, and how to pick the right one for your project.
ONTO vs LangChain: When Typed Memory Beats Chains
LangChain is the largest agent orchestration ecosystem in the world. So why pick ONTO instead? A direct comparison on memory, planning, consent, and operational concerns.
ONTO vs LlamaIndex: Agents vs RAG, Explained
LlamaIndex is the most popular RAG framework. ONTO is an agent SDK. They solve adjacent problems — but treating them as substitutes leads to bad architecture. Here's how to pick.
ONTO vs Mem0: Choosing a Memory Layer for AI Agents
Mem0 is a popular dedicated memory layer for LLMs. ONTO is an agent SDK with memory built in. What's the difference, and which one is right for your project?
ONTO vs Letta (MemGPT): Two Approaches to Long-Term Agent Memory
Letta (formerly MemGPT) pioneered the idea of agents managing their own memory through tool calls. ONTO takes a typed-graph approach. Same problem, different solutions.
ONTO vs CrewAI: Multi-Agent Without the Black Box
CrewAI made multi-agent orchestration accessible. ONTO takes a different shape — subagents under explicit consent scopes, a depth budget, and rolled-up cost. Which fits your problem?
ONTO vs AutoGen: Human-in-the-Loop, Done Right
AutoGen pioneered conversational multi-agent and human-in-the-loop patterns. ONTO takes a different approach to the same problem — Plan Mode as a runtime invariant. Here's how they compare.