Of the five best-known open-source deep-research agents, one archived itself in August 2026, one rewrote itself into a general agent harness, and one has not taken a commit since September 2025. The research loop became a default feature of every harness, so the only axis left worth choosing on is where your corpus lives and who gets to see the query.
A human approves a $40 refund and the runtime executes something else — no injection, no sandbox escape, just an approval stored as a boolean against an identifier while the arguments stayed writable. Loopjacking reproduced it across seven Agno releases; one SDK in the sample rejected it, and the difference is three lines of design.
The four leading TypeScript agent frameworks agree almost completely on the tool loop and disagree on one thing that decides your architecture: where the run lives when the HTTP request ends. That single axis picks your database, your deploy story and your exit cost — and the AI SDK's own troubleshooting page, where a user pressing Stop is indistinguishable from a closed tab, is the cleanest proof that it is the real axis.
Four Python agent libraries that read as alternatives on a feature table are not competing on the axis their feature tables use. Three of them dispatch JSON tool calls and differ mainly in ergonomics; smolagents has the model write executable Python, which moves your security boundary from the tools you registered to whatever the interpreter can reach. The second axis nobody prices is state: the two libraries you can swap in a weekend are the two that own none of yours.
Four open-source voice frameworks that look interchangeable on a feature table have their centres of gravity in four different columns — the runtime, the media server, the graph, the phone line — and only one of those is expensive to change later. The pipeline ergonomics everyone benchmarks are also the part a full-duplex model is busy commoditising, so pick on transport ownership, telephony breadth and maintenance velocity, and read TEN’s licence before you ship.
Flowise archived itself on 13 August 2026 and its maintainers named the reason: coding agents now handle the complexity that a rigid low-code workflow hits a wall on. The three still standing are not surviving on the canvas either — each is defending something underneath it, and each licence says exactly what. n8n forbids offering it to others, Dify forbids multi-tenant operation, Langflow forbids nothing and is owned by IBM. Read the clause before the feature list.
All four build agents with tool calling, RAG and MCP, so features are not the decision. What separates them is what each one demands of the runtime you already operate — and for the JVM pair that demand is a Spring Boot major version.
Framework comparisons argue about graphs versus crews versus handoffs, but the metaphor stops mattering by week three. What you cannot re-pick eighteen months in is where a run lives, what resume means after a crash, and whether a human can pause a half-finished task — so choose on the state model and the rest of the comparison resolves itself.
A 128K-token context window degrades past the first thousand tokens and vanishes the moment the session ends. The agent-memory infrastructure market crossed $6 billion in 2026 because "throw it all in the context" stopped being a strategy — and four frameworks now bet differently on what memory should rank, store, and forget.
Four orchestration frameworks let you wire up the same workflow — and the feature lists nearly match. The thing that decides which one survives production is invisible there: where your agent's state actually lives.
Three of 2026’s fastest-growing open-source agents look almost identical on a feature list — and behave like completely different species the moment you run them. A diagram-by-diagram tour of where the architectures diverge.