MemPalace tops the long-term memory benchmark by refusing to summarize. That's not a trick — it's an argument that the field spent two years solving the wrong half of the problem.
Five real, self-hostable gateways that put one endpoint in front of many MCP servers — and why the stateless spec is about to change what a gateway is even for.
Liquid AI's smallest model yet fits in under 400MB and runs on a Raspberry Pi. The interesting part isn't how small it is — it's what a model this size is actually for.
Every checkpoint a long-running LangGraph agent writes re-serializes its entire state. DeltaChannel, per-node timeouts, and the v2 stream in 1.1–1.2 are the runtime quietly admitting the naive durability model doesn't scale.
Schema-constrained output gives you a valid object. It can't tell you whether a value was read from the document or invented to satisfy the schema. Google's LangExtract returns the exact character span each field came from — which turns extraction from a trust exercise into a verification one.
Three places to get an RL environment, and they don't compete on the axis you think. The dividing line is where step() runs — a cheap function call or a network trip to a live machine.
Loading the right MCP tools on demand is a solved problem. Removing the wrong ones over a long conversation is not — and a new benchmark finds that letting the agent do it itself only works if the model reasons.
Google open-sourced a text diffusion model that reads documents better than the autoregressive Gemma it's built on — and does multi-step math worse. That split tells you exactly where to wire it in.
CrewAI 1.14 lets you swap the default memory, knowledge, RAG, and flow backends for your own. It reads like a config change. It's actually the framework conceding that batteries-included storage was a production liability.
Anthropic's new research workbench isn't a smarter model — it's two orthogonal layers, an independent reviewer agent and a reproducibility package, that any agentic pipeline can steal.
Subagents can now spawn subagents five levels deep. The number that decides whether you should is not the depth — it's how much billed work happens at branches the root never reads.
Seven real, self-hostable RL frameworks for post-training tool-using agents — and why the one you pick should be decided by the environment, not the algorithm.
Letta's .af format serializes an agent's memory, tools, and history into one JSON file. The pitch is portability. What it delivers best is something quieter — a savefile — and that distinction decides whether it's useful to you.
The ground-up Beta that becomes mainline in AG2 1.0 replaces the chatty ConversableAgent with a MemoryStream — a per-channel append-only event log. It's the framework conceding that an agent has to survive its second concurrent user, not just the demo.
Per chip, Google's Ironwood and Nvidia's B200 are now within ten percent of each other on every number that used to decide this. The real fork is the interconnect — and vLLM just deleted the reason you couldn't cross it.
SGLang v0.5.14 reports 5x throughput serving DeepSeek-V4 on GB300. The lever isn't Blackwell Ultra — it's a per-batch linear program that reroutes tokens across expert replicas. Static replication plans for the average; no batch looks like the average.
The load balancer you already trust is the wrong tool for a fleet of inference servers. Spreading requests evenly is exactly what destroys the cache that sets your latency and your bill.
OpenClaw runs on your own machine, so it feels private and therefore safe. The security crisis of the last three months is a lesson in why those are not the same thing — self-hosting moved the data, not the trust boundary.
OpenAI's first silicon claims roughly 50% cheaper inference than Nvidia. The number is self-reported and unverifiable — but the vertical-integration bet underneath it is the part actually worth understanding.
"Open weights" is a spectrum, not a permission. The license — not the benchmark — decides whether you can ship a coding agent on GLM-5.2, Kimi K2.7, or MiniMax M3, and whether you own the tokens it generates.
Adding a tenant_id to your WHERE clause is the easy part and the part that never leaks. The breaches live in the three stateful surfaces that filter never reaches — the cache, the vector index, and the tool call.
Anthropic's MCP tunnels connect a hosted agent to servers inside your private network over an outbound-only link. The clever part is the direction of the connection — and the threat it doesn't touch.
The rename reads like marketing housekeeping. It isn't. Folding deploy into LangSmith and handing every deployed agent an MCP endpoint quietly reclassifies your agent from an application into a tool other agents can call.
Moonshot's new coding model cuts reasoning tokens ~30% while nudging its own benchmarks up — a wager that per-step cost, not raw smarts, now decides agentic coding.
Every prompt tool sells the same feature — edit the prompt without shipping code. Stated precisely, that feature is: change production behavior with no PR, no eval run, and no pinned model. Here's how to keep the convenience without the shadow deploy.
Boson AI's 4B model speaks before the sentence is finished, which is the right shape for a voice agent. The catch isn't quality or speed — it's the non-commercial license on the exact use case it was built for.
Microsoft's new agent runtime scales to zero like a serverless function but keeps the filesystem and a machine identity — quietly moving the lock-in from your framework down to the sandbox your agent lives in.
The field spent a year making the orchestrator smarter. Microsoft's Conductor argues the routing layer should be dumb — and spend zero tokens deciding what runs next.
CrewAI ships two orchestration models in one framework. Picking wrong is why your multi-agent demo worked and your production run didn't — and the fix is usually not choosing between them.