This release brings the HFresh disk-based vector index and the built-in MCP Server to general availability, rebuilds cluster-wide async replication to run from a single scheduler (on by default), and adds two previews: the Boost API and Nested Object Filtering.
This release introduces the built-in MCP Server, Extensible Tokenizers, Diversity Search (MMR), and Query Profiling as previews, along with Incremental Backups, Gemini audio support for multi2vec-google, and the new BlobHash property type.
Agent harnesses are becoming the dominant way to build agents, and they are not going anywhere. These harnesses are intimately tied to agent memory. If you used a closed harness - especially if it’s behind a proprietary API - you are choosing to yield control of your agent&
Clarifai 12.3 introduces KV Cache-Aware Routing. Routes requests to replicas with relevant cache state for faster inference. Zero configuration required.
Deep Agents Deploy: an open alternative to Claude Managed Agents
Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way.Deep Agents deploy is built for an open world. It’s built on Deep Agents - an open
AI agents work best when they reflect the knowledge and judgment your team has built over time. Some of that is institutional knowledge that’s already documented and easy for an agent to use as-is. But most great organizations also rely on tacit knowledge that lives inside their employees’ minds.
Better Harness: A Recipe for Harness Hill-Climbing with Evals
By Vivek Trivedy, Product Manager @ LangChain💡TL;DR: We can build better agents by building better harnesses. But to autonomously build a “better” harness, we need a strong learning signal to “hill-climb” on. We share how we use evals as that signal, plus design decisions
TL;DR: We’ve released new minor versions of deepagents & deepagentsjs, featuring async (non-blocking) subagents, expanded multi-modal filesystem support, and more.See the changelog for details.Async subagentsDeep Agents can now delegate work to remote agents that run in the background. As opposed to...
Run Gemma 4 Locally: Deploy Frontier AI on Your Hardware with Public API Access
Run Google's Gemma 4 models on your own hardware while exposing them via public API using Clarifai Local Runners. Apache 2.0 licensed, multimodal support, and production-ready.
Arcade is the MCP runtime for production agents, delivering secure agent authorization, reliable tools, and governance. This integration gives your agents access to Arcade’s collection of 7,500+ agent-optimized tools through a single secure gateway.
Most discussions of continual learning in AI focus on one thing: updating model weights. But for AI agents, learning can happen at three distinct layers: the model, the harness, and the context. Understanding the difference changes how you think about building systems that improve over time.The thre...
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I built a self-healing deployment pipeline for our GTM Agent. After every deploy, it detects regressions, triages whether the change caused them, and kicks off an agent to open a PR with a fix, with no manual intervention needed until review time.
💡TL;DR: Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and latency. Here's what our evals show and how to start using them
Two weeks of dogfooding Engram, Weaviate's memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.
It feels like spring has sprung here, and so has a new NVIDIA integration, ticket sales for Interrupt 2026, and announcing LangSmith Fleet (formerly Agent Builder).
Your Code is Your Schema: Weaviate Managed CClient
Use semantic search and RAG in C# with the Weaviate Managed .NET client — attribute-driven schema, type-safe queries, and safe migrations, all in idiomatic .NET.
How Kensho built a multi-agent framework with LangGraph to solve trusted financial data retrieval
Discover how Kensho, S&P Global’s AI innovation engine, leveraged LangGraph to create its Grounding framework–a unified agentic access layer solving fragmented financial data retrieval at enterprise scale.