AI Tools & Frameworks Directory

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.

A deep dive into Engram, our managed memory service for agents which is simple to get started but adaptable to any use case.

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.

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.

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 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...

--- 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).

Build production AI agents on MongoDB Atlas — with vector search, persistent memory, natural-language querying, and end-to-end observability built in.

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.

A practical checklist for agent evaluation: error analysis, dataset construction, grader design, offline & online evals, and production readiness.

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.

💡TLDR: The best agent evals directly measure an agent behavior we care about. Here's how we source data, create metrics, and run well-scoped, targeted experiments over time to make agents more accurate and reliable.Evals shape agent behaviorWe’ve been curating evaluations to measure and

Agent harnesses are what help build an agent, they connect an LLM to its environment and let it do things.When you’re building an agent, it’s likely you’ll want build an application specific agent harness. “Agent Middleware” empowers you to build on

Fleet now supports shareable skills, so you equip agents across your team with knowledge for specialized tasks.

Moda uses a multi-agent system built on Deep Agents and traced through LangSmith to let non-designers create and iterate on professional-grade visuals.

If you're attending Google Cloud Next 2026 in Las Vegas this year and working on agent development, here's what we have planned.Visit Us at Booth #5006We'll be at Booth #5006 in the Expo Hall at the Mandalay Bay Convention Center, April 22-24.