⚡Trending:
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LangChain@langchain·33m ago
🚀 Release

LangChain.js @langchain/openai 1.6.0: explicit prompt cache options + content-block breakpoints

LangChain.js shipped `@langchain/[email protected]` on 2026-09-27: ports explicit prompt-cache options across Chat Completions and Responses, preserves content-block breakpoints, and maps cache-write usage to `cache_creation`. A same-tag fix serializes `promptCacheRetention: "in-memory"` as API-legal `"in_memory"` and unifies precedence (per-call → modelKwargs → constructor); Chat Completions no longer drops modelKwargs.

LangChain.js @langchain/openai 1.6.0: explicit prompt cache options + content-block breakpoints
⚡ Key Takeaways
  • •Shipped: @langchain/[email protected] / npm 1.6.0
  • •Explicit prompt cache options + content-block breakpoints; cache-write → cache_creation (PR #11232)
  • •Fix: serialize in-memory as in_memory; precedence per-call > modelKwargs > constructor (PR #11735)
  • •Install: npm i @langchain/[email protected]
  • •Follow-on: @langchain/[email protected] bumps this dependency the same day
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ByteDance DeerFlow@bytedance·34m ago
🚀 Release

DeerFlow 2.1.0: 772-PR milestone — verifiable subagent delegation, pluggable memory, five remote sandboxes

ByteDance’s DeerFlow shipped v2.1.0 on 2026-09-24, closing the milestone with **772** merged PRs (~83k GitHub stars). Focus: trust and ops—verifiable subagent delegation, optional durable `batch_task`, pluggable memory (DeerMem / OpenViking / mem0 HTTP), plus E2B, BoxLite micro-VM, Tenki and OpenSandbox providers. Breakings: unconditional `X-Trace-Id`, reserved `/mnt/skills`, memory config under `memory.backend_config`, Docker bind defaults to `127.0.0.1`.

DeerFlow 2.1.0: 772-PR milestone — verifiable subagent delegation, pluggable memory, five remote sandboxes
⚡ Key Takeaways
  • •Shipped: v2.1.0 — 772 PRs; site deerflow.tech
  • •Verifiable subagent delegation + tamper-evident tool stamps; optional durable batch_task
  • •Pluggable memory.manager_class; FTS5/BM25; OpenViking & mem0 HTTP backends
  • •Sandbox: E2B replicas cap; BoxLite / Tenki / OpenSandbox among new providers
  • •Breakings: always-on X-Trace-Id, PAT auth, loopback Docker bind — read CHANGELOG before upgrade
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Pydantic@pydantic·34m ago
🚀 Release

Pydantic Monty 1.0.0 GA: Rust Python sandbox for agents — ~0.8ms warm checkout vs ~1500ms remote services

Pydantic shipped Monty v1.0.0 on 2026-09-25 (PyPI pydantic-monty, npm @pydantic/monty, crates.io monty), dropping the experimental label. OSS Monty is a from-scratch Rust Python bytecode VM (Ruff parser) with zero FS/network/env by default—host access only via explicit external functions. Official alternatives table: ~0.8ms warm checkout / ~5ms new pool vs ~195ms Docker exec, ~1500ms remote sandbox services, ~2700ms Pyodide. Built for agent-written code, not full VMs.

Pydantic Monty 1.0.0 GA: Rust Python sandbox for agents — ~0.8ms warm checkout vs ~1500ms remote services
⚡ Key Takeaways
  • •Shipped: v1.0.0 — uv add pydantic-monty / npm i @pydantic/monty / cargo add monty
  • •Official latency: ~0.8ms warm OSS checkout, ~5ms new pool vs ~195ms Docker, ~1500ms remote services, ~2700ms Pyodide
  • •Default deny FS/network/env; host reachability only via explicit external functions
  • •OSS Monty (MIT package) + commercial Full Monty (~2ms warm); Python ≥3.10
  • •Powers Pydantic AI Code Mode; docs at pydantic.dev/docs/monty
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Confident AI@confident-ai·5h ago
🛠️ Tooling

DeepEval 4.2: Jev for LLM-as-judge decisions at ~$0.042/M input tokens and ~100ms

Confident AI’s deepeval 4.2.x (notes for 4.2.2/4.2.4; PyPI at 4.2.6) wires TypeSafe Jev into LLM-as-a-judge: evaluation LLMs still handle language tasks, while closed verdicts go to System One for typed decisions with calibrated probabilities. Claimed decision-stage pricing is about $0.042 per million input tokens with no output charge, ~100ms typical latency, and batched independent questions. Faithfulness, GEval, DAGMetric, and Classifiers are integrated; enable via `deepeval set-mode experimental` plus `typesafe-sdk`—missing keys fail loudly instead of silent fallback.

DeepEval 4.2: Jev for LLM-as-judge decisions at ~$0.042/M input tokens and ~100ms
⚡ Key Takeaways
  • •Notes: python-v4.2.4; blog Introducing Jev for Evals; PyPI deepeval 4.2.6
  • •Official: ~$0.042/M input tokens for decisions, no output charge; ~100ms typical; batch independent questions
  • •Faithfulness/GEval/DAGMetric keep language work on the judge LLM; closed verdicts use Jev Noul/Choice/Score
  • •New Classifiers for categorical labels (e.g. RefusalClassifier)
  • •Opt-in: deepeval set-mode experimental + typesafe-sdk + TYPESAFE_API_KEY (loud failure if missing)
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xAI@xai-org·5h ago
🚀 Release

xAI Python SDK 1.20.0: pin first/last frames and up to 4 keyframes on grok-imagine-video-1.5

xAI shipped Python SDK v1.20.0 on 2026-09-24 (PyPI xai-sdk). For grok-imagine-video-1.5 it adds last_frame_url/last_frame_file_id to pin the exact closing frame (optionally interpolating from a pinned first frame) and a keyframes list to pin up to four in-clip reference images at second timestamps strictly inside the duration. Docs list video at about $0.08/sec with 480p/720p/1080p. Aimed at Imagine pipelines that need controllable start/end framing or storyboard alignment.

xAI Python SDK 1.20.0: pin first/last frames and up to 4 keyframes on grok-imagine-video-1.5
⚡ Key Takeaways
  • •Shipped: v1.20.0 — pip install -U xai-sdk
  • •last_frame_url/file_id pins the exact end frame; combine with image_* to interpolate first→last on grok-imagine-video-1.5
  • •keyframes: up to 4 {image_url|image_file_id, timestamp} entries with in-clip second timestamps
  • •Docs list ~$0.08/sec video with 480p/720p/1080p
  • •See CHANGELOG and docs.x.ai Imagine / video guides
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Assaf Elovic@assafelovic·5h ago
🚀 Release

GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance

GPT Researcher shipped v3.7.0 on 2026-09-26 (PyPI gpt-researcher 0.16.x). The context filter now defaults to TypeSafe Jev, scoring passage usefulness for each sub-query instead of embedding similarity. On a 28-task replay with identical sources, kept-passage relevance is 73% for Jev vs 46% for embeddings (~59% relative lift). Without TYPESAFE_API_KEY it falls back to local BM25—no embedding provider required. Also requires Python 3.12+ and adds a retriever plugin entry point.

GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance
⚡ Key Takeaways
  • •Shipped: v3.7.0 / PyPI 0.16.1 — pip install -U gpt-researcher
  • •Official 28-task replay: Jev kept-passage relevance 73% vs embeddings 46% (~+59%) at similar cost
  • •CONTEXT_FILTER=auto uses Jev with TYPESAFE_API_KEY else BM25; Jev failures fall back to keyword
  • •Chunking: 50k page cap, 1k/100 overlap; keep ≤10 chunks (Jev/embeddings) or ≤25 (keyword) per sub-query
  • •Requires Python 3.12+; retrievers via gpt_researcher.retrievers entry points
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Spring@spring-projects·9h ago
🚀 Release

Spring AI 2.1.0-M1: OpenAI Responses API, ordered MessagePart multimodal, VectorStore.upsert for precomputed embeddings

Spring shipped Spring AI 2.1.0-M1 on 2026-09-25. Headliners: OpenAI Responses API support (maps reasoning/function_call/image items to ReasoningPart/ToolCallPart/MediaPart while preserving item id/status for replay); ordered MessagePart lists on AbstractMessage so text, reasoning, tool calls, and media round-trip in model order; VectorStore.upsert for caller-supplied precomputed embeddings (wired for pgvector, Redis, Elasticsearch, Qdrant). Bumps Spring Boot to 4.2. First usable 2.1 milestone for teams adopting Responses and precomputed vector pipelines.

Spring AI 2.1.0-M1: OpenAI Responses API, ordered MessagePart multimodal, VectorStore.upsert for precomputed embeddings
⚡ Key Takeaways
  • •Shipped: v2.1.0-M1 — spring-ai-bom:2.1.0-M1 on Maven Central / Spring milestone
  • •Responses API: reasoning/function_call/image → MessagePart types; only function_call becomes ToolCalls
  • •MessagePart: ordered multimodal list; legacy accessors remain views; Jackson 3 type discriminator
  • •VectorStore.upsert for precomputed embeddings; pgvector/Redis/ES/Qdrant replace-by-id
  • •See 2.1 upgrade notes; Boot 4.2 — try from milestone before GA
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LangChain4j@langchain4j·9h ago
🚀 Release

LangChain4j 1.20.1: solidifies non-blocking AI Services (CompletableFuture/Flow.Publisher), opt-in Jackson 3, MCP Supplier with params

LangChain4j 1.20.1 landed 2026-09-25 atop the 1.20.0 line. AI Services now choose sync, TokenStream, CompletableFuture/CompletionStage, or Flow.Publisher by return type; non-blocking reaches tools, memory, guardrails, and the retrieval graph (@Experimental). Opt-in langchain4j-jackson3 routes JSON through Jackson 3 with typed JsonRead/WriteException. 1.20.1 lets @McpClientSupplier methods also take supplier parameters for per-request MCP clients. Aimed at Quarkus/Vert.x/WebFlux teams needing concurrent agents.

LangChain4j 1.20.1: solidifies non-blocking AI Services (CompletableFuture/Flow.Publisher), opt-in Jackson 3, MCP Supplier with params
⚡ Key Takeaways
  • •Shipped: 1.20.1 on Central; headline features from 1.20.0 non-blocking line
  • •Pick CompletableFuture/Flow.Publisher return types; async tool defaults differ from sync
  • •Docs: Non-blocking and Reactive tutorial
  • •Opt-in langchain4j-jackson3 for Jackson 3 + typed JSON exceptions
  • •1.20.1: @McpClientSupplier methods may take supplier parameters
Read details→
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LangChain@langchain-ai·9h ago
🚀 Release

LangGraph.js SDK 1.12.0: useStream enqueue can use durable server-side run queues across reloads/sessions

LangGraph.js shipped `@langchain/[email protected]` on 2026-09-25 (with `@langchain/[email protected]` and Vue/Svelte twins). `useStream`'s `"enqueue"` multitask strategy can set `queue: "server"` so queued runs hit backend Runs REST instead of an in-memory client queue—surviving reloads, visible across sessions, with server-side cancelQueued/clearQueue. Default remains `queue: "local"`; custom AgentServerAdapter transports are unsupported. For frontends that need durable, collaborative agent queues.

LangGraph.js SDK 1.12.0: useStream enqueue can use durable server-side run queues across reloads/sessions
⚡ Key Takeaways
  • •Shipped: langgraph-sdk 1.12.0 + react/vue/svelte 1.2.0
  • •Opt in with queue: "server" on useStream enqueue; local remains default
  • •Backend must implement Runs REST create/list/cancel endpoints
  • •Docs: useStream React howto + npm package
  • •Not supported with custom AgentServerAdapter transports
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ByteDance@bytedance·14h ago
🔥 Trending

ByteDance Releases DeerFlow 2.0: Ground-Up Rewrite of 83K-Star Long-Horizon SuperAgent Harness

ByteDance launched DeerFlow 2.0.0, completing a ground-up rewrite of its 83k-star open-source agent harness with 182 merged PRs. Built around a SuperAgent architecture, DeerFlow 2.0 orchestrates sub-agents, persistent memory, and sandboxed execution with extensible tools. It introduces self-updating SOUL memory, user-owned IM channel integrations (Feishu, DingTalk, WeChat, Slack, Discord, Telegram), Docker DooD sandbox protection, and deep integration with BytePlus InfoQuest crawling technology for robust multi-hour autonomous execution.

ByteDance Releases DeerFlow 2.0: Ground-Up Rewrite of 83K-Star Long-Horizon SuperAgent Harness
⚡ Key Takeaways
  • •Full architectural rewrite: migrated from rigid workflows to a SuperAgent harness orchestrating sub-agents, memory, and sandboxes
  • •Evolving agent souls: agents can modify and persist their own SOUL.md and YAML configs across chats with strict user isolation
  • •Universal IM gateway: native support for Feishu/Lark, DingTalk, WeChat/WeCom, Slack, Discord, and Telegram integrations
  • •Enterprise-grade sandbox: DooD Docker isolation with automated zip-bomb protection and sensitive MCP credential masking
  • •Production observability: Langfuse distributed tracing with parent-child attribution and thread-indexed O(1) query acceleration
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Docker Engineering@docker·14h ago
🚀 Release

Docker Agent (cagent) 1.144.0: ACP v1 sessions/terminals/remote MCP; sandbox --cloud without local Docker

Docker Engineering shipped docker-agent/cagent v1.144.0 on 2026-09-25. It widens ACP v1 compatibility: session delete + elicitation, model reasoning/safety options via session/set_config_option, shell via client terminal/create, client-supplied Streamable HTTP/SSE MCP on session new/resume/load, host credential auth/logout, audio prompts with bounded history replay. Sandbox gains --cloud and v3 kit so sandboxes run without a local Docker dependency. Plus TUI render wins and sandbox/filesystem confinement fixes. Aimed at teams embedding agents in IDEs/gateways over ACP.

Docker Agent (cagent) 1.144.0: ACP v1 sessions/terminals/remote MCP; sandbox --cloud without local Docker
⚡ Key Takeaways
  • •Shipped: v1.144.0 — `docker agent` / docker-agent binary
  • •ACP: session delete, elicitation, set_config_option, client terminals, remote MCP, host creds, audio prompts
  • •Sandbox: --cloud + v3 kit without local Docker; confinement fixes
  • •Install: Docker Desktop 4.63+ or brew install docker-agent; docs at docker.github.io/docker-agent
  • •See agentclientprotocol.com; MCP still first-class
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Kilo-Org@kilocode·14h ago
🚀 Release

Kilo Code 7.8.1: Agent Manager browser for public HTTPS/CDN previews; CycloneDX SBOM on every artifact

Kilo-Org shipped open-source coding agent platform Kilo Code v7.8.1 (includes 7.8.0) on 2026-09-25. 7.8.0 brings a high-resolution streamed Agent Manager browser for public HTTPS pages and CDN assets in localhost previews, with install/retry/settings guidance, native cross-origin checks, ordered clipboard, and stalled-session cleanup; plus close-task commands and a stoppable session-cleanup pass. 7.8.1 publishes CycloneDX SBOMs for every CLI archive, npm package, container image, VS Code extension, and JetBrains plugin—SHA-256 bound and GitHub-attestation signed. Azure Entra ID accepts resource name or full endpoint URL.

Kilo Code 7.8.1: Agent Manager browser for public HTTPS/CDN previews; CycloneDX SBOM on every artifact
⚡ Key Takeaways
  • •Shipped: v7.8.1 / @kilocode/[email protected]
  • •7.8.0: Agent Manager browser for public HTTPS/CDN; close-task cmds; stoppable session cleanup
  • •7.8.1: CycloneDX SBOM + SHA-256 + GitHub attestation on every artifact
  • •Azure Entra ID: resource name or full endpoint; block nested var refs in project MCP headers
  • •Install: VS Code Marketplace / CLI / JetBrains; docs at kilo.ai/docs
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Zed Industries@zeddotdev·14h ago
🚀 Release

Zed 1.21.0: Agent Panel BYOK for Claude Opus 5.5 and GPT-6 Astra/Sol/Luna; prevent idle sleep on long agent turns

Zed Industries shipped editor Zed v1.21.0 on 2026-09-23. AI: Agent Panel BYOK for Claude Opus 5.5 (Anthropic) and GPT-6 Astra/Sol/Luna (OpenAI); SuperGrok sign-in for Grok models; DeepSeek Flash 4.1; Z.ai GLM via Mistral provider; `agent.prevent_idle_sleep` on by default so long agent turns do not idle-sleep the machine. Also: language-server command picker, `showDocument` open file/URL, faster syntax/Markdown code-block rendering, ACP compatibility and async task wakeup improvements.

Zed 1.21.0: Agent Panel BYOK for Claude Opus 5.5 and GPT-6 Astra/Sol/Luna; prevent idle sleep on long agent turns
⚡ Key Takeaways
  • •Shipped: v1.21.0 — zed.dev/releases/stable
  • •BYOK: Opus 5.5 (Anthropic) + GPT-6 Astra/Sol/Luna (OpenAI)
  • •Default agent.prevent_idle_sleep for long agent turns
  • •Also: SuperGrok, DeepSeek Flash 4.1, Z.ai GLM via Mistral; ACP improvements
  • •Docs: AI overview, Agent Panel, LLM providers
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wong2@wong2__·17h ago
🔥 Trending

cf-mailroom: Open-Source Self-Hosted Email System for Humans and AI Agents on Cloudflare via MCP

Indie developer wong2 released cf-mailroom, a fully self-hosted email system powered by Cloudflare (Workers, Email Routing, D1, R2, Web Push) designed for seamless human-agent collaboration. While humans interact through a responsive Gmail-like web UI, AI agents access mailboxes via the Model Context Protocol (MCP) with scoped OAuth permissions. The platform features automated natural-language email labeling, intelligent agent draft generation with custom playbooks, and one-click human approval workflows.

cf-mailroom: Open-Source Self-Hosted Email System for Humans and AI Agents on Cloudflare via MCP
⚡ Key Takeaways
  • •Serverless native: runs completely on Cloudflare Workers, D1 database, R2 object storage, and Email Routing
  • •Dual access: Gmail-style web UI for humans and MCP server endpoints for AI coding/productivity agents
  • •Human-in-the-loop: inbound mail auto-triaged and replied by agent drafts, pending one-click approval
  • •Zero Trust security: protected by Cloudflare Access and scoped OAuth authentication
  • •Instant deployment: one-click Cloudflare deploy button with automatic D1 schema migrations
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Unsloth AI@UnslothAI·18h ago
🚀 Release

Unsloth Ships v0.1.815 & Prebuilt CUDA 13 Wheels: Run Qwen-Image-2.1 Locally with Claude-Compatible Agent Skills

Unsloth AI released version v0.1.815-beta alongside official prebuilt Linux x86_64 CUDA 13 wheels for Flash-Attention 2.8.4, Causal-Conv1D 1.7.0, and Mamba-SSM 2.3.2.post1 targeting PyTorch 2.13/2.14 on Python 3.13. The release enables fast local deployment of Alibaba's Qwen-Image-2.1 across both consumer and datacenter GPUs using Fast FP8 and GGUF quantization (defaulting to Q4_K_M) for combined image generation and precise image editing. Crucially, the Unsloth workspace introduces custom Agent Skills compatible with Claude Code and .agents formats, featuring @skill tagging in chat and 2x faster reasoning blocks (boosting UI rendering from 30 FPS to 60 FPS).

Unsloth Ships v0.1.815 & Prebuilt CUDA 13 Wheels: Run Qwen-Image-2.1 Locally with Claude-Compatible Agent Skills
⚡ Key Takeaways
  • •Official CUDA 13 prebuilt wheels: instant binaries for Flash-Attention 2.8.4 and Mamba-SSM on PyTorch 2.13/2.14, eliminating compilation errors
  • •Local Qwen-Image-2.1 execution: Fast FP8 and GGUF Q4_K_M support for both high-fidelity image generation and interactive image editing
  • •Claude Code skill interoperability: native support for importing and executing custom skills from .agents and Claude Code folders with @skill
  • •60 FPS reasoning blocks: doubled chain-of-thought rendering performance from 30 FPS to 60 FPS with reworked UX
  • •Expanded platform support: in-app Debian upgrades and a dedicated installer for Ubuntu 24.04+ on ARM64 architectures
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unclecode@unclecode·19h ago
🚀 Release

Crawl4AI 0.9.4: three Docker SSRF/env-leak advisories closed; PruningContentFilterLXML ~10× faster

unclecode shipped Crawl4AI v0.9.4 on 2026-09-23 (PyPI crawl4ai 0.9.4). It closes three coordinated advisories: blind SSRF via robots.txt (GHSA-f77g-77vp-r96v), SSRF with response disclosure via link_preview (GHSA-wh5w-hmj3-vgg7, high), and a dict-wrapper trust-boundary bypass that let non-admin clients read server env vars (GHSA-5w5p-vcv6-mm3f, high). A process-wide egress_policy wires the Docker PinningProxy into library HTTP clients. Default pruning switches to lxml-native PruningContentFilterLXML (~10×: 134→13 ms medium page; 2200→260 ms on a 6000-card page). Self-hosted Docker servers should upgrade.

Crawl4AI 0.9.4: three Docker SSRF/env-leak advisories closed; PruningContentFilterLXML ~10× faster
⚡ Key Takeaways
  • •Shipped: v0.9.4 / PyPI 0.9.4; Docker: unclecode/crawl4ai:0.9.4
  • •Security: three GHSAs closed; robots + link_preview SSRF and env-leak via dict laundering; pinning egress proxy
  • •Perf: PruningContentFilterLXML default ~10× faster pruning with byte-identical output
  • •Ops: CRAWL4AI_MAX_TIMEOUT_MS; pool max_pages_before_recycle=200
  • •Install: docs.crawl4ai.com + pip install crawl4ai==0.9.4
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Anthropic@AnthropicAI·19h ago
🛠️ Tooling

Anthropic Python SDK 1.8.0: Claude Opus 5.5, inline tool definitions, MCP tool-list pinning (beta)

Anthropic shipped the official Python SDK v1.8.0 on 2026-09-22 (PyPI anthropic 1.8.0). Versus 1.7.0 it adds `claude-opus-5-5`, inline tool definitions, and MCP tool-list pinning (beta) into client types/runners; fixes a shared `evaluated_permission` enum across Managed Agents events, a Python 3.13 crash when a stream is left open at exit, immediate `add_tools()` effect, and stripping reply-only params from the tool runner compaction request. For stacks already on Opus 5.5 and MCP-managed tools, this is the typed client surface that matches the server features.

Anthropic Python SDK 1.8.0: Claude Opus 5.5, inline tool definitions, MCP tool-list pinning (beta)
⚡ Key Takeaways
  • •Shipped: v1.8.0 / PyPI anthropic 1.8.0
  • •Model id: claude-opus-5-5 on the typed client surface
  • •Tools/MCP: inline tool definitions + MCP tool-list pinning (beta)
  • •Stability: add_tools() applies immediately; Py3.13 open-stream exit fix; compaction omits reply-only params
  • •Install: pip install anthropic==1.8.0; see Client SDKs docs
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Model Context Protocol@modelcontextprotocol·19h ago
🛠️ Tooling

MCP TypeScript @modelcontextprotocol/server 2.1.0: request-time OAuth scopeChallenge + 4 MiB body cap

The official MCP TypeScript SDK shipped `@modelcontextprotocol/[email protected]` on 2026-09-23 (npm). Headline: request-time `scopeChallenge` callbacks on tools, resources, resource templates, and prompts that return the exact scope set for an `insufficient_scope` challenge; `createMcpHandler` and Streamable HTTP answer HTTP 403 before handler/SSE setup. Patches add a default **4 MiB** request body limit and JSON-RPC batch cap of 100, treat request id `0` as a real id, stop sending `notifications/cancelled` for `initialize`, and enforce `Mcp-Name` on modern tasks routes. For Agent gateways wiring MCP to OAuth resource servers, this is a permissions + DoS hardening minor.

MCP TypeScript @modelcontextprotocol/server 2.1.0: request-time OAuth scopeChallenge + 4 MiB body cap
⚡ Key Takeaways
  • •Shipped: @modelcontextprotocol/[email protected] on GitHub + npm
  • •Auth: scopeChallenge/requireScopes (#1624); 403 insufficient_scope per Authorization spec
  • •Hardening: 4MiB body default, batch≤100; request id 0 treated as real
  • •Protocol: no cancel notify on initialize; Mcp-Name checked on modern tasks routes
  • •Docs: MCP architecture + typescript-sdk repo; npm i @modelcontextprotocol/[email protected]
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BerriAI@Berri_AI·1d ago
🛠️ Tooling

LiteLLM 1.102.1: Claude Code safeguards on stable + TypeSafe Jev router/guardrails backported

BerriAI shipped LiteLLM v1.102.1 on 2026-09-23 (PyPI litellm 1.102.1). Versus 1.102.0 it backports Anthropic/Bedrock/Vertex `safeguards`→`safeguard_results` passthrough so Claude Code Auto mode through a LiteLLM gateway can show Auto mode server: Enabled, and cherry-picks seven TypeSafe Jev changes (passthrough, complexity router, relevance compaction guardrail, auto-router classifier, OpenRouter jev-1.13 pricing, etc.) from the 1.103 RC onto `stable/1.102.x`. Docker images keep cosign verify instructions.

LiteLLM 1.102.1: Claude Code safeguards on stable + TypeSafe Jev router/guardrails backported
⚡ Key Takeaways
  • •Shipped: v1.102.1 / PyPI 1.102.1
  • •Claude Code: gateway forwards `safeguards` and returns `safeguard_results` (#42538) so Auto mode server can stay Enabled
  • •TypeSafe Jev: seven PRs backported to stable/1.102.x (#42595)
  • •Deploy: pip install litellm==1.102.1; cosign-verify ghcr.io/berriai/litellm:v1.102.1
  • •Docs: Proxy Quick Start + Anthropic provider pages
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agno-agi@agnoagi·1d ago
🚀 Release

Agno 3.0.11: knowledge-level retrieval pipeline, MMR/Recency rerankers, page migration, cancellation_stage

agno-agi shipped Agno v3.0.11 on 2026-09-23 (PyPI agno 3.0.11). `Knowledge.search()` now runs a knowledge-level retrieval pipeline that can widen candidates before reranking, with new `MMRReranker` (relevance vs diversity) and pgvector-focused `RecencyReranker` (score × exponential time decay); adds hostname migration for indexed page sources (dry-run by default), typed `PageCommandResult` with truncation/completeness metadata, and machine-readable `cancellation_stage` on Run/Team/Workflow outputs. Per-vector-db `reranker` is deprecated in favor of Knowledge-level rerankers.

Agno 3.0.11: knowledge-level retrieval pipeline, MMR/Recency rerankers, page migration, cancellation_stage
⚡ Key Takeaways
  • •Shipped: v3.0.11 / PyPI 3.0.11
  • •Retrieval: knowledge-level pipeline + MMRReranker (#10115)
  • •Recency: RecencyReranker with exponential time decay; page-source migrate dry-run by default
  • •Observability: cancellation_stage enum + typed PageCommandResult metadata
  • •Migration: deprecate vector-db reranker → Knowledge-level; see Knowledge overview docs
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