cloudflareAug 7, 2026

Introducing Radar Researcher: An AI tool for exploring Internet data in plain language (opens in new tab)

Cloudflare Radar Researcher is an AI-powered assistant that lets users explore Radar’s Internet data through natural-language questions. It replaces manual API queries, filters, and chart hunting with interactive visualizations, explanations, follow-up questions, and auditable analysis. The beta tool is designed for both casual users and technical researchers, while grounding responses in real data from Radar’s API. ## Why Cloudflare Built Radar Researcher - Cloudflare Radar publishes global Internet data covering: - DNS queries from the 1.1.1.1 resolver - HTTP traffic across Cloudflare’s network - Internet quality measurements from Cloudflare Speed Test - Network outages and other datasets - Radar serves a wide audience, from journalists and curious users to network operators and academics. - AI lowers the barrier to using complex datasets by allowing users to ask questions without knowing a dataset’s structure, terminology, or API. - The tool is especially useful for people working under time pressure, such as journalists investigating current Internet disruptions. ## Natural-Language Data Exploration - Radar Researcher is available from every Radar page in a docked panel. - Users can: - Ask questions in plain language. - Receive short answers or more detailed reports. - View real, interactive Radar charts alongside explanations. - Continue with suggested follow-up questions. - Use text, voice input, or Radar’s search bar. - Conversations are saved in searchable history, can be pinned, and can be shared through links that expire after 30 days. - Users can inspect how the assistant interpreted their question, which datasets it queried, and how it derived its answer. ## Explaining Existing Charts - An “Explain with AI” action lets users start a conversation from any Radar visualization. - The assistant receives: - A screenshot of the chart for visual context. - The exact underlying data from Radar’s API. - The current location, date range, and applied filters. - This combination lets the model explain the exact chart being viewed while preserving numerical accuracy and context. ## Example: Internet Quality in Portugal - A user can ask about home Internet quality in Portugal without manually selecting API parameters or searching Radar pages. - Researcher queries the Internet quality API and presents the results through familiar interactive charts. - Users can ask follow-up questions, such as comparing Portugal with Spain or another neighboring country. - The assistant can also suggest related investigations, including common Internet outages. ## Example: Investigating an Internet Shutdown - Researcher can support more open-ended investigations for engineers, researchers, journalists, and network operators. - For Iran’s 2026 government-directed shutdowns, it: - Retrieved recorded outage events. - Collected corresponding HTTP traffic data. - Explained the timeline in natural language. - The analysis described traffic falling from approximately 0.58 on January 7 to nearly zero by January 9, partially recovering around January 17, and approaching normal levels by January 27. - It displayed the findings as an annotated traffic chart and a table of outages, including another shutdown beginning February 28. - Suggested follow-ups included comparing Iran’s traffic with neighboring countries. ## Cloudflare’s Technical Implementation - The application runs entirely on Cloudflare’s developer platform. - A Cloudflare Worker uses the Cloudflare Agents SDK. - Each conversation is stored in a stateful Durable Object with its own SQLite database. - Chat history, titles, and streaming responses persist even if users leave the page during generation. - Workers AI runs open models, including Kimi K2.7. - An ordered fallback chain across three model families helps maintain availability if one model provider reaches capacity. - Requests are routed through AI Gateway. Radar Researcher makes Cloudflare’s extensive public datasets easier to use without sacrificing precision or transparency. It is particularly valuable when users need fast, context-specific analysis backed by interactive charts and verifiable source data.

cloudflare-agents-sdkllmcloudflare-workersdurable-objects+3

cloudflareAug 6, 2026

The next generation of MCP (opens in new tab)

MCP’s latest specification makes the protocol fully stateless, removing the session management and persistent connections that complicated remote deployments. Requests now carry the information they need, enabling MCP servers to run on simpler request-scoped infrastructure such as serverless Workers. The update also redesigns elicitation, improves HTTP observability, and strengthens authorization practices. ## MCP Becomes Stateless - Earlier MCP implementations required an `initialize`/`initialized` handshake and an `Mcp-Session-Id` for subsequent requests. - Stateful sessions created operational challenges: - Sticky-session routing - Open-stream management - Session migration during deployments - Reconnection problems when instances disappeared - The new protocol removes: - The mandatory handshake - `Mcp-Session-Id` - Protocol sessions from the core request path - Each request includes its protocol version, client identity, and capabilities. - `server/discover` is available for optional server inspection. - MCP servers can now execute a request and return its result without storing protocol state. - Cloudflare’s `McpAgent` is no longer required for MCP itself; developers can use `createMcpHandler` and deploy on Workers. - Durable Objects remain useful when the application—not MCP—requires state, persistence, or real-time coordination. ## Elicitation Uses Multi Round-Trip Requests - Elicitation allows servers to request additional information or approval, such as confirming a deployment or refund. - Previously, `elicitation/create` depended on an open stream, adding timeout, scaling, and infrastructure complexity. - The new Multi Round-Trip Request model works as follows: - The server returns an `input_required` result. - The client gathers the user’s response. - The client retries the operation with the requested input. - This is a breaking change from the previous approach but removes the need to preserve a transport session. ## HTTP Infrastructure Can Inspect MCP Requests - Streamable HTTP requests now include `Mcp-Method` and `Mcp-Name` headers. - Gateways, rate limiters, and web application firewalls can identify operations such as `tools/call` without parsing JSON-RPC bodies. - Operators can apply method-specific policies and collect tool-level metrics using standard HTTP infrastructure. - Results from `tools/list`, `prompts/list`, `resources/list`, and `resources/read` can include: - `ttlMs` hints - `cacheScope` hints - Deterministically ordered tool catalogs help clients reuse results and maintain stable prompt caches. ## Authorization Continues to Evolve - The specification prioritizes authorization approaches in this order: - Pre-registered clients when an established relationship exists - Client ID Metadata Documents (CIMD) for dynamic registration - Dynamic Client Registration (DCR) as a fallback - DCR is deprecated for new implementations, although the provided text ends before describing the full authorization changes. The new MCP specification significantly reduces deployment complexity by making the protocol request-oriented rather than session-oriented. Developers should migrate from `McpAgent` to `createMcpHandler` where application state is unnecessary, adopt MRTR for elicitation, and update HTTP and authorization integrations to use the new headers and registration guidance.

cloudflare-agents-sdkmodel-context-protocolcloudflare-workersdurable-objects+2

cloudflareJun 17, 2026

Bringing more agent harnesses to Cloudflare, starting with Flue (opens in new tab)

Cloudflare argues that production AI agents need more than an agent harness: they require platform primitives for durable state, execution, storage, and secure compute. It presents a three-layer stack—framework, harness, and runtime—and introduces Flue as the first framework built on the Cloudflare Agents SDK. Flue uses a declarative approach based on Pi, while Cloudflare supplies the infrastructure needed to resume interrupted work and run agents reliably at scale. ## The Three-Layer Agent Stack - **Framework — Flue** - Provides project structure, conventions, integrations, CLI commands, and developer experience. - **Harness — Pi or Project Think** - Runs the agentic loop: calls tools, processes results, manages context, and continues until a task is complete. - **Runtime/platform — Cloudflare Agents SDK** - Supplies compute, state, storage, durable execution, sandboxing, and workflow primitives. - Cloudflare’s goal is to make these runtime capabilities available to any harness or framework. ## Flue’s Declarative Agent Model - Flue 1.0 Beta is built on the Pi harness, which also powers OpenClaw. - Developers describe what an agent knows rather than explicitly scripting its orchestration. - An agent is defined through its: - Model - Skills - Sandbox - Instructions - This allows relatively compact agents to autonomously handle tasks such as reproducing and diagnosing bug reports. ## Flue’s Developer Experience - **Integrated channels** - Preconfigured integrations let agents work in Slack, GitHub, Linear, and Discord. - Channels handle event verification and dispatch boilerplate. - **Headless and UI-ready operation** - Agents can run as background processes. - `@flue/react` provides hooks for streaming agent state, tool execution, and messages into frontend applications. - **Ecosystem integrations** - Commands such as `flue add channel slack` generate Markdown blueprints that coding agents can modify and integrate into a project. ## Durable Execution with Durable Streams - Production agents face host crashes, LLM API timeouts, restarts, and interrupted tool calls. - Flue records prompts, tool responses, model decisions, and other execution events in an append-only log. - This durable event history prevents in-memory state from being lost. - If a process fails, another process can replay the log and resume from the exact point of interruption. ## Deployment Across Clouds - On Node.js, Flue agents run as long-lived processes on VMs, containers, GitHub Actions, or existing servers. - On Cloudflare, each agent runs in its own Durable Object. - This provides: - Isolated storage and compute - Automatic scaling - No need to provision servers or manage sticky sessions - Protection from noisy neighbors - Cloudflare deployments use Agents SDK features including `runFiber()`, `stash()`, and `onFiberRecovered()` for durable execution. - Sandboxed code execution uses `@cloudflare/codemode` and `@cloudflare/shell` with a durable workspace. ## Requirements for Production Agent Harnesses - An agent turn is a multi-step process that may involve token streaming, tool calls, human approval, or delegated subagents. - These operations can last seconds or minutes and may fail at any point. - Persisting only conversation history is insufficient because it does not preserve active execution state, pending tool calls, or the agent’s current position. - Cloudflare’s fiber-based primitives provide checkpointing so interrupted agent turns can recover instead of leaving users with stalled requests. Cloudflare’s recommendation is to treat the framework, harness, and runtime as separate but coordinated layers. Frameworks like Flue make agents easy to build, while the Agents SDK supplies the durable execution and infrastructure primitives required to operate them reliably in production.

cloudflare-agents-sdkdurable-objectsdurable-executionflue+3