How can I get started with iCall
Hi all . Recently, I want to learn how to use iCall to do some automated operations work, but I haven't seen any comprehensive tutorials about iCall on askf5. Are there any good articles I can refer to for learning? Do I need to systematically learn Tcl first? I still have a question about iCall. What is the difference between using iCall and using shell scripts with scheduled tasks to achieve automated management and configuration of F5? Best RegardsF5 Distributed Cloud (XC) Custom Routes: Capabilities, Limitations, and Key Design Considerations
This article explores how Custom Routes work in F5 Distributed Cloud (XC), why they differ architecturally from standard Load Balancer routes, and what to watch out for in real-world deployments, covering backend abstraction, Endpoint/Cluster dependencies, and critical TLS trust and Root CA requirements.I Tried to Beat OpenAI with Ollama in n8n—Here’s Why It Failed (and the Bug I’m Filing)
Hey, community. I wanted to share a story about how I built the n8n Labs workflow. It watches a YouTube channel, summarizes the latest videos with AI agents, and sends a clean HTML newsletter via Gmail. In the video, I show it working flawlessly with OpenAI. But before I got there, I spent a lot of time trying to copy the same flow using open source models through Ollama with the n8n Ollama node. My results were all over the map. I really wanted this to be a great “open source first” build. I tried many local models via Ollama, tuned prompts, adjusted parameters, and re‑ran tests. The outputs were always unpredictable: sometimes I’d get partial JSON, sometimes extra text around the JSON. Sometimes fields would be missing. Sometimes it would just refuse to stick to the structure I asked for. After enough iterations, I started to doubt whether my understanding of the agent setup was off. So, I built a quick proof inside the n8n Code node. If the AI Agent step is supposed to take the XML→JSON feed and reshape it into a structured list—title, description, content URL, thumbnail URL—then I should be able to do that deterministically in JavaScript and compare. I wrote a tiny snippet that reads the entries array, grabs the media fields, and formats a minimal output. And guess what? Voila. It worked on the first try and my HTML generator lit up exactly the way I wanted. That told me two things: one, my upstream data (HTTP Request + XML→JSON) was solid; and two, my desired output structure was clear and achievable without any trickery. With that proof in hand, I turned to OpenAI. I wired the same agent prompt, the same structured output parser, and the same workflow wiring—but swapped the Ollama node for an OpenAI chat model. It worked immediately. Fast, cheap, predictable. The agent returned a perfectly clean JSON with the fields I requested. My code node transformed it into HTML. The preview looked right, and Gmail sent the newsletter just like in the demo. So at that point, I felt confident the approach was sound and the transcript you saw in the video was repeatable—at least with OpenAI in the loop. Where does that leave Ollama and open source models? I’m not throwing shade—I love open source, and I want this path to be great. My current belief is the failure is somewhere inside the n8n Ollama node code path. I don’t think it’s the models themselves in isolation; I think the node may be mishandling one or more of these details: how messages are composed (system vs user). Whether “JSON mode” or a grammar/format hint is being passed, token/length defaults that cause truncation, stop settings that let extra text leak into the output; or the way the structured output parser constraints are communicated. If you’ve worked with local models, you know they can follow structure very well when you give them a strict format or grammar. If the node isn’t exposing that (or is dropping it on the floor), you get variability. To make sure this gets eyes from the right folks, my intent is to file a bug with n8n for the Ollama node. I’ll include a minimal, reproducible workflow: the same RSS fetch, the same XML→JSON conversion, the same agent prompt and required output shape, and a comparison run where OpenAI succeeds and Ollama does not. I’ll share versions, logs, model names, and settings so the team can trace exactly where the behavior diverges. If there’s a missing parameter (like format: json) or a message-role mix‑up, great—let’s fix it. If it needs a small enhancement to pass a grammar or schema to the model, even better. The net‑net is simple: for AI agents inside n8n to feel predictable with Ollama, we need the node to enforce reliably structured outputs the same way the OpenAI path does. That unlocks a ton of practical automation for folks who prefer local models. In the meantime, if you’re following the lab and want a rock‑solid fallback, you can use the Code node to do the exact transformation the agent would do. Here’s the JavaScript I wrote and tested in the workflow: const entries = $input.first().json.feed?.entry ?? []; function truncate(str, max) { if (!str) return ''; const s = String(str).trim(); return s.length > max ? s.slice(0, max) + '…' : s; // If you want total length (including …) to be max, use: // return s.length > max ? s.slice(0, Math.max(0, max - 1)) + '…' : s; } const output = entries.map(entry => { const g = entry['media:group'] ?? {}; return { title: g['media:title'] ?? '', description: truncate(g['media:description'], 60), contentUrl: g['media:content']?.url ?? '', thumbnailUrl: g['media:thumbnail']?.url ?? '' }; }); return [{ json: { output } }]; That snippet proves the data is there and your HTML builder is fine. If OpenAI reproduces the same structured JSON as the code, and Ollama doesn’t, the issue is likely in the node’s request/response handling rather than your workflow logic. I’ll keep pushing on the bug report so we can make agents with Ollama as predictable as they need to be. Until then, if you want speed and consistency to get the job done, OpenAI works great. If you’re experimenting with open source, try enforcing stricter formats and shorter outputs—and keep an eye on what the node actually sends to the model. As always, I’ll share updates, because I love sharing knowledge—and I want the open-source path to shine right alongside the rest of our AI, agents, n8n, Gmail, and OpenAI workflows. As always, community, if you have a resolution and can pull it off, please share!
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I am trying to check for live updates of attack signatures in F5, but I am getting a message. In passive devices, the signature list does not display — it keeps loading and never shows the updated signatures. Has the destination or location of the signature updates changed in version 17?ACME DNS RFC-2136 Let's Encrypt certs
I've been pushing on certbot to handle CNAME entries when ordering certs, and finally given up. https://github.com/certbot/certbot/issues/6787 https://github.com/certbot/certbot/pull/9970 https://github.com/certbot/certbot/pull/7244 This repo contains scripts that: Create an ACME account with Let's Encrypt use TSIG credentials to talk to bind (RFC-2136) create TXT record in correct zone by following CNAME and SOA entries if present downloads certs installs certs on one or more F5s. The F5 credentials requires Administrator rights as Certificate Manager can't upload files. https://github.com/timriker/certmgr CNAME records are recommended to a zone with minimal or no replication and a low TTL. ie: _acme-challenge.example.com CNAME example.com._tls.example.com _acme-challenge.example.net CNAME example.net._tls.example.com _tls.example.com would have one name server and 30 second TTL or so a TSIG key would be created that only needs update access to _tls.example.com Comments welcome. JRahm I'm looking at you. 😎 More info: https://letsencrypt.org/docs/challenge-types/Modernizing F5 Platforms with Ansible
I’ve been meaning to publish this article for some time now. Over the past few months, I’ve been building Ansible automation that I believe will help customers modernize their F5 infrastructure. This especially true for those looking to migrate from legacy BIG-IP hardware to next-generation platforms like VELOS and rSeries. As I explored tools like F5 Journeys and traditional CLI-based migration methods, I noticed a significant amount of manual pre-work was still required. This includes: Ensuring the Master Key used to encrypt the UCS archive is preserved and securely handled Storing UCS, Master Key and information assets in a backup host Pre-configuring all VLANs and properly tagging them on the VELOS partition before deploying a Tenant OS To streamline this, I created an Ansible Playbook with supporting roles tailored for Red Hat Ansible Automation Platform. It’s built to perform a lift-and-shift migration of a F5 BIG-IP configuration from one device to another—with optional OS upgrades included. In the demo video below, you’ll see an automated migration of a F5 i10800 running 15.1.10 to a VELOS BX110 Tenant OS running 17.5.0—demonstrating a smooth, hands-free modernization process. Currently Working Velos Velos Controller/Partition running (F5OS-C 1.8.1) - which allows Tenant Management IP to be in a different VLAN Migrates a standalone F5 BIG-IP i10800 to a VELOS BX110 Tenant OS VLAN'ed Source tenant required (Doesn’t support non-vlan tenants) rSeries Shares MGMT IP with the same subnet as the Chassis Partition. Migrates a standalone F5 BIG-IP i10800 to a R5000 Tenant OS VLAN'ed Source tenant required (Doesn’t support non-vlan tenants) Handles: Configuration and crypto backup UCS creation, transfer, and validation F5OS System VLAN Creation, and Association to Tenant - (Does Not manage Interface to VLAN Mapping) F5 OS Tenant provisioning and deployment inline OS upgrades during the migration Roadmap / What's Next Expanding Testing to include Viprion/iSeries (Using VCMP) Tenant Testing. Supporting hardware-to-virtual platform migrations Adding functionality for HA (High Availability) environments Watch the Demo Video View the Source Code on GitHub https://github.com/f5devcentral/f5-bd-ansible-platform-modernization This project is built for the community—so feel free to take it, fork it, and expand it. Let’s make F5 platform modernization as seamless and automated as possible.
BIG-IP Next Edge Firewall CNF for Edge workloads
Introduction The CNF architecture aligns with cloud-native principles by enabling horizontal scaling, ensuring that applications can expand seamlessly without compromising performance. It preserves the deterministic reliability essential for telecom environments, balancing scalability with the stringent demands of real-time processing. More background information about what value CNF brings to the environment, https://community.f5.com/kb/technicalarticles/from-virtual-to-cloud-native-infrastructure-evolution/342364 Telecom service providers make use of CNFs for performance optimization, Enable efficient and secure processing of N6-LAN traffic at the edge to meet the stringent requirements of 5G networks. Optimize AI-RAN deployments with dynamic scaling and enhanced security, ensuring that AI workloads are processed efficiently and securely at the edge, improving overall network performance. Deploy advanced AI applications at the edge with the confidence of carrier-grade security and traffic management, ensuring real-time processing and analytics for a variety of edge use cases. CNF Firewall Implementation Overview Let’s start with understanding how different CRs are enabled within a CNF implementation this allows CNF to achieve more optimized performance, Capex and Opex. The traditional way of inserting services to the Kubernetes is as below, Moving to a consolidated Dataplane approach saved 60% of the Kubernetes environment’s performance The F5BigFwPolicy Custom Resource (CR) applies industry-standard firewall rules to the Traffic Management Microkernel (TMM), ensuring that only connections initiated by trusted clients will be accepted. When a new F5BigFwPolicy CR configuration is applied, the firewall rules are first sent to the Application Firewall Management (AFM) Pod, where they are compiled into Binary Large Objects (BLOBs) to enhance processing performance. Once the firewall BLOB is compiled, it is sent to the TMM Proxy Pod, which begins inspecting and filtering network packets based on the defined rules. Enabling AFM within BIG-IP Controller Let’s explore how we can enable and configure CNF Firewall. Below is an overview of the steps needed to set up the environment up until the CNF CRs installations [Enabling the AFM] Enabling AFM CR within BIG-IP Controller definition global: afm: enabled: true pccd: enabled: true f5-afm: enabled: true cert-orchestrator: enabled: true afm: pccd: enabled: true image: repository: "local.registry.com" [Configuration] Example for Firewall policy settings apiVersion: "k8s.f5net.com/v1" kind: F5BigFwPolicy metadata: name: "cnf-fw-policy" namespace: "cnf-gateway" spec: rule: - name: allow-10-20-http action: "accept" logging: true servicePolicy: "service-policy1" ipProtocol: tcp source: addresses: - "2002::10:20:0:0/96" zones: - "zone1" - "zone2" destination: ports: - "80" zones: - "zone3" - "zone4" - name: allow-10-30-ftp action: "accept" logging: true ipProtocol: tcp source: addresses: - "2002::10:30:0:0/96" zones: - "zone1" - "zone2" destination: ports: - "20" - "21" zones: - "zone3" - "zone4" - name: allow-us-traffic action: "accept" logging: true source: geos: - "US:California" destination: geos: - "MX:Baja California" - "MX:Chihuahua" - name: drop-all action: "drop" logging: true ipProtocol: any source: addresses: - "::0/0" - "0.0.0.0/0" [Logging & Monitoring] CNF firewall settings allow not only local logging but also to use HSL logging to external logging destinations. apiVersion: "k8s.f5net.com/v1" kind: F5BigLogProfile metadata: name: "cnf-log-profile" namespace: "cnf-gateway" spec: name: "cnf-logs" firewall: enabled: true network: publisher: "cnf-hsl-pub" events: aclMatchAccept: true aclMatchDrop: true tcpEvents: true translationFields: true Verifying the CNF firewall settings can be done through the sidecar container kubectl exec -it deploy/f5-tmm -c debug -n cnf-gateway – bash tmctl -d blade fw_rule_stat context_type context_name ------------ ------------------------------------------ virtual cnf-gateway-cnf-fw-policy-SecureContext_vs rule_name micro_rules counter last_hit_time action ------------------------------------ ----------- ------- ------------- ------ allow-10-20-http-firewallpolicyrule 1 2 1638572860 2 allow-10-30-ftp-firewallpolicyrule 1 5 1638573270 2 Conclusion To conclude our article, we showed how CNFs with consolidated data planes help with optimizing CNF deployments. In this article we went through the overview of BIG-IP Next Edge Firewall CNF implementation, sample configuration and monitoring capabilities. More use cases to cover different use cases to be following. Related content F5BigFwPolicy BIG-IP Next Cloud-Native Network Functions (CNFs) CNF Home
