API AI Tools

tiun. is the AI-native backend for builders, positioned as one system for authentication, payments, a customer database, and analytics. According to the website, tiun gives AI and SaaS companies the backend they need to ship, scale, and grow their business in one unified platform. Rather than assembling a stack of separate services, teams get a single place where user accounts, billing, transactions, and product usage data live together. The product describes itself as the backend powering the AI engineering era, built from an ecosystem of services that are designed to work together from the start. Its stated goal is to remove webhook logic and business logic that developers would otherwise have to write and maintain themselves, so a builder can launch a paid product the same day they start building. The problem tiun addresses is the hidden complexity created by single-purpose tools. When authentication, payments, customer data, and analytics each come from a different provider, teams end up juggling multiple accounts, scattered data, and costs that compound as they scale. Keeping those separate systems in sync requires maintaining business logic purely for the sake of consistency, and that maintenance burden grows alongside the business. The website notes that this fragmentation also makes the insights a company needs harder to reach, because the information required to understand customers, usage, and revenue sits in disconnected places. tiun's answer is an ecosystem of services designed to work together from the start, so there is no webhook logic and no business logic to handle just to keep tools aligned. The way to adopt tiun is described as installing its skills, connecting its MCP endpoint, and letting an AI agent do the hard work. The site provides a single command — npx skills add https://mcp.tiun.business — and links to documentation at docs.tiun.io. This integration path is highlighted by customers on the page: one founding member at Braintonic comments that it worked so well there was no backend, no webhooks, and no custom logic, while a founder describes integrating it for a side project as working like a charm and an absolute no brainer for future solo builders and founders. The significance of this approach is that it shifts setup work away from manual backend engineering and toward an agent-driven installation flow, which lowers the barrier for builders who want working infrastructure without writing and maintaining the usual glue code. tiun's authentication section aims to provide everything needed for user authentication. Sign up, login, and logout are ready to use out of the box, and the platform describes them as simple and secure. Beyond those basics, tiun supplies a User Button and User Profile, giving users a dropdown menu where they can access their account and manage their profile and security settings. Multifactor authentication is included, with SMS passcodes, email, and social SSO listed as supported methods. For a builder, this means the account layer that normally requires careful implementation — credential handling, profile management, and stronger sign-in options — is available as pre-built functionality rather than something to design from scratch. Payments are handled without the need to write payment code or wrangle webhooks. With tiun, builders can create products and billing plans and accept one-time payments, subscriptions, and usage-based billing from day one. Pre-built checkout components can be dropped in as an overlay, so users never leave the page during the purchase flow. tiun also acts as the Merchant of Record: it processes payments, pays out monthly, and includes tax compliance and chargebacks. The site states that this model brings better fees and more functionality, and its example pricing shows transaction fees of 2.9% + $0.30, for a total of roughly 3.4% + $0.30 on international transactions. The third part of the system is a customer database where every user, transaction, and session is stored in one place, with no syncing between tools. User management keeps customers' subscription status up to date and stored alongside their user data, removing the need to build or maintain complex synchronization logic. Advanced event and session tracking logs every login, purchase, and product interaction at profile level, so teams can understand how users move through the product. The same area covers transactional emails for key user actions such as confirmations, password resets, and purchases, invoice history that lets customers view and download receipts and invoices from their profile, and plan management so users can upgrade, downgrade, or cancel directly without a support ticket. Data APIs expose one queryable API built on a consistent model that stays in sync and is ready to plug into an existing stack. Analytics is presented as one system your entire team can work with, so the full picture is finally visible and actionable. All data lives in one place, letting business, engineering, product, and marketing see who is signing up, who is paying, how they use the product, where they get value, and how to price it. Because authentication, billing, and product events share the same underlying model, these questions can be answered from a single source instead of being stitched together across separate tools. The site illustrates the value with a case study: Res Publica reported a 21% increase in paying users and grew its user base by 21% in the last 12 months after introducing usage-based billing with tiun, as described by CEO Martin Stedler. Overall, tiun's approach is to treat authentication, payments, the customer database, and analytics as one connected system rather than four independent products. The website frames this as an ecosystem of services designed to work together from the start, which is why there is no webhook logic and no business logic to handle simply to keep systems in sync. Integration follows an AI-native path: install the skills, connect the MCP endpoint, and let an agent carry out the setup, with the command npx skills add https://mcp.tiun.business and documentation available for reference. Installation is described as one command, and the promise is that a builder can launch a paid product the same day they start building. The benefits follow directly from that consolidation. Teams avoid multiple accounts and scattered data, and they avoid the compounding costs that come with maintaining several single-purpose tools as they scale. Because subscription status, session activity, and transactions all sit with the user record, there is no synchronization logic to build or maintain. Customers can manage their own plans, invoices, and profiles, which reduces the need for support tickets. And because business, engineering, product, and marketing all read from the same data, the insights needed to understand signups, payments, usage, and pricing are reachable rather than buried in disconnected systems. tiun is aimed at AI and SaaS companies and the builders behind them, including solo founders and developers who want to launch a paid product quickly; commenters on the site describe using it for side projects. Typical scenarios reflect the product's shape: adding sign-up, login, and multifactor authentication to a new application; accepting one-time payments, subscriptions, or usage-based billing from day one; offering checkout as an overlay so users stay on the page; giving customers self-service control over plans, receipts, and invoices; and asking who is signing up, who is paying, and how to price the product from a single place. tiun can be tried for free, pricing details are published on its pricing page, and the platform is used through the web as well as its MCP and data APIs. tiun's core value proposition is consolidation: one system that supplies the authentication, payments, customer database, and AI analytics an AI or SaaS business needs, installed with one command and connected through MCP so an agent can do the heavy lifting. By removing webhook and synchronization logic and keeping every user, transaction, and session in one place, tiun promises to help builders ship, scale, and grow from a single backend — and start charging on day one.

Web Search Agents by Nimble are self-learning agents that become experts at your specific research task. They are web crawling and research agents built for a specific domain — company enrichment, regulations research, and other focused use cases — and they crawl the web with surgical accuracy. Instead of returning generic results, the agents self-learn your use case to go deeper into the sources that matter most to you, giving your AI deeper and more relevant web context. The product is aimed at agent builders and teams that need expert-level web search for their AI agents, delivering higher accuracy at a fraction of the token cost. You can start by giving your AI the Nimble agent onboarding link, start building for free, or book a demo with the team. Web search is usually judged on generic benchmarks that do not resemble the queries a real agent builder faces. Nimble evaluates web search by domain instead, because that lets agent builders judge solutions against queries that resemble their own rather than generic benchmarks. Nimble argues that specialized intelligence needs a specialized web search, and invites teams whose domain is not listed to contact the company to see how Web Search Agents adapt to their use case. A second problem is cost: retrieving web context typically means redundant searches and parsing raw pages with an LLM, which consumes tokens. Nimble positions Web Search Agents as a way to retrieve exactly what is needed — with no redundant searches and no parsing of raw pages with an LLM — so teams get expert-level web search for their AI agents with higher accuracy at a fraction of the token cost. Web Search Agents are built to execute hyper-specific research workflows, crawling the web with surgical accuracy for the task at hand. They can also build and enrich web datasets: you define your schema and the agents return consistent results on every run, which makes it practical to assemble structured web data without manual cleanup. A monitoring capability, currently in beta, lets you continuously track any data point on any webpage in real time, so changes on the web surface as they happen. Together these three capabilities — hyper-specific research, schema-driven dataset building, and continuous monitoring — cover the common shapes of web data work an agent needs to perform, from answering a single research question to maintaining a dataset that stays current. Three capabilities underpin how the agents adapt to your use case and self-improve. First, compounding domain knowledge: the agents accumulate web context over time to master your domain, so their understanding of relevant sources grows with use. Second, deep web access for your sources: the agents combine web search with domain crawling to reach subpages that other tools cannot access, which matters when the useful information sits deeper than a top-level page. Third, full control over search methodology: the agents retrieve data within the scope and guardrails defined by your search plan, so you decide what is in bounds. Nimble summarizes this as agents that adapt to your use case and self-improve, rather than behaving the same way for every customer and every query. Governance is part of the design. Web Search Agents operate with full governance and control through auditable Search Plans that show exactly what was searched, where, and why — so you can inspect the path the agent took rather than trusting an opaque set of results. Accuracy compounds over time through a Proprietary Index and Memory that gets smarter with every query, meaning the agents retain and reuse what they have learned. The same retrieval discipline addresses cost: by retrieving exactly what is needed, the system avoids redundant searches and avoids the expense of parsing raw pages with an LLM. These three elements — auditable Search Plans, compounding memory, and precise retrieval — are the core promises Nimble makes for expert-level web search delivered to AI agents. The overall approach is that the agents self-learn your use case. Rather than being configured once and left static, Web Search Agents learn from the searches they run, building a memory and a Proprietary Index that improve the relevance of later results. They combine two access paths — web search and domain crawling — to reach both broad results and the deeper subpages that other tools cannot access. Each retrieval stays inside the scope and guardrails you define for the search plan, and every search is recorded so you can audit what was searched, where, and why. Nimble describes this as specialized intelligence for a specialized web search, and documents an onboarding path so your AI agent can be pointed at the product and begin building. The stated benefits concentrate on accuracy and cost. Nimble says Web Search Agents deliver expert-level web search for your AI agents with higher accuracy at a fraction of the token cost. Because retrieval returns exactly what is needed, there are no redundant searches and no need to parse raw pages with an LLM — two of the main sources of token spend in agentic web research. Accuracy compounds over time as the Proprietary Index and Memory get smarter with every query, so results improve rather than plateau. Control and trust are the other stated outcomes: auditable Search Plans show exactly what was searched, where, and why, and the agents work within the scope and guardrails you set, which makes it easier for teams to explain how a result was produced. Nimble publishes cookbook examples of what teams can build. Company research and due diligence can be run from a single prompt at audit grade. Teams can research case laws and regulations, enrich dependencies with health indicators, and find assortment gaps on the digital shelf. Retail and brand teams can find where products are sold to enforce MAP compliance, and go-to-market teams can discover businesses that match an ideal customer profile. Finance workflows include tracking analyst earnings predictions against actuals, and recruiting workflows include building a dataset of job candidates. Nimble also names the domains it evaluates and adapts to: market analysis, real estate, social media monitoring, travel and hospitality, company research, finance, product intelligence, and GTM. In those benchmarks, contestants independently completed 96 tasks per domain — covering reports, enrichment, and discovery — with each result graded fact-by-fact by an independent AI judge against a gold standard built without any contestant's input. Web Search Agents are aimed at agent builders and teams that need their AI agents to research the web reliably. Nimble says it is trusted by organizations including Databricks, Qudo and Uber under a "Trusted By" heading, and its site also displays a broader logo wall featuring brands such as Microsoft, Coca-Cola, L'Oréal, LG, TripAdvisor, Semrush and Browserbase. Native integrations are offered including Anthropic, GPT, LangChain and Vercel, and the product is documented as a Nimble SDK with an agent onboarding page you can give to your AI to get started. Security and compliance features include zero data retention, flexible PII masking, audit logs, data encryption in transit, and no training, alongside CCPA, GDPR and SOC 2 badges. Nimble invites teams to start building for free, try the product now, or book a demo to discuss use cases and see how Nimble delivers higher accuracy at a fraction of the token cost. For teams building AI agents that need reliable web context, Web Search Agents by Nimble offer a self-learning approach: agents that adapt to your domain, crawl the web with surgical accuracy, build and enrich datasets against your schema, and monitor pages for change. Auditable Search Plans provide governance, while a Proprietary Index and Memory compound accuracy over time and precise retrieval reduces token cost. The result is deeper, more relevant web context for your AI, evaluated by domain against queries that resemble your own rather than generic benchmarks.

Cadenya is a hosted agent runtime that layers tools, agents, and objectives on top of the APIs you already run. Rather than a framework you bolt into your application stack, Cadenya runs the agentic loop for you, so you can build, test, and improve agents without rebuilding your stack. You connect your MCP servers, OpenAPI specs, and existing endpoints through a single tool layer agents can use as they are. From there you define an agent, shape its abilities, and run objectives. It is built for teams that already have working systems and want to add agentic capability on top of them. Teams that want agents in their product usually face an awkward choice: adopt a framework and integrate and maintain it inside their own stack, or build the agentic loop themselves — handling context windows, approvals, event delivery, and model comparison along the way. Cadenya starts from the opposite assumption, that the APIs already exist and already work. Its stated purpose is to layer tools, agents, and objectives on top of the APIs you already run so you can build, test, and improve agents without rebuilding your stack. Because the runtime is hosted and model-agnostic, teams can adopt frontier models fast, test behaviors, compare approaches, and add functionality rather than complexity. You start with your stack. Cadenya connects your MCP servers, OpenAPI specs, and existing endpoints through a single tool layer agents can use, and the documentation states plainly that you do not rewrite your APIs to use it. Tools are connected using specs you already know, so existing endpoints become usable capabilities for agents as they are. Inference is kept separate from that tool layer: Cadenya is model-agnostic, so you point it at OpenRouter or any OpenAI-compatible endpoint and it uses that for inference. Each new account comes with $5 in credits on OpenRouter pre-configured for you, and after that you provide your own LLM provider credentials. That separation is what allows you to swap models, evolve behaviors, and expand capabilities while retaining infrastructure. Defining an agent means assembling concrete building blocks. An agent has assignments — individual tools, tool sets, and sub-agents — shown in the product's interface as items like a reroute shipment tool, an update ETA tool, a dispatch API tool set, and a customs broker sub-agent. It has memory layers, such as a carrier playbook or SLA policies, and a system prompt that describes the agent's role and how it should behave. Agents can dispatch sub-agents, and the model configuration for sub-agents can be changed to best suit the job, which the product describes as the most efficient approach for token usage and outcome. Objectives are then run against that configuration, and each objective keeps its trail. Token usage is managed inside the runtime rather than left to chance. Cadenya provides live token metering so you can stay on top of costs, reduce waste through progressive discovery, and improve efficiency as agents adapt. Progressive tool discovery keeps tool schemas out of the context window until the agent asks for them — only names ride along, so every request gets smaller. It is configurable: you can enable progressive tool discovery, set the maximum number of tools per search, provide search hints such as delays, reroutes, or customs, and set a rerank threshold, which can be left blank to skip reranking. Context compaction is handled out of the box, and context window compaction is also emitted as a webhook event type. Real-time behavior is a first-class part of the runtime. Webhooks and SSE push agent events into your apps as they happen, so downstream services react immediately, and the documentation notes that Cadenya makes it easy to wire agent events into your applications. Every event in your agentic loop is sent to a webhook endpoint you provide; the interface lists event types including assistant message, tool result, tool approval requested, sub-agent spawned, context window compacted, and timed out, each with a delivery status such as HTTP 204 and a completed state. When a tool call needs sign-off, approval-gated tools pause the agent and deliver a tool_approval_requested event so a person or system can approve before anything runs. Cadenya also ships Widgets that can be dropped into any frontend to enable agentic features like conversations and more, alongside SDKs in four languages. Observation and experimentation are built in. Cadenya lets you monitor outcomes and understand how behaviors take shape in the real world, on the premise that clear visibility means your agents show their worth. You can run variations — the interface shows a Default and a Canary side by side with different models, creation dates, assignments, memory layers, and system prompts — which lets you test behaviors and compare approaches without uprooting what already works. Feedback is captured against variations and objectives with sentiment-style scores, so a reroute that happened before an SLA breach scores positively while a case where the agent held at a facility when a reroute was available scores negatively. Every objective keeps its trail: tool calls, webhook deliveries, token usage, and the feedback people leave on the outcome. The benefits follow from that structure. You iterate without uprooting: swap models, evolve behaviors, and expand capabilities while retaining infrastructure. You evolve with what's next by adopting frontier models fast, testing behaviors, and comparing approaches, so the unified runtime lets you add functionality, not complexity. You experiment safely because variations and feedback let you evaluate behavior before and while real objectives run. Costs stay visible through live token metering and progressive discovery. And because agents can talk to your systems in real time and every objective keeps its trail, you can answer the question of what an agent actually did. Concrete workflows run through the product's own material. A freight shipment-exceptions agent watches for stalled deliveries and reroutes them via a dispatch API; the interface describes a system prompt for the shipment-exceptions agent that instructs it to reroute when a delivery stalls. A related feedback comment credits the agent with catching a customs hold and updating the ETA proactively, and another with escalating a frozen-goods lane correctly. Approval-gated tool calls pause for sign-off before running. Webhook deliveries notify an application endpoint as events occur. Widgets embed agentic conversation into a frontend. And canary variations let the same objectives be run against different models for comparison. Cadenya is aimed at developers and teams that already run APIs and want agentic capabilities without a rewrite. Getting started is deliberately short — kick off an agent using APIs you already have, and the first step is signing up. New accounts receive $5 in OpenRouter credits pre-configured, after which you bring your own LLM provider credentials; the team also offers a free month for those who email support@cadenya.com. API documentation is published, SDKs come in four languages, and the product runs as a hosted runtime rather than something you install and maintain in your own stack. Cadenya's value proposition is straightforward: bring agentic possibilities to life on top of the stack you already have. By layering tools, agents, and objectives over your MCP servers, OpenAPI specs, and existing endpoints, keeping inference model-agnostic, and shipping the operational pieces — context compaction, tool approvals, webhooks and SSE, widgets, SDKs, token metering, and observability — it lets teams start with one agent and grow from there.

Desert Ant Labs is building the intelligence layer for every app. Rather than one large model that tries to do everything, the company publishes a family of small, specialized AI models, each of which does one job very well across speech, text, and vision. The models are designed to run on-device — on a phone or in a browser — and are added to any product through one native SDK in just a few lines of code. The company's stated goal is "little brains in every product," giving developers the fastest model for their specific task instead of the overhead of a general-purpose model. The product addresses the cost and dependency that come with cloud-based AI. Because the models run on the device itself, they need no internet connection and involve no per-use or token cost. That matters for two reasons the site calls out directly: developers never have to meter their users, and sensitive data such as personally identifiable information can be filtered on the device instead of being sent away. The company explicitly positions its approach against using one big model for everything, arguing that small models dedicated to a single task deliver the fastest result for that task. Speech, text, and vision each get purpose-built models rather than a single general system. Speech is the deepest area of the model catalog. Voz handles speech recognition, transcribing ten minutes of audio in two seconds on an iPhone. Clear provides speech enhancement for studio sound without a cloud bill. Uhm detects and removes filler words in seconds, which is useful when cleaning up recorded conversations before publishing. Align produces accurate word timestamps for any transcript, even though it is listed more briefly than the others. Ear detects spoken language from 30 seconds of audio, while Tongue identifies language from as little as three words. Together these models cover the path from raw audio to clean, searchable, and well-labelled text, and each one runs on the device, so none of that processing depends on a remote service. On the text side, Gist generates topics and tags for posts and articles, helping content be organised and discovered. Title suggests a title and description for any text, cutting the friction out of publishing. Several models are marked as beta. Schemer performs structured extraction, turning any text into typed JSON, which is directly useful for developers who need machine-readable output from unstructured input. Moderator flags nudity before content is uploaded or displayed, and Toxic is built for hate speech triage, catching hate speech before it posts. Those moderation models are described as running on the device, so content checks happen locally rather than after the fact in the cloud. Vision and media tasks are covered as well. Shapes performs shape recognition, turning a rough sketch into a perfect shape, which suits drawing and design tools where users draw imprecisely and expect clean geometric output. Clips handles clip selection, creating short videos and highlight clips. Emo suggests emoji faster than a user can type, aimed at messaging and social products. Redact filters personally identifiable information on the device, a model the site presents alongside Clear as a way to process sensitive material locally rather than in the cloud. Each of these models is described in one line because each does one narrow job rather than many. The unifying mechanism is a single native SDK. The company describes it as one SDK that drops the models into any product in a few lines of code, which means a developer does not need a different integration for each capability. The models themselves are published on Hugging Face, the SDK is available on GitHub, and documentation is provided separately. Because inference happens on-device, the working method is local execution: the model runs on the user's phone or in their browser rather than calling a remote endpoint. That on-device approach is what removes the need for tokens, logins, and per-use billing from the developer's perspective. The stated benefits follow from that design. Speech enhancement delivers studio sound without a cloud bill; PII redaction keeps sensitive filtering on the device; and transcription is fast enough to process ten minutes of audio in two seconds on an iPhone. Developers can build without metering their users, and the pricing model reinforces this: every model is free up to 100k monthly active devices per platform, with no limit on how often each person runs it. The company frames the outcome simply — build your wildest ideas and best products, and never meter a user. Concrete scenarios follow from the model list. A recording or podcast app can run Voz to transcribe audio and Uhm to strip filler words before publishing, with Align supplying word timestamps for captions or search. A social or community platform can call Moderator before an upload is displayed and Toxic before a comment is posted, checking content on-device. A notes or publishing tool can use Gist for topic tags and Title for suggested titles and descriptions. A drawing app can use Shapes to snap rough sketches into clean shapes, while a messaging app can use Emo for emoji suggestions. A developer pipeline can use Schemer to extract typed JSON from unstructured text, and a privacy-conscious product can run Redact to filter PII locally before data leaves the device. The product is aimed at developers and product teams who are adding speech, text, or vision features to an app and want to avoid cloud costs, per-use token billing, and remote data processing. It is offered as an SDK, with the SDK available on GitHub, documentation on the company's site, and models published on Hugging Face, and it is listed under the topics Artificial Intelligence and SDK. On pricing, every model is free up to 100k monthly active devices per platform, and there is no limit on how often each person runs it. In short, Desert Ant Labs supplies small, task-specific AI models that run on-device and plug into any product through one native SDK. Fast transcription, speech enhancement, on-device redaction, clip selection, and a growing catalog of text and vision models — all free up to 100k monthly active devices per platform — make the pitch simple: the fastest model for the job, with no tokens and no meter.

The Frigade Assist API makes the AI agent you already built an expert in your product. With one tool call, your agent can answer product questions and guide users through workflows, right inside the agent you already built. Frigade presents it simply: you add Frigade in one call, and your agent answers questions and guides users through your product. The API is described as a lightweight SDK and two primitives — you register it as a tool your agent can call in a few lines, and your agent can then run a live product tour or return a grounded product answer. It is aimed at product and engineering teams who have built their own agents and want those agents to know the product. It is also available to teams with no agent yet, since Frigade ships a full in-product assistant that learns your product and guides users in real time, with no code required. The problem Frigade addresses is that most in-app AI agents answer "how do I do this?" with a wall of text, because they cannot see the screen. As Frigade puts it, your agent has read your docs but has never used your product. Written documentation is accurate exactly once; Frigade shows a help center article updated nine months ago, featuring a broken 404 screenshot, telling users to open "Webhooks (formerly Integrations)" and paste a URL. Answers like that go stale the day you ship. The Assist API closes that gap by giving your agent the same product your user is looking at, so it can walk them through the workflow instead of linking a document from two releases ago. Integration is deliberately small. The example in Frigade's documentation defines a single tool, frigade_guide_tool, with a description telling the agent to call it to answer product questions or guide the user through a task, and a query parameter describing what the user is asking or wants to do. The tool's run function calls frigade.assist({ query }), and you add that tool to your agent's existing toolset. It is framework-agnostic by design: it works cleanly with the Vercel AI SDK today, and any agent that can call a tool can call Frigade, regardless of how the agent was built or which models it runs. Your agent keeps its own reasoning and voice, decides when to call Frigade, and decides what to do with the result. Frigade learns your product by using it. It deploys agents that work through your real workflows the way your users do, documenting how each one behaves and taking in your existing knowledge base. The site describes this in three steps: you invite Frigade the way you would a user, with nothing to document or configure first; it works through real workflows, clicking the same paths your users click and mapping how features actually connect; and it re-learns on every release, so the map updates itself and your agent is never a version behind. A visual list of product areas — Security, Retention, Dashboards, Webhooks, Notifications, Environments, Provisioning, Custom fields, Imports, Integrations, Data export, and Members — shows items marked as relearned, moved, mapped, or unchanged after a September release. Guidance is one of the two primitives. Rather than returning text alone, Frigade draws a step-by-step guide right on the page. In an example settings screen, a Frigade panel shows "Step 1 / 3" and instructs the user to "Open Security to manage SSO. Follow the highlight," with the real steps rendered inside your own UI. In another example, while a user adds a webhook, the guide reads: "Add the URL and I'll send a test event to confirm it's live," with a step counter showing 4 of 6 and navigation controls. The Assist API also tells the agent what the user is looking at, which is what allows the guide to be placed in context. Answers are grounded and controlled by your team. Frigade generates answers from how your product works in the current release, so they hold up even when the help center is two releases behind. Your team stays in control: anyone can rate any answer and write the behavior they want instead — no code required — and the change holds from the next conversation onward. An example conversation shows an agent answering whether the Growth plan includes SSO, with a note underneath: "Also mention SAML is on Enterprise only," which is saved without code. Frigade calls this steering: the more your team puts in, the better it gets. Frigade logs every reply your agent gives. You can see every conversation your agent handled through Frigade in the dashboard, in Slack, or over the API. A list of example queries shows how calls resolve: "Does the Growth plan include SSO?" resolved, "How do I connect Slack?" guided, "Our contractor needs API access" guided, "I want to cancel my account" handoff, "Why did my sync fail last night?" guided, and "Delete our workspace and all data" handoff. Insights let you see where users get stuck, where the agent helps, and where it hands off. Frigade also knows its limits: when it cannot help it says so and passes the conversation to your team, returning fast so your agent never stalls or burns latency. Alongside answering and guiding, Frigade can proactively surface the right feature to a user when they would benefit from it — the same idea as Frigade's Suggestions product — helping drive feature adoption and expansion revenue. Underneath the tool call, Frigade describes an entire engine that relearns your product, plus a platform your team manages with no code. Four components are named: the product model, built by using your product and rebuilt every release; grounded answers, written from your actual product rather than just your docs; guidance, the real steps rendered inside your own UI; and steering, where the more your team puts in, the better it gets. The company frames the difference bluntly: it is not just a tool call. The benefits are described throughout. Your agent stops linking stale documents and starts walking users through the workflow. Answers stay current because Frigade relearns on every release, so you do not have to retrain the agent or rewrite prompts. Support, CS, and CX teams own the answers without filing tickets to engineering. Every answer is logged, giving your team visibility in the dashboard or over the API. The agent stays in control of the conversation — Frigade adds product expertise, it never takes over. And when Frigade cannot help, it hands off cleanly. A customer story from Valley reports that Frigade solved over 400 queries a month that would otherwise have gone to support, equivalent to two hires the company did not have to make, paying for itself within the first two months. Typical scenarios appear directly in Frigade's content. A user asks whether the Growth plan includes SSO and gets a grounded answer about turning it on under Settings, then Security. A user needs to add a webhook: the agent starts a guided flow in the app, the user pastes an endpoint URL, and the guide confirms it will send a test event. A user asks how to set up SAML, connect Slack, or grant API access to a contractor, and the agent guides them. A user asks to cancel an account or delete a workspace and all data, and Frigade hands the conversation to the human team. A user asks why a sync failed or why a webhook stopped firing and gets guided help. Morning Consult reported a working prototype deployed in less than a few hours of automated training, able to generate product tours on the fly without manual configuration. Frigade Assist API is used by product and engineering teams who want the agents they built to gain real expertise in their product. Frigade says it is trusted by teams building the best products, naming Sanity, Arc, Merge, Productboard, Legora, Retell, Logicbroker, Hotplate, Spellbook, Perfect Venue, Simplify, and Typewise, and quotes Vercel CEO Guillermo Rauch calling Frigade "mind-blowingly good." On integration, the Vercel AI SDK is supported and the API stays framework-agnostic. On security, Frigade is SOC 2 Type II certified and fully GDPR compliant, with data encrypted in transit with TLS 1.2+ and at rest with AES-256, EU data residency, a zero-retention LLM policy, and automatic PII scrubbing. Guidance runs with the user's own permissions, so the agent only sees what the user can already see, and teams needing full data control can self-host Frigade with their own LLM keys. Pricing starts at $1,000 per month with usage-based scaling, and enterprise plans with custom pricing are available. For teams that have built their own agents, the Frigade Assist API is one tool call that turns those agents into product experts — grounded in the live product, able to draw step-by-step guidance inside your own UI, tunable by your team without code, and able to hand off cleanly when it cannot help. Frigade's own summary puts it simply: give your agent product superpowers.

GoModel is an open-source AI gateway written in Go and released under the MIT license. It places a single OpenAI- and Anthropic-compatible endpoint in front of 31 AI model providers, so applications keep the request shape they already use while the gateway handles authentication, workflow resolution, provider routing, caching, budgets, guardrails, failover, audit logging, and usage tracking behind that endpoint. It is built for engineering teams, platform teams, and developers who want the provider-switching and governance logic that would otherwise leak into application code to live in one self-hosted layer instead. GoModel ships as one small binary with an embedded admin dashboard, so there is nothing else to deploy. The gateway exists to solve a specific set of problems that appear once AI workloads reach production. Without a gateway layer, provider switching, debugging, and usage tracking start leaking into application code, and teams that integrate a provider directly find that switching vendors becomes a code project rather than a configuration change. A single behavior rarely fits every team or every app: one path may need cache, another audit logging, another guardrails. Identical prompts can be paid for twice when duplicate requests are dispatched. Provider dashboards show one aggregate total, which makes it hard to attribute spend to teams, tenants, and features. When a fallback fires during an incident, nobody can reconstruct why it happened. And running a gateway can become its own scaling project if the software sitting on every request is heavy to operate. GoModel moves that logic into one gateway layer so that provider choice is decoupled from the application. GoModel's routing and provider layer covers a broad range of models behind a single endpoint. OpenAI, Anthropic, Gemini, Bedrock, Vertex, Azure, Groq, Ollama, vLLM, and more are supported, with round-robin rotation across multiple keys per provider, and suffixed environment variables that register extra instances of the same provider type. Aliases and virtual models let teams publish stable names such as smart-chat and remap the real provider and model behind them, which is a config change rather than an application change. Load balancing spreads a virtual model across targets with weighted round-robin, or lets cost-based routing pick the cheapest capable model for each request. Automatic failover sends availability errors to the next model or provider, with retries using backoff and a circuit breaker absorbing flaky upstreams. Provider passthrough lets you call any provider's native API through /p/:provider/* while keeping GoModel's auth, usage tracking, and audit on the way through. Control and safety features are configured through scoped workflows. A workflow toggles cache, audit, usage, budgets, guardrails, and failover per provider, model, or user path, and the most specific matching scope wins; workflow versions are immutable so you can see exactly which policy a given request ran under. Guardrails can inject system prompts or rewrite messages with an LLM before dispatch, running as ordered steps that execute in parallel groups. Virtual API keys hand teams managed keys bound to a user path and labels instead of raw provider credentials, and those keys can be revoked and rotated from the admin UI. Rate limits cap request rate and concurrency per user path, provider, or model, and saturated routes are routed around when alternatives exist, returning 429 with Retry-After when they do not. On the cost side, budgets enforce hard spend limits per user path or label, evaluated from tracked usage cost and enforced before a request is dispatched, so the run stops at the cap rather than at the invoice. Response caching works in two ways: exact-match caching returns identical non-streaming requests straight from the gateway with no provider call and no cost, while semantic caching matches similar prompts and is backed by Qdrant, pgvector, Pinecone, or Weaviate. Usage and cost tracking records token and dollar accounting per request, user path, and label, with per-model pricing overrides for when list prices do not match your contract. Cache lookups run after alias and workflow resolution, so policy decisions still apply, and cache hits are visible in the dashboard. Observability and surface area extend well beyond chat completions. Audit logs capture every request with its resolved route, workflow, cache result, and provider attempts, with bodies and headers logged only when explicitly enabled. The embedded admin dashboard shows live request logs, usage breakdowns, keys, budgets, workflows, and provider status without a separate deployment. Request tagging flows labels from headers or key metadata into usage and audit, so spend and incidents map to teams, tenants, and features. Prometheus /metrics exposes request, provider, and circuit-breaker gauges alongside health endpoints and optional pprof profiling, and OpenTelemetry traces and metrics cover every inbound request and provider call on the GenAI semantic conventions, readable by Jaeger, Tempo, Honeycomb, or Datadog as they are. GoModel also serves the full OpenAI surface including chat, embeddings, the Responses API with gateway-managed conversations, files, and batches, plus the Anthropic Messages API with native /v1/messages and token counting, audio and realtime features such as text-to-speech, transcription, and realtime speech over WebSocket and WebRTC, an MCP gateway that aggregates MCP servers behind one endpoint with namespaced tools, and a built-in playground for sending a real request against any model or alias. Overall, GoModel authenticates each request, applies the matching workflow with its guardrails, cache, budgets, and rate limits, and routes it to the right provider with automatic failover, all behind OpenAI- and Anthropic-compatible APIs. Every response records usage and cost, writes an audit trail, and updates the live dashboard, while cache hits return instantly without a provider call. Because the gateway runs as one Go binary with Docker, Compose, and Helm recipes and the admin UI is embedded, deployment is minimal. Storage starts on SQLite with zero setup and moves to PostgreSQL or MongoDB when traffic and retention demand it, using the same binary with a different config. Session keeping makes requests from one conversation or agent task stick to the target and key that served the first, which keeps provider prompt caches warm, audit logs threaded, and cost attributable per session, with zero configuration required. The benefits follow from that architecture. Teams get one stable API that decouples provider choice from the application, so models can be swapped with a config change. Duplicate prompts stop paying full price twice because caching returns them faster and cheaper. Spend becomes attributable per team, tenant, model, and label instead of appearing as a single provider total. Incidents become reconstructable: a fallback that fired can be traced through audit logs and runtime metadata showing the resolved route and provider attempts. Compliance reviews can replay any request, including guardrail versions and full bodies where logging is explicitly enabled. And the gateway itself stays lightweight: a single binary with storage that grows with the workload, so the gateway does not out-scale the application it serves. Concrete workflows show how it is used. A multi-tenant SaaS issues a virtual key per customer, tracks usage by user path, and enforces per-tenant budgets so invoices come from the dashboard rather than guesswork. A platform team publishes aliases like smart-chat with scoped workflows behind them, letting product teams ship features without ever holding provider keys. Production traffic rides failover chains with retries and circuit breakers, turning a provider incident into a routing event instead of a customer-facing one. Caching absorbs duplicate prompts, cost-based routing picks the cheapest capable model, and budgets stop end-of-month surprises without code changes. Compliance reviews replay any request with its resolved route, guardrail versions, provider attempts, and full bodies where logging is explicitly enabled. Developers run Ollama or vLLM locally behind the same endpoint the cloud providers serve in production, so moving from laptop to production is config, not code. GoModel supports 31 providers, including OpenAI, Anthropic, Google Gemini and Vertex AI, Azure OpenAI, Amazon Bedrock, OpenRouter, Cohere, Groq, xAI, DeepSeek, Fireworks AI, Alibaba Bailian, MiniMax, Z.ai, ElevenLabs, Ollama, vLLM, and any OpenAI-compatible backend registered as its own instance. Hundreds of models are read from live provider catalogs, and connection is typically a single environment variable. Deployment options include a one-command binary install for macOS, Linux, and Windows, a compressed 14.4 MB Docker image, Docker Compose, and Kubernetes with a Helm chart. The gateway is MIT licensed, and GoModel Pro is a commercial distribution that adds prompt compression, OIDC single sign-on, per-child quota templates, and intelligent routing for $4,999 per year or $499 per month, flat per company, with a 30-day money-back guarantee and an offline signed license token. A roadmap toward v0.2.0 tracks remaining work such as plugins starting with guardrails, guardrails hardening with custom and response-side guardrails, passthrough for every provider, and failover and streaming for image endpoints. In short, GoModel is an open-source AI gateway that collapses provider routing, governance, cost control, and observability into a single small binary with one OpenAI- and Anthropic-compatible endpoint, giving teams a self-hosted alternative to OpenRouter and LiteLLM without running a Python service on the hot path.

OpenStatus is an open-source uptime and synthetic monitoring platform. Its main purpose is to monitor APIs and websites globally and showcase reliability with a public status page. Key features include global uptime and synthetic monitoring for APIs and websites. It provides a public status page to showcase service reliability. The platform is open-source, allowing for community contributions and transparency. It offers a free tier to get started. For MCP servers specifically, the tool performs a true protocol-level check, acting exactly like a real AI client. It goes beyond basic HTTP pings to validate the actual JSON-RPC handshake required for AI agents to function. The MCP Health Checker provides deep visibility by inspecting exact JSON-RPC payloads and negotiated versions. It offers smart authentication parsing, analyzing RFC 9728 headers on 401 responses to surface exact token requirements. This helps developers debug connectivity issues that standard uptime monitors would miss. The product benefits developers and teams building and maintaining MCP servers by ensuring their endpoints are truly functional for AI clients like Claude Desktop or Cursor. It solves the common problem where a server returns a 200 OK HTTP status but fails during the JSON-RPC handshake, which breaks AI agents. Target users are developers working with the Model Context Protocol (MCP) and AI agents. It is particularly useful for those building MCP servers who need to ensure protocol compliance and real client connectivity. The tool is open-source and integrates with the broader OpenStatus synthetic monitoring platform.

Wingbits AI is a platform that enables users to create AI agents for real-time monitoring of airspace activity and receive alerts when specific events occur. It is powered by an independent global network of over 5,600 antennas across 120 countries, processing terabytes of ADS-B data daily to provide insights into aircraft movements, including military, private, and government jets. Key features include the ability to ask questions in plain English about current flights, such as "where is Air Force One right now?" or "Which private jets visited Davos last weekend?". Users can create monitoring agents that send alerts to platforms like Slack, email, Telegram, or Teams when criteria are met. The platform also offers scheduled reports and analysis on topics like competitor routes and can compare GPS jamming events across different regions. The system is built on a proprietary data infrastructure that ingests and cleans approximately 3TB of raw ADS-B data daily with under 1-second latency. It deduplicates and processes transponder messages before agents query the clean data. The platform identifies common query patterns and pre-aggregates relevant data to enable efficient querying over longer time windows. Benefits include extracting geopolitical or operational insights from aviation data without requiring a data science team. Use cases include tracking military aircraft, monitoring private jet movements for competitive analysis or news reporting, detecting GPS jamming spikes, and receiving alerts about specific aircraft like those of traveling friends or family members. The platform helps users get fewer, higher-confidence alerts by using alert history to determine if something has meaningfully changed. The product is designed for reporters, prediction markets, competitive analysts, route planners, and aviation enthusiasts. It integrates with communication tools like Slack, Teams, Telegram, and email for alert delivery. The underlying technology includes a global antenna network for data collection and real-time stream processing to handle high-frequency event streams with low latency.

21st Agents SDK is the fastest way to add an AI agent to your app. It provides built-in UI, chat history, spend limits, tool execution, memory, and observability.

Anything API turns any browser work into production-ready APIs by creating custom functions that call websites directly. Describe your task and get a deployable API endpoint.