AnyFrame is a platform designed for every agent your team builds, enabling you to spin up swarms of agents in minutes for any use case on any harness. It bridges the gap between internal tooling and customer-facing products by providing a unified environment where agents bind templates to runtimes like Claude Code, Codex, or Cursor. Ideal for teams that ship software together—engineering, product, design, and data professionals—AnyFrame's core value lies in its flexibility and ease of deployment. With a simple interface and SDK integration, teams can quickly create agents that live in the tools they already use, from Slack to GitHub to Linear. This makes it a go-to platform for those seeking to automate complex workflows without reinventing infrastructure.
The concrete problem AnyFrame solves is the fragmentation of agent development and deployment across multiple tools and platforms. Teams often build custom agents but then struggle to integrate them into daily workflows, manage different runtimes, or ensure they can act reliably. AnyFrame eliminates these pain points by offering a single platform that abstracts away the underlying harness and connector details. Instead of wiring up each agent individually, users define templates and select runtimes and connectors in a few clicks. This matters because it reduces the time from idea to production agent from weeks to minutes, allowing teams to focus on the logic and outcomes rather than the plumbing. The result is a seamless experience where agents become true team members.
First major feature group: harness flexibility. AnyFrame supports multiple harnesses including Claude Code, Codex, Cursor, OpenCode, and Gemini CLI, with managed agents coming soon for Claude and Gemini. The platform allows users to switch between these harnesses at any time while everything else remains unchanged—agents, connectors, and triggers all persist. This is implemented by binding an agent template to a runtime; the template defines the agent's behavior and tools, while the runtime is the execution engine. The benefit is that teams are not locked into a single AI provider or toolchain. They can experiment with different models and paradigms without rebuilding their agents. For example, a team using Claude Code can switch to Codex if they need different capabilities, with zero code changes.
Second major feature group: triggered workflows where work happens. AnyFrame integrates deeply with communication and project management tools such as Slack, Discord, Linear, GitHub, and Jira. Agents can be triggered by a message, ticket, or PR comment, making them responsive to real-time events. For instance, tagging @anyframe on a Slack message in #deploys can immediately trigger an agent to roll back a deploy. Similarly, a comment on a GitHub PR can invoke an agent to write tests or review code. The agent processes the request in a sandboxed environment and reports back with results. This feature transforms agents from passive chatbots into active contributors that handle tasks directly within the tools teams already use daily, reducing context switching and accelerating response times.
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Third major feature group: browser-based "hands and eyes" capability. Not just tool calls, but agents can click through a real browser like a person, operating on any web interface without requiring an API. This is showcased in the product with an example: an agent upgrading a customer's plan by clicking through a billing UI, no API needed. This feature is crucial for tasks that involve legacy systems, third-party SaaS platforms, or any interface lacking robust APIs. The agent sees the screen, interacts with elements, and performs actions as a human would. It even shows a live desktop view. This expands the range of automation possibilities to include UI-based workflows that were previously impossible to automate, giving teams a powerful way to handle any web-based task.
How the product works overall: Agents are created by binding a template to a runtime. Templates come bundled with connectors like PostHog, Slack, and skills. Users define the agent's name, select a template, and pick a runtime—Claude Code or Codex, for example. Once created, agents run in sandboxed environments (fresh Ubuntu + repo, 4 vCPU) that ensure security and isolation. Sessions are triggered manually via messages or automatically via schedule or webhook. The agent executes its task, using tools and browser when needed, and returns a summary. AnyFrame also provides an SDK (Python and TypeScript) for embedding agents directly into a product's backend, allowing customer-facing agent capabilities with just a few lines of code.
Concrete use cases and outcomes include rolling back a deployment directly from a Slack message, fixing high-latency issues by ingesting a Linear ticket with a flamegraph attached, writing tests for new edge cases from a GitHub PR comment, and updating a marketing site's hero and testimonial section based on a Slack request. In each case, the agent performs the work autonomously, producing real results such as a live preview URL or a commit. The outcomes are faster incident response, reduced manual coding, and more efficient collaboration. Teams report that agents handle routine tasks, freeing humans to focus on higher-level decisions. These scenarios demonstrate AnyFrame's ability to turn natural language instructions into completed work products.
Target users are software-shipping teams: engineering, product, design, and data professionals who already work in Slack, Linear, GitHub, and similar tools. AnyFrame is built for both internal tooling and customer-facing products, with an SDK that runs the runtime while you write the integration. Pricing starts with a free tier offering 500 credits (no card required), enough for several full sessions. Pay-as-you-go continues after that. SSO, self-hosting, and custom SLAs are available for enterprise customers via scheduling a call. An open-source version is coming soon. AnyFrame positions itself as the platform that lets teams build agents that do the work, not just suggest it, reinforcing its core value of making every agent your team builds actionable and integrated.
AnyFrame is built for software teams—engineers, product managers, designers, and data professionals—who collaborate in tools like Slack, Linear, and GitHub. It is ideal for teams that want to automate complex workflows without switching contexts, whether for internal operations or customer-facing features. Engineering leads, platform teams, and AI/ML engineers will find it especially valuable for rapid agent deployment. The platform serves companies of all sizes, from startups to enterprises, looking to integrate AI agents into their daily processes.