AnyFrame is a platform for every agent your team builds, designed for engineering, product, and operations teams that need to deploy AI agents across multiple contexts. It allows teams to spin up swarms of agents in minutes for any use case on any harness, whether for internal tools or customer-facing products. The core value lies in its abstraction: agents are built by binding templates—like PostHog or Slack—to runtimes such as Claude Code, Cursor, Codex, and others. This makes AnyFrame a unified control plane for agent deployment, eliminating the need to manage each runtime separately. Teams get a single interface to create, monitor, and update agents that work seamlessly across their stack.
The concrete problem AnyFrame solves is the fragmentation of AI agent tools across different platforms and workflows. Teams often struggle to integrate agents into their existing communication and development tools, leading to context switching and underutilization. Without a platform like AnyFrame, each agent requires separate infrastructure for triggering, execution, and monitoring. This pain point is particularly acute for teams that rely on Slack for operations, Linear for issue tracking, and GitHub for code reviews. AnyFrame unifies these by allowing agents to be triggered directly from a message, ticket, or PR comment, and execute in a consistent sandboxed environment. The result is a seamless workflow where agents become active participants in daily tasks, not isolated tools.
The first major feature group is "Any harness, swappable." AnyFrame supports multiple AI agent runtimes including Claude Code, Cursor, Codex, OpenCode, and Gemini CLI, with managed agents from Claude and Gemini coming soon. This works by allowing users to select a runtime when creating an agent, and they can switch runtimes at any time without modifying the agent's template or integrations. The benefit is flexibility: teams can experiment with different models and tools without rebuilding their agents. For example, a team might use Claude Code for complex reasoning tasks and switch to Codex for code generation, all within the same agent definition. This feature also future-proofs agents as new runtimes emerge, ensuring teams can always use the best available tool.
The second major feature is "Triggered where you work." AnyFrame agents can be invoked directly from Slack messages, Linear tickets, GitHub PRs and issues, Discord, Jira, and other platforms using a simple @anyframe mention. This works by connecting these tools as connectors in the agent's configuration. When triggered, the agent receives the full context—message body, thread, ticket details, or PR diff—and executes its task in a sandbox. This eliminates the need to switch to a separate dashboard or terminal. For instance, a developer can ask the agent to roll back a deployment from Slack, or a product manager can request a code change from a Linear ticket. The agent runs in a fresh Ubuntu sandbox with the repository and secrets scoped to that agent, ensuring security and isolation.
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The third feature group is "It has hands and eyes." Beyond simple tool calls, AnyFrame agents can interact with real web browsers, clicking through interfaces like a human user. This capability is showcased in the demo where an agent navigates to a billing page, clicks a plan dropdown, and upgrades a customer's plan. The agent uses a live desktop environment with a Chromium browser, meaning it can perform actions on any web application without requiring an API. The benefit is that agents can handle tasks that involve visual elements, authentication flows, or legacy interfaces that lack programmatic access. This makes AnyFrame suitable for tasks like data entry, UI testing, and account management that previously required manual effort.
How AnyFrame works overall: agents are created by selecting a template (which bundles connectors like PostHog, Slack, and skills) and a runtime (like Claude Code or Codex). Each agent runs in a fresh sandboxed environment—Ubuntu with 4 vCPU—that includes the repository and any scoped secrets. Agents can be triggered on-demand via tags or set on schedules, webhooks, or queues. The platform also provides an SDK (Python, TypeScript, cURL) to embed agents directly into your own product. The workflow is straightforward: describe the task in natural language, optionally attach files or context, and the agent executes in its sandbox. Results are streamed back to the triggering channel, with live previews for tasks like website updates.
Concrete use cases include rolling back a deployment from Slack after an alert, fixing a code issue from a Linear ticket with an attached flamegraph, writing tests for a PR from a GitHub comment, and updating a marketing website hero section from a Slack message. In each scenario, the agent receives the full context and executes autonomously, providing real-time feedback. For the deployment rollback, the agent runs the appropriate commands in its sandbox and confirms success. For the latency fix, it ingests the attached flamegraph and codebase context to identify and implement the fix. The outcome is faster incident response, reduced manual coding, and the ability to push changes directly from communication tools. These use cases demonstrate how AnyFrame turns agents into productive team members that do the work, not just suggest it.
AnyFrame is built for teams that ship software together—engineering, product, design, and data people who already work in Slack, Linear, and GitHub. The platform supports multiple runtimes (Claude Code, Cursor, Codex, etc.) and integrates with Slack, Discord, Linear, GitHub, Jira, and more. It offers a free tier with 500 credits (no card required) and pay-as-you-go pricing. For enterprise needs, SSO, self-hosting, and custom SLAs are available via scheduling a call. An open-source version is planned, allowing full self-hosting with own keys and secrets. The technology stack includes Python and TypeScript SDKs, making it easy to embed agents into any product. In summary, AnyFrame provides a single platform to build, deploy, and manage agents that work where your team works, turning AI into a practical, integrated collaborator.
AnyFrame is built for engineering teams, product managers, operations staff, and data professionals who ship software together in tools like Slack, Linear, and GitHub. It is ideal for teams that want to deploy AI agents as active collaborators in their daily workflows—whether for internal automation (e.g., deployment rollbacks, code fixes) or customer-facing features (e.g., agent-powered actions inside products). Specific roles include software engineers, engineering managers, product managers, DevOps engineers, QA engineers, data analysts, and technical founders building AI-enhanced software. The platform is also suited for startups and scale-ups that need a simple, unified way to manage multiple agents across different runtimes and integrations without reinventing infrastructure.