Use General Augment when the agent belongs inside your product.
The app already owns users, permissions, billing, and workflows. You need the backend layer around agent turns: memory, tools, approvals, channels, traces, and usage.
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Category comparisons for app teams.
General Augment fits at the agent-backend layer. These pages explain where adjacent categories fit and when they can work together.
Use adjacent platforms when they own one narrow primitive.
Model APIs, frameworks, sandboxes, and messaging providers are useful engines. General Augment fits above them when the product needs one governed agent backend.
Category comparisons
Choose the right layer for the job.
raw LLM APIs
Raw LLM APIs vs an agent backend
Use raw model APIs for direct model calls. Use General Augment when an app needs identity, memory, governed tools, approvals, channels, usage, and observability.
Best when: The agent lives inside an existing product.
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chatbot builders
Chatbot builders vs an agent backend
Chatbot builders help teams launch chat surfaces. General Augment provides the backend layer for agents embedded in existing apps.
Best when: The agent should feel native to the app experience.
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agent frameworks
Agent frameworks vs a managed agent backend
Agent frameworks help teams build custom agent systems. General Augment gives product teams a managed backend for embedded app agents.
Best when: You want an app-agent backend instead of a custom platform project.
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execution sandboxes
Execution sandboxes vs an agent backend
Execution sandboxes provide compute environments. General Augment governs product agents and can connect to private capacity when an app needs it.
Best when: The product needs one governed agent backend across users and surfaces.
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messaging APIs
Messaging APIs vs an agent backend
Messaging APIs deliver messages. General Augment gives the same product agent identity, memory, tools, approvals, and observability across app and channel surfaces.
Best when: The same agent should work in-app and through channels.
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Platforms and SDKs
How General Augment fits with the agent tools buyers already know.
OpenAI Agents SDK
OpenAI Agents SDK vs an app-agent backend
OpenAI Agents SDK helps developers build agent loops with tools, handoffs, guardrails, and tracing. General Augment adds the app-agent backend layer for product teams.
Best when: The agent belongs inside an existing product experience.
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LangGraph and LangSmith
LangGraph and LangSmith vs an app-agent backend
LangGraph provides durable orchestration and persistence; LangSmith provides tracing and evaluation. General Augment gives app teams the managed product backend around customer-facing agents.
Best when: The agent should be a product capability, not a new platform project.
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Cloudflare Agents
Cloudflare Agents vs an app-agent backend
Cloudflare Agents provides stateful agent infrastructure on Cloudflare with memory, WebSockets, scheduling, and long-running patterns. General Augment provides the managed app-agent backend for existing products.
Best when: Your app needs a managed agent backend above runtime primitives.
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Vercel AI SDK
Vercel AI SDK vs an app-agent backend
Vercel AI SDK helps TypeScript teams build AI apps and now exposes agent harness abstractions. General Augment provides the managed app-agent backend around product users, memory, tools, approvals, channels, usage, and traces.
Best when: You need a managed backend that existing app backends can call.
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