Sogentic AI is an embeddable runtime for building agents that take real actions inside your product. Imagine Claude Code or Codex for your app, and friendly enough for your users. You describe what the agent can do in plain declarations. The runtime handles the hard part.
Your application supplies four things in plain English: the tools an agent can use, the context it knows, the prompts that shape how it behaves, and the relationships between agents. The runtime does everything else: running the loop, picking tools, assembling context, spawning sub-agents, and surfacing every action so users can see, edit, and undo it.
There's a gap in the industry right now. Plenty of engineers can build great software, but building a reliable agentic system has meant wrestling with execution loops, context windows, tool routing, retries, and orchestration plumbing. That's a different skill set, and it's kept most teams out. Sogentic closes that gap. The architecture is still yours to design; that's a real skill. But the technical machinery underneath is no longer your problem.
The hard part was never the plumbing. It was knowing what to build. Now that's the only part left.
The same runtime powers both a conversational copilot and a headless background worker. One primitive, two very different products.
Drop a copilot into your product that takes action on the user's behalf. Simple requests run in one step. Complex ones get a visible plan the agent works through. It can ask clarifying questions, chain multiple actions, and explain itself as it goes.
Every action is visible before it runs. Users can edit a step inline, or undo one with a click. Think of the power of Claude Code or Codex, delivered to your users in a way that feels safe and friendly.
Not every agent needs a person typing to it. Trigger a module from an event in your app or on a timer, and let it carry out a multi-step job autonomously, with the same tools, context, and safety guarantees.
A new record appears, a threshold is crossed, a schedule fires; the agent wakes up, reasons about what's needed, takes the right actions, and logs everything for review. Agentic automation that runs while your users sleep.
An agent can't misuse a tool it was never given. Permissions strip tools out before the model can even see them; restraint is built into the shape, not bolted on with prompts.
Every action streams to your UI before it commits. Users can correct a payload inline or revert a completed action with one click. Undo logic lives right on the tool.
Instead of dragging an ever-growing history, the runtime rebuilds context fresh each turn, pulling only what's relevant. Lower cost, tighter focus, no drift.
A tiny text-formatter and a sprawling multi-agent coordinator share the exact same shape. Compose agents by having modules reference modules. Nothing new to learn.
Tools fail with messages the agent can act on, so it self-corrects instead of stalling. When it truly can't proceed, it asks; it never guesses silently.
An unopinionated package: no database to adopt, no platform to deploy to. Run it in a web server, a CLI, or a background queue. Your stack stays your stack.
LangGraph, the Vercel AI SDK, and LlamaIndex are excellent at what they're built for. Sogentic is built for something they're not: cleanly embedding an action-taking agent into an existing product, with enterprise-grade visibility and control.
| Capability | Sogentic AIembeddable runtime | LangGraphstate-graph engine | Vercel AI SDKfrontend-first | LlamaIndexRAG-first |
|---|---|---|---|---|
| Core primitive | One module: same shape for a single agent or a whole coordinator | State graph: nodes and routing edges you draw in code | A tool-loop agent wired into web primitives | Indices and query engines, built around retrieval |
| How you configure it | Plain declarations: describe tools, context, prompts, relations | Code-first: explicitly build the graph by hand | Functional hooks and callbacks in code | Class instantiation with heavy data-loader abstraction |
| Context handling | Rebuilt fresh each turn: only what's relevant, no drift | Accumulates everything in a growing state array | A standard, ever-growing message history array | Vector search injects chunks before each call |
| Permissions | Structural: unusable tools are invisible to the model | Prompt or custom-node guardrails | Intercepted at runtime in your app code | Not a primary concern |
| Before an action runs | Live edit: users can change the action inline, then continue | Execution pauses; you update state via API | A yes/no approval hook | Not a primary concern |
| After an action runs | One-click undo: reversal logic defined on the tool | "Time travel": rewind graph state to a checkpoint | You catch and reverse it manually | Not a primary concern |
| Runs headless | Yes: chat, CLI, worker, or timer, same runtime | Yes, but inside its own state server | Hard: built around the front-end stream | Yes, for retrieval tasks |
You're running a long, crash-recoverable background pipeline with thousands of non-linear transitions, closer to a workflow engine than a copilot.
You're a frontend developer who wants a standard B2C chatbot streaming straight into a React view, with near-zero setup.
You have a real product and want to add an agent that performs multi-step actions for users with strict permissions, full visibility, and undo, without rewriting your business logic.
Sogentic AI is open source. Read the code, fork it, extend it, ship it in production, no strings. We believe the agent runtime that powers your product should be something you can fully see and own.
The runtime is free and open. But if you'd rather not wire it up yourself, our managed service builds and hosts your agents for you: designed, integrated, and run end-to-end.
We score the difficulty of a build in points. Points map directly to a one-time setup fee, plus a monthly service fee and API costs.