> ## Documentation Index
> Fetch the complete documentation index at: https://aomi.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Architecture
> See how the hosted runtime loads Apps, restores threads, schedules tools and models, and simulates transactions on warm chain forks.
Aomi runs Apps for users across the widget, headless library, Telegram, and CLI. You ship an App with your tools and a prompt. The hosted runtime supplies the model loop, thread state, simulation forks, signing flow, scheduling, and billing. This page explains which parts the runtime manages for you.
## Runtime structure
A traditional deployment gives each bot its own server. Aomi represents agents as data and executes them in a shared runtime.
Each user and App conversation enters through the same chat endpoint. Aomi does not create a container, server instance, or dedicated bot process for each App or user.
The runtime combines three pieces of data for each request:
* **Your App.** The tools and preamble you shipped are shared by each thread that selects the App.
* **The thread.** The database stores the conversation history and working state.
* **The user's context.** Each request carries wallet state, signing policy, and account identity.
Because agents are data, adding your App adds no infrastructure. The runtime that already serves every other App starts serving yours the moment it loads.
## Runtime diagram
The left side shows the transaction pipeline from intent through signature. The right side shows the runtime components that Aomi manages for you.
## Stateless dispatch
Stateless dispatch means no request depends on which server it lands on. That matters because it is what lets a conversation survive a restart, a deploy, or a move between hosts without the user noticing.
Each chat request carries a thread id in a header. The server uses that id to find the selected App, rebuild the user's authorization snapshot, and resolve payment before the turn runs. The request does not assume the server has seen the thread before.
If the thread is already in memory, the turn starts immediately. Otherwise, the runtime reconstructs the thread from the database. It loads the stored messages, rebinds the App, and resumes the conversation. The state-polling path uses the same recovery process, so an open chat window can survive a host change.
This design does not require session affinity. A thread is not tied to a machine, so a machine failure does not delete the conversation. A response that is still generating when a process stops is not replayed. The thread remains available, but that response does not.
## Thread state and communication
A thread is one conversation: its messages, its working state, and its identity bindings. It is the unit everything else in the runtime is scoped to.
While a thread is active, the runtime gives it a lightweight in-process task rather than an operating-system process. Message channels deliver user input, commands, streamed output, and interrupts. An event bus sends wallet requests, notices, and transaction outcomes to the active client. A second task polls tool completions so slow tools do not block the loop. The agent loop, tool layer, and transport communicate through these messages instead of sharing state directly.
Memory between turns is the message history plus a running summary of context, and it is durable by construction:
* After each completed turn, the thread's new messages are flushed to the database.
* Idle threads are evicted from memory. Eviction flushes first, then drops the in-memory copy.
* The next request on an evicted thread reconstructs it from the database, history intact.
A thread's durable memory lives in the database. RAM is only a cache. This
makes [Multi-threading](/docs/concepts/multi-threading) durable across requests. It
also lets an [asynchronous task](/docs/concepts/asynchronous-tasks) save an intent
now and start a fresh thread with that context when its trigger is ready.
## Tool scheduler and LLM interface
A tool is a function the model can call. With many Apps loaded in one process, the runtime needs to keep their tools from colliding and show each thread only the tools its App declared. That is the tool scheduler's job.
The scheduler is a process-wide registry. Each tool belongs to either an App namespace or a shared host namespace such as the EVM primitives. This prevents conflicts when two Apps define tools with the same name. When a thread starts, the scheduler exposes only the namespaces declared by its App. Per-tool pricing and hooks attach to the same registry entries.
The LLM interface supports Anthropic, OpenAI, and OpenRouter through one interface. A thread can select a model without changing the App architecture.
* **Per-thread model switching.** A thread can change its model between turns through the API; the selection persists with the thread and survives reconstruction.
* **Bring your own key (BYOK).** A user can register an Anthropic, OpenAI, or OpenRouter API key. The key lives in a secret vault associated with that user. Model clients that contain keys are never shared between users. Turns that use a user's key bypass platform metering for model usage.
## Forks infrastructure
A fork is a private copy of live chain state. It has the same balances, contracts, and prices, but changes do not reach the live chain. Aomi uses forks to show a transaction's expected result before requesting a signature.
Starting a fork on demand is slow, so the runtime keeps a managed fork warm for each supported chain. By default, each fork syncs to the live chain tip every 10 seconds and starts fresh every 5 minutes. During simulation, the runtime takes a snapshot, executes each transaction in order, and then reverts the snapshot. State-dependent flows such as approve-then-swap validate as a unit without changing the shared fork.
Every transaction is simulated against recent chain state, and the user can review the balance changes before signing. A fork may trail the live chain by its sync interval. Simulation is therefore a prediction rather than a guarantee of the final result.
## What you don't build
An App contains a plugin, which is a compiled Rust library built against the public SDK. The runtime loads the plugin dynamically, checks its SDK version, and registers its tools. It can replace a plugin without restarting the server. Existing threads keep the version they started with, while new threads receive the update. The runtime removes idle App instances from memory and reloads them when needed.
Everything else on this page is the part you inherit rather than build:
* The HTTP surface, streaming, and thread dispatch
* Thread persistence, reconstruction, and memory
* The model loop, provider integrations, and BYOK handling
* Tool registration, namespacing, and per-tool pricing
* Warm chain forks and transaction simulation
* The signing flow and per-wallet policy enforcement
* Multi-threading, asynchronous tasks, and metering
You ship tools and a prompt. The runtime does the rest.
How a user message becomes a simulated, signed transaction, end to end.
How parent and child threads separate work while preserving context.
How detached, scheduled, and condition-triggered work runs safely.
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