Capabilities
AI Mastra Bridge focuses on one job: make a Mastra runtime easier for assistant frontends to consume. It does that by translating streaming behavior, exposing a practical route surface, and handling the run and thread patterns that frontend assistant clients typically need.
Backend Contract Layer
AI Mastra Bridge makes Mastra usable as an assistant backend without forcing frontend teams to speak Mastra directly.
The bridge sits in the middle of the integration and cleans up the parts that are otherwise awkward: streaming translation, proxy-based frontend tool execution, route consistency, and practical thread access.
Capability Areas at a Glance
Protocol event streaming
Translate Mastra stream chunks into the AG-UI-based event sequence used by io.Intelligence frontend clients.
Route surface for clients
Expose endpoints for runs, agents, threads, and messages through one backend package.
Frontend tool proxying
Expose frontend-declared tools to Mastra as temporary backend tools so Mastra can keep ownership of the tool loop and memory.
Thread persistence model
Use Mastra memory when available so assistant conversations can behave like a real thread system.
Translate Mastra Streams into Protocol Events
Mastra's native streaming model is not the same contract expected by io.Intelligence frontend packages. AI Mastra Bridge converts that native stream into the io.Intelligence Agent Protocol, including its AG-UI-based event sequence and lifecycle guarantees.
| Stream behavior | Frontend benefit |
|---|---|
| Run lifecycle events | Easier loading, completion, and error handling |
| Text event translation | Cleaner progressive rendering in chat UIs |
| Tool lifecycle translation | Better visibility into tool start, arguments, completion, and result phases |
| Deduplication handling | Fewer frontend workarounds for repeated or inconsistent backend chunks |
This capability is what lets frontend packages like AI Web and io.Assist NG stay focused on UX instead of backend-specific stream parsing.
Expose a Frontend-Friendly Route Surface
The bridge provides Hono route definitions that cover the most common assistant backend requirements.
| Method | Path | Purpose |
|---|---|---|
POST | /io-bridge/run | Start a streaming assistant run |
GET | /io-bridge/agents | List available agents |
GET | /io-bridge/agents/:agentId | Inspect a single agent |
POST | /io-bridge/threads | Create a thread |
GET | /io-bridge/threads | List threads |
GET | /io-bridge/threads/:threadId | Get one thread |
PATCH | /io-bridge/threads/:threadId | Update a thread |
DELETE | /io-bridge/threads/:threadId | Delete a thread |
GET | /io-bridge/threads/:threadId/messages | Read thread messages |
That route inventory is small enough to understand quickly and broad enough to support a real assistant client.
Support Frontend Tool Execution Loops
One of the harder assistant integration patterns is the frontend tool loop:
- the model decides to call a frontend-executed tool
- the frontend performs that tool action
- the frontend reports the result back while the same run is still alive
AI Mastra Bridge supports that pattern by proxying frontend-declared tools into Mastra toolsets and accepting per-tool results on a dedicated route. It does not require the frontend to build a second run request just to continue the conversation.
This matters whenever the frontend is responsible for tool execution, including MCP-driven product workflows.
Capture Tool Results More Reliably
The bridge also improves visibility around tool execution by capturing server-side tool results and emitting TOOL_CALL_RESULT events when Mastra does not provide a clean corresponding stream chunk.
| Reliability improvement | Why it matters |
|---|---|
| Server tool result capture | Frontend clients can present a clearer tool lifecycle |
| Mastra-owned persistence | Tool outcomes and follow-up model responses stay in Mastra's normal memory flow |
| Cleaner run boundaries | Clients can reason about the run as a series of explicit phases |
Use Mastra Memory as the Thread Backbone
When at least one Mastra agent has memory configured, the bridge can back thread CRUD operations with that memory system. That gives frontend assistant clients a practical thread model without forcing them to integrate Mastra internals directly.
Thread CRUD behavior depends on at least one Mastra agent having memory configured. Without that, streaming can still work, but the bridge cannot behave as a fully persistent thread API.
Fit into Host-Application Middleware
The bridge does not force a full application framework around itself. Instead, it returns route definitions that host applications can register into Mastra and surround with their own middleware for authentication, logging, monitoring, or deployment-specific controls.
const bridge = IoMastraBridgeFactory({ prefix: "/io-bridge" });
export const mastra = new Mastra({
agents: { myAgent },
server: {
apiRoutes: [...bridge.createHonoRoutes()],
},
});
That makes the package feel operationally practical, not just conceptually clean.
Next Steps
- Overview for the high-level package framing
- Agent Protocol Reference for the shared contract the bridge implements
- AI Mastra Bridge API Reference for the factory and public types
- AI Mastra Bridge Routes and Events for event behavior details
- AI Mastra Bridge Examples for registration patterns