AI Studio Agent Plugins - Tyk Documentation

Availability

Edition	Deployment Type
Community & Enterprise	Self-Managed, Hybrid

Experimental Feature: Agent plugins are currently experimental. The API and behavior may change in future releases.

AI Studio Agent plugins enable conversational AI experiences in the Chat Interface using the Unified Plugin SDK. Build custom agents that wrap LLMs, add specialized logic, integrate external services, and create sophisticated multi-turn conversations with streaming responses. Agent plugins use the same pkg/plugin_sdk as other plugin types, automatically detecting the Studio runtime and providing access to Studio Services (LLM calls, tool execution, datasource queries).

Architecture Overview

Agent plugins follow a three-tier binding model:

Key Concepts

Component	Purpose
Plugin	Long-running gRPC plugin implementing `AgentPlugin` interface
Agent Object	Configuration binding a plugin to an App, with access controls
App Object	Resource container providing LLMs, tools, datasources, and credentials

Important points:

Plugins are reusable: A single plugin can power multiple Agent Objects
Apps provide resources: The App determines which LLMs the agent can call
Access via Groups: Users access agents based on group membership
Budget enforcement: LLM calls are routed through the proxy, enforcing budgets
Portal integration: Active agents appear in the Chat section of the AI Portal alongside managed chats

Overview

Agent plugins enable you to:

Stream Responses: Real-time server-streaming for interactive conversations
Call LLMs: Access managed LLMs via Context Services or direct SDK calls
Execute Tools: Run registered tools and integrate external services
Query Datasources: Access configured datasources for RAG and context
Maintain Context: Access full conversation history
Custom Configuration: Per-agent config with JSON schema validation
Universal Services: KV storage and logging via Context.Services

Unified SDK Integration

Agent plugins use the Unified Plugin SDK (pkg/plugin_sdk), just like all other plugin types. Key patterns:

Import and Structure

import "github.com/TykTechnologies/midsommar/v2/pkg/plugin_sdk"

type MyAgentPlugin struct {
    plugin_sdk.BasePlugin
}

func NewMyAgentPlugin() *MyAgentPlugin {
    return &MyAgentPlugin{
        BasePlugin: plugin_sdk.NewBasePlugin(
            "my-agent", "1.0.0", "Description",
        ),
    }
}

Lifecycle Methods

Expandable

// Initialize - called when plugin starts
func (p *MyAgentPlugin) Initialize(ctx plugin_sdk.Context, config map[string]string) error {
    // Extract broker ID for Service API calls
    if brokerIDStr, ok := config["_service_broker_id"]; ok {
        var brokerID uint32
        fmt.Sscanf(brokerIDStr, "%d", &brokerID)
        ai_studio_sdk.SetServiceBrokerID(brokerID)
    }
    return nil
}

// Shutdown - called when plugin stops
func (p *MyAgentPlugin) Shutdown(ctx plugin_sdk.Context) error {
    return nil
}

Agent Capability

Implement the AgentPlugin capability:

Expandable

// HandleAgentMessage processes incoming messages and streams responses
func (p *MyAgentPlugin) HandleAgentMessage(
    req *pb.AgentMessageRequest,
    stream pb.PluginService_HandleAgentMessageServer) error {

    // Stream chunks to user
    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_CONTENT,
        Content: "Hello!",
        IsFinal: false,
    })

    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_DONE,
        IsFinal: true,
    })

    return nil
}

Serving the Plugin

func main() {
    plugin_sdk.Serve(NewMyAgentPlugin())
}

Service API Access

Agent plugins can call LLMs, execute tools, and query datasources via the ai_studio_sdk helper functions (requires broker ID):

// Call LLM
llmStream, err := ai_studio_sdk.CallLLM(
    ctx, llmID, model, messages, temperature, maxTokens, tools, stream,
)

// Execute tool
result, err := ai_studio_sdk.ExecuteTool(ctx, toolID, operation, params)

// Query datasource
result, err := ai_studio_sdk.QueryDatasource(ctx, dsID, query)

Quick Start

1. Project Structure

my-agent-plugin/
├── server/
│   ├── main.go                # Plugin server
│   ├── plugin.manifest.json   # Plugin manifest
│   └── config.schema.json     # Configuration schema
└── go.mod

2. Create Manifest

server/plugin.manifest.json:

Expandable

{
  "id": "com.example.my-agent",
  "name": "My Agent",
  "version": "1.0.0",
  "plugin_type": "agent",
  "description": "Custom conversational agent",
  "permissions": {
    "services": [
      "llms.proxy",
      "tools.execute",
      "datasources.query"
    ]
  },
  "ui": {
    "slots": []
  }
}

3. Implement Agent Plugin

server/main.go:

Expandable

package main

import (
    _ "embed"
    "fmt"
    "io"
    "log"

    "github.com/TykTechnologies/midsommar/v2/pkg/ai_studio_sdk"
    "github.com/TykTechnologies/midsommar/v2/pkg/plugin_sdk"
    pb "github.com/TykTechnologies/midsommar/v2/proto"
    mgmt "github.com/TykTechnologies/midsommar/v2/proto/ai_studio_management"
)

//go:embed plugin.manifest.json
var manifestFile []byte

//go:embed config.schema.json
var configSchemaFile []byte

type MyAgent struct {
    plugin_sdk.BasePlugin
}

func NewMyAgent() *MyAgent {
    return &MyAgent{
        BasePlugin: plugin_sdk.NewBasePlugin(
            "my-agent",
            "1.0.0",
            "Custom conversational agent",
        ),
    }
}

// Initialize is called when plugin starts
func (p *MyAgent) Initialize(ctx plugin_sdk.Context, config map[string]string) error {
    log.Printf("My Agent initializing")

    // Extract broker ID for Service API access
    brokerIDStr := ""
    if id, ok := config["_service_broker_id"]; ok {
        brokerIDStr = id
    } else if id, ok := config["service_broker_id"]; ok {
        brokerIDStr = id
    }

    if brokerIDStr != "" {
        var brokerID uint32
        fmt.Sscanf(brokerIDStr, "%d", &brokerID)
        ai_studio_sdk.SetServiceBrokerID(brokerID)
        log.Printf("My Agent: Set broker ID %d for Service API", brokerID)
    }

    return nil
}

// Shutdown is called when plugin stops
func (p *MyAgent) Shutdown(ctx plugin_sdk.Context) error {
    log.Printf("My Agent shutting down")
    return nil
}

// GetManifest returns the plugin manifest
func (p *MyAgent) GetManifest() ([]byte, error) {
    return manifestFile, nil
}

// GetConfigSchema returns the configuration schema
func (p *MyAgent) GetConfigSchema() ([]byte, error) {
    return configSchemaFile, nil
}

// HandleAgentMessage processes incoming messages and streams responses
func (p *MyAgent) HandleAgentMessage(
    req *pb.AgentMessageRequest,
    stream pb.PluginService_HandleAgentMessageServer) error {

    log.Printf("Received message: %s", req.UserMessage)

    // Select LLM (use first available or configured default)
    var selectedLLM *pb.AgentLLMInfo
    if len(req.AvailableLlms) > 0 {
        selectedLLM = req.AvailableLlms[0]
    }

    if selectedLLM == nil {
        return stream.Send(&pb.AgentMessageChunk{
            Type:    pb.AgentMessageChunk_ERROR,
            Content: "No LLM available",
            IsFinal: true,
        })
    }

    // Build messages from history + current message
    messages := buildMessages(req.History, req.UserMessage)

    // Call LLM via SDK
    llmStream, err := ai_studio_sdk.CallLLM(
        stream.Context(),
        selectedLLM.Id,
        selectedLLM.DefaultModel,
        messages,
        0.7,  // temperature
        1000, // max tokens
        nil,  // tools
        false, // non-streaming
    )
    if err != nil {
        return stream.Send(&pb.AgentMessageChunk{
            Type:    pb.AgentMessageChunk_ERROR,
            Content: fmt.Sprintf("Failed to call LLM: %v", err),
            IsFinal: true,
        })
    }

    // Receive and forward response
    var llmContent string
    for {
        resp, err := llmStream.Recv()
        if err == io.EOF {
            break
        }
        if err != nil {
            return stream.Send(&pb.AgentMessageChunk{
                Type:    pb.AgentMessageChunk_ERROR,
                Content: fmt.Sprintf("Error receiving response: %v", err),
                IsFinal: true,
            })
        }

        if !resp.Success {
            return stream.Send(&pb.AgentMessageChunk{
                Type:    pb.AgentMessageChunk_ERROR,
                Content: fmt.Sprintf("LLM error: %s", resp.ErrorMessage),
                IsFinal: true,
            })
        }

        llmContent += resp.Content

        if resp.Done {
            break
        }
    }

    // Send content chunk
    if err := stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_CONTENT,
        Content: llmContent,
        IsFinal: false,
    }); err != nil {
        return err
    }

    // Send done chunk
    return stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_DONE,
        Content: "completed",
        IsFinal: true,
    })
}

func buildMessages(history []*pb.AgentConversationMessage, userMessage string) []*mgmt.LLMMessage {
    messages := make([]*mgmt.LLMMessage, 0, len(history)+1)

    // Add history
    for _, msg := range history {
        messages = append(messages, &mgmt.LLMMessage{
            Role:    msg.Role,
            Content: msg.Content,
        })
    }

    // Add current message
    messages = append(messages, &mgmt.LLMMessage{
        Role:    "user",
        Content: userMessage,
    })

    return messages
}

func main() {
    log.Printf("Starting My Agent")
    plugin_sdk.Serve(NewMyAgent())
}

4. Build and Deploy

Expandable

# Build plugin
go build -o my-agent main.go

# Create plugin in AI Studio
curl -X POST http://localhost:3000/api/v1/plugins \
  -H "Authorization: Bearer $TOKEN" \
  -d '{
    "name": "My Agent",
    "slug": "my-agent",
    "command": "file:///path/to/my-agent",
    "hook_type": "agent",
    "plugin_type": "agent",
    "is_active": true
  }'

5. Create Agent Object

Create an Agent Object that binds the plugin to an App: Prerequisites:

An active plugin with hook_type: agent
An App with at least one LLM assigned, active credential, and optionally tools and datasources

Expandable

curl -X POST http://localhost:3000/api/v1/agents \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "My Custom Agent",
    "description": "An agent that helps with specific tasks",
    "plugin_id": 1,
    "app_id": 1,
    "config": {
      "system_prompt": "You are a helpful assistant",
      "temperature": 0.7
    },
    "group_ids": [1, 2],
    "is_active": true
  }'

Agent Object Fields:

Field	Type	Description
`name`	`string`	Display name for the agent
`description`	`string`	Description shown to users
`plugin_id`	`uint`	ID of the agent plugin to use
`app_id`	`uint`	ID of the App providing resources
`config`	`object`	Plugin-specific configuration (passed as `ConfigJson`)
`group_ids`	`[]uint`	Groups that can access this agent (empty = public)
`is_active`	`bool`	Whether the agent is available to users
`namespace`	`string`	Optional namespace for multi-tenant deployments

6. Use in Chat Interface

Active agents appear in the Chat section of the AI Portal alongside managed chats. Users see agents they have access to based on group membership. SSE Communication Flow:

Establish SSE connection:
```
GET /api/agents/{id}/stream?token=...
```

Receive session info (first message):

{
  "session_id": "agent-1-123",
  "agent_id": 1,
  "agent_name": "My Agent",
  "available_llms": 3,
  "available_tools": 2,
  "available_datasources": 1
}

Send messages via POST:

curl -X POST "http://localhost:3000/api/agents/1/message?session_id=agent-1-123" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Hello, can you help me?",
    "history": []
  }'

Receive streaming response via SSE:

event: content
data: {"type":"CONTENT","content":"Hello! How can I help?","is_final":false}

event: done
data: {"type":"DONE","content":"completed","is_final":true}

Agent Message Request

The AgentMessageRequest provides rich context for your agent:

type AgentMessageRequest struct {
    SessionId            string                       // Unique session ID
    UserMessage          string                       // Current user message
    AvailableTools       []*AgentToolInfo             // Tools agent can use
    AvailableDatasources []*AgentDatasourceInfo       // Datasources available
    AvailableLlms        []*AgentLLMInfo              // LLMs available
    ConfigJson           string                       // Agent configuration (JSON)
    History              []*AgentConversationMessage  // Conversation history
    Context              *PluginContext               // Request context
}

Available Tools

for _, tool := range req.AvailableTools {
    log.Printf("Tool: %s (%s) - %s", tool.Name, tool.Slug, tool.Description)
}

Available Datasources

for _, ds := range req.AvailableDatasources {
    log.Printf("Datasource: %s (%s) - %s", ds.Name, ds.DbSourceType, ds.Description)
}

Available LLMs

for _, llm := range req.AvailableLlms {
    log.Printf("LLM: %s - %s %s", llm.Name, llm.Vendor, llm.DefaultModel)
}

Configuration

Parse custom configuration from JSON:

type Config struct {
    SystemPrompt string  `json:"system_prompt"`
    Temperature  float64 `json:"temperature"`
    MaxTokens    int     `json:"max_tokens"`
}

var config Config
if req.ConfigJson != "" {
    if err := json.Unmarshal([]byte(req.ConfigJson), &config); err == nil {
        log.Printf("Using config: system_prompt=%s, temp=%.2f",
            config.SystemPrompt, config.Temperature)
    }
}

Conversation History

log.Printf("Conversation has %d messages", len(req.History))
for i, msg := range req.History {
    log.Printf("  [%d] %s: %s", i, msg.Role, msg.Content)
}

Streaming Responses

Agent plugins use server-streaming gRPC to send real-time responses:

Chunk Types

const (
    CONTENT     = "CONTENT"      // Response content
    TOOL_CALL   = "TOOL_CALL"    // Tool is being called
    TOOL_RESULT = "TOOL_RESULT"  // Tool result
    THINKING    = "THINKING"     // Agent reasoning
    ERROR       = "ERROR"        // Error occurred
    DONE        = "DONE"         // Response complete
)

Send Content

stream.Send(&pb.AgentMessageChunk{
    Type:    pb.AgentMessageChunk_CONTENT,
    Content: "Here is my response...",
    IsFinal: false,
})

Send Thinking

stream.Send(&pb.AgentMessageChunk{
    Type:    pb.AgentMessageChunk_THINKING,
    Content: "Analyzing the question...",
    IsFinal: false,
})

Send Tool Call

metadata := map[string]interface{}{
    "tool_name":  "weather_api",
    "operation":  "get_forecast",
    "parameters": map[string]string{"city": "San Francisco"},
}
metadataJSON, _ := json.Marshal(metadata)

stream.Send(&pb.AgentMessageChunk{
    Type:         pb.AgentMessageChunk_TOOL_CALL,
    Content:      "Calling weather API...",
    MetadataJson: string(metadataJSON),
    IsFinal:      false,
})

Send Error

stream.Send(&pb.AgentMessageChunk{
    Type:    pb.AgentMessageChunk_ERROR,
    Content: "Failed to process request: invalid input",
    IsFinal: true,
})

Send Done

stream.Send(&pb.AgentMessageChunk{
    Type:    pb.AgentMessageChunk_DONE,
    Content: "completed",
    IsFinal: true, // Always set IsFinal=true for DONE
})

Complete Example: Echo Agent

A simple agent that wraps LLM responses with custom formatting:

Expandable

package main

import (
    _ "embed"
    "encoding/json"
    "fmt"
    "io"
    "log"

    "github.com/TykTechnologies/midsommar/v2/pkg/ai_studio_sdk"
    "github.com/TykTechnologies/midsommar/v2/pkg/plugin_sdk"
    pb "github.com/TykTechnologies/midsommar/v2/proto"
    mgmt "github.com/TykTechnologies/midsommar/v2/proto/ai_studio_management"
)

//go:embed plugin.manifest.json
var manifestFile []byte

//go:embed config.schema.json
var configSchemaFile []byte

type EchoAgentPlugin struct {
    plugin_sdk.BasePlugin
    prefix          string
    suffix          string
    includeMetadata bool
}

type Config struct {
    Prefix          string `json:"prefix"`
    Suffix          string `json:"suffix"`
    IncludeMetadata bool   `json:"include_metadata"`
}

func NewEchoAgentPlugin() *EchoAgentPlugin {
    return &EchoAgentPlugin{
        BasePlugin: plugin_sdk.NewBasePlugin(
            "echo-agent",
            "1.0.0",
            "Wraps LLM responses with prefix/suffix",
        ),
        prefix:          "<<",
        suffix:          ">>",
        includeMetadata: false,
    }
}

func (p *EchoAgentPlugin) Initialize(ctx plugin_sdk.Context, config map[string]string) error {
    log.Printf("EchoAgent: Initialize called")

    // Extract broker ID for Service API access
    brokerIDStr := ""
    if id, ok := config["_service_broker_id"]; ok {
        brokerIDStr = id
    } else if id, ok := config["service_broker_id"]; ok {
        brokerIDStr = id
    }

    if brokerIDStr != "" {
        var brokerID uint32
        fmt.Sscanf(brokerIDStr, "%d", &brokerID)
        ai_studio_sdk.SetServiceBrokerID(brokerID)
        log.Printf("EchoAgent: Set broker ID %d for Service API", brokerID)
    }

    log.Println("✅ EchoAgent: Initialized successfully")
    return nil
}

func (p *EchoAgentPlugin) Shutdown(ctx plugin_sdk.Context) error {
    log.Println("EchoAgent: Shutdown called")
    return nil
}

func (p *EchoAgentPlugin) GetManifest() ([]byte, error) {
    return manifestFile, nil
}

func (p *EchoAgentPlugin) GetConfigSchema() ([]byte, error) {
    return configSchemaFile, nil
}

func (p *EchoAgentPlugin) HandleAgentMessage(
    req *pb.AgentMessageRequest,
    stream pb.PluginService_HandleAgentMessageServer) error {

    log.Printf("EchoAgent: Received message: %s", req.UserMessage)

    // Parse config from request if present
    if req.ConfigJson != "" {
        var config Config
        if err := json.Unmarshal([]byte(req.ConfigJson), &config); err == nil {
            if config.Prefix != "" {
                p.prefix = config.Prefix
            }
            if config.Suffix != "" {
                p.suffix = config.Suffix
            }
            p.includeMetadata = config.IncludeMetadata
            log.Printf("EchoAgent: Using custom config - prefix: %s, suffix: %s",
                p.prefix, p.suffix)
        }
    }

    // Select LLM to use
    var selectedLLM *pb.AgentLLMInfo
    if len(req.AvailableLlms) > 0 {
        selectedLLM = req.AvailableLlms[0]
    }

    // Fall back to echo mode if no LLM available
    if selectedLLM == nil {
        log.Println("EchoAgent: No LLM available, using echo mode")
        return p.echoMode(req.UserMessage, stream)
    }

    log.Printf("EchoAgent: Using LLM: %s (ID: %d, Vendor: %s, Model: %s)",
        selectedLLM.Name, selectedLLM.Id, selectedLLM.Vendor, selectedLLM.DefaultModel)

    // Call LLM via SDK
    return p.callLLM(req, selectedLLM, stream)
}

// echoMode is the fallback mode that just echoes the message
func (p *EchoAgentPlugin) echoMode(userMessage string, stream pb.PluginService_HandleAgentMessageServer) error {
    wrappedContent := fmt.Sprintf("%s %s %s", p.prefix, userMessage, p.suffix)
    log.Printf("EchoAgent: Sending wrapped echo response: %s", wrappedContent)

    // Send content chunk
    if err := stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_CONTENT,
        Content: wrappedContent,
        IsFinal: false,
    }); err != nil {
        return err
    }

    // Send done chunk
    return stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_DONE,
        Content: "completed",
        IsFinal: true,
    })
}

// callLLM calls the LLM via SDK and streams back the wrapped response
func (p *EchoAgentPlugin) callLLM(
    req *pb.AgentMessageRequest,
    llm *pb.AgentLLMInfo,
    stream pb.PluginService_HandleAgentMessageServer) error {

    ctx := stream.Context()

    // Build LLM messages from history + current message
    messages := []*mgmt.LLMMessage{}

    // Add history
    for _, histMsg := range req.History {
        messages = append(messages, &mgmt.LLMMessage{
            Role:    histMsg.Role,
            Content: histMsg.Content,
        })
    }

    // Add current user message
    messages = append(messages, &mgmt.LLMMessage{
        Role:    "user",
        Content: req.UserMessage,
    })

    log.Printf("EchoAgent: Calling LLM %d with %d messages via SDK", llm.Id, len(messages))

    // Use SDK's CallLLM helper
    llmStream, err := ai_studio_sdk.CallLLM(
        ctx,
        llm.Id,
        llm.DefaultModel,
        messages,
        0.7,  // temperature
        1000, // max tokens
        nil,  // no tools
        false, // non-streaming
    )
    if err != nil {
        log.Printf("EchoAgent: Failed to call LLM via SDK: %v", err)
        return stream.Send(&pb.AgentMessageChunk{
            Type:    pb.AgentMessageChunk_ERROR,
            Content: fmt.Sprintf("Failed to call LLM: %v", err),
            IsFinal: true,
        })
    }

    // Receive response from LLM
    var llmContent string
    for {
        resp, err := llmStream.Recv()
        if err == io.EOF {
            break
        }
        if err != nil {
            log.Printf("EchoAgent: Error receiving from LLM: %v", err)
            return stream.Send(&pb.AgentMessageChunk{
                Type:    pb.AgentMessageChunk_ERROR,
                Content: fmt.Sprintf("Error receiving LLM response: %v", err),
                IsFinal: true,
            })
        }

        if !resp.Success {
            log.Printf("EchoAgent: LLM returned error: %s", resp.ErrorMessage)
            return stream.Send(&pb.AgentMessageChunk{
                Type:    pb.AgentMessageChunk_ERROR,
                Content: fmt.Sprintf("LLM error: %s", resp.ErrorMessage),
                IsFinal: true,
            })
        }

        llmContent += resp.Content

        if resp.Done {
            break
        }
    }

    log.Printf("EchoAgent: Received LLM response (%d chars)", len(llmContent))

    // Wrap LLM response with prefix/suffix
    wrappedContent := fmt.Sprintf("%s %s %s", p.prefix, llmContent, p.suffix)

    // Send wrapped content
    if err := stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_CONTENT,
        Content: wrappedContent,
        IsFinal: false,
    }); err != nil {
        return err
    }

    // Optionally include metadata
    if p.includeMetadata {
        metadata := map[string]interface{}{
            "llm_id":    llm.Id,
            "llm_name":  llm.Name,
            "llm_model": llm.DefaultModel,
            "tokens":    len(llmContent),
        }
        metadataJSON, _ := json.Marshal(metadata)

        stream.Send(&pb.AgentMessageChunk{
            Type:         pb.AgentMessageChunk_CONTENT,
            Content:      fmt.Sprintf("\n\n---\nMetadata: %s", string(metadataJSON)),
            MetadataJson: string(metadataJSON),
            IsFinal:      false,
        })
    }

    // Send done chunk
    return stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_DONE,
        Content: "completed",
        IsFinal: true,
    })
}

func main() {
    log.Printf("🤖 Starting Echo Agent Plugin")
    plugin_sdk.Serve(NewEchoAgentPlugin())
}

Configuration Schema

config.schema.json:

Expandable

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "title": "Echo Agent Configuration",
  "properties": {
    "prefix": {
      "type": "string",
      "description": "Prefix to add before LLM response",
      "default": "<<"
    },
    "suffix": {
      "type": "string",
      "description": "Suffix to add after LLM response",
      "default": ">>"
    },
    "include_metadata": {
      "type": "boolean",
      "description": "Include metadata in response",
      "default": false
    }
  }
}

Advanced Patterns

Multi-Step Reasoning

Expandable

func (p *MyAgent) HandleAgentMessage(
    req *pb.AgentMessageRequest,
    stream pb.PluginService_HandleAgentMessageServer) error {

    // Step 1: Think
    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_THINKING,
        Content: "Analyzing the question...",
        IsFinal: false,
    })

    // Step 2: Search datasource
    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_TOOL_CALL,
        Content: "Searching knowledge base...",
        IsFinal: false,
    })

    searchResults := p.searchKnowledgeBase(req.UserMessage)

    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_TOOL_RESULT,
        Content: fmt.Sprintf("Found %d results", len(searchResults)),
        IsFinal: false,
    })

    // Step 3: Generate response with context
    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_THINKING,
        Content: "Generating response with retrieved context...",
        IsFinal: false,
    })

    // Build prompt with context
    contextualPrompt := buildPromptWithContext(req.UserMessage, searchResults)

    // Call LLM
    response := p.callLLMWithContext(contextualPrompt)

    // Step 4: Send final response
    stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_CONTENT,
        Content: response,
        IsFinal: false,
    })

    return stream.Send(&pb.AgentMessageChunk{
        Type:    pb.AgentMessageChunk_DONE,
        Content: "completed",
        IsFinal: true,
    })
}

Tool Integration

Expandable

func (p *MyAgent) executeTool(ctx context.Context, toolID uint32, operation string, params map[string]interface{}) (string, error) {
    // Execute tool via SDK
    result, err := ai_studio_sdk.ExecuteTool(ctx, toolID, operation, params)
    if err != nil {
        return "", err
    }

    return result.Data, nil
}

func (p *MyAgent) HandleAgentMessage(
    req *pb.AgentMessageRequest,
    stream pb.PluginService_HandleAgentMessageServer) error {

    // Check if user wants to search
    if strings.Contains(strings.ToLower(req.UserMessage), "search") {
        // Find search tool
        var searchTool *pb.AgentToolInfo
        for _, tool := range req.AvailableTools {
            if tool.Slug == "web-search" {
                searchTool = tool
                break
            }
        }

        if searchTool != nil {
            stream.Send(&pb.AgentMessageChunk{
                Type:    pb.AgentMessageChunk_TOOL_CALL,
                Content: "Searching the web...",
                IsFinal: false,
            })

            result, err := p.executeTool(stream.Context(), searchTool.Id, "search", map[string]interface{}{
                "query": extractSearchQuery(req.UserMessage),
            })

            if err == nil {
                stream.Send(&pb.AgentMessageChunk{
                    Type:    pb.AgentMessageChunk_TOOL_RESULT,
                    Content: fmt.Sprintf("Search results: %s", result),
                    IsFinal: false,
                })
            }
        }
    }

    // Continue with LLM call...
    return p.callLLM(req, stream)
}

Best Practices

Performance

Use non-blocking I/O for external calls
Stream responses as they arrive
Set appropriate timeouts
Cache frequently accessed data
Minimize LLM calls where possible

Error Handling

Always send ERROR chunk on failures
Set IsFinal=true for ERROR chunks
Provide descriptive error messages
Log errors with context (session ID, plugin ID)
Implement graceful fallbacks

User Experience

Send THINKING chunks for long operations
Stream content as it’s generated
Provide progress updates
Use metadata for rich responses
Always send DONE chunk

Security

Validate all inputs
Sanitize user messages
Don’t expose sensitive data in responses
Use permission scopes appropriately
Log security-relevant events

API Reference

Agent Management (Admin Only)

Endpoint	Method	Description
`/api/v1/agents`	GET	List all accessible agents
`/api/v1/agents`	POST	Create new agent config
`/api/v1/agents/{id}`	GET	Get agent details
`/api/v1/agents/{id}`	PUT	Update agent config
`/api/v1/agents/{id}`	DELETE	Delete agent config
`/api/v1/agents/{id}/activate`	POST	Activate agent
`/api/v1/agents/{id}/deactivate`	POST	Deactivate agent

Agent Communication (Users)

Endpoint	Method	Description
`/api/agents/{id}/stream`	GET	Establish SSE connection
`/api/agents/{id}/message`	POST	Send message to active session

SessionAware Pattern (Recommended)

Agent plugins should implement the SessionAware interface to warm up the Service API connection:

// OnSessionReady is called when the session broker is ready
func (p *MyAgentPlugin) OnSessionReady(ctx plugin_sdk.Context) {
    // Warm up Service API connection
    if ai_studio_sdk.IsInitialized() {
        _, _ = ai_studio_sdk.GetPluginsCount(context.Background())
    }
}

// OnSessionClosing is called when the session is closing
func (p *MyAgentPlugin) OnSessionClosing(ctx plugin_sdk.Context) {
    // Clean up session resources
}

Service Broker ID

For LLM calls via the Service API, always set the service broker ID from the request:

func (p *MyAgentPlugin) HandleAgentMessage(req *pb.AgentMessageRequest, stream pb.PluginService_HandleAgentMessageServer) error {
    // Critical: Set service broker ID for LLM calls
    if req.ServiceBrokerId != 0 {
        ai_studio_sdk.SetServiceBrokerID(req.ServiceBrokerId)
    }
    // ... rest of handler
}

Troubleshooting

Agent Not Appearing in Chat

Check plugin_type is "agent"
Verify agent configuration is created for an app
Ensure plugin is active (is_active: true)
Check logs for initialization errors

LLM Calls Failing

Verify llms.proxy permission in manifest
Check SDK is initialized
Ensure LLMs are available in app configuration
Review LLM provider credentials

Streaming Not Working

Ensure you’re sending chunks sequentially
Always set IsFinal=true for final chunk
Send DONE chunk at the end
Check for errors in stream.Send()

Context Not Available

Verify plugin context is passed correctly
Check broker ID is set for service API calls
Ensure session ID is provided
Review plugin initialization

Working Example

See the Echo Agent example in the repository: Path: examples/plugins/studio/echo-agent/ The Echo Agent demonstrates:

Basic HandleAgentMessage implementation
LLM selection from available LLMs
Calling LLMs via ai_studio_sdk.CallLLM()
Streaming responses back to users
Configuration handling via JSON schema
Session warmup with OnSessionReady

Limitations (Experimental)

No built-in conversation persistence (agents must manage their own state if needed)
Tool execution must be implemented by the agent
No automatic retry on LLM failures
Single concurrent conversation per session

General

AI Studio

MCPs

​Availability

​Architecture Overview

​Key Concepts

​Overview

​Unified SDK Integration

​Import and Structure

​Lifecycle Methods

​Agent Capability

​Serving the Plugin

​Service API Access

​Quick Start

​1. Project Structure

​2. Create Manifest

​3. Implement Agent Plugin

​4. Build and Deploy

​5. Create Agent Object

​6. Use in Chat Interface

​Agent Message Request

​Available Tools

​Available Datasources

​Available LLMs

​Configuration

​Conversation History

​Streaming Responses

​Chunk Types

​Send Content

​Send Thinking

​Send Tool Call

​Send Error

​Send Done

​Complete Example: Echo Agent

​Configuration Schema

​Advanced Patterns

​Multi-Step Reasoning

​Tool Integration

​Best Practices

​Performance

​Error Handling

​User Experience

​Security

​API Reference

​Agent Management (Admin Only)

​Agent Communication (Users)

​SessionAware Pattern (Recommended)

​Service Broker ID

​Troubleshooting

​Working Example

​Limitations (Experimental)

Availability

Architecture Overview

Key Concepts

Overview

Unified SDK Integration

Import and Structure

Lifecycle Methods

Agent Capability

Serving the Plugin

Service API Access

Quick Start

1. Project Structure

2. Create Manifest

3. Implement Agent Plugin

4. Build and Deploy

5. Create Agent Object

6. Use in Chat Interface

Agent Message Request

Available Tools

Available Datasources

Available LLMs

Configuration

Conversation History

Streaming Responses

Chunk Types

Send Content

Send Thinking

Send Tool Call

Send Error

Send Done

Complete Example: Echo Agent

Configuration Schema

Advanced Patterns

Multi-Step Reasoning

Tool Integration

Best Practices

Performance

Error Handling

User Experience

Security

API Reference

Agent Management (Admin Only)

Agent Communication (Users)

SessionAware Pattern (Recommended)

Service Broker ID

Troubleshooting

Working Example

Limitations (Experimental)