Documentation Index Fetch the complete documentation index at: https://docs.elizaos.ai/llms.txt
Use this file to discover all available pages before exploring further.
Real Patterns from Real Plugins
These aren’t theoretical - they’re extracted from production plugins. Each pattern solves a specific problem you’ll encounter when building agents that actually work.
Action Chaining Multi-step workflows where actions build on each other
Callbacks Send progress updates before actions complete
State Composition Combine multiple providers elegantly
Error Recovery Graceful degradation and retry strategies
Action Chaining and Callbacks
Action chaining allows multiple actions to execute sequentially, with each action accessing previous results. This enables complex workflows where actions build upon each other’s outputs.
The ActionResult Interface
Actions return an ActionResult object that standardizes how actions communicate their outcomes. This interface includes:
success (required): Boolean indicating whether the action completed successfully
text : Optional human-readable description of the result
values : Key-value pairs to merge into the state for subsequent actions
data : Raw data payload with action-specific results
error : Error information if the action failed
The success field is the only required field, making it easy to create simple results while supporting complex data passing for action chaining.
For interface definitions, see Plugin Reference . For component basics, see Plugin Components .
Handler Callbacks
The HandlerCallback provides a mechanism for actions to send immediate feedback to users before the action completes:
export type HandlerCallback = ( response : Content , files ?: any ) => Promise < Memory []>;
Example usage:
async handler (
runtime : IAgentRuntime ,
message : Memory ,
_state ?: State ,
_options ?: Record < string , unknown > ,
callback ?: HandlerCallback
): Promise < ActionResult > {
try {
// Send immediate feedback
await callback ?.({
text: `Starting to process your request...` ,
source: message . content . source
});
// Perform action logic
const result = await performComplexOperation ();
// Send success message to user via callback
await callback ?.({
text: `Created issue: ${ result . title } ( ${ result . identifier } ) \n\n View it at: ${ result . url } ` ,
source: message . content . source
});
// Return structured result for potential chaining
return {
success: true ,
text: `Created issue: ${ result . title } ` ,
data: {
issueId: result . id ,
identifier: result . identifier ,
url: result . url
}
};
} catch (error) {
// Send error message to user
await callback?.({
text : `Failed to create issue: ${ error . message } ` ,
source : message . content . source
});
return {
success : false ,
text : `Failed to create issue: ${ error . message } ` ,
error : error instanceof Error ? error : new Error ( String ( error ))
};
}
}
Action Context and Previous Results
When multiple actions are executed in sequence, each action receives an ActionContext that provides access to previous action results:
export interface ActionContext {
/** Results from previously executed actions in this run */
previousResults : ActionResult [];
/** Get a specific previous result by action name */
getPreviousResult ?: ( actionName : string ) => ActionResult | undefined ;
}
The runtime automatically provides this context in the options parameter:
async handler (
runtime : IAgentRuntime ,
message : Memory ,
state ?: State ,
options ?: Record < string , unknown > ,
callback ?: HandlerCallback
): Promise < ActionResult > {
// Access the action context
const context = options ?. context as ActionContext ;
// Get results from a specific previous action
const previousResult = context ?. getPreviousResult ?.( 'CREATE_LINEAR_ISSUE' );
if ( previousResult ? .data?.issueId) {
// Use data from previous action
const issueId = previousResult . data . issueId ;
// ... continue with logic using previous result ...
}
}
Action Execution Flow
The runtime’s processActions method manages the execution flow:
Action Planning : When multiple actions are detected, the runtime creates an execution plan
Sequential Execution : Actions execute in the order specified by the agent
State Accumulation : Each action’s results are merged into the accumulated state
Working Memory : Results are stored in working memory for access during execution
Error Handling : Failed actions don’t stop the chain unless marked as critical
Working Memory Management
The runtime maintains a working memory that stores recent action results:
// Results are automatically stored in state.data.workingMemory
const memoryEntry : WorkingMemoryEntry = {
actionName: action . name ,
result: actionResult ,
timestamp: Date . now ()
};
The system keeps the most recent 50 entries (configurable) to prevent memory bloat.
Action Patterns
Decision-Making Actions
Actions can use the LLM to make intelligent decisions based on context:
export const muteRoomAction : Action = {
name: 'MUTE_ROOM' ,
similes: [ 'SHUT_UP' , 'BE_QUIET' , 'STOP_TALKING' , 'SILENCE' ],
description: 'Mutes a room if asked to or if the agent is being annoying' ,
validate : async ( runtime , message ) => {
// Check if already muted
const roomState = await runtime . getParticipantUserState ( message . roomId , runtime . agentId );
return roomState !== 'MUTED' ;
},
handler : async ( runtime , message , state ) => {
// Create a decision prompt
const shouldMuteTemplate = `# Task: Should {{agentName}} mute this room?
{{recentMessages}}
Should {{agentName}} mute and stop responding unless mentioned?
Respond YES if:
- User asked to stop/be quiet
- Agent responses are annoying users
- Conversation is hostile
Otherwise NO.` ;
const prompt = composePromptFromState ({ state , template: shouldMuteTemplate });
const decision = await runtime . useModel ( ModelType . TEXT_SMALL , {
prompt ,
runtime ,
});
if ( decision . toLowerCase (). includes ( 'yes' )) {
await runtime . setParticipantUserState ( message . roomId , runtime . agentId , 'MUTED' );
return {
success: true ,
text: 'Going silent in this room' ,
values: { roomMuted: true },
};
}
return {
success: false ,
text: 'Continuing to participate'
};
}
};
Multi-Step Actions
Actions that need to perform multiple steps with intermediate feedback:
export const deployContractAction : Action = {
name: 'DEPLOY_CONTRACT' ,
description: 'Deploy a smart contract with multiple steps' ,
handler : async ( runtime , message , state , options , callback ) => {
try {
// Step 1: Compile
await callback ?.({
text: '📝 Compiling contract...' ,
actions: [ 'DEPLOY_CONTRACT' ]
});
const compiled = await compileContract ( state . contractCode );
// Step 2: Estimate gas
await callback ?.({
text: '⛽ Estimating gas costs...' ,
actions: [ 'DEPLOY_CONTRACT' ]
});
const gasEstimate = await estimateGas ( compiled );
// Step 3: Deploy
await callback ?.({
text: `🚀 Deploying with gas: ${ gasEstimate } ...` ,
actions: [ 'DEPLOY_CONTRACT' ]
});
const deployed = await deploy ( compiled , gasEstimate );
// Step 4: Verify
await callback ?.({
text: '✅ Verifying deployment...' ,
actions: [ 'DEPLOY_CONTRACT' ]
});
await verifyContract ( deployed . address );
return {
success: true ,
text: `Contract deployed at ${ deployed . address } ` ,
values: {
contractAddress: deployed . address ,
transactionHash: deployed . txHash ,
gasUsed: deployed . gasUsed
},
data: {
deployment: deployed ,
verification: true
}
};
} catch ( error ) {
return {
success: false ,
text: `Deployment failed: ${ error . message } ` ,
error
};
}
}
};
API Integration Actions
Pattern for external API calls with retries and error handling:
export const apiAction : Action = {
name: 'API_CALL' ,
handler : async ( runtime , message , state , options , callback ) => {
const maxRetries = 3 ;
let lastError : Error | null = null ;
for ( let attempt = 1 ; attempt <= maxRetries ; attempt ++ ) {
try {
await callback ?.({
text: `Attempt ${ attempt } / ${ maxRetries } ...`
});
const result = await callExternalAPI ({
endpoint: state . endpoint ,
data: state . data ,
timeout: 5000
});
return {
success: true ,
text: 'API call successful' ,
data: result
};
} catch ( error ) {
lastError = error as Error ;
if ( attempt < maxRetries ) {
await callback ?.({
text: `Attempt ${ attempt } failed, retrying...`
});
await new Promise ( r => setTimeout ( r , 1000 * attempt )); // Exponential backoff
}
}
}
return {
success: false ,
text: `API call failed after ${ maxRetries } attempts` ,
error: lastError
};
}
};
Context-Aware Actions
Actions that adapt based on conversation context:
export const contextAwareAction : Action = {
name: 'CONTEXT_RESPONSE' ,
handler : async ( runtime , message , state , options , callback ) => {
// Analyze conversation sentiment
const sentiment = await analyzeSentiment ( state . recentMessages );
// Adjust response based on context
let responseStrategy : string ;
if ( sentiment . score < - 0.5 ) {
responseStrategy = 'empathetic' ;
} else if ( sentiment . score > 0.5 ) {
responseStrategy = 'enthusiastic' ;
} else {
responseStrategy = 'neutral' ;
}
// Generate context-appropriate response
const response = await generateResponse (
state ,
responseStrategy ,
runtime
);
return {
success: true ,
text: response . text ,
values: {
sentiment: sentiment . score ,
strategy: responseStrategy
}
};
}
};
Action Composition
Compose Multiple Actions
// Compose multiple actions into higher-level operations
export const compositeAction : Action = {
name: 'SEND_AND_TRACK' ,
description: 'Send a message and track its delivery' ,
handler : async ( runtime , message , state , options , callback ) => {
// Execute sub-actions
const sendResult = await sendMessageAction . handler (
runtime ,
message ,
state ,
options ,
callback
);
if ( ! sendResult . success ) {
return sendResult ; // Propagate failure
}
// Track the sent message
const trackingId = generateTrackingId ();
await runtime . createMemory ({
id: trackingId ,
entityId: message . entityId ,
roomId: message . roomId ,
content: {
type: 'message_tracking' ,
sentTo: sendResult . data . targetId ,
sentAt: Date . now (),
messageContent: sendResult . data . messageContent ,
}
}, 'tracking' );
return {
success: true ,
text: `Message sent and tracked ( ${ trackingId } )` ,
values: {
... sendResult . values ,
trackingId ,
tracked: true ,
},
data: {
sendResult ,
trackingId ,
}
};
}
};
Workflow Orchestration
export const workflowAction : Action = {
name: 'COMPLEX_WORKFLOW' ,
handler : async ( runtime , message , state , options , callback ) => {
const workflow = [
{ action: 'VALIDATE_INPUT' , required: true },
{ action: 'FETCH_DATA' , required: true },
{ action: 'PROCESS_DATA' , required: false },
{ action: 'STORE_RESULTS' , required: true },
{ action: 'NOTIFY_USER' , required: false }
];
const results : ActionResult [] = [];
for ( const step of workflow ) {
const action = runtime . getAction ( step . action );
if ( ! action ) {
if ( step . required ) {
return {
success: false ,
text: `Required action ${ step . action } not found`
};
}
continue ;
}
const result = await action . handler (
runtime ,
message ,
state ,
{ context: { previousResults: results } },
callback
);
results . push ( result );
if ( ! result . success && step . required ) {
return {
success: false ,
text: `Workflow failed at ${ step . action } ` ,
data: { failedStep: step . action , results }
};
}
// Merge values into state for next action
state = {
... state ,
values: {
... state . values ,
... result . values
}
};
}
return {
success: true ,
text: 'Workflow completed successfully' ,
data: { workflowResults: results }
};
}
};
Self-Modifying Actions
Actions that learn and adapt their behavior:
export const learningAction : Action = {
name: 'ADAPTIVE_RESPONSE' ,
handler : async ( runtime , message , state ) => {
// Retrieve past performance
const history = await runtime . getMemories ({
tableName: 'action_feedback' ,
roomId: message . roomId ,
count: 100 ,
});
// Analyze what worked well
const analysis = await runtime . useModel ( ModelType . TEXT_LARGE , {
prompt: `Analyze these past interactions and identify patterns:
${ JSON . stringify ( history ) }
What response strategies were most effective?` ,
});
// Adapt behavior based on learning
const strategy = determineStrategy ( analysis );
const response = await generateResponse ( state , strategy );
// Store for future learning
await runtime . createMemory ({
id: generateId (),
content: {
type: 'action_feedback' ,
strategy: strategy . name ,
context: state . text ,
response: response . text ,
}
}, 'action_feedback' );
return {
success: true ,
text: response . text ,
values: {
strategyUsed: strategy . name ,
confidence: strategy . confidence ,
}
};
}
};
Provider Patterns
Conditional Providers
Providers that only provide data under certain conditions:
export const conditionalProvider : Provider = {
name: 'PREMIUM_DATA' ,
private: true ,
get : async ( runtime , message , state ) => {
// Check if user has premium access
const user = await runtime . getUser ( message . entityId );
if ( ! user . isPremium ) {
return {
text: '' ,
values: {},
data: { available: false }
};
}
// Provide premium data
const premiumData = await fetchPremiumData ( user );
return {
text: formatPremiumData ( premiumData ),
values: premiumData ,
data: { available: true , ... premiumData }
};
}
};
Aggregating Providers
Providers that combine data from multiple sources:
export const aggregateProvider : Provider = {
name: 'MARKET_OVERVIEW' ,
position: 50 , // Run after individual data providers
get : async ( runtime , message , state ) => {
// Aggregate from multiple sources
const [ stocks , crypto , forex ] = await Promise . all ([
fetchStockData (),
fetchCryptoData (),
fetchForexData ()
]);
const overview = {
stocksUp: stocks . filter ( s => s . change > 0 ). length ,
stocksDown: stocks . filter ( s => s . change < 0 ). length ,
cryptoMarketCap: crypto . reduce (( sum , c ) => sum + c . marketCap , 0 ),
forexVolatility: calculateVolatility ( forex )
};
return {
text: `Market Overview:
- Stocks: ${ overview . stocksUp } up, ${ overview . stocksDown } down
- Crypto Market Cap: $ ${ overview . cryptoMarketCap . toLocaleString () }
- Forex Volatility: ${ overview . forexVolatility } ` ,
values: overview ,
data: { stocks , crypto , forex }
};
}
};
Best Practices for Action Chaining
Always Return ActionResult : Even for simple actions, return a proper ActionResult object:
return {
success: true ,
text: "Action completed" ,
data: { /* any data for next actions */ }
};
Use Callbacks for User Feedback : Send immediate feedback via callbacks rather than waiting for the action to complete:
await callback ?.({
text: "Processing your request..." ,
source: message . content . source
});
Store Identifiers in Data : When creating resources, store identifiers that subsequent actions might need:
return {
success: true ,
data: {
resourceId: created . id ,
resourceUrl: created . url
}
};
Handle Missing Dependencies : Check if required previous results exist:
const previousResult = context ?. getPreviousResult ?.( 'REQUIRED_ACTION' );
if ( ! previousResult ?. success ) {
return {
success: false ,
text: "Required previous action did not complete successfully"
};
}
Maintain Backward Compatibility : The runtime handles legacy action returns (void, boolean) but new actions should use ActionResult.
Example: Multi-Step Workflow
Here’s an example of a multi-step workflow using action chaining:
// User: "Create a bug report for the login issue and assign it to John"
// Agent executes: REPLY, CREATE_LINEAR_ISSUE, UPDATE_LINEAR_ISSUE
// Action 1: CREATE_LINEAR_ISSUE
{
success : true ,
data : {
issueId : "abc-123" ,
identifier : "BUG-456"
}
}
// Action 2: UPDATE_LINEAR_ISSUE (can access previous result)
async handler ( runtime , message , state , options , callback ) {
const context = options ?. context as ActionContext ;
const createResult = context ?. getPreviousResult ?.( 'CREATE_LINEAR_ISSUE' );
if ( createResult ?. data ?. issueId ) {
// Use the issue ID from previous action
await updateIssue ( createResult . data . issueId , { assignee: "John" });
return {
success: true ,
text: "Issue assigned to John"
};
}
}
Common Patterns
Create and Configure : Create a resource, then configure it
Search and Update : Find resources, then modify them
Validate and Execute : Check conditions, then perform actions
Aggregate and Report : Collect data from multiple sources, then summarize
The action chaining system provides a powerful way to build complex, multi-step workflows while maintaining clean separation between individual actions.
Real-World Implementation Patterns
This section documents actual patterns and structures used in production elizaOS plugins based on examination of real implementations.
Basic Plugin Structure Pattern
Every plugin follows this core structure pattern (from plugin-starter):
import type { Plugin } from '@elizaos/core' ;
export const myPlugin : Plugin = {
name: 'plugin-name' ,
description: 'Plugin description' ,
// Core components
actions: [], // Actions the plugin provides
providers: [], // Data providers
services: [], // Background services
evaluators: [], // Response evaluators
// Optional components
init : async ( config ) => {}, // Initialization logic
models: {}, // Custom model implementations
routes: [], // HTTP routes
events: {}, // Event handlers
tests: [], // Test suites
dependencies: [], // Other required plugins
};
Bootstrap Plugin Pattern
The most complex and comprehensive plugin that provides core functionality:
export const bootstrapPlugin : Plugin = {
name: 'bootstrap' ,
description: 'Agent bootstrap with basic actions and evaluators' ,
actions: [
actions . replyAction ,
actions . followRoomAction ,
actions . ignoreAction ,
actions . sendMessageAction ,
actions . generateImageAction ,
// ... more actions
],
providers: [
providers . timeProvider ,
providers . entitiesProvider ,
providers . characterProvider ,
providers . recentMessagesProvider ,
// ... more providers
],
services: [ TaskService ],
evaluators: [ evaluators . reflectionEvaluator ],
events: {
[EventType. MESSAGE_RECEIVED ]: [ messageReceivedHandler ],
[EventType. POST_GENERATED ]: [ postGeneratedHandler ],
// ... more event handlers
}
};
Service Plugin Pattern (Discord, Telegram)
Platform integration plugins focus on service implementation:
// Discord Plugin
const discordPlugin : Plugin = {
name: "discord" ,
description: "Discord service plugin for integration with Discord servers" ,
services: [ DiscordService ],
actions: [
chatWithAttachments ,
downloadMedia ,
joinVoice ,
leaveVoice ,
summarize ,
transcribeMedia ,
],
providers: [ channelStateProvider , voiceStateProvider ],
tests: [ new DiscordTestSuite ()],
init : async ( config , runtime ) => {
// Check for required API tokens
const token = runtime . getSetting ( "DISCORD_API_TOKEN" );
if ( ! token ) {
logger . warn ( "Discord API Token not provided" );
}
},
};
// Telegram Plugin (minimal)
const telegramPlugin : Plugin = {
name: TELEGRAM_SERVICE_NAME ,
description: 'Telegram client plugin' ,
services: [ TelegramService ],
tests: [ new TelegramTestSuite ()],
};
Action Implementation Pattern
Actions follow a consistent structure with validation and execution:
const helloWorldAction : Action = {
name: 'HELLO_WORLD' ,
similes: [ 'GREET' , 'SAY_HELLO' ], // Alternative names
description: 'Responds with a simple hello world message' ,
validate : async ( runtime , message , state ) => {
// Return true if action can be executed
return true ;
},
handler : async ( runtime , message , state , options , callback , responses ) => {
try {
const responseContent : Content = {
text: 'hello world!' ,
actions: [ 'HELLO_WORLD' ],
source: message . content . source ,
};
if ( callback ) {
await callback ( responseContent );
}
return responseContent ;
} catch ( error ) {
logger . error ( 'Error in HELLO_WORLD action:' , error );
throw error ;
}
},
examples: [
[
{
name: '{{name1}}' ,
content: { text: 'Can you say hello?' }
},
{
name: '{{name2}}' ,
content: {
text: 'hello world!' ,
actions: [ 'HELLO_WORLD' ]
}
}
]
]
};
Complex Action Pattern (Reply Action)
export const replyAction = {
name: 'REPLY' ,
similes: [ 'GREET' , 'REPLY_TO_MESSAGE' , 'SEND_REPLY' , 'RESPOND' ],
description: 'Replies to the current conversation' ,
validate : async ( runtime ) => true ,
handler : async ( runtime , message , state , options , callback , responses ) => {
// Compose state with providers
state = await runtime . composeState ( message , [ 'RECENT_MESSAGES' ]);
// Generate response using LLM
const prompt = composePromptFromState ({ state , template: replyTemplate });
const response = await runtime . useModel ( ModelType . OBJECT_LARGE , { prompt });
const responseContent = {
thought: response . thought ,
text: response . message || '' ,
actions: [ 'REPLY' ],
};
await callback ( responseContent );
return true ;
}
};
Provider Implementation Pattern
Providers supply contextual data to the agent:
export const timeProvider : Provider = {
name: 'TIME' ,
get : async ( runtime , message ) => {
const currentDate = new Date ();
const options = {
timeZone: 'UTC' ,
dateStyle: 'full' as const ,
timeStyle: 'long' as const ,
};
const humanReadable = new Intl . DateTimeFormat ( 'en-US' , options ). format ( currentDate );
return {
data: { time: currentDate },
values: { time: humanReadable },
text: `The current date and time is ${ humanReadable } .` ,
};
},
};
Plugin Initialization Pattern
Plugins can have initialization logic:
const myPlugin : Plugin = {
name: 'my-plugin' ,
config: {
EXAMPLE_VARIABLE: process . env . EXAMPLE_VARIABLE ,
},
async init ( config : Record < string , string >) {
// Validate configuration
const validatedConfig = await configSchema . parseAsync ( config );
// Set environment variables
for ( const [ key , value ] of Object . entries ( validatedConfig )) {
if ( value ) process . env [ key ] = value ;
}
},
};
See Also
Plugin Architecture Understand the overall plugin system design
Development Guide Build your first plugin with step-by-step guidance
Plugin Schemas Learn about configuration and validation schemas
Plugin Migration Migrate existing plugins to new patterns