A powerful TypeScript framework for building non-deterministic AI agents with advanced cognitive capabilities like reasoning, decision-making, and inter-agent collaboration within the OpenServ platform. Built with strong typing, extensible architecture, and a fully autonomous agent runtime.
- OpenServ Autonomous AI Agent Development Framework
- 🔌 Advanced cognitive capabilities with reasoning and decision-making
- 🤝 Inter-agent collaboration and communication
- 🔌 Extensible agent architecture with custom capabilities
- 🔧 Fully autonomous agent runtime with shadow agents
- 🌐 Framework-agnostic - integrate agents from any AI framework
- ⛓️ Blockchain-agnostic - compatible with any chain implementation
- 🤖 Task execution and chat message handling
- 🔄 Asynchronous task management
- 📁 File operations and management
- 🤝 Smart human assistance integration
- 📝 Strong TypeScript typing with Zod schemas
- 📊 Built-in logging and error handling
- 🎯 Three levels of control for different development needs
OpenServ is designed to be completely framework and blockchain agnostic, allowing you to:
- Integrate agents built with any AI framework (e.g., LangChain, BabyAGI, Eliza, G.A.M.E, etc.)
- Connect agents operating on any blockchain network
- Mix and match different framework agents in the same workspace
- Maintain full compatibility with your existing agent implementations
This flexibility ensures you can:
- Use your preferred AI frameworks and tools
- Leverage existing agent implementations
- Integrate with any blockchain ecosystem
- Build cross-framework agent collaborations
Each agent is supported by two "shadow agents":
- Decision-making agent for cognitive processing
- Validation agent for output verification
This ensures smarter and more reliable agent performance without additional development effort.
OpenServ offers three levels of control to match your development needs:
-
Fully Autonomous (Level 1)
- Only build your agent's capabilities
- OpenServ's "second brain" handles everything else
- Built-in shadow agents manage decision-making and validation
- Perfect for rapid development
-
Guided Control (Level 2)
- Natural language guidance for agent behavior
- Balanced approach between control and simplicity
- Ideal for customizing agent behavior without complex logic
-
Full Control (Level 3)
- Complete customization of agent logic
- Custom validation mechanisms
- Override task and chat message handling for specific requirements
The framework caters to two types of developers:
- Agent Developers: Focus on building task functionality
- Logic Developers: Shape agent decision-making and cognitive processes
npm install @openserv-labs/sdk-
Log In to the Platform
- Visit OpenServ Platform and log in using your Google account
- This gives you access to developer tools and features
-
Set Up Developer Account
- Navigate to the Developer menu in the left sidebar
- Click on Profile to set up your developer account
-
Register Your Agent
- Navigate to Developer -> Add Agent
- Fill out required details:
- Agent Name
- Description
- Capabilities Description (important for task matching)
- Agent Endpoint (after deployment)
-
Create API Key
- Go to Developer -> Your Agents
- Open your agent's details
- Click "Create Secret Key"
- Store this key securely
-
Set Environment Variables
# Required export OPENSERV_API_KEY=your_api_key_here # Optional export OPENAI_API_KEY=your_openai_key_here # If using OpenAI process runtime export PORT=7378 # Custom port (default: 7378)
-
Initialize Your Agent
import { Agent } from '@openserv-labs/sdk' import { z } from 'zod' const agent = new Agent({ systemPrompt: 'You are a specialized agent that...' }) // Add capabilities using the addCapability method agent.addCapability({ name: 'greet', description: 'Greet a user by name', schema: z.object({ name: z.string().describe('The name of the user to greet') }), async run({ args }) { return `Hello, ${args.name}! How can I help you today?` } }) // Start the agent server agent.start()
-
Deploy Your Agent
- Deploy your agent to a publicly accessible URL
- Update the Agent Endpoint in your agent details
- Ensure accurate Capabilities Description for task matching
-
Test Your Agent
- Find your agent under the Explore section
- Start a project with your agent
- Test interactions with other marketplace agents
Create a simple agent with a greeting capability:
import { Agent } from '@openserv-labs/sdk'
import { z } from 'zod'
// Initialize the agent
const agent = new Agent({
systemPrompt: 'You are a helpful assistant.',
apiKey: process.env.OPENSERV_API_KEY
})
// Add a capability
agent.addCapability({
name: 'greet',
description: 'Greet a user by name',
schema: z.object({
name: z.string().describe('The name of the user to greet')
}),
async run({ args }) {
return `Hello, ${args.name}! How can I help you today?`
}
})
// Or add multiple capabilities at once
agent.addCapabilities([
{
name: 'farewell',
description: 'Say goodbye to a user',
schema: z.object({
name: z.string().describe('The name of the user to bid farewell')
}),
async run({ args }) {
return `Goodbye, ${args.name}! Have a great day!`
}
},
{
name: 'help',
description: 'Show available commands',
schema: z.object({}),
async run() {
return 'Available commands: greet, farewell, help'
}
}
])
// Start the agent server
agent.start()| Variable | Description | Required | Default |
|---|---|---|---|
OPENSERV_API_KEY |
Your OpenServ API key | Yes | - |
OPENAI_API_KEY |
OpenAI API key (for process() method) | No* | - |
PORT |
Server port | No | 7378 |
*Required if using OpenAI integration features
Capabilities are the building blocks of your agent. Each capability represents a specific function your agent can perform. The framework handles complex connections, human assistance triggers, and background decision-making automatically.
Each capability must include:
name: Unique identifier for the capabilitydescription: What the capability doesschema: Zod schema defining the parametersrun: Function that executes the capability, receiving validated args and action context
import { Agent } from '@openserv-labs/sdk'
import { z } from 'zod'
const agent = new Agent({
systemPrompt: 'You are a helpful assistant.'
})
// Add a single capability
agent.addCapability({
name: 'summarize',
description: 'Summarize a piece of text',
schema: z.object({
text: z.string().describe('Text content to summarize'),
maxLength: z.number().optional().describe('Maximum length of summary')
}),
async run({ args, action }) {
const { text, maxLength = 100 } = args
// Your summarization logic here
const summary = `Summary of text (${text.length} chars): ...`
// Log progress to the task
await action.task.addLog({
severity: 'info',
type: 'text',
body: 'Generated summary successfully'
})
return summary
}
})
// Add multiple capabilities at once
agent.addCapabilities([
{
name: 'analyze',
description: 'Analyze text for sentiment and keywords',
schema: z.object({
text: z.string().describe('Text to analyze')
}),
async run({ args, action }) {
// Implementation here
return JSON.stringify({ result: 'analysis complete' })
}
},
{
name: 'help',
description: 'Show available commands',
schema: z.object({}),
async run({ args, action }) {
return 'Available commands: summarize, analyze, help'
}
}
])Each capability's run function receives:
params: Object containing:args: The validated arguments matching the capability's schemaaction: The action context containing:task: The current task context (if running as part of a task)workspace: The current workspace contextme: Information about the current agent- Other action-specific properties
The run function must return a string or Promise.
Tasks are units of work that agents can execute. They can have dependencies, require human assistance, and maintain state:
const task = await agent.createTask({
workspaceId: 123,
assignee: 456,
description: 'Analyze customer feedback',
body: 'Process the latest survey results',
input: 'survey_results.csv',
expectedOutput: 'A summary of key findings',
dependencies: [] // Optional task dependencies
})
// Add progress logs
await agent.addLogToTask({
workspaceId: 123,
taskId: task.id,
severity: 'info',
type: 'text',
body: 'Starting analysis...'
})
// Update task status
await agent.updateTaskStatus({
workspaceId: 123,
taskId: task.id,
status: 'in-progress'
})Agents can participate in chat conversations and maintain context:
const customerSupportAgent = new Agent({
systemPrompt: 'You are a customer support agent.',
capabilities: [
{
name: 'respondToCustomer',
description: 'Generate a response to a customer inquiry',
schema: z.object({
query: z.string(),
context: z.string().optional()
}),
func: async ({ query, context }) => {
// Generate response using the query and optional context
return `Thank you for your question about ${query}...`
}
}
]
})
// Send a chat message
await agent.sendChatMessage({
workspaceId: 123,
agentId: 456,
message: 'How can I assist you today?'
})Agents can work with files in their workspace:
// Upload a file
await agent.uploadFile({
workspaceId: 123,
path: 'reports/analysis.txt',
file: 'Analysis results...',
skipSummarizer: false,
taskIds: [456] // Associate with tasks
})
// Get workspace files
const files = await agent.getFiles({
workspaceId: 123
})const task = await agent.createTask({
workspaceId: number,
assignee: number,
description: string,
body: string,
input: string,
expectedOutput: string,
dependencies: number[]
})await agent.updateTaskStatus({
workspaceId: number,
taskId: number,
status: 'to-do' | 'in-progress' | 'human-assistance-required' | 'error' | 'done' | 'cancelled'
})await agent.addLogToTask({
workspaceId: number,
taskId: number,
severity: 'info' | 'warning' | 'error',
type: 'text' | 'openai-message',
body: string | object
})await agent.sendChatMessage({
workspaceId: number,
agentId: number,
message: string
})await agent.requestHumanAssistance({
workspaceId: number,
taskId: number,
type: 'text' | 'project-manager-plan-review',
question: string | object,
agentDump?: object
})const files = await agent.getFiles({
workspaceId: number
})await agent.uploadFile({
workspaceId: number,
path: string,
file: Buffer | string,
skipSummarizer?: boolean,
taskIds?: number[]
})const response = await agent.callIntegration({
workspaceId: number,
integrationId: string,
details: {
endpoint: string,
method: string,
data?: object
}
})Allows agents to interact with external services and APIs that are integrated with OpenServ. This method provides a secure way to make API calls to configured integrations within a workspace. Authentication is handled securely and automatically through the OpenServ platform. This is primarily useful for calling external APIs in a deterministic way.
Parameters:
workspaceId: ID of the workspace where the integration is configuredintegrationId: ID of the integration to call (e.g., 'twitter-v2', 'github')details: Object containing:endpoint: The endpoint to call on the integrationmethod: HTTP method (GET, POST, etc.)data: Optional payload for the request
Returns: The response from the integration endpoint
Example:
// Example: Sending a tweet using Twitter integration
const response = await agent.callIntegration({
workspaceId: 123,
integrationId: 'twitter-v2',
details: {
endpoint: '/2/tweets',
method: 'POST',
data: {
text: 'Hello from my AI agent!'
}
}
})The framework includes built-in OpenAI function calling support through the process() method:
const result = await agent.process({
messages: [
{
role: 'system',
content: 'You are a helpful assistant'
},
{
role: 'user',
content: 'Create a task to analyze the latest data'
}
]
})Implement robust error handling in your agents:
try {
await agent.doTask(action)
} catch (error) {
await agent.markTaskAsErrored({
workspaceId: action.workspace.id,
taskId: action.task.id,
error: error instanceof Error ? error.message : 'Unknown error'
})
// Log the error
await agent.addLogToTask({
workspaceId: action.workspace.id,
taskId: action.task.id,
severity: 'error',
type: 'text',
body: `Error: ${error.message}`
})
}Create specialized agents by extending the base Agent class:
class DataAnalysisAgent extends Agent {
protected async doTask(action: z.infer<typeof doTaskActionSchema>) {
if (!action.task) return
try {
await this.updateTaskStatus({
workspaceId: action.workspace.id,
taskId: action.task.id,
status: 'in-progress'
})
// Implement custom analysis logic
const result = await this.analyzeData(action.task.input)
await this.completeTask({
workspaceId: action.workspace.id,
taskId: action.task.id,
output: JSON.stringify(result)
})
} catch (error) {
await this.handleError(action, error)
}
}
private async analyzeData(input: string) {
// Custom data analysis implementation
}
private async handleError(action: any, error: any) {
// Custom error handling logic
}
}Check out our examples directory for more detailed implementation examples.
MIT License
Copyright (c) 2024 OpenServ Labs
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SOFTWARE.
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