TypeScript SDK Framework Architecture

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.

Features

🔌 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

Framework Architecture

Framework & Blockchain Compatibility

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

Shadow Agents

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.

Control Levels

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

Developer Focus

The framework caters to two types of developers:

Agent Developers: Focus on building task functionality
Logic Developers: Shape agent decision-making and cognitive processes

Installation

npm install @openserv-labs/sdk

Getting Started

Platform Setup

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

Agent Registration

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

Development Setup

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

Quick Start

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()

Environment Variables

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

7378

*Required if using OpenAI integration features

Core Concepts

Capabilities

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 capability
description: What the capability does
schema: Zod schema defining the parameters
run: 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 schema
- action: The action context containing:
  - task: The current task context (if running as part of a task)
  - workspace: The current workspace context
  - me: Information about the current agent
  - Other action-specific properties

The run function must return a string or Promise.

Tasks

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'
})

Chat Interactions

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?'
})

File Operations

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
})

API Reference

Task Management

Create Task

const task = await agent.createTask({
  workspaceId: number,
  assignee: number,
  description: string,
  body: string,
  input: string,
  expectedOutput: string,
  dependencies: number[]
})

Update Task Status

await agent.updateTaskStatus({
  workspaceId: number,
  taskId: number,
  status: 'to-do' | 'in-progress' | 'human-assistance-required' | 'error' | 'done' | 'cancelled'
})

Add Task Log

await agent.addLogToTask({
  workspaceId: number,
  taskId: number,
  severity: 'info' | 'warning' | 'error',
  type: 'text' | 'openai-message',
  body: string | object
})

Chat & Communication

Send Message

await agent.sendChatMessage({
  workspaceId: number,
  agentId: number,
  message: string
})

Request Human Assistance

await agent.requestHumanAssistance({
  workspaceId: number,
  taskId: number,
  type: 'text' | 'project-manager-plan-review',
  question: string | object,
  agentDump?: object
})

Workspace Management

Get Files

const files = await agent.getFiles({
  workspaceId: number
})

Upload File

await agent.uploadFile({
  workspaceId: number,
  path: string,
  file: Buffer | string,
  skipSummarizer?: boolean,
  taskIds?: number[]
})

Integration Management

Call Integration

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 configured
integrationId: ID of the integration to call (e.g., 'twitter-v2', 'github')
details: Object containing:
- endpoint: The endpoint to call on the integration
- method: 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!'
    }
  }
})

Advanced Usage

OpenAI Process Runtime

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'
    }
  ]
})

Error Handling

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}`
  })
}

Custom Agents

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
  }
}

Examples

Check out our examples directory for more detailed implementation examples.

License

MIT License

Copyright (c) 2024 OpenServ Labs

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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