lite_agent_core_dart 0.1.1

LLM AI Agent sessions service in Dart.

LiteAgent core for dart

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LLM AI Agent multi sessions service.

Features

• Support OpenAPI/OpenRPC/OpenModbus/OpenTool JSON Spec.
• Support LLM Function calling to HTTP API/json-rpc 2.0 over HTTP/Modbus and more custom tools.

Usage

Prepare

1. Some OpenSpec json file, according to /example/json/*.json, which is callable.
2. Run your tool server, which is described in json file.
3. Add .env file in the example folder, and add below content in the .env file：

   baseUrl = https://xxx.xxx.com         # LLM API BaseURL
   apiKey = sk-xxxxxxxxxxxxxxxxxxxx      # LLM API ApiKey
   

4. Use below method to run agent service.

Method 1(Recommend): AgentService

• According to /example/agent_service_example.dart
• Support multi agent session via session id.

Future<void> main() async {
  CapabilityDto capabilityDto = CapabilityDto(
      llmConfig: _buildLLMConfig(),       // LLM Config
      systemPrompt: _buildSystemPrompt(), // System Prompt
      openSpecList: await _buildOpenSpecList()  // OpenSpec Description String List
  );
  SessionDto sessionDto = await agentService.initChat(
      capabilityDto,
      listen        // Subscribe AgentMessage, Agent chat with User/Client/LLM/Tools Role
  ); // Get Session Id
  
  String prompt = "<USER PROMPT, e.g. call any one tool>";
  await agentService.startChat(
      sessionDto.id, // Start chat with the Session Id
      [UserMessageDto(type: UserMessageType.text, message: prompt)] // User Content List, support type text/imageUrl
  );
}

Method 2: ToolAgent

• According to /example/tool_agent_example.dart
• Pure native calling. Support single session.
• Method 1 AgentService is friendly encapsulation for this.

Future<void> main() async {
  ToolAgent toolAgent = ToolAgent(
      llmRunner: _buildLLMRunner(),
      session: _buildSession(),
      toolRunnerList: await _buildToolRunnerList(),
      systemPrompt: _buildSystemPrompt()
  );
  String prompt = "<USER PROMPT, e.g. call any one tool>";
  toolAgent.userToAgent([Content(type: ContentType.text, message: prompt)]);
}