io.agentscope.*
-
"_bypass_multiagent_history_merge"
"_cache_control"
"_chat_usage"
"_structured_output"
"_structured_output_reminder"
"_structured_output_reminder_type"
-
io.agentscope.core.message.Msg
"agentscope_generate_reason"
-
-
-
-
"https://dashscope.aliyuncs.com"
"/api/v1/services/aigc/multimodal-generation/generation"
"/api/v1/public-keys/latest"
"/api/v1/services/aigc/text-generation/generation"
-
"/api/chat"
"http://localhost:11434"
"/api/embed"
-
"/v1/chat/completions"
"https://api.openai.com"
"https://api.openai.com/v1"
-
"X-AgentScope-Stream-Format"
"ndjson"
-
io.agentscope.core.model.transport.websocket.CloseInfo
public static final int
1006
public static final int
1001
public static final int
1000
public static final int
1002
-
"https://dashscope.aliyuncs.com"
"/api/v1/services/aigc/multimodal-generation/generation"
-
"The plan-related tools. Activate this tool when you need to execute complex task, e.g. building a website or a game. Once activated, you\'ll enter the plan mode, where you will be guided to complete the given query by creating and following a plan, and hint message wrapped by <system-hint></system-hint> will guide you to complete the task. If you think the user no longer wants to perform the current task, you need to confirm with the user and call the \'finish_plan\' function."
-
"Operation interrupted due to system shutting down, please retry"
-
"## Available Skills\n\n<usage>\nSkills provide specialized capabilities and domain knowledge. Use them when they match your current task.\n\nHow to use skills:\n- Load skill: load_skill_through_path(skillId=\"<skill-id>\", path=\"SKILL.md\")\n- The skill will be activated and its documentation loaded with detailed instructions\n- Additional resources (scripts, assets, references) can be loaded using the same tool with different paths\n\nExample:\n1. User asks to analyze data \u2192 find a matching skill below (e.g. <skill-id>data-analysis_builtin</skill-id>)\n2. Load it: load_skill_through_path(skillId=\"data-analysis_builtin\", path=\"SKILL.md\")\n3. Follow the instructions returned by the skill\n\nMetadata is rendered as XML under each <skill> element:\n- scalar metadata becomes a simple child element\n- nested maps become nested XML elements\n- lists become repeated <item> elements\n- <skill-id> is always appended for tool loading\n</usage>\n\n<available_skills>\n\n"
"\n## Code Execution\n\n<code_execution>\nYou have access to the execute_shell_command tool. When a task can be accomplished by running \na pre-deployed skill script, you MUST execute it yourself using execute_shell_command rather \nthan describing or suggesting commands to the user.\n\nSkills root directory: %s\nEach skill\'s files are located under a subdirectory named by its <skill-id>:\n %s/<skill-id>/scripts/\n %s/<skill-id>/assets/\n\nWorkflow:\n1. After loading a skill, use ls to explore its directory structure and discover available scripts/assets\n2. Once you find the right script, execute it immediately with its absolute path\n3. If execution fails, diagnose and retry \u2014 do not fall back to describing the command\n\nRules:\n- Always use absolute paths when executing scripts\n- If a script exists for the task, run it directly \u2014 do not rewrite its logic inline\n- If asset/data files exist for the task, read them directly \u2014 do not recreate them\n\nExample:\n # Explore what scripts are available for a skill\n execute_shell_command(command=\"ls %s/data-analysis_builtin/scripts/\")\n\n # Run an existing script with absolute path\n execute_shell_command(command=\"python3 %s/data-analysis_builtin/scripts/analyze.py\")\n</code_execution>\n"