Release/v2.0.0

.gitignore

@@ -21,6 +21,7 @@ docker/volumes/db/data
 docker/.env
 docker/.run
 docker/deploy.options
+k8s/helm/.deploy.options
 
 frontend_standalone/
 .pnpm-store/
@@ -32,4 +33,4 @@ model-assets/
 *coverage_html
 *.pytest_cache
 *.coverage
-*coverage.xml
+*coverage.xml

README.md

@@ -7,7 +7,7 @@
 [![Docker Pulls](https://img.shields.io/docker/pulls/nexent/nexent?logo=docker&label=DockerPull)](https://hub.docker.com/repositories/nexent)
 [![Codecov (with branch)](https://img.shields.io/codecov/c/github/ModelEngine-Group/nexent/develop?logo=codecov&color=green)](https://codecov.io/gh/ModelEngine-Group/nexent)
 
-Nexent is a zero-code platform for auto-generating agents — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want. Built on the MCP ecosystem with rich tool integration, Nexent also provides various built-in agents to meet your intelligent service needs in different scenarios such as work, travel, and daily life. Nexent offers powerful capabilities for agent running control, multi-agent collaboration, data processing and knowledge tracing, multimodal dialogue, and batch scaling.
+Nexent is a zero-code platform for auto-generating production-grade AI agents, built on **Harness Engineering** principles. It provides unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want.
 
 > One prompt. Endless reach.
 
@@ -58,7 +58,7 @@ Most of all, we need visibility. Star ⭐ and watch the repo, share it with frie
 
 ## 💬 Community & contact
 
-- Browse the [Documentation](https://modelengine-group.github.io/nexent) for more information.  
+- Browse the [Documentation](https://modelengine-group.github.io/nexent) for more information.
 - Join our [Discord community](https://discord.gg/tb5H3S3wyv) to chat with other developers and get help!
 - Conntact us by Wechat, find our QR Code in our [website](https://nexent.tech/en/contact)
 
@@ -119,5 +119,4 @@ Prefer to run Nexent from source code? Follow our [Developer Guide](https://mode
 
 # 📄 License
 
-Nexent is licensed under the [MIT](LICENSE) with additional conditions. Please read the [LICENSE](LICENSE) file for details.
-
+Nexent is licensed under the [MIT License](LICENSE).

README_CN.md

@@ -7,7 +7,7 @@
 [![Docker Pulls](https://img.shields.io/docker/pulls/nexent/nexent?logo=docker&label=DockerPull)](https://hub.docker.com/repositories/nexent)
 [![Codecov (with branch)](https://img.shields.io/codecov/c/github/ModelEngine-Group/nexent/develop?logo=codecov&color=green)](https://codecov.io/gh/ModelEngine-Group/nexent)
 
-Nexent 是一个零代码智能体自动生成平台 —— 无需编排，无需复杂的拖拉拽操作，使用纯语言开发你想要的任何智能体。基于MCP生态，具备丰富的工具集成，同时提供多种自带智能体，满足你的工作、旅行、生活等不同场景的智能服务需要。Nexent 还提供强大的智能体运行控制、多智能体协作、数据处理和知识溯源、多模态对话、批量扩展能力。
+Nexent 是一个基于 **Harness Engineering** 原则打造的零代码智能体自动生成平台。集统一工具、技能、记忆和编排能力于一体，内置约束机制、反馈循环和控制平面。无需编排，无需复杂的拖拉拽操作，使用纯语言开发你想要的任何智能体。
 
 > 一个提示词，无限种可能。
 
@@ -58,7 +58,7 @@ bash deploy.sh
 
 ## 💬 社区与联系方式
 
-- 浏览 [文档](https://modelengine-group.github.io/nexent) 了解更多信息。  
+- 浏览 [文档](https://modelengine-group.github.io/nexent) 了解更多信息。
 - 加入我们的 [Discord 社区](https://discord.gg/tb5H3S3wyv) 与其他开发者交流并获取帮助！
 - 通过微信联系我们，在我们的[网站](https://nexent.tech/zh/contact)找到二维码
 
@@ -119,4 +119,4 @@ bash deploy.sh
 
 # 📄 许可证
 
-Nexent 采用 [MIT](LICENSE) 许可证，并附有额外条件。请阅读 [LICENSE](LICENSE) 文件了解详情。
+Nexent 采用 [MIT 许可证](LICENSE)。

backend/agents/create_agent_info.py

@@ -1,10 +1,10 @@
 import threading
 import logging
+from typing import List
 from urllib.parse import urljoin
 from datetime import datetime
 
 from jinja2 import Template, StrictUndefined
-from smolagents.utils import BASE_BUILTIN_MODULES
 from nexent.core.utils.observer import MessageObserver
 from nexent.core.agents.agent_model import AgentRunInfo, ModelConfig, AgentConfig, ToolConfig
 from nexent.memory.memory_service import search_memory_in_levels
@@ -27,11 +27,119 @@
 from utils.prompt_template_utils import get_agent_prompt_template
 from utils.config_utils import tenant_config_manager, get_model_name_from_config
 from consts.const import LOCAL_MCP_SERVER, MODEL_CONFIG_MAPPING, LANGUAGE, DATA_PROCESS_SERVICE
+import re
 
 logger = logging.getLogger("create_agent_info")
 logger.setLevel(logging.DEBUG)
 
 
+def _get_skills_for_template(
+    agent_id: int,
+    tenant_id: str,
+    version_no: int = 0
+) -> List[dict]:
+    """Get skills list for prompt template injection.
+
+    Args:
+        agent_id: Agent ID
+        tenant_id: Tenant ID
+        version_no: Version number
+
+    Returns:
+        List of skill dicts with name and description
+    """
+    try:
+        from services.skill_service import SkillService
+        skill_service = SkillService()
+        enabled_skills = skill_service.get_enabled_skills_for_agent(
+            agent_id=agent_id,
+            tenant_id=tenant_id,
+            version_no=version_no
+        )
+        return [
+            {"name": s.get("name", ""), "description": s.get("description", "")}
+            for s in enabled_skills
+        ]
+    except Exception as e:
+        logger.warning(f"Failed to get skills for template: {e}")
+        return []
+
+
+def _get_skill_script_tools(
+    agent_id: int,
+    tenant_id: str,
+    version_no: int = 0
+) -> List[ToolConfig]:
+    """Get tool config for skill script execution and skill reading.
+
+    Args:
+        agent_id: Agent ID for filtering available skills in error messages.
+        tenant_id: Tenant ID for filtering available skills in error messages.
+        version_no: Version number for filtering available skills.
+
+    Returns:
+        List of ToolConfig for skill execution and reading tools
+    """
+    from consts.const import CONTAINER_SKILLS_PATH
+
+    skill_context = {
+        "agent_id": agent_id,
+        "tenant_id": tenant_id,
+        "version_no": version_no,
+    }
+
+    try:
+        return [
+            ToolConfig(
+                class_name="RunSkillScriptTool",
+                name="run_skill_script",
+                description="Execute a skill script with given parameters. Use this to run Python or shell scripts that are part of a skill.",
+                inputs='{"skill_name": "str", "script_path": "str", "params": "dict"}',
+                output_type="string",
+                params={"local_skills_dir": CONTAINER_SKILLS_PATH},
+                source="builtin",
+                usage="builtin",
+                metadata=skill_context,
+            ),
+            ToolConfig(
+                class_name="ReadSkillMdTool",
+                name="read_skill_md",
+                description="Read skill execution guide and optional additional files. Always reads SKILL.md first, then optionally reads additional files.",
+                inputs='{"skill_name": "str", "additional_files": "list[str]"}',
+                output_type="string",
+                params={"local_skills_dir": CONTAINER_SKILLS_PATH},
+                source="builtin",
+                usage="builtin",
+                metadata=skill_context,
+            ),
+            ToolConfig(
+                class_name="ReadSkillConfigTool",
+                name="read_skill_config",
+                description="Read the config.yaml file from a skill directory. Returns JSON containing configuration variables needed for skill workflows.",
+                inputs='{"skill_name": "str"}',
+                output_type="string",
+                params={"local_skills_dir": CONTAINER_SKILLS_PATH},
+                source="builtin",
+                usage="builtin",
+                metadata=skill_context,
+            ),
+            ToolConfig(
+                class_name="WriteSkillFileTool",
+                name="write_skill_file",
+                description="Write content to a file within a skill directory. Creates parent directories if they do not exist.",
+                inputs='{"skill_name": "str", "file_path": "str", "content": "str"}',
+                output_type="string",
+                params={"local_skills_dir": CONTAINER_SKILLS_PATH},
+                source="builtin",
+                usage="builtin",
+                metadata=skill_context,
+            )
+        ]
+    except Exception as e:
+        logger.warning(f"Failed to load skill script tool: {e}")
+        return []
+
+
 async def create_model_config_list(tenant_id):
     records = get_model_records({"model_type": "llm"}, tenant_id)
     model_list = []
@@ -169,22 +277,26 @@
         logger.error(f"Failed to build knowledge base summary: {e}")
 
     # Assemble system_prompt
-    if duty_prompt or constraint_prompt or few_shots_prompt:
-        system_prompt = Template(prompt_template["system_prompt"], undefined=StrictUndefined).render({
-            "duty": duty_prompt,
-            "constraint": constraint_prompt,
-            "few_shots": few_shots_prompt,
-            "tools": {tool.name: tool for tool in tool_list},
-            "managed_agents": {agent.name: agent for agent in managed_agents},
-            "authorized_imports": str(BASE_BUILTIN_MODULES),
-            "APP_NAME": app_name,
-            "APP_DESCRIPTION": app_description,
-            "memory_list": memory_list,
-            "knowledge_base_summary": knowledge_base_summary,
-            "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        })
-    else:
-        system_prompt = agent_info.get("prompt", "")
+    # Get skills list for prompt template
+    skills = _get_skills_for_template(agent_id, tenant_id, version_no)
+
+    render_kwargs = {
+        "duty": duty_prompt,
+        "constraint": constraint_prompt,
+        "few_shots": few_shots_prompt,
+        "tools": {tool.name: tool for tool in tool_list},
+        "skills": skills,
+        "managed_agents": {agent.name: agent for agent in managed_agents},
+        "APP_NAME": app_name,
+        "APP_DESCRIPTION": app_description,
+        "memory_list": memory_list,
+        "knowledge_base_summary": knowledge_base_summary,
+        "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "user_id": user_id,
+    }
+    system_prompt = Template(prompt_template["system_prompt"], undefined=StrictUndefined).render(render_kwargs)
+
+    _print_prompt_with_token_count(system_prompt, agent_id, "BEFORE_INJECTION")
 
     if agent_info.get("model_id") is not None:
         model_info = get_model_by_model_id(agent_info.get("model_id"))
@@ -197,9 +309,10 @@
         prompt_templates=await prepare_prompt_templates(
             is_manager=len(managed_agents) > 0,
             system_prompt=system_prompt,
-            language=language
+            language=language,
+            agent_id=agent_id
         ),
-        tools=tool_list,
+        tools=tool_list + _get_skill_script_tools(agent_id, tenant_id, version_no),
         max_steps=agent_info.get("max_steps", 10),
         model_name=model_name,
         provide_run_summary=agent_info.get("provide_run_summary", False),
@@ -296,23 +409,46 @@
     return langchain_tools
 
 
-async def prepare_prompt_templates(is_manager: bool, system_prompt: str, language: str = 'zh'):
+async def prepare_prompt_templates(
+    is_manager: bool,
+    system_prompt: str,
+    language: str = 'zh',
+    agent_id: int = None,
+):
     """
     Prepare prompt templates, support multiple languages
 
     Args:
         is_manager: Whether it is a manager mode
         system_prompt: System prompt content
         language: Language code ('zh' or 'en')
+        agent_id: Agent ID for fetching skill instances
 
     Returns:
         dict: Prompt template configuration
     """
     prompt_templates = get_agent_prompt_template(is_manager, language)
     prompt_templates["system_prompt"] = system_prompt
+
+    # Print final prompt with all injections
+    _print_prompt_with_token_count(prompt_templates["system_prompt"], agent_id, "FINAL_PROMPT")
+
     return prompt_templates
 
 
+def _print_prompt_with_token_count(prompt: str, agent_id: int = None, stage: str = "PROMPT"):
+    """Print prompt content and estimate token count using tiktoken."""
+    try:
+        import tiktoken
+        encoding = tiktoken.get_encoding("cl100k_base")
+        token_count = len(encoding.encode(prompt))
+        logger.info(f"[Skill Debug][{stage}] Agent {agent_id} token count: {token_count}")
+        logger.info(f"[Skill Debug][{stage}] Agent {agent_id} prompt:\n{prompt}")
+    except Exception as e:
+        logger.warning(f"[Skill Debug][{stage}] Failed to count tokens: {e}")
+        logger.info(f"[Skill Debug][{stage}] Agent {agent_id} prompt:\n{prompt}")
+
+
 async def join_minio_file_description_to_query(minio_files, query):
     final_query = query
     if minio_files and isinstance(minio_files, list):

backend/apps/agent_app.py

@@ -20,7 +20,8 @@
     run_agent_stream,
     stop_agent_tasks,
     get_agent_call_relationship_impl,
-    clear_agent_new_mark_impl
+    clear_agent_new_mark_impl,
+    get_agent_by_name_impl,
 )
 from services.agent_version_service import (
     publish_version_impl,
@@ -100,6 +101,27 @@ async def search_agent_info_api(
             status_code=HTTPStatus.INTERNAL_SERVER_ERROR, detail="Agent search info error.")
 
 
+@agent_config_router.get("/by-name/{agent_name}")
+async def get_agent_by_name_api(
+    agent_name: str,
+    tenant_id: Optional[str] = Query(
+        None, description="Tenant ID for filtering (uses auth if not provided)"),
+    authorization: Optional[str] = Header(None)
+):
+    """
+    Look up an agent by name and return its agent_id and highest published version_no.
+    """
+    try:
+        _, auth_tenant_id = get_current_user_id(authorization)
+        effective_tenant_id = tenant_id or auth_tenant_id
+        result = get_agent_by_name_impl(agent_name, effective_tenant_id)
+        return JSONResponse(status_code=HTTPStatus.OK, content=result)
+    except Exception as e:
+        logger.error(f"Agent by name lookup error: {str(e)}")
+        raise HTTPException(
+            status_code=HTTPStatus.INTERNAL_SERVER_ERROR, detail="Agent not found.")
+
+
 @agent_config_router.get("/get_creating_sub_agent_id")
 async def get_creating_sub_agent_info_api(authorization: Optional[str] = Header(None)):
     """

backend/apps/config_app.py

@@ -14,6 +14,7 @@
 from apps.model_managment_app import router as model_manager_router
 from apps.prompt_app import router as prompt_router
 from apps.remote_mcp_app import router as remote_mcp_router
+from apps.skill_app import router as skill_router
 from apps.tenant_config_app import router as tenant_config_router
 from apps.tool_config_app import router as tool_config_router
 from apps.user_management_app import router as user_management_router
@@ -52,6 +53,7 @@
 
 app.include_router(summary_router)
 app.include_router(prompt_router)
+app.include_router(skill_router)
 app.include_router(tenant_config_router)
 app.include_router(remote_mcp_router)
 app.include_router(tenant_router)