version: 发布1.1.6版本

CHANGELOG.md

@@ -1,5 +1,20 @@
 # Changelog
 
+## [1.1.6](https://github.com/trpc-group/trpc-agent-python/releases/tag/v1.1.6) (2026-06-03)
+
+### Features
+
+* Skill: Added a recoverable Cube sandbox runtime for skills, including `CubeClientConfig`, a unified `create_cube_sandbox_client` entry point, optional `auto_recover` support in `CubeSandboxClient`, sandbox lifecycle helpers, and direct `CubeWorkspaceRuntime` creation from the client.
+* Skill: Unified skill load/run/exec/stager paths around repository-level workspace runtime resolution via `repository.get_workspace_runtime(ctx)`, so tools under the same skill repository share one workspace runtime context.
+* MCP: Added MCP tool caching to avoid repeated network access.
+* Tools: Added `GraphAgent` support in `AgentTool`, allowing wrapped graph agents to return results from tool context state.
+* Examples/Eval: Restored evaluation examples that were previously removed during open-source cleanup.
+* Optimizer: Added support for the prompt self-optimization `AgentOptimizer`.
+
+### Bug Fixes
+
+* Storage: Fixed frequent sqlite warnings in `SqlSessionService` by consistently using database-side `func.now()` for update timestamps.
+
 ## [1.1.5](https://github.com/trpc-group/trpc-agent-python/releases/tag/v1.1.5) (2026-05-19)
 
 ### Features
@@ -23,8 +38,6 @@
 
 * Tools: Removed default `mempalace_tool` exports from `trpc_agent_sdk.tools` to avoid forcing MemPalace optional dependencies during base package import.
 
-
-
 ## [1.1.3](https://github.com/trpc-group/trpc-agent-python/releases/tag/v1.1.3) (2026-05-12)
 
 ### Features

examples/evaluation/callbacks/.env

@@ -0,0 +1 @@
+# Set TRPC_AGENT_API_KEY、TRPC_AGENT_BASE_URL、TRPC_AGENT_MODEL_NAME

examples/evaluation/callbacks/README.md

@@ -0,0 +1,22 @@
+# 回调 Callbacks 示例
+
+在评测中注册 **Callbacks**：在推理集/用例推理、打分集/用例打分的 8 个生命周期节点挂载钩子，用于打点、日志、采样或上报。
+
+## 目录结构
+
+- `callbacks/`：示例根目录
+- `agent/`：内含 `agent.py`、`callbacks_example.evalset.json`、`test_config.json`、`config.py`
+- `test_callbacks.py`：调用 `AgentEvaluator.evaluate(..., callbacks=callbacks)`，注册 `before_inference_set`、`after_inference_case`、`before_evaluate_set`、`after_evaluate_case` 并打日志
+
+## 环境要求
+
+Python 3.10+。需配置 `TRPC_AGENT_API_KEY` 等环境变量（同 quickstart）。
+
+## 运行
+
+```bash
+cd examples/evaluation/callbacks
+pytest test_callbacks.py -v --tb=short -s
+```
+
+`-s` 可看到回调中的 print 输出。

examples/evaluation/callbacks/agent/__init__.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+from .agent import root_agent
+
+__all__ = ["root_agent"]

examples/evaluation/callbacks/agent/agent.py

@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+"""Weather agent for callbacks example."""
+
+from typing import Any, Dict
+
+from trpc_agent_sdk.agents import LlmAgent
+from trpc_agent_sdk.models import OpenAIModel
+from trpc_agent_sdk.tools import FunctionTool
+
+from .config import get_model_config
+
+
+def get_weather(city: str) -> Dict[str, Any]:
+    """查询指定城市当前天气。"""
+    data = {
+        "上海": {"temperature": 18, "condition": "多云"},
+        "北京": {"temperature": 15, "condition": "晴"},
+    }
+    result = data.get(city, {"temperature": 20, "condition": "未知"})
+    return {"city": city, **result}
+
+
+def create_agent() -> LlmAgent:
+    api_key, url, model_name = get_model_config()
+    return LlmAgent(
+        name="weather_agent",
+        description="天气查询助手",
+        model=OpenAIModel(model_name=model_name, api_key=api_key, base_url=url),
+        instruction="你是天气助手，用 get_weather 查询城市天气并简要回答。",
+        tools=[FunctionTool(get_weather)],
+    )
+
+
+root_agent = create_agent()

examples/evaluation/callbacks/agent/callbacks_example.evalset.json

@@ -0,0 +1,37 @@
+{
+  "eval_set_id": "callbacks_example",
+  "name": "回调示例",
+  "description": "演示在评测中注册 Callbacks，在推理/打分生命周期打日志或计数",
+  "eval_cases": [
+    {
+      "eval_id": "callbacks_001",
+      "conversation": [
+        {
+          "invocation_id": "e-1",
+          "user_content": {
+            "parts": [{"text": "上海天气怎么样"}],
+            "role": "user"
+          },
+          "final_response": {
+            "parts": [{"text": "18"}],
+            "role": "model"
+          },
+          "intermediate_data": {
+            "tool_uses": [
+              {
+                "id": "t1",
+                "name": "get_weather",
+                "args": {"city": "上海"}
+              }
+            ]
+          }
+        }
+      ],
+      "session_input": {
+        "app_name": "weather_agent",
+        "user_id": "user",
+        "state": {}
+      }
+    }
+  ]
+}

examples/evaluation/callbacks/agent/config.py

@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+"""Agent config module."""
+
+import os
+
+
+def get_model_config() -> tuple[str, str, str]:
+    api_key = (
+        os.environ.get("TRPC_AGENT_API_KEY") or os.environ.get("API_KEY", "")
+    )
+    url = os.environ.get(
+        "TRPC_AGENT_BASE_URL", "http://v2.open.venus.woa.com/llmproxy"
+    )
+    model_name = os.environ.get("TRPC_AGENT_MODEL_NAME", "glm-4.7")
+    if not api_key:
+        raise ValueError(
+            "TRPC_AGENT_API_KEY or API_KEY must be set in environment variables"
+        )
+    return api_key, url, model_name

examples/evaluation/callbacks/agent/test_config.json

@@ -0,0 +1,27 @@
+{
+  "metrics": [
+    {
+      "metric_name": "tool_trajectory_avg_score",
+      "threshold": 0.8,
+      "criterion": {
+        "tool_trajectory": {
+          "default": {
+            "name": {"match": "exact", "case_insensitive": false},
+            "arguments": {"match": "exact"}
+          },
+          "order_sensitive": false,
+          "subset_matching": false
+        }
+      }
+    },
+    {
+      "metric_name": "final_response_avg_score",
+      "threshold": 0.6,
+      "criterion": {
+        "final_response": {
+          "text": {"match": "contains", "case_insensitive": true}
+        }
+      }
+    }
+  ]
+}

examples/evaluation/callbacks/test_callbacks.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+"""回调示例：在评测中注册 8 个生命周期 Callbacks，打日志并演示 context 传递。"""
+
+import os
+import pytest
+from trpc_agent_sdk.evaluation import (
+    AgentEvaluator,
+    Callbacks,
+    Callback,
+    CallbackResult,
+    BeforeInferenceSetArgs,
+    AfterInferenceSetArgs,
+    BeforeInferenceCaseArgs,
+    AfterInferenceCaseArgs,
+    BeforeEvaluateSetArgs,
+    AfterEvaluateSetArgs,
+    BeforeEvaluateCaseArgs,
+    AfterEvaluateCaseArgs,
+)
+
+triggered: list[str] = []
+
+
+def before_inference_set(ctx, args: BeforeInferenceSetArgs):
+    triggered.append("before_inference_set")
+    print("[callback] 推理集开始", args.request.eval_set_id, flush=True)
+    return None
+
+
+def after_inference_set(ctx, args: AfterInferenceSetArgs):
+    triggered.append("after_inference_set")
+    n = len(args.results) if args.results else 0
+    print("[callback] 推理集结束，共", n, "个用例", flush=True)
+    return None
+
+
+def before_inference_case(ctx, args: BeforeInferenceCaseArgs):
+    triggered.append("before_inference_case")
+    print("[callback] 用例推理开始", args.eval_case_id, flush=True)
+    return None
+
+
+def after_inference_case(ctx, args: AfterInferenceCaseArgs):
+    triggered.append("after_inference_case")
+    print("[callback] 用例推理结束", args.result.eval_case_id, flush=True)
+    return None
+
+
+def before_evaluate_set(ctx, args: BeforeEvaluateSetArgs):
+    triggered.append("before_evaluate_set")
+    n = len(args.request.inference_results)
+    print("[callback] 打分集开始 cases=", n, flush=True)
+    return CallbackResult(context={"phase": "evaluate"})
+
+
+def after_evaluate_set(ctx, args: AfterEvaluateSetArgs):
+    triggered.append("after_evaluate_set")
+    n = len(args.result.eval_case_results) if args.result else 0
+    phase = (ctx.get("context") or {}).get("phase", "?")
+    print("[callback] 打分集结束，共", n, "个用例，ctx.phase=", phase, flush=True)
+    return None
+
+
+def before_evaluate_case(ctx, args: BeforeEvaluateCaseArgs):
+    triggered.append("before_evaluate_case")
+    print("[callback] 用例打分开始", args.eval_case_id, flush=True)
+    return None
+
+
+def after_evaluate_case(ctx, args: AfterEvaluateCaseArgs):
+    triggered.append("after_evaluate_case")
+    print("[callback] 用例打分结束", args.result.eval_id, flush=True)
+    return None
+
+
+@pytest.mark.asyncio
+async def test_with_callbacks():
+    triggered.clear()
+    test_dir = os.path.dirname(os.path.abspath(__file__))
+    eval_set_path = os.path.join(test_dir, "agent", "callbacks_example.evalset.json")
+
+    callbacks = Callbacks()
+    callbacks.register(
+        "demo",
+        Callback(
+            before_inference_set=before_inference_set,
+            after_inference_set=after_inference_set,
+            before_inference_case=before_inference_case,
+            after_inference_case=after_inference_case,
+            before_evaluate_set=before_evaluate_set,
+            after_evaluate_set=after_evaluate_set,
+            before_evaluate_case=before_evaluate_case,
+            after_evaluate_case=after_evaluate_case,
+        ),
+    )
+
+    await AgentEvaluator.evaluate(
+        agent_module="agent",
+        agent_name="weather_agent",
+        eval_dataset_file_path_or_dir=eval_set_path,
+        callbacks=callbacks,
+    )
+
+    expected = [
+        "before_inference_set",
+        "before_inference_case",
+        "after_inference_case",
+        "after_inference_set",
+        "before_evaluate_set",
+        "before_evaluate_case",
+        "after_evaluate_case",
+        "after_evaluate_set",
+    ]
+    assert triggered == expected, triggered

examples/evaluation/context_messages/.env

@@ -0,0 +1 @@
+# Set TRPC_AGENT_API_KEY、TRPC_AGENT_BASE_URL、TRPC_AGENT_MODEL_NAME

examples/evaluation/context_messages/README.md

@@ -0,0 +1,20 @@
+# 上下文注入示例
+
+在评测用例上配置 **context_messages**：评估服务在每轮推理前会将其中内容注入会话上下文，用于传递系统说明、领域知识或格式约束。
+
+## 目录结构
+
+- `context_messages/`：示例根目录
+- `agent/`：内含 `agent.py`、`context_example.evalset.json`（含带 context_messages 的用例）、`test_config.json`、`config.py`
+- `test_context_messages.py`：调用 `AgentEvaluator.evaluate` 跑评测
+
+## 环境要求
+
+Python 3.10+。需配置 `TRPC_AGENT_API_KEY` 等环境变量（同 quickstart）。
+
+## 运行
+
+```bash
+cd examples/evaluation/context_messages
+pytest test_context_messages.py -v --tb=short -s
+```

examples/evaluation/context_messages/agent/__init__.py

@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+from .agent import root_agent
+
+__all__ = ["root_agent"]

examples/evaluation/context_messages/agent/agent.py

@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+"""Weather agent for context_messages example."""
+
+from typing import Any, Dict
+
+from trpc_agent_sdk.agents import LlmAgent
+from trpc_agent_sdk.models import OpenAIModel
+from trpc_agent_sdk.tools import FunctionTool
+
+from .config import get_model_config
+
+
+def get_weather(city: str) -> Dict[str, Any]:
+    """查询指定城市当前天气。"""
+    data = {
+        "上海": {"temperature": 18, "condition": "多云"},
+        "北京": {"temperature": 15, "condition": "晴"},
+    }
+    result = data.get(city, {"temperature": 20, "condition": "未知"})
+    return {"city": city, **result}
+
+
+def create_agent() -> LlmAgent:
+    api_key, url, model_name = get_model_config()
+    return LlmAgent(
+        name="weather_agent",
+        description="天气查询助手",
+        model=OpenAIModel(model_name=model_name, api_key=api_key, base_url=url),
+        instruction="你是天气助手，用 get_weather 查询城市天气并简要回答。",
+        tools=[FunctionTool(get_weather)],
+    )
+
+
+root_agent = create_agent()

examples/evaluation/context_messages/agent/config.py

@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright @ 2025 Tencent.com
+"""Agent config module."""
+
+import os
+
+
+def get_model_config() -> tuple[str, str, str]:
+    api_key = (
+        os.environ.get("TRPC_AGENT_API_KEY") or os.environ.get("API_KEY", "")
+    )
+    url = os.environ.get(
+        "TRPC_AGENT_BASE_URL", "http://v2.open.venus.woa.com/llmproxy"
+    )
+    model_name = os.environ.get("TRPC_AGENT_MODEL_NAME", "glm-4.7")
+    if not api_key:
+        raise ValueError(
+            "TRPC_AGENT_API_KEY or API_KEY must be set in environment variables"
+        )
+    return api_key, url, model_name