补齐 Anthropic 原生工具与三协议流式工具调用

Anthropic 请求发送 tools 与对象形态 tool_choice 并解析 tool_use block，解除本地对 function tools 的拦截 Chat / Responses / Anthropic 三种协议的流式工具增量按槽位聚合，收尾校验参数为完整 JSON，截断流不返回半截参数解除 App 侧 Anthropic 降级为文本 JSON 协议的两处守卫与对应提示词分支新增依赖真实凭据的流式工具验收用例，默认 ignore Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-25 13:38:41 +00:00
parent 006846e667
commit 7c61e9a532
4 changed files with 927 additions and 130 deletions
@@ -93,17 +93,13 @@ fn agent_interaction_function_tools() -> Vec<platform_llm::LlmFunctionTool> {
        .collect()
 }

-fn agent_interaction_system_prompt(agent_id: &str, native_tools: bool) -> String {
+fn agent_interaction_system_prompt(agent_id: &str) -> String {
    let role_prompt = if agent_id == GAME_CREATOR_PROJECT_SUPERVISOR_AGENT_ID {
        game_creator_project_supervisor_chat_system_prompt()
    } else {
        game_creator_role_agent_chat_system_prompt()
    };
-    let protocol = if native_tools {
-        "普通问答、身份说明、架构解释、方案讨论和必要澄清直接用自然语言回复。只有确实需要宿主持久能力时才调用一个 function tool，调用工具时不要同时输出回复文本。不要根据单个关键词决定是否执行，要理解整句的否定、假设、范围和上下文。"
-    } else {
-        "你必须只输出一个 JSON 对象，不要代码块或额外文字。允许的结构为：{\"action\":\"reply\",\"reply\":\"自然语言回复\"}、{\"action\":\"execute\"}、{\"action\":\"resume\"}、{\"action\":\"project_location\"}。不要根据单个关键词决定 action，要理解整句的否定、假设、范围和上下文。"
-    };
+    let protocol = "普通问答、身份说明、架构解释、方案讨论和必要澄清直接用自然语言回复。只有确实需要宿主持久能力时才调用一个 function tool，调用工具时不要同时输出回复文本。不要根据单个关键词决定是否执行，要理解整句的否定、假设、范围和上下文。";
    format!(
        "{role_prompt}\n\n你现在位于统一的 Agent interaction loop。{protocol} 高影响请求仍不明确时直接追问，不要擅自启动 Runtime。"
    )
@@ -114,7 +110,7 @@ fn build_agent_interaction_request_for_session(
    agent_id: &str,
    session_id: &str,
    prompt: &str,
-) -> Result<(GameCreatorLlmConfig, String, LlmRunRequest, bool), String> {
+) -> Result<(GameCreatorLlmConfig, String, LlmRunRequest), String> {
    let prompt = prompt.trim();
    if prompt.is_empty() {
        return Err("交互内容不能为空".to_string());
@@ -125,7 +121,6 @@ fn build_agent_interaction_request_for_session(
    let (llm, config_path, context) =
        build_game_creator_role_agent_context_for_session(root, agent_id, Some(session_id))?;
    let api_kind = parse_game_creator_llm_api_kind(&llm.api_kind)?;
-    let native_tools = api_kind != LlmApiKind::Anthropic;
    let user_prompt = if context.trim().is_empty() {
        format!("用户这轮输入：\n{prompt}")
    } else {
@@ -133,18 +128,15 @@ fn build_agent_interaction_request_for_session(
            "项目上下文如下。只把它当作背景，不要逐字复述。\n\n{context}\n\n用户这轮输入：\n{prompt}"
        )
    };
-    let mut request = LlmRunRequest::new(vec![
-        LlmMessage::system(agent_interaction_system_prompt(agent_id, native_tools)),
+    let request = LlmRunRequest::new(vec![
+        LlmMessage::system(agent_interaction_system_prompt(agent_id)),
        LlmMessage::user(user_prompt),
    ])
    .with_api_kind(api_kind)
-    .with_max_output_tokens(AGENT_INTERACTION_MAX_OUTPUT_TOKENS);
-    if native_tools {
-        request = request
-            .with_function_tools(agent_interaction_function_tools())
-            .with_tool_choice(platform_llm::LlmToolChoice::Auto);
-    }
-    Ok((llm, config_path, request, native_tools))
+    .with_max_output_tokens(AGENT_INTERACTION_MAX_OUTPUT_TOKENS)
+    .with_function_tools(agent_interaction_function_tools())
+    .with_tool_choice(platform_llm::LlmToolChoice::Auto);
+    Ok((llm, config_path, request))
 }

 pub(crate) async fn decide_game_creator_agent_interaction_turn_for_session_at<F>(
@@ -157,10 +149,10 @@ pub(crate) async fn decide_game_creator_agent_interaction_turn_for_session_at<F>
 where
    F: FnMut(&platform_llm::LlmStreamDelta),
 {
-    let (llm, config_path, request, native_tools) =
+    let (llm, config_path, request) =
        build_agent_interaction_request_for_session(root, agent_id, session_id, prompt)?;
    let client = build_game_creator_agent_runtime_llm_client(&llm, &config_path)?;
-    let response = if native_tools && llm.stream {
+    let response = if llm.stream {
        let fallback_request = request.clone();
        match client.stream_run(request, |delta| on_delta(delta)).await {
            Ok(response) => response,
@@ -139,11 +139,7 @@ pub(in crate::agent) fn build_game_creator_agent_background_tool_plan_request(
        "command.start 使用 {\"program\":\"受信任 PATH 中的裸可执行名\"",
    );
    let api_kind = parse_game_creator_llm_api_kind(&llm.api_kind)?;
-    let protocol_prompt = if api_kind == platform_llm::LlmApiKind::Anthropic {
-        "当前 Provider 不提供 function tools，请返回上述 schema 的单个完整 JSON object；不要解释、markdown 或代码围栏。"
-    } else {
-        "必须直接调用当前请求提供的原生函数：需要更新持久计划时调用 update_agent_plan，需要行动时调用对应动作工具，已有观察足够时调用 respond_to_user。只有步骤或状态真实变化时才单独调用 update_agent_plan；当前 in_progress 步骤已具备执行条件时必须在同一响应调用对应动作工具，不能只改计划解释。不要调用未广告的旧 submit_agent_tool_plan，也不要把计划或动作放在普通文本中。"
-    };
+    let protocol_prompt = "必须直接调用当前请求提供的原生函数：需要更新持久计划时调用 update_agent_plan，需要行动时调用对应动作工具，已有观察足够时调用 respond_to_user。只有步骤或状态真实变化时才单独调用 update_agent_plan；当前 in_progress 步骤已具备执行条件时必须在同一响应调用对应动作工具，不能只改计划解释。不要调用未广告的旧 submit_agent_tool_plan，也不要把计划或动作放在普通文本中。";
    let mut system_prompt = game_creator_agent_runtime_tool_plan_system_prompt_for_agent(agent_id);
    if autonomous_game_build {
        system_prompt.push_str(
@@ -167,12 +163,9 @@ pub(in crate::agent) fn build_game_creator_agent_background_tool_plan_request(
    ])
    .with_api_kind(api_kind)
    .with_max_output_tokens(AGENT_RUNTIME_TOOL_PLAN_MAX_OUTPUT_TOKENS)
-    .with_response_text_verbosity(platform_llm::LlmResponseTextVerbosity::Low);
-    if api_kind != platform_llm::LlmApiKind::Anthropic {
-        request = request
-            .with_function_tools(build_agent_runtime_native_function_tools(mcp_catalog)?)
-            .with_tool_choice(platform_llm::LlmToolChoice::Required);
-    }
+    .with_response_text_verbosity(platform_llm::LlmResponseTextVerbosity::Low)
+    .with_function_tools(build_agent_runtime_native_function_tools(mcp_catalog)?)
+    .with_tool_choice(platform_llm::LlmToolChoice::Required);
    request = apply_game_creator_llm_web_search(
        apply_game_creator_llm_reasoning_effort(request, &llm)?,
        &llm,
@@ -0,0 +1,101 @@
+//! 真实端点的流式工具调用验收。默认 `#[ignore]`，只在显式指定环境变量时运行：
+//!
+//! ```powershell
+//! $env:PLATFORM_LLM_LIVE_BASE_URL = 'https://api.minimaxi.com/anthropic'
+//! $env:PLATFORM_LLM_LIVE_API_KEY  = '...'
+//! $env:PLATFORM_LLM_LIVE_MODEL    = 'MiniMax-M3'
+//! $env:PLATFORM_LLM_LIVE_API_KIND = 'anthropic'   # 或 openai_chat / openai_responses
+//! cargo test -p platform-llm --test live_stream_tool_calls -- --ignored --nocapture
+//! ```
+//!
+//! 单测里的 SSE 是转录的真实报文，这个用例负责证明转录没有偏差。
+
+use platform_llm::{
+    LlmApiKind, LlmClient, LlmConfig, LlmFunctionTool, LlmMessage, LlmProvider, LlmRunRequest,
+    LlmToolChoice,
+};
+
+fn env_var(name: &str) -> Option<String> {
+    std::env::var(name).ok().filter(|value| !value.trim().is_empty())
+}
+
+fn parse_api_kind(value: &str) -> LlmApiKind {
+    match value.trim().to_ascii_lowercase().replace('-', "_").as_str() {
+        "anthropic" => LlmApiKind::Anthropic,
+        "openai_chat" => LlmApiKind::OpenAiChat,
+        _ => LlmApiKind::OpenAiResponses,
+    }
+}
+
+#[tokio::test]
+#[ignore = "需要真实 Provider 凭据，用 --ignored 显式运行"]
+async fn live_stream_run_returns_native_tool_calls() {
+    let (Some(base_url), Some(api_key), Some(model)) = (
+        env_var("PLATFORM_LLM_LIVE_BASE_URL"),
+        env_var("PLATFORM_LLM_LIVE_API_KEY"),
+        env_var("PLATFORM_LLM_LIVE_MODEL"),
+    ) else {
+        panic!("缺少 PLATFORM_LLM_LIVE_BASE_URL / _API_KEY / _MODEL");
+    };
+    let api_kind = parse_api_kind(&env_var("PLATFORM_LLM_LIVE_API_KIND").unwrap_or_default());
+
+    let config = LlmConfig::new(
+        LlmProvider::OpenAiCompatible,
+        base_url,
+        api_key,
+        model,
+        120_000,
+        0,
+        1_000,
+    )
+    .expect("live config should be valid");
+    let client = LlmClient::new(config).expect("live client should be created");
+
+    let request = LlmRunRequest::new(vec![
+        LlmMessage::system("你可以使用工具。需要外部数据时必须调用工具，不要凭空回答。"),
+        LlmMessage::user("杭州现在天气怎么样？"),
+    ])
+    .with_api_kind(api_kind)
+    .with_max_output_tokens(512)
+    .with_function_tools(vec![LlmFunctionTool::new(
+        "get_weather",
+        "查询指定城市的当前天气。",
+        serde_json::json!({
+            "type": "object",
+            "properties": { "city": { "type": "string" } },
+            "required": ["city"]
+        }),
+    )])
+    .with_tool_choice(LlmToolChoice::Required);
+
+    let mut streamed_chars = 0usize;
+    let response = client
+        .stream_run(request, |delta| {
+            streamed_chars += delta.delta_text.chars().count();
+        })
+        .await
+        .expect("live stream_run should succeed");
+
+    println!(
+        "api_kind={api_kind:?} finish_reason={:?} streamed_chars={streamed_chars} text={:?}",
+        response.finish_reason, response.text
+    );
+    for call in &response.tool_calls {
+        println!("tool_call id={} name={} args={}", call.id, call.name, call.arguments);
+    }
+
+    assert!(
+        !response.tool_calls.is_empty(),
+        "流式必须解析出工具调用，实际 finish_reason={:?}",
+        response.finish_reason
+    );
+    let call = &response.tool_calls[0];
+    assert_eq!(call.name, "get_weather");
+    assert!(!call.id.trim().is_empty(), "工具调用必须带 id");
+    let arguments: serde_json::Value =
+        serde_json::from_str(&call.arguments).expect("参数必须是完整 JSON");
+    assert!(
+        arguments.get("city").is_some(),
+        "参数应包含 city，实际为 {arguments}"
+    );
+}