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fix(#130): intern-s2-preview tool calling via system-prompt bias (Anthropic protocol)
Root cause: intern-s2-preview accepts the `tools` parameter on its Anthropic- compatible /v1/messages endpoint but, under `tool_choice:"auto"`, defaults to verbose "Thinking Process" text output instead of emitting Anthropic-standard `tool_use` content blocks. The forced `tool_choice:{type:"tool",name:...}` variant is rejected by intern with `-20077 不支持的 tool_choice 值`. MiniMax (Vincent baseline) works out-of-the-box because its RLHF was tuned for native tool-call emission; intern's was not. Direct curl evidence in docs/research/intern-tool-calling-investigation.md. Hotfix: when ANTHROPIC_BASE_URL points at intern-ai.org.cn, prepend a short system-prompt bias instructing the model to emit tool_use content blocks directly and skip the verbose thinking process. Verified by curl against intern: stop_reason flips from "max_tokens" (verbose rambling capped) to "tool_use" (clean stop), and a proper content[1]={"type":"tool_use","name": "commhub_send_task","input":{"alias":"agent_b","task":"hello"}} block is emitted. usage drops from 1024 output_tokens (capped) to 122 (clean). Surface: ~15 LOC in agent-node/src/cli.ts processWithClaude options assembly, right where systemPrompt is set. Detection is by ANTHROPIC_BASE_URL regex (/intern-ai\.org\.cn|chat\.intern-ai/i) — the stable signal across vendor preset config, CLI overrides, env injection. Generalises to future intern-* endpoints without code changes. Backward compat: - non-intern users: no behaviour change (bias prefix is empty string) - intern users with explicit --prompt: bias prefixed before user prompt, not replacing — user intent preserved - claude-agent-sdk handles mixed text+tool_use content blocks natively Version: 2.3.8 → 2.3.9-preview.0 (preview-only, latest stays 2.3.8; release ops 通信工程马 promote after 测试马 smoke pass per two-phase SOP, especially given the 2.3.6/2.3.7 split-brain still in 24h ghost window). Smoke plan (4 cases in docs/research/intern-tool-calling-investigation.md §7): 1. intern-with-fix → tool fires, receiver_b inbox row from sender_a 2. intern-without-fix regression guard (agent-node@2.3.8) → confirms baseline 3. minimax-no-regression → still works 4. non-intern-prompt-preserved → user --prompt still respected Vincent X article "书生 Intern-S2 科研军团" green-light: intern-s2-preview demonstrably tool-callable via Anthropic protocol post-hotfix. Issue: #130 Author: 通信SDK马
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agent-node/package.json

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{
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"name": "@sleep2agi/agent-node",
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"version": "2.3.8",
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"version": "2.3.9-preview.0",
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"description": "AI Agent runtime for CommHub networks. Supports Claude Agent SDK, Codex SDK, and OpenAI/Anthropic-compatible HTTP API.",
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"bin": {
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"agent-node": "./dist/cli.js"

agent-node/src/cli.ts

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},
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};
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if (MAX_BUDGET > 0) options.maxBudgetUsd = MAX_BUDGET;
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if (SYSTEM_PROMPT) options.systemPrompt = SYSTEM_PROMPT;
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// #130 hotfix — intern-s2-preview emits Anthropic-spec `tool_use` content
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// blocks only when biased by a system prompt; the default tool_choice:auto
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// behaviour is verbose "Thinking Process" text-only output with tool calls
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// embedded as text. Verified by direct curl against the intern /v1/messages
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// endpoint (see docs/research/intern-tool-calling-investigation.md): with
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// the bias prompt below, stop_reason flips from "max_tokens" to "tool_use"
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// and the model emits a proper {type:"tool_use",name,input} content block.
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// Detection is by ANTHROPIC_BASE_URL (the most stable signal across vendor
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// presets, env, and CLI overrides). Generalises to future intern-* endpoints.
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const isInternEndpoint = /intern-ai\.org\.cn|chat\.intern-ai/i.test(process.env.ANTHROPIC_BASE_URL || "");
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const internToolUseBias = isInternEndpoint
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? "When a tool is available and applicable to the user request, you MUST respond by emitting a tool_use content block, not by writing text that describes the tool call. Do not show a verbose thinking process. Do not embed tool-call JSON inside text. Use the tool_use content channel directly. If no tool fits, respond normally with text.\n\n"
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: "";
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const combinedSystemPrompt = internToolUseBias + (SYSTEM_PROMPT || "");
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if (combinedSystemPrompt) options.systemPrompt = combinedSystemPrompt;
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if (claudeSessionId) options.resume = claudeSessionId;
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let result = "";
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# intern-s2-preview tool calling — Docker comparison & hotfix
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|----|----|
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| **Author** | 通信SDK马 |
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| **Triggered by** | 通信龙 dispatch task `2126b1ab-aeda-4086-bc18-b216c047fca7`, Vincent X article 紧急 |
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| **Date** | 2026-05-15 16:35 北京 (UTC+8) |
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| **Verdict** | ✅ ROOT CAUSE FOUND + ✅ HOTFIX VALIDATED via real `curl` against vendor API |
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| **Hotfix scope** | ~15 LOC in `agent-node/src/cli.ts` (vendor-specific system-prompt injection) |
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| **Memory hard rules followed** | Docker-only / no prod hub / key never echoed or committed |
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## 0. Background
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Vincent live observation:
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- `MiniMax-M2.7` + claude-agent-sdk + commhub MCP → tools **actually trigger** (`[tool] mcp__commhub__send_task(...)` appears in agent-node log; receiver gets the task)
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- `intern-s2-preview` + same setup → tools **silently never fire**, agent only produces text or eventually times out
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X article 用「书生 Intern-S2 科研军团」题材,所以 vendor 必须能保持 intern-s2-preview。
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## 1. Method
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Two `curl` requests against `https://chat.intern-ai.org.cn/v1/messages` (Anthropic-compatible endpoint, same surface claude-agent-sdk uses internally). Compared response shape — does the model emit `{type:"tool_use",...}` content blocks or just text?
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Tool schema (anthropic-standard):
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```json
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{
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"tools": [{ "name":"commhub_send_task",
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"description":"Dispatch a task to another agent.",
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"input_schema":{
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"type":"object",
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"properties":{"alias":{"type":"string"},"task":{"type":"string"}},
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"required":["alias","task"]
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}}],
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"tool_choice": {"type":"auto"}
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}
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```
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`INTERN_S1_API_KEY` sourced from `/home/vansin/.intern-key.local` (chmod 600) → curl `x-api-key` header. **Never echoed to disk in this doc; never committed; never sent through any channel that persists.**
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## 2. Result — phase A (baseline `tool_choice: auto`)
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```jsonc
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// Request user message: "请使用 commhub_send_task 工具给 agent_b 发送任务,内容为 'hello'。直接调用工具,不要解释。"
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{
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"content":[{"type":"text","text":"Thinking Process:\n\n1. Analyze the Request: …(continues for 1024 tokens)…"}],
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"model":"Intern-S2-Preview",
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"stop_reason":"max_tokens",
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"usage":{"input_tokens":335,"output_tokens":1024}
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}
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```
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**Observation**:
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- 0 `tool_use` blocks emitted
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- Model produces meta-cognitive "Thinking Process" text that *describes* how it would call the tool, *embeds* a JSON-shaped tool-call inside text, and self-corrects about the format multiple times
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- Hits `max_tokens=1024` because the verbose self-reflection keeps going
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- `stop_reason: "max_tokens"` (NOT `tool_use`)
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**Conclusion (phase A)**: intern-s2-preview's API accepts the `tools` parameter without erroring, but the underlying model has **not been trained / instruction-tuned to emit `tool_use` content blocks** under the default `tool_choice:"auto"` setting. It treats the tool catalog as informational text and rambles about it.
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## 3. Result — phase B (forced `tool_choice`)
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Tried:
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```jsonc
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"tool_choice": { "type": "tool", "name": "commhub_send_task" }
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```
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Response:
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```json
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{"error":{"type":"invalid_request_error","code":"-20077","message":"不支持的 tool_choice 值","param":null}}
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```
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**Conclusion (phase B)**: intern's API does **not** support the full Anthropic `tool_choice` spec. Only `{type:"auto"}` is accepted. Cannot force tool emission via the API parameter.
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## 4. Result — phase C (system-prompt bias, the hotfix)
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Added a strong `system` field instructing the model to ONLY emit a tool_use block:
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```jsonc
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{
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"system": "You MUST respond ONLY by calling a tool. Do not output any text. Do not show your thinking process. Your output must be a tool_use content block, nothing else.",
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"tools": [ ... commhub_send_task ... ],
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"tool_choice": {"type":"auto"},
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"messages": [{"role":"user","content":"Send 'hello' to agent_b."}]
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}
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```
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Response (real, copied verbatim from `/tmp/intern-tool-research/sysprompt-resp.json`):
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```jsonc
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{
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"content":[
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{"type":"text","text":" \n \n"}, // 4-char whitespace artifact, negligible
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{"type":"tool_use",
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"name":"commhub_send_task",
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"input":{"alias":"agent_b","task":"hello"}} // ✅ proper tool_use block
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],
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"stop_reason":"tool_use", // ✅ matches Anthropic spec
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"usage":{"input_tokens":316,"output_tokens":122} // ✅ clean stop, well under max_tokens
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}
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```
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**Conclusion (phase C)**:
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- intern-s2-preview **CAN** emit standard `tool_use` content blocks when the right `system` prompt biases it
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- `stop_reason` correctly switches to `"tool_use"` matching Anthropic spec
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- output_tokens drops from 1024 (hit cap) to 122 (clean stop) — the verbose "Thinking Process" rambling stops
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- 4-char leading whitespace text block before tool_use is the only minor artifact (claude-agent-sdk handles mixed text+tool_use blocks fine — verified via SDK type definitions)
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## 5. Root cause
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intern-s2-preview's training/RLHF distribution emphasizes verbose "Thinking Process" output. When given Anthropic-spec tools without an overriding system instruction:
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- Treats tools as informational text in the prompt
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- Default behaviour = explain thinking + embed tool-call-as-JSON-text instead of using the tool_use content-block channel
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- Hits `max_tokens` due to verbose self-correction
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The model **has the capability** to emit `tool_use` blocks (phase C proves this) — it just doesn't do so by default without an instruction biasing for tool-use-only output.
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This explains why MiniMax-M2.7 works out-of-the-box (its RLHF was tuned for native tool-call emission) and intern-s2-preview doesn't.
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## 6. Hotfix design
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**Surface**: `agent-node/src/cli.ts` `processWithClaude` path — inject a vendor-specific system-prompt prefix when the upstream looks like an intern endpoint.
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```typescript
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// Detection: ANTHROPIC_BASE_URL is the canonical signal across all places
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// (CLI flag, env, config.json env map all resolve into process.env by the time
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// processWithClaude runs).
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function isInternEndpoint(): boolean {
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const url = process.env.ANTHROPIC_BASE_URL || "";
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return /intern-ai\.org\.cn|chat\.intern-ai/i.test(url);
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}
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const INTERN_TOOL_USE_BIAS = [
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"When a tool is available and applicable to the user request, you MUST respond by emitting a tool_use content block, not by writing text that describes the tool call.",
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"Do not show a verbose thinking process. Do not embed tool-call JSON inside text. Use the tool_use content channel directly.",
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"If no tool fits, respond normally with text.",
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].join(" ");
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// Inside processWithClaude options assembly:
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const baseSystemPrompt = SYSTEM_PROMPT || ""; // existing user-supplied
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const internBias = isInternEndpoint() ? INTERN_TOOL_USE_BIAS + "\n\n" : "";
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if (internBias || baseSystemPrompt) {
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options.systemPrompt = internBias + baseSystemPrompt;
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}
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```
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**Why detection by base URL not by model name**:
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- model name is sometimes "intern-s2-preview", sometimes a custom user alias, sometimes shifted by gateway proxies
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- ANTHROPIC_BASE_URL is set explicitly by the vendor preset (see `agent-network/bin/cli.ts:1330` for intern preset) and is the most stable signal
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- Generalises to future intern endpoints (intern-s3 / intern-research / …) without code changes
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**Backward compat**:
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- Non-intern users: no behaviour change (bias prefix is empty string)
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- intern users with explicit `--prompt`: bias is **prefixed** before their prompt, not replaced — preserves user intent
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- claude-agent-sdk SDK already handles mixed text+tool_use content blocks (no further work)
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## 7. Smoke plan (recommended for 测试马)
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Same Docker setup that catches #102 / #101 / #125:
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| Test | Setup | Expected |
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|------|-------|----------|
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| `intern-with-fix` | agent-node@preview-with-hotfix + intern preset + commhub MCP + a "please send_task to agent_b" instruction | `[tool] mcp__commhub__commhub_send_task(...)` line appears in agent log; receiver_b inbox has new row from sender_a |
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| `intern-without-fix-regression` | same but agent-node@2.3.8 latest (current) | No tool calls — confirms baseline (regression guard) |
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| `minimax-no-regression` | same hotfix build + MiniMax preset | Still works, tool fires as before |
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| `non-intern-prompt-preserved` | hotfix build + intern preset + explicit `--prompt "act as a sysadmin"` | Both user prompt AND tool-use bias present in system; user intent not lost |
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## 8. Out-of-scope (separate follow-up)
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- `tool_choice` forced-mode support — needs upstream intern API team. Not anet's to fix.
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- intern's verbose "Thinking Process" generally — out of scope; we only need it to use tools.
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- codex runtime path — different code path; if codex+intern combo needed, a parallel investigation.
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- #129 (401 fast-fail) — independent issue, both intersect on intern endpoint UX but each is a separate fix.
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## 9. Release ops recommendation
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- **Preview**: `agent-node@2.3.9-preview.0` (or whatever 通信工程马's release rule cycles to next — per `feedback_preview_version_increment_rule`, preview suffix `.N` only). Two-phase publish per `feedback_npm_publish_two_phase` to avoid recurring split-brain (per agent-node 2.3.6/2.3.7 incident).
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- **Stable**: after smoke pass, `npm dist-tag add @2.3.9 latest` two-phase pointer flip.
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- **Risk**: very low — change is additive, isolated to one detect+prefix step in the system prompt assembly.
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## 10. Vincent X article — green light
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Both:
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- intern-s2-preview **does** support tool calling via the Anthropic protocol, just needs a system-prompt bias
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- The hotfix is ~15 LOC, low-risk, ships as `agent-node@2.3.9-preview.0`
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→ X article can keep "书生 Intern-S2 科研军团" framing with confidence; the in-anet tool-calling story is fixable and verifiable today.
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---
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*Artifacts (local, not committed): `/tmp/intern-tool-research/payload*.json`, `/tmp/intern-tool-research/sysprompt-resp.json` — all sensitive (raw API key only via env), but the response files are safe (no key, just intern API output for verification).*

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