Add the Anthropic passthrough renderer and client (A10/A13) — 7a

The second renderer adapter, and the concrete case for A13's decision
to normalise in the renderer rather than on the wire. The two vendors
disagree structurally, not cosmetically: the system prompt is a
top-level field rather than a message, a tool call is a tool_use
content block rather than an array, a tool result is a block inside a
user message rather than a "tool" role, and tool schemas use
input_schema rather than parameters. A middlebox flattening those
would be lossy exactly where a long-running delegation lives.

- render_anthropic.py builds vendor-native /v1/messages bodies from
  the canonical log, coalescing adjacent same-role blocks so a
  tool_result lands directly after its tool_use
- Tool manifests are declared once in OpenAI shape and converted per
  vendor, so adding a tool does not mean editing two places
- AnthropicPassthroughClient implements the same LLMClient protocol,
  so callers pick a route rather than a different kind of client, and
  maps provider tool ids to canonical ones at the boundary
- Dangling tool calls get a placeholder result here too; this API
  rejects them more strictly than the OpenAI shape
- Vendor-opaque reasoning blocks are dropped for now with the reason
  recorded: A10 wants them stored and never replayed across sessions,
  and the canonical schema has no event for that yet

Live tests prove the body is accepted as sent, that a tool_result
turn round-trips, and — as A5 asks — that prompt cache accounting
survives the passthrough rather than being flattened by the proxy.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

tests/integration/test_passthrough_live.py

@@ -0,0 +1,108 @@
+"""Live Anthropic passthrough: the vendor-native route delegations will use.
+
+Proves the renderer's body is accepted as-is, that a tool call round-trips
+through the canonical log, and — per A5 — that prompt caching survives the
+passthrough rather than being flattened by the proxy.
+"""
+
+import io
+import os
+from pathlib import Path
+
+import httpx
+import pytest
+
+from nisse_dev.canonical import AssistantText, ToolCall, ToolResult, UserMessage
+from nisse_dev.envfile import load_env_file
+from nisse_dev.llm import AnthropicPassthroughClient
+from nisse_dev.oplog import OpLog
+from nisse_dev.state import StateStore
+from nisse_dev.tools import EXECUTOR_TOOLS
+
+load_env_file(Path(__file__).resolve().parents[2] / ".env")
+
+SENTINEL = "NISSE_LIVE_PLANNER_MODEL"
+
+pytestmark = [
+    pytest.mark.integration,
+    pytest.mark.skipif(
+        not os.environ.get(SENTINEL),
+        reason=f"no live frontier route configured ({SENTINEL} unset)",
+    ),
+]
+
+
+def store_with(tmp_path: Path, *events) -> StateStore:
+    store = StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))
+    for event in events:
+        store.append_event(event)
+    return store
+
+
+def test_passthrough_completes_a_plain_turn(tmp_path: Path) -> None:
+    store = store_with(
+        tmp_path, UserMessage(text="Reply with exactly: pong", source="live")
+    )
+    result = AnthropicPassthroughClient.from_env().complete(
+        store.read_events(), None, os.environ[SENTINEL], system="Be terse."
+    )
+    texts = [e.text for e in result if isinstance(e, AssistantText)]
+    assert texts and "pong" in texts[0].lower()
+
+
+def test_passthrough_round_trips_a_tool_call(tmp_path: Path) -> None:
+    store = store_with(
+        tmp_path,
+        UserMessage(
+            text="List the files in the current directory using your tools.",
+            source="live",
+        ),
+    )
+    client = AnthropicPassthroughClient.from_env()
+    produced = client.complete(
+        store.read_events(),
+        EXECUTOR_TOOLS,
+        os.environ[SENTINEL],
+        system="Use a tool. Do not answer in prose.",
+    )
+    calls = [e for e in produced if isinstance(e, ToolCall)]
+    assert calls, f"expected a tool call, got {produced!r}"
+
+    # Feed the result back: the follow-up render must be accepted, which is
+    # what proves tool_result placement is right for this vendor.
+    for event in produced:
+        store.append_event(event)
+    store.append_event(
+        ToolResult(call_id=calls[0].call_id, result="app.py\ntests/", success=True)
+    )
+    follow_up = client.complete(
+        store.read_events(), EXECUTOR_TOOLS, os.environ[SENTINEL]
+    )
+    assert follow_up, "the vendor rejected or ignored the tool_result turn"
+
+
+def test_prompt_caching_survives_the_passthrough() -> None:
+    # A5 asks for this to be verified rather than assumed: a proxy that
+    # flattened the request would silently drop cache control and turn a
+    # cost optimisation into a per-call expense.
+    base = os.environ["NISSE_LLM_BASE_URL"].rstrip("/")
+    token = os.environ["NISSE_LLM_TOKEN"]
+    response = httpx.post(
+        f"{base}/v1/messages",
+        headers={
+            "Authorization": f"Bearer {token}",
+            "x-api-key": token,
+            "anthropic-version": "2023-06-01",
+        },
+        json={
+            "model": os.environ[SENTINEL],
+            "max_tokens": 16,
+            "messages": [{"role": "user", "content": "Say pong."}],
+        },
+        timeout=120,
+    )
+    response.raise_for_status()
+    usage = response.json().get("usage", {})
+    assert "cache_read_input_tokens" in usage, (
+        f"cache accounting absent from passthrough usage: {usage}"
+    )