The call record: every model call, for the operator's study (M6.6a)

nisse-core records each model call of every agent into a file beside its
state store (<state>.calls.db), for the status page (L7, X6):

- the request body exactly as sent (never headers), the raw response with
  its reasoning, the model asked and the one that answered, time, tokens
  and llama-server's own prompt reuse (timings.cache_n);
- its work item, role, run and purpose: Driver.run and nisse-dev's
  delegation loop open runs, nested; compaction, expert consults, the
  classifier and the go-ahead reader label their calls;
- provenance, worked out in BudgetedClient by comparing the events at
  each stage: each event sent with its log id and content as sent, which
  a compaction replaced, which fitting shortened (by how much) and
  dropped.

Content is stored once by hash, compressed. It is diagnostic only: the
agent never reads it, a failed write is logged once and recording stops
while the call goes on, and the file is deleted when the agent starts,
so its layout never migrates; CallReader refuses a layout it doesn't
know. While recording, the system prompt ends with one sentence saying
so. `call_record: false` in either agent's YAML turns it off.

The benchmark records each run beside that run's own state store, and
`--keep-runs DIR` keeps both for the page.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01J5Utyfzw7gF7UhjRDEtjTk

nisse-review/src/nisse_review/build.py

@@ -9,6 +9,7 @@ from nisse_core.knowledge.memory import Memory
 from nisse_core.knowledge.vault import Vault
 from nisse_core.loop.driver import Driver
 from nisse_core.loop.expert import ExpertDesk
+from nisse_core.models import calls
 from nisse_core.models.catalog import ModelCatalog
 from nisse_core.models.compact import Compactor
 from nisse_core.models.context import BudgetedClient
@@ -94,6 +95,7 @@ def llm_client(config: ReviewConfig, oplog: OpLog | None = None) -> BudgetedClie
         safety_margin=tuning.safety_margin,
         enforces_tool_choice=lambda m: catalog.facts(m).supports_tool_choice is True,
         oplog=oplog,  # a cut that fitting has to make is logged
+        call_recorder=calls.recorder_for(config.state_db, config.call_record, oplog),
     )