DESIGN_STRATEGIST.md §2. Four read-only view tools the strategist uses
to ground its decision each round.
graph_overview() — hypotheses table (log_odds, conf, edges_in,
distinct_sources, recent_flip), sources table,
pending leads. distinct_sources is the
critical signal: a hypothesis with 23 edges
but only 1 distinct_source has fragile cross-
source independence and is a candidate for
a corroboration-seeking lead.
source_coverage(src) — per-source ✓/✗ against an expected-artefact
catalogue. Catalogue is heuristic hints,
NOT a forced checklist. Footer reminds the
strategist to investigate ✗ items only when
an active hypothesis depends on them — this
is the "应试能力存在但不被绑死" guardrail.
marginal_yield(N) — new phenomena / edges / status flips per
recent round. Two consecutive zero-yield
rounds = strong signal to declare complete.
budget_status() — usage vs caps (tool_calls, rounds, wall
clock). Pacing warnings at 70% / 90%.
tools/strategy.py also exports EXPECTED_ARTEFACTS, a per-source-type
table of (name, detector, value_for) entries. Detectors are
substring patterns on tool name + args; the matcher resolves at
call time against graph.tool_invocations. Catalogue covers iOS /
Android / Windows disk / media-collection / archive source types.
All four tools registered in tool_registry, listed as read-only in
llm_client.READ_ONLY_TOOLS for parallel execution. They go through
the invocation-logging wrapper so the strategist's reads are
themselves auditable (the wrapper does NOT cache them — graph
state changes between calls).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Move _safe_json_loads from orchestrator.py to llm_client.py and have
_extract_tool_calls use it when parsing <tool_call> JSON blocks from
model output. orchestrator now imports it from llm_client.
Background: in the first full DeepSeek run (runs/2026-05-12T17-25-38),
~10 'Failed to parse tool call JSON' warnings appeared, all from regex
patterns where the LLM wrote \. or \* inside JSON string values:
Failed to parse tool call JSON: {..., "pattern": "Outlook Express|...|\.dbx"}
Failed to parse tool call JSON: {..., "pattern": "ethereal.*\.pcap"}
Failed to parse tool call JSON: {..., "pattern": "lookatlan.*\.txt|..."}
These are exactly the kind of stray-backslash errors stage-1 sanitize
already handles for orchestrator JSON calls — but tool-call extraction
was using bare json.loads. Result: each failed tool call silently dropped
on the floor, the LLM never got a result, and at least one network agent
burned 14m26s spinning before hitting max_iterations=40.
Now the sanitize/log-on-failure path is shared. Verified against the
three failure cases from yesterday's log: all three now parse cleanly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The previous LLMClient used raw httpx + Claude Messages API (/v1/messages,
x-api-key, Anthropic SSE event types). Incompatible with DeepSeek.
Rewrite LLMClient.__init__/chat/close to use openai.AsyncOpenAI:
- /v1/chat/completions endpoint, OpenAI message format
- Bearer auth, native SDK error types
- Stream chunks via async for + chunk.choices[0].delta.content
Tool calling protocol (ReAct text-based tags) and all surrounding helpers
(_apply_progressive_decay, _fold_old_messages, _partition_tool_calls,
tool_call_loop, etc.) are unchanged — endpoint-agnostic by design.
New optional config params surfaced to config.yaml.agent:
- reasoning_effort: "high" | "medium" | "low" — DeepSeek/o1-style depth
- thinking_enabled: bool — DeepSeek extra_body.thinking switch
main.py and regenerate_report.py pass these through to LLMClient.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
