docs: add TODO marker for adaptive edge weights

Note that the hard-coded HYPOTHESIS_EDGE_WEIGHTS table is a temporary choice; an adaptive scheme should be explored once the full pipeline is stable. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
fix: address agent boundary / JSON robustness / Phase 4 no-op from CFReDS run
2026-05-12 17:14:23 +08:00 · 2026-05-12 17:14:16 +08:00 · 2026-05-12 17:13:54 +08:00
11 changed files with 480 additions and 149 deletions
--- a/README.md
+++ b/README.md
@@ -36,6 +36,8 @@ main.py                          入口：配置加载、镜像选择、断连
 | `Entity` | `ent-*` | 人、程序、主机、IP 等可复现的实体 |

 Phenomenon → Hypothesis 的边类型与权重写死在 `HYPOTHESIS_EDGE_WEIGHTS`：
+# TODO 
+当前流程跑通以后，寻找自适应方案

 | 边类型 | 权重 | 语义 |
 |---|---:|---|
--- a/agents/hypothesis.py
+++ b/agents/hypothesis.py
@@ -1,7 +1,9 @@
 """Hypothesis Agent — generates investigative hypotheses from phenomena.

 Generates hypotheses only. Phenomenon→Hypothesis linking is handled centrally
-by Orchestrator._judge_new_phenomena, so all link logic lives in one place.
+by Orchestrator._judge_new_phenomena. Tool set is restricted to read-only
+graph queries + add_hypothesis to prevent the agent from creating phenomena,
+leads, or entity links.
 """

 from __future__ import annotations
@@ -27,6 +29,10 @@ class HypothesisAgent(BaseAgent):
        super().__init__(llm, graph)
        self._register_hypothesis_tools()

+    def _register_graph_tools(self) -> None:
+        """Restrict to read-only graph tools. add_hypothesis is registered separately."""
+        self._register_graph_read_tools()
+
    def _register_hypothesis_tools(self) -> None:
        self.register_tool(
            name="add_hypothesis",
@@ -51,6 +57,32 @@ class HypothesisAgent(BaseAgent):
            executor=self._add_hypothesis,
        )

+    def _build_system_prompt(self, task: str) -> str:
+        """Focused prompt — no INVESTIGATE/RECORD/LINK workflow."""
+        return (
+            f"You are {self.name}, a forensic hypothesis analyst.\n"
+            f"Role: {self.role}\n\n"
+            f"Image: {self.graph.image_path}\n"
+            f"Current investigation state: {self.graph.stats_summary()}\n\n"
+            f"Your task: {task}\n\n"
+            f"WORKFLOW:\n"
+            f"1. Call list_phenomena and search_graph to review existing findings.\n"
+            f"2. For each hypothesis you want to record, call add_hypothesis (title + description).\n"
+            f"3. Wrap a short summary in <answer> when you have generated 3-7 hypotheses.\n\n"
+            f"STRICT BOUNDARIES:\n"
+            f"- Your only mutation tool is add_hypothesis. Do NOT attempt list_directory, "
+            f"parse_registry_key, extract_file, or any disk-image investigation tools — "
+            f"they are not yours and you will get 'unknown tool' errors.\n"
+            f"- You CANNOT create phenomena, leads, or entity links. The orchestrator handles "
+            f"all phenomenon↔hypothesis linking after you finish.\n"
+            f"- Each hypothesis must be specific and testable. Avoid generic templates like "
+            f"'Unauthorized Remote Access' or 'Malware Deployment' unless concrete phenomena "
+            f"in the graph already point to them.\n"
+            f"- If the graph is empty, generate broad starting hypotheses and mark them "
+            f"clearly as exploratory in their description so downstream agents know they "
+            f"still need evidence."
+        )
+
    async def _add_hypothesis(self, title: str, description: str) -> str:
        hid = await self.graph.add_hypothesis(
            title=title,
--- a/agents/report.py
+++ b/agents/report.py
@@ -25,6 +25,10 @@ class ReportAgent(BaseAgent):
        super().__init__(llm, graph)
        self._register_tools()

+    def _register_graph_tools(self) -> None:
+        """Restrict to read-only graph tools. Report agent does not mutate state."""
+        self._register_graph_read_tools()
+
    def _build_system_prompt(self, task: str) -> str:
        """Report agent gets a clean prompt — no Phase A/B/C/D workflow."""
        return (
--- a/agents/timeline.py
+++ b/agents/timeline.py
@@ -1,14 +1,21 @@
-"""Timeline Agent — correlates evidence across time."""
+"""Timeline Agent — connects existing phenomena with temporal edges.
+
+Operates on phenomena already in the graph. Does NOT investigate the disk
+image itself. The agent's only useful output is the temporal edges it
+creates between phenomena.
+"""

 from __future__ import annotations

-import json
+import logging

 from base_agent import BaseAgent
 from evidence_graph import EvidenceGraph
 from llm_client import LLMClient
 from tool_registry import TOOL_CATALOG

+logger = logging.getLogger(__name__)
+

 class TimelineAgent(BaseAgent):
    name = "timeline"
@@ -22,24 +29,33 @@ class TimelineAgent(BaseAgent):
        super().__init__(llm, graph)
        self._register_tools()

+    def _register_graph_tools(self) -> None:
+        """Restrict to read-only graph tools — Timeline does not add phenomena."""
+        self._register_graph_read_tools()
+
    def _register_tools(self) -> None:
-        # Filesystem timeline tool from catalog
        td = TOOL_CATALOG.get("build_filesystem_timeline")
        if td:
            self.register_tool(td.name, td.description, td.input_schema, td.executor)

-        # Custom tool to get all phenomena with timestamps for correlation
        self.register_tool(
            name="get_timestamped_phenomena",
-            description="Get all phenomena that have timestamps, sorted chronologically. Use for timeline correlation.",
+            description=(
+                "Get all phenomena that have timestamps, sorted chronologically. "
+                "Returns each phenomenon's id, category, title, and a short description "
+                "preview. Use this as your primary input for temporal correlation."
+            ),
            input_schema={"type": "object", "properties": {}},
            executor=self._get_timestamped_phenomena,
        )

-        # Tool to add temporal edges between phenomena
        self.register_tool(
            name="add_temporal_edge",
-            description="Add a temporal relationship between two phenomena (before, after, or concurrent).",
+            description=(
+                "Add a temporal relationship edge between two existing phenomena. "
+                "Use 'before' when source phenomenon happened before target, "
+                "'concurrent' when they occurred within seconds of each other."
+            ),
            input_schema={
                "type": "object",
                "properties": {
@@ -56,6 +72,42 @@ class TimelineAgent(BaseAgent):
            executor=self._add_temporal_edge,
        )

+    def _build_system_prompt(self, task: str) -> str:
+        """Focused prompt — Timeline connects existing phenomena, doesn't investigate."""
+        return (
+            f"You are {self.name}, a forensic timeline correlation analyst.\n"
+            f"Role: {self.role}\n\n"
+            f"Image: {self.graph.image_path}\n"
+            f"Current state: {self.graph.stats_summary()}\n\n"
+            f"Your task: {task}\n\n"
+            f"WORKFLOW:\n"
+            f"1. Call build_filesystem_timeline once to materialize MAC times for the disk.\n"
+            f"2. Call get_timestamped_phenomena to see all phenomena with timestamps, "
+            f"sorted chronologically. THIS IS YOUR PRIMARY INPUT.\n"
+            f"3. For each meaningful temporal relationship between phenomena, call "
+            f"add_temporal_edge(source_id, target_id, relation). Use 'before' when "
+            f"source happened first (the common case); 'concurrent' for events within "
+            f"a few seconds of each other.\n"
+            f"   Examples of meaningful connections:\n"
+            f"     - 'Cain installer executed' (before) 'Cain.exe first execution'\n"
+            f"     - 'WHOIS first lookup'      (before) 'WHOIS second lookup'\n"
+            f"     - 'Recon tool cluster'      (before) 'Anti-forensics defrag'\n"
+            f"     - 'Tool installation'       (before) 'Tool execution'\n"
+            f"4. Aim for 15-40 temporal edges that connect the major events into a "
+            f"forensic story.\n"
+            f"5. Wrap a short summary in <answer> when done.\n\n"
+            f"STRICT BOUNDARIES:\n"
+            f"- Your job is to CONNECT existing phenomena, NOT to discover new ones. "
+            f"You CANNOT call add_phenomenon — the tool isn't yours.\n"
+            f"- Use ONLY phenomenon IDs returned by get_timestamped_phenomena or "
+            f"list_phenomena. NEVER fabricate IDs.\n"
+            f"- Connect events that tell a forensic story (recon -> exploit -> cover-up). "
+            f"Do not exhaustively pair every two phenomena; focus on causally-relevant "
+            f"sequences.\n"
+            f"- The orchestrator handles report writing in the next phase. Your only "
+            f"output that propagates is the temporal edges you create."
+        )
+
    async def _get_timestamped_phenomena(self) -> str:
        items = [
            ph for ph in self.graph.phenomena.values()
--- a/base_agent.py
+++ b/base_agent.py
@@ -93,12 +93,20 @@ class BaseAgent:
            f"  NEVER guess or fabricate a phenomenon ID. If an ID is not in list_phenomena output, it does not exist.\n\n"
            f"Phase D — ANSWER:\n"
            f"  Only give your <answer> AFTER completing Phases B and C.\n\n"
-            f"IMPORTANT:\n"
-            f"- You MUST call add_phenomenon at least once before finishing\n"
-            f"- Complete each phase before starting the next\n"
-            f"- Other agents can ONLY see what you write to the graph\n"
-            f"- If you don't record findings, they are LOST\n"
-            f"- Include relevant file paths, inode numbers, timestamps, and raw data\n\n"
+            f"CRITICAL — RECORDING REQUIREMENT:\n"
+            f"- Your <answer> block is DISCARDED by the orchestrator. Only graph mutations propagate.\n"
+            f"- Other agents and the final report read ONLY the evidence graph "
+            f"(phenomena, entities, edges).\n"
+            f"- You MUST call add_phenomenon for EVERY significant finding BEFORE you end.\n"
+            f"- NEGATIVE findings count too. If you searched X (a directory, a pattern, "
+            f"a registry key) and found NOTHING, that absence IS evidence — call "
+            f"add_phenomenon with a 'No matches for X' title and the search scope in "
+            f"raw_data. Negative findings constrain the hypothesis space and prevent "
+            f"the next agent from wasting time re-searching.\n"
+            f"- If you produce <answer> without having called add_phenomenon at least once, "
+            f"the task is FAILED regardless of what you wrote in <answer>.\n"
+            f"- Include exact file paths, inode numbers, timestamps, and the source_tool "
+            f"that produced each finding.\n\n"
            f"ANTI-HALLUCINATION RULES — STRICTLY ENFORCED:\n"
            f"- ONLY record findings that appear VERBATIM in tool results you received\n"
            f"- NEVER invent or guess timestamps, file paths, inode numbers, or program names\n"
@@ -145,9 +153,17 @@ class BaseAgent:
    # ---- Graph interaction tools --------------------------------------------

    def _register_graph_tools(self) -> None:
-        """Register tools for querying and writing to the evidence graph."""
+        """Register graph query + mutation tools.

-        # --- Read tools ---
+        Subclasses can override to restrict the toolset. For example, a
+        read-only agent (hypothesis, report) overrides this to skip
+        _register_graph_write_tools.
+        """
+        self._register_graph_read_tools()
+        self._register_graph_write_tools()
+
+    def _register_graph_read_tools(self) -> None:
+        """Register read-only graph + asset query tools."""

        self.register_tool(
            name="list_phenomena",
@@ -213,7 +229,49 @@ class BaseAgent:
            executor=self._get_hypothesis_status,
        )

-        # --- Write tools ---
+        self.register_tool(
+            name="list_assets",
+            description=(
+                "List all files extracted from the disk image. "
+                "Shows filename, category, size, local path, and inode. "
+                "Check this before calling extract_file to avoid re-extraction."
+            ),
+            input_schema={
+                "type": "object",
+                "properties": {
+                    "category": {
+                        "type": "string",
+                        "enum": [
+                            "registry_hive", "chat_log", "prefetch", "network_capture",
+                            "config_file", "address_book", "recycle_bin", "executable",
+                            "text_log", "other",
+                        ],
+                        "description": "Filter by category. Omit to list all.",
+                    },
+                },
+            },
+            executor=self._list_assets,
+        )
+
+        self.register_tool(
+            name="find_extracted_file",
+            description=(
+                "Find an already-extracted file by inode or filename. "
+                "Returns the local path so you can use it directly with "
+                "parse_registry_key, read_text_file, etc. without re-extracting."
+            ),
+            input_schema={
+                "type": "object",
+                "properties": {
+                    "inode": {"type": "string", "description": "Inode to look up."},
+                    "filename": {"type": "string", "description": "Filename or partial name to search."},
+                },
+            },
+            executor=self._find_extracted_file,
+        )
+
+    def _register_graph_write_tools(self) -> None:
+        """Register graph mutation tools (add_phenomenon, add_lead, link_to_entity)."""

        self.register_tool(
            name="add_phenomenon",
@@ -282,49 +340,6 @@ class BaseAgent:
            executor=self._link_to_entity,
        )

-        # --- Asset library tools ---
-
-        self.register_tool(
-            name="list_assets",
-            description=(
-                "List all files extracted from the disk image. "
-                "Shows filename, category, size, local path, and inode. "
-                "Check this before calling extract_file to avoid re-extraction."
-            ),
-            input_schema={
-                "type": "object",
-                "properties": {
-                    "category": {
-                        "type": "string",
-                        "enum": [
-                            "registry_hive", "chat_log", "prefetch", "network_capture",
-                            "config_file", "address_book", "recycle_bin", "executable",
-                            "text_log", "other",
-                        ],
-                        "description": "Filter by category. Omit to list all.",
-                    },
-                },
-            },
-            executor=self._list_assets,
-        )
-
-        self.register_tool(
-            name="find_extracted_file",
-            description=(
-                "Find an already-extracted file by inode or filename. "
-                "Returns the local path so you can use it directly with "
-                "parse_registry_key, read_text_file, etc. without re-extracting."
-            ),
-            input_schema={
-                "type": "object",
-                "properties": {
-                    "inode": {"type": "string", "description": "Inode to look up."},
-                    "filename": {"type": "string", "description": "Filename or partial name to search."},
-                },
-            },
-            executor=self._find_extracted_file,
-        )
-
    # ---- Tool executors -----------------------------------------------------

    async def _list_phenomena(self, category: str | None = None) -> str:
--- a/llm_client.py
+++ b/llm_client.py
@@ -1,8 +1,9 @@
-"""Custom LLM client using httpx for Claude Messages API via third-party proxy.
+"""LLM client via the OpenAI SDK (works with DeepSeek's OpenAI-compatible API).

-The proxy does not support Claude's native tool_use format (it strips the `tools`
-field from requests). So we embed tool definitions in the system prompt and parse
-structured JSON tool calls from the model's text output (ReAct-style).
+Tool calling is text-based (ReAct pattern): tool schemas are embedded in
+the system prompt and tool calls are parsed as <tool_call> JSON blocks
+from model output. This keeps the protocol independent of whether the
+underlying API supports native function/tool calling.
 """

 from __future__ import annotations
@@ -18,6 +19,7 @@ from dataclasses import dataclass, field
 from typing import Any

 import httpx
+from openai import APIConnectionError, APIError, APITimeoutError, AsyncOpenAI

 logger = logging.getLogger(__name__)

@@ -301,44 +303,51 @@ _FOLD_SUMMARY_SYSTEM = (


 class LLMClient:
-    """Calls Claude Messages API through a third-party proxy using raw httpx.
+    """Async LLM client via the OpenAI SDK.

-    Uses prompt-based tool calling (ReAct pattern) since the proxy does not
-    support Claude's native tool_use format.
+    Works with any OpenAI-compatible endpoint (OpenAI, DeepSeek, ...).
+    Tool calling is text-based (ReAct) — see module docstring.
    """

    def __init__(
        self,
        base_url: str,
        api_key: str,
-        model: str = "claude-sonnet-4-6",
+        model: str = "deepseek-v4-pro",
        max_tokens: int = 4096,
        proxy: str | None = "auto",
+        reasoning_effort: str | None = None,
+        thinking_enabled: bool = False,
    ) -> None:
        self.base_url = base_url.rstrip("/")
        self.api_key = api_key
        self.model = model
        self.max_tokens = max_tokens
-        # proxy="auto": read from env; proxy=None/""/"none": no proxy; proxy="http://...": use it
+        self.reasoning_effort = reasoning_effort
+        self.thinking_enabled = thinking_enabled
+
+        # proxy="auto": read from env; proxy=None/""/"none": no proxy
        if proxy == "auto":
            proxy_url = os.environ.get("https_proxy") or os.environ.get("HTTPS_PROXY")
        elif proxy and proxy.lower() != "none":
            proxy_url = proxy
        else:
            proxy_url = None
-        self._client = httpx.AsyncClient(
+
+        http_client = (
+            httpx.AsyncClient(proxy=proxy_url, timeout=300.0)
+            if proxy_url else None
+        )
+
+        self._client = AsyncOpenAI(
+            api_key=self.api_key,
            base_url=self.base_url,
-            headers={
-                "x-api-key": self.api_key,
-                "anthropic-version": "2023-06-01",
-                "content-type": "application/json",
-            },
            timeout=300.0,
-            proxy=proxy_url,
+            http_client=http_client,
        )

    async def close(self) -> None:
-        await self._client.aclose()
+        await self._client.close()

    async def chat(
        self,
@@ -346,79 +355,53 @@ class LLMClient:
        system: str | None = None,
        max_retries: int = 5,
    ) -> str:
-        """Send a streaming chat request and return the assembled text response.
+        """Send a streaming chat completion and return the assembled text."""
+        full_messages: list[dict] = []
+        if system:
+            full_messages.append({"role": "system", "content": system})
+        full_messages.extend(messages)

-        Uses SSE streaming to keep the connection alive and avoid gateway
-        timeouts (504/524) on long-running completions.
-        """
-        import asyncio as _asyncio
-
-        payload: dict[str, Any] = {
+        kwargs: dict[str, Any] = {
            "model": self.model,
+            "messages": full_messages,
            "max_tokens": self.max_tokens,
-            "messages": messages,
            "stream": True,
        }
-        if system:
-            payload["system"] = system
+        if self.reasoning_effort:
+            kwargs["reasoning_effort"] = self.reasoning_effort
+        if self.thinking_enabled:
+            kwargs["extra_body"] = {"thinking": {"type": "enabled"}}

        for attempt in range(max_retries):
-            logger.debug("LLM request (stream): %d messages (attempt %d)", len(messages), attempt + 1)
+            logger.debug(
+                "LLM request (stream): %d messages (attempt %d)",
+                len(messages), attempt + 1,
+            )
            text_parts: list[str] = []
            try:
-                async with self._client.stream(
-                    "POST", "/v1/messages", json=payload,
-                ) as resp:
-                    # Check for HTTP errors before consuming stream
-                    if resp.status_code >= 400:
-                        body = await resp.aread()
-                        raise httpx.HTTPStatusError(
-                            f"Server error '{resp.status_code}' for url '{resp.url}'",
-                            request=resp.request,
-                            response=resp,
-                        )
-
-                    # Parse SSE events
-                    async for line in resp.aiter_lines():
-                        if not line.startswith("data: "):
-                            continue
-                        data_str = line[6:]  # strip "data: " prefix
-                        if data_str.strip() == "[DONE]":
-                            break
-                        try:
-                            event = json.loads(data_str)
-                        except json.JSONDecodeError:
-                            continue
-
-                        event_type = event.get("type", "")
-                        if event_type == "content_block_delta":
-                            delta = event.get("delta", {})
-                            if delta.get("type") == "text_delta":
-                                text_parts.append(delta["text"])
-                        elif event_type == "message_stop":
-                            break
-                        elif event_type == "error":
-                            err_msg = event.get("error", {}).get("message", "Unknown streaming error")
-                            raise httpx.HTTPStatusError(
-                                err_msg, request=resp.request, response=resp,
-                            )
+                stream = await self._client.chat.completions.create(**kwargs)
+                async for chunk in stream:
+                    if not chunk.choices:
+                        continue
+                    delta = chunk.choices[0].delta
+                    if delta.content:
+                        text_parts.append(delta.content)

                text = "".join(text_parts)
                logger.debug("LLM response (stream): %d chars", len(text))
                return text

-            except (httpx.HTTPStatusError, httpx.ConnectError, httpx.ReadTimeout, httpx.RemoteProtocolError) as e:
+            except (APIConnectionError, APITimeoutError, APIError) as e:
                if attempt < max_retries - 1:
                    wait = 2 ** attempt * 10
                    logger.warning("Request failed (%s), retrying in %ds...", e, wait)
-                    await _asyncio.sleep(wait)
+                    await asyncio.sleep(wait)
                else:
                    raise LLMAPIError(
                        f"LLM API unreachable after {max_retries} attempts: {e}",
                        attempts=max_retries,
                    ) from e

-        # Should not reach here, but just in case
        return ""

    async def tool_call_loop(
--- a/main.py
+++ b/main.py
@@ -219,6 +219,8 @@ async def async_main() -> None:
        model=agent_cfg["model"],
        max_tokens=agent_cfg.get("max_tokens", 4096),
        proxy=agent_cfg.get("proxy", "auto"),
+        reasoning_effort=agent_cfg.get("reasoning_effort"),
+        thinking_enabled=agent_cfg.get("thinking_enabled", False),
    )

    # Initialize evidence graph
--- a/orchestrator.py
+++ b/orchestrator.py
@@ -17,6 +17,36 @@ from llm_client import LLMClient
 logger = logging.getLogger(__name__)


+def _safe_json_loads(text: str):
+    """Parse JSON with progressive sanitization for LLM-produced output.
+
+    Tries: (0) as-is, (1) escape stray backslashes outside valid JSON
+    escapes (\\" \\\\ \\/ \\b \\f \\n \\r \\t \\uXXXX). On final failure,
+    logs raw input (first 600 chars) so we can diagnose what the model
+    actually emitted.
+    """
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        pass
+
+    # Escape backslashes not followed by a valid JSON escape character.
+    # NOTE: \\u must be followed by exactly 4 hex digits to be valid.
+    stage1 = re.sub(
+        r'\\(?!["\\/bfnrt]|u[0-9a-fA-F]{4})',
+        r'\\\\',
+        text,
+    )
+    try:
+        return json.loads(stage1)
+    except json.JSONDecodeError as e:
+        logger.warning(
+            "_safe_json_loads failed after sanitize (%s); raw head[:600]=%r",
+            e, text[:600],
+        )
+        raise
+
+
 def _log(msg: str, **extra) -> None:
    """Emit a structured log message with extra fields for the terminal formatter."""
    logger.info(msg, extra=extra)
@@ -216,6 +246,52 @@ class Orchestrator:
            "The ultimate goal is to reconstruct a detailed timeline of what happened on this host."
        )

+    # ---- LLM JSON helper -----------------------------------------------------
+
+    async def _call_llm_for_json(
+        self,
+        system: str,
+        user_prompt: str,
+        schema: str = "array",
+        max_retries: int = 2,
+    ):
+        """Call LLM expecting JSON output; self-correct on parse failure.
+
+        Two layers of safety:
+        1. _safe_json_loads escapes stray backslashes outside valid JSON escapes.
+        2. On any remaining parse error, append the error to the prompt and ask
+           the LLM to retry, up to max_retries additional attempts.
+        """
+        error_hint = ""
+        last_err: Exception | None = None
+        pattern = r'\[.*?\]' if schema == "array" else r'\{.*?\}'
+
+        for attempt in range(max_retries + 1):
+            messages = [{"role": "user", "content": user_prompt + error_hint}]
+            response = await self.llm.chat(messages=messages, system=system)
+            match = re.search(pattern, response, re.DOTALL)
+            candidate = match.group() if match else response
+            try:
+                return _safe_json_loads(candidate)
+            except (json.JSONDecodeError, ValueError) as e:
+                last_err = e
+                if attempt < max_retries:
+                    logger.info(
+                        "JSON parse attempt %d/%d failed (%s); retrying with hint",
+                        attempt + 1, max_retries + 1, e,
+                    )
+                error_hint = (
+                    f"\n\n[Your previous response could not be parsed as JSON: {e}]\n"
+                    f"Output STRICT JSON only — no markdown fences, no code blocks. "
+                    f"Do NOT include literal backslash characters in any string value; "
+                    f"rephrase using forward slashes or describe paths in English "
+                    f"(e.g. 'the Cain folder under Program Files' instead of "
+                    f"'C:\\Program Files\\Cain'). "
+                    f"The response must be a single JSON {schema}."
+                )
+        assert last_err is not None
+        raise last_err
+
    # ---- Hypothesis-directed investigation -----------------------------------

    async def _generate_hypothesis_leads(self) -> None:
@@ -252,15 +328,11 @@ class Orchestrator:
        )

        try:
-            response = await self.llm.chat(
-                messages=[{"role": "user", "content": prompt}],
+            tasks = await self._call_llm_for_json(
                system=self._LEAD_GEN_SYSTEM,
+                user_prompt=prompt,
+                schema="array",
            )
-            match = re.search(r'\[.*?\]', response, re.DOTALL)
-            if match:
-                tasks = json.loads(match.group())
-            else:
-                tasks = json.loads(response)

            for task in tasks:
                hyp_id = task.get("hypothesis_id", "")
@@ -300,12 +372,11 @@ class Orchestrator:
                f'[{{"agent": "agent_type", "task": "what to investigate", "priority": 1-10}}]'
            )
            try:
-                response = await self.llm.chat(
-                    messages=[{"role": "user", "content": prompt}],
+                tasks = await self._call_llm_for_json(
                    system=self._LEAD_GEN_SYSTEM,
+                    user_prompt=prompt,
+                    schema="array",
                )
-                match = re.search(r'\[.*?\]', response, re.DOTALL)
-                tasks = json.loads(match.group()) if match else json.loads(response)
                for task in tasks:
                    await self.graph.add_lead(
                        target_agent=task.get("agent", "filesystem"),
@@ -351,15 +422,11 @@ class Orchestrator:
        )

        try:
-            response = await self.llm.chat(
-                messages=[{"role": "user", "content": prompt}],
+            judgments = await self._call_llm_for_json(
                system=self._JUDGE_SYSTEM,
+                user_prompt=prompt,
+                schema="array",
            )
-            match = re.search(r'\[.*?\]', response, re.DOTALL)
-            if match:
-                judgments = json.loads(match.group())
-            else:
-                judgments = json.loads(response)

            for j in judgments:
                hyp_id = j.get("hypothesis_id", "")
@@ -402,12 +469,11 @@ class Orchestrator:
                f'[{{"phenomenon_id": "ph-xxx", "edge_type": "supports|contradicts|...", "reason": "brief explanation"}}]'
            )
            try:
-                response = await self.llm.chat(
-                    messages=[{"role": "user", "content": prompt}],
+                judgments = await self._call_llm_for_json(
                    system=self._JUDGE_SYSTEM,
+                    user_prompt=prompt,
+                    schema="array",
                )
-                match = re.search(r'\[.*?\]', response, re.DOTALL)
-                judgments = json.loads(match.group()) if match else json.loads(response)
                for j in judgments:
                    ph_id = j.get("phenomenon_id", "")
                    edge_type = j.get("edge_type", "")
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,7 @@ description = "Multi-Agent System for Digital Forensics"
 requires-python = ">=3.14"
 dependencies = [
    "httpx[socks]>=0.28.1",
+    "openai>=2.36.0",
    "pyyaml",
    "regipy>=6.2.1",
 ]
--- a/regenerate_report.py
+++ b/regenerate_report.py
@@ -45,6 +45,8 @@ async def main() -> None:
        api_key=agent_cfg["api_key"],
        model=agent_cfg["model"],
        max_tokens=16384,
+        reasoning_effort=agent_cfg.get("reasoning_effort"),
+        thinking_enabled=agent_cfg.get("thinking_enabled", False),
    )

    register_all_tools(graph.image_path, graph.partition_offset, graph)
--- a/uv.lock
+++ b/uv.lock
@@ -2,6 +2,15 @@ version = 1
 revision = 3
 requires-python = ">=3.14"

+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.13.0"
@@ -41,6 +50,15 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/b2/fb/08b3f4bf05da99aba8ffea52a558758def16e8516bc75ca94ff73587e7d3/construct-2.10.70-py3-none-any.whl", hash = "sha256:c80be81ef595a1a821ec69dc16099550ed22197615f4320b57cc9ce2a672cb30", size = 63020, upload-time = "2023-11-29T08:44:46.876Z" },
 ]

+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -110,12 +128,48 @@ wheels = [
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 ]

+[[package]]
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+version = "0.14.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
 [[package]]
 name = "masforensics"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
    { name = "httpx", extra = ["socks"] },
+    { name = "openai" },
    { name = "pyyaml" },
    { name = "regipy" },
 ]
@@ -129,6 +183,7 @@ dev = [
 [package.metadata]
 requires-dist = [
    { name = "httpx", extras = ["socks"], specifier = ">=0.28.1" },
+    { name = "openai", specifier = ">=2.36.0" },
    { name = "pyyaml" },
    { name = "regipy", specifier = ">=6.2.1" },
 ]
@@ -139,6 +194,25 @@ dev = [
    { name = "pytest-asyncio", specifier = ">=1.3.0" },
 ]

+[[package]]
+name = "openai"
+version = "2.36.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
+]
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+wheels = [
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+
 [[package]]
 name = "packaging"
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