Merge pull request #72 from The-Obstacle-Is-The-Way/dev

## Summary
Merges dev → main with complete SPEC-07 + SPEC-08 implementation.

### Included:
- SPEC-07: LangGraph Cognitive Memory Architecture
- SPEC-08: Shared Memory Layer Integration
- CodeRabbit critical fixes (async add_evidence with proper deduplication)
- Security: CVE fixes for langgraph-checkpoint and urllib3
- CI hardening: Security scans now fail on vulnerabilities

All 178 tests pass. No known vulnerabilities.

.github/workflows/ci.yml

@@ -40,11 +40,9 @@ jobs:
 
       - name: Security scan with bandit
         run: uv run bandit -r src -ll -q
-        continue-on-error: true  # Don't fail CI, just report
 
       - name: Dependency vulnerability audit
         run: uv run pip-audit
-        continue-on-error: true  # Informational - deps may have known issues
 
       - name: Run tests with coverage
         run: uv run pytest tests/unit/ -v --cov=src --cov-report=xml --cov-report=term-missing

Dockerfile

@@ -1,4 +1,5 @@
 # Dockerfile for DeepBoner
+# Build: 2025-11-29-v2 (force rebuild after orchestrator refactor)
 FROM python:3.11-slim
 
 # Set working directory

docs/bugs/ACTIVE_BUGS.md

@@ -10,13 +10,22 @@
 
 ## P3 - Architecture/Enhancement
 
-### P3 - Missing Structured Cognitive Memory
+### ~~P3 - Missing Structured Cognitive Memory~~ FIXED (Phase 1)
 **File:** `P3_ARCHITECTURAL_GAP_STRUCTURED_MEMORY.md`
 **Spec:** [SPEC_07_LANGGRAPH_MEMORY_ARCH.md](../specs/SPEC_07_LANGGRAPH_MEMORY_ARCH.md)
+**PR:** [#72](https://github.com/The-Obstacle-Is-The-Way/DeepBoner/pull/72)
 
 **Problem:** AdvancedOrchestrator uses chat-based state (context drift on long runs).
-**Solution:** Implement LangGraph StateGraph with explicit hypothesis/conflict tracking.
-**Status:** Spec complete, implementation pending.
+**Solution:** Implemented LangGraph StateGraph with explicit hypothesis/conflict tracking (`src/agents/graph`).
+**Status:** ✅ Memory layer built. ⏳ Integration pending (SPEC_08).
+
+### P1 - Memory Layer Not Integrated (Post-Hackathon)
+**Issue:** [#73](https://github.com/The-Obstacle-Is-The-Way/DeepBoner/issues/73)
+**Spec:** [SPEC_08_INTEGRATE_MEMORY_LAYER.md](../specs/SPEC_08_INTEGRATE_MEMORY_LAYER.md)
+
+**Problem:** Structured memory (hypotheses, conflicts) is isolated in "God Mode" only.
+**Solution:** Extract memory into shared service, integrate into Simple and Advanced modes.
+**Status:** Spec written. Blocked until post-hackathon.
 
 ### P3 - Ephemeral Memory (No Persistence)
 **File:** `P3_ARCHITECTURAL_GAP_EPHEMERAL_MEMORY.md`

docs/bugs/P3_ARCHITECTURAL_GAP_STRUCTURED_MEMORY.md

@@ -69,11 +69,13 @@ Based on [comprehensive analysis](https://latenode.com/blog/langgraph-multi-agen
 ### Target Architecture
 
 ```python
-# src/agents/graph/state.py (PROPOSED)
+# src/agents/graph/state.py (IMPLEMENTED)
 from typing import Annotated, TypedDict, Literal
 import operator
+from pydantic import BaseModel, Field
+from langchain_core.messages import BaseMessage
 
-class Hypothesis(TypedDict):
+class Hypothesis(BaseModel):
     id: str
     statement: str
     status: Literal["proposed", "validating", "confirmed", "refuted"]
@@ -81,7 +83,7 @@ class Hypothesis(TypedDict):
     supporting_evidence_ids: list[str]
     contradicting_evidence_ids: list[str]
 
-class Conflict(TypedDict):
+class Conflict(BaseModel):
     id: str
     description: str
     source_a_id: str

docs/specs/SPEC_07_LANGGRAPH_MEMORY_ARCH.md

@@ -120,26 +120,30 @@ Based on [comprehensive framework comparison](https://kanerika.com/blogs/langcha
 from typing import Annotated, TypedDict, Literal
 import operator
 from langchain_core.messages import BaseMessage
+from pydantic import BaseModel, Field
 
 
-class Hypothesis(TypedDict):
+class Hypothesis(BaseModel):
     """A research hypothesis with evidence tracking."""
-    id: str
-    statement: str
-    status: Literal["proposed", "validating", "confirmed", "refuted"]
-    confidence: float  # 0.0 - 1.0
-    supporting_evidence_ids: list[str]
-    contradicting_evidence_ids: list[str]
+    id: str = Field(description="Unique identifier for the hypothesis")
+    statement: str = Field(description="The hypothesis statement")
+    status: Literal["proposed", "validating", "confirmed", "refuted"] = Field(
+        default="proposed", description="Current validation status"
+    )
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0, description="Confidence score (0.0-1.0)")
+    supporting_evidence_ids: list[str] = Field(default_factory=list)
+    contradicting_evidence_ids: list[str] = Field(default_factory=list)
+    reasoning: str | None = Field(default=None, description="Reasoning for current status")
 
 
-class Conflict(TypedDict):
+class Conflict(BaseModel):
     """A detected contradiction between sources."""
-    id: str
-    description: str
-    source_a_id: str
-    source_b_id: str
-    status: Literal["open", "resolved"]
-    resolution: str | None
+    id: str = Field(description="Unique identifier for the conflict")
+    description: str = Field(description="Description of the contradiction")
+    source_a_id: str = Field(description="ID of the first conflicting source")
+    source_b_id: str = Field(description="ID of the second conflicting source")
+    status: Literal["open", "resolved"] = Field(default="open")
+    resolution: str | None = Field(default=None, description="Resolution explanation if resolved")
 
 
 class ResearchState(TypedDict):
@@ -151,11 +155,12 @@ class ResearchState(TypedDict):
     # Immutable context
     query: str
 
-    # Cognitive state (the "blackboard")
+    # Cognitive state (The "Blackboard")
+    # Note: We store these as lists of Pydantic models.
     hypotheses: Annotated[list[Hypothesis], operator.add]
     conflicts: Annotated[list[Conflict], operator.add]
 
-    # Evidence links (actual content in ChromaDB)
+    # Evidence links (actual content stored in ChromaDB)
     evidence_ids: Annotated[list[str], operator.add]
 
     # Chat history (for LLM context)
@@ -169,90 +174,78 @@ class ResearchState(TypedDict):
 
 ### 4.2 Graph Nodes
 
-Each node is a pure function: `(state: ResearchState) -> dict`
+Each node is an async function that receives the state and injected dependencies.
 
 **File:** `src/agents/graph/nodes.py`
 
 ```python
 """Graph node implementations."""
-from langchain_core.messages import HumanMessage, AIMessage
-from src.tools.pubmed import search_pubmed
-from src.tools.clinicaltrials import search_clinicaltrials
-from src.tools.europepmc import search_europepmc
+from typing import Any
+from langchain_core.messages import AIMessage
+from src.services.embeddings import EmbeddingService
+from src.tools.search_handler import SearchHandler
 
 
-async def search_node(state: ResearchState) -> dict:
+async def search_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
     """Execute search across all sources.
 
-    Returns partial state update (additive via operator.add).
+    Uses SearchHandler to query PubMed, ClinicalTrials, and EuropePMC.
+    Deduplicates evidence using EmbeddingService.
     """
-    query = state["query"]
-    # Reuse existing tools
-    results = await asyncio.gather(
-        search_pubmed(query),
-        search_clinicaltrials(query),
-        search_europepmc(query),
-    )
-    new_evidence_ids = [...]  # Store in ChromaDB, return IDs
+    # ... implementation ...
     return {
-        "evidence_ids": new_evidence_ids,
-        "messages": [AIMessage(content=f"Found {len(new_evidence_ids)} papers")],
+        "evidence_ids": new_ids,
+        "messages": [AIMessage(content=message)],
     }
 
 
-async def judge_node(state: ResearchState) -> dict:
+async def judge_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
     """Evaluate evidence and update hypothesis confidence.
 
-    Key responsibility: Detect conflicts and flag them.
+    Uses pydantic_ai Agent to generate structured HypothesisAssessment.
     """
-    # LLM call to evaluate hypotheses against evidence
-    # If contradiction found: add to conflicts list
+    # ... implementation ...
     return {
-        "hypotheses": updated_hypotheses,  # With new confidence scores
-        "conflicts": new_conflicts,  # Any detected contradictions
-        "messages": [...],
+        "hypotheses": new_hypotheses,
+        "messages": [AIMessage(content=f"Judge: Generated {len(new_hypotheses)} hypotheses.")],
+        "next_step": "resolve",
     }
 
 
-async def resolve_node(state: ResearchState) -> dict:
-    """Handle open conflicts via tie-breaker logic.
-
-    Triggers targeted search or reasoning to resolve.
-    """
-    open_conflicts = [c for c in state["conflicts"] if c["status"] == "open"]
-    # For each conflict: search for decisive evidence or make judgment call
-    return {
-        "conflicts": resolved_conflicts,
-        "messages": [...],
-    }
-
+async def resolve_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Handle open conflicts."""
+    # ... implementation ...
+    return {"messages": messages}
 
-async def synthesize_node(state: ResearchState) -> dict:
-    """Generate final research report.
 
-    Only uses confirmed hypotheses and resolved conflicts.
-    """
-    confirmed = [h for h in state["hypotheses"] if h["status"] == "confirmed"]
-    # Generate structured report
-    return {
-        "messages": [AIMessage(content=report_markdown)],
-        "next_step": "finish",
-    }
+async def synthesize_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Generate final research report."""
+    # ... implementation ...
+    return {"messages": [AIMessage(content=report_markdown)], "next_step": "finish"}
 
 
-def supervisor_node(state: ResearchState) -> dict:
-    """Route to next node based on state.
+async def supervisor_node(
+    state: ResearchState, llm: BaseChatModel | None = None
+) -> dict[str, Any]:
+    """Route to next node based on state using robust Pydantic parsing.
 
     This is the "brain" - uses LLM to decide next action
-    based on STRUCTURED STATE (not just chat).
+    based on STRUCTURED STATE.
     """
-    # Decision logic:
-    # 1. If open conflicts exist -> "resolve"
-    # 2. If hypotheses need more evidence -> "search"
-    # 3. If evidence is sufficient -> "judge"
-    # 4. If all hypotheses confirmed -> "synthesize"
-    # 5. If max iterations -> "synthesize" (forced)
-    return {"next_step": decided_step, "iteration_count": state["iteration_count"] + 1}
+    # ... implementation ...
+    return {
+        "next_step": decision.next_step,
+        "iteration_count": state["iteration_count"] + 1,
+        "messages": [AIMessage(content=f"Supervisor: {decision.reasoning}")],
+    }
 ```
 
 ### 4.3 Graph Definition
@@ -261,57 +254,40 @@ def supervisor_node(state: ResearchState) -> dict:
 
 ```python
 """LangGraph workflow definition."""
+from functools import partial
 from langgraph.graph import StateGraph, END
-from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.graph.state import CompiledStateGraph
 
 from src.agents.graph.state import ResearchState
-from src.agents.graph.nodes import (
-    search_node,
-    judge_node,
-    resolve_node,
-    synthesize_node,
-    supervisor_node,
-)
+from src.services.embeddings import EmbeddingService
+# ... imports ...
 
 
-def create_research_graph(checkpointer=None):
+def create_research_graph(
+    llm=None,
+    checkpointer=None,
+    embedding_service: EmbeddingService | None = None,
+) -> CompiledStateGraph:
     """Build the research state graph.
 
     Args:
-        checkpointer: Optional SqliteSaver/MongoDBSaver for persistence
+        llm: Supervisor LLM
+        checkpointer: Optional persistence layer
+        embedding_service: Service for evidence storage
     """
     graph = StateGraph(ResearchState)
 
-    # Add nodes
-    graph.add_node("supervisor", supervisor_node)
-    graph.add_node("search", search_node)
-    graph.add_node("judge", judge_node)
-    graph.add_node("resolve", resolve_node)
-    graph.add_node("synthesize", synthesize_node)
-
-    # Define edges (supervisor routes based on state.next_step)
-    graph.add_edge("search", "supervisor")
-    graph.add_edge("judge", "supervisor")
-    graph.add_edge("resolve", "supervisor")
-    graph.add_edge("synthesize", END)
-
-    # Conditional routing from supervisor
-    graph.add_conditional_edges(
-        "supervisor",
-        lambda state: state["next_step"],
-        {
-            "search": "search",
-            "judge": "judge",
-            "resolve": "resolve",
-            "synthesize": "synthesize",
-            "finish": END,
-        },
-    )
+    # Bind dependencies using partial
+    bound_supervisor = partial(supervisor_node, llm=llm) if llm else supervisor_node
+    bound_search = partial(search_node, embedding_service=embedding_service)
+    # ... binding other nodes ...
 
-    # Entry point
-    graph.set_entry_point("supervisor")
+    # Add nodes
+    graph.add_node("supervisor", bound_supervisor)
+    graph.add_node("search", bound_search)
+    # ...
 
-    return graph.compile(checkpointer=checkpointer)
+    # ... edges ...
 ```
 
 ### 4.4 Orchestrator Integration

docs/specs/SPEC_08_INTEGRATE_MEMORY_LAYER.md

@@ -0,0 +1,308 @@
+# SPEC 08: Integrate Memory Layer into All Modes
+
+**Status:** APPROVED
+**Priority:** P1 (Post-Hackathon)
+**Author:** Architecture Team
+**Date:** 2025-11-29
+**Depends On:** SPEC_07 (LangGraph Memory - IMPLEMENTED)
+**Related Issue:** #73
+
+---
+
+## 1. Executive Summary
+
+Integrate the structured memory layer (built in SPEC_07 as "God Mode") into Simple and Advanced modes. Remove the separate "God Mode" - memory becomes a shared capability, not a separate mode.
+
+**Before (current - accidental):**
+```
+Simple Mode     → No memory
+Advanced Mode   → Chat-based memory
+God Mode        → Structured memory  ← ISOLATED
+```
+
+**After (target):**
+```
+Simple Mode     → Structured memory ✓
+Advanced Mode   → Structured memory ✓
+(God Mode removed from UI)
+```
+
+---
+
+## 2. What SPEC_07 Built (Already Done)
+
+| Component | File | Status |
+|-----------|------|--------|
+| `ResearchState` TypedDict | `src/agents/graph/state.py` | ✅ Done |
+| `Hypothesis` model | `src/agents/graph/state.py` | ✅ Done |
+| `Conflict` model | `src/agents/graph/state.py` | ✅ Done |
+| `EmbeddingService` | `src/services/embeddings.py` | ✅ Done |
+| Hypothesis conversion | `src/agents/graph/nodes.py` | ✅ Done |
+
+**This is the memory layer. It works. We just need to wire it into Simple and Advanced modes.**
+
+---
+
+## 3. Integration Plan
+
+### Phase 1: Create Shared Memory Service
+
+Extract the memory logic from LangGraph nodes into a standalone service.
+
+**New File:** `src/services/research_memory.py`
+
+```python
+"""Shared research memory layer for all orchestration modes."""
+
+from typing import Literal
+
+from src.agents.graph.state import Conflict, Hypothesis
+from src.services.embeddings import EmbeddingService
+from src.utils.models import Citation, Evidence
+
+
+class ResearchMemory:
+    """Shared cognitive state for research workflows.
+
+    This is the memory layer that ALL modes use.
+    It mimics the LangGraph state management but for manual orchestration.
+    """
+
+    def __init__(self, query: str, embedding_service: EmbeddingService | None = None):
+        self.query = query
+        self.hypotheses: list[Hypothesis] = []
+        self.conflicts: list[Conflict] = []
+        self.evidence_ids: list[str] = []
+        self.iteration_count: int = 0
+        
+        # Injected service
+        self._embedding_service = embedding_service or EmbeddingService()
+
+    async def store_evidence(self, evidence: list[Evidence]) -> list[str]:
+        """Store evidence and return new IDs (deduped)."""
+        if not self._embedding_service:
+            return []
+
+        unique = await self._embedding_service.deduplicate(evidence)
+        new_ids = []
+
+        for ev in unique:
+            ev_id = ev.citation.url
+            await self._embedding_service.add_evidence(
+                evidence_id=ev_id,
+                content=ev.content,
+                metadata={
+                    "source": ev.citation.source,
+                    "title": ev.citation.title,
+                    "date": ev.citation.date,
+                    "authors": ",".join(ev.citation.authors or []),
+                    "url": ev.citation.url,
+                },
+            )
+            new_ids.append(ev_id)
+
+        self.evidence_ids.extend(new_ids)
+        return new_ids
+
+    async def get_relevant_evidence(self, n: int = 20) -> list[Evidence]:
+        """Retrieve relevant evidence for current query."""
+        if not self._embedding_service:
+            return []
+            
+        results = await self._embedding_service.search_similar(self.query, n_results=n)
+        evidence_list = []
+        
+        for r in results:
+            meta = r.get("metadata", {})
+            authors_str = meta.get("authors", "")
+            authors = authors_str.split(",") if authors_str else []
+            
+            # Reconstruct Evidence object
+            # Note: SourceName validation might be needed, defaulting to 'web' or similar if unknown
+            source_raw = meta.get("source", "web")
+            
+            citation = Citation(
+                source=source_raw, # type: ignore
+                title=meta.get("title", "Unknown"),
+                url=meta.get("url", r["id"]),
+                date=meta.get("date", "Unknown"),
+                authors=authors
+            )
+            
+            evidence_list.append(Evidence(
+                content=r["content"],
+                citation=citation,
+                relevance=1.0 - r.get("distance", 0.5) # Approx conversion
+            ))
+            
+        return evidence_list
+
+    def add_hypothesis(self, hypothesis: Hypothesis) -> None:
+        """Add a hypothesis to tracking."""
+        self.hypotheses.append(hypothesis)
+
+    def add_conflict(self, conflict: Conflict) -> None:
+        """Add a detected conflict."""
+        self.conflicts.append(conflict)
+
+    def get_open_conflicts(self) -> list[Conflict]:
+        """Get unresolved conflicts."""
+        return [c for c in self.conflicts if c.status == "open"]
+
+    def get_confirmed_hypotheses(self) -> list[Hypothesis]:
+        """Get high-confidence hypotheses."""
+        return [h for h in self.hypotheses if h.confidence > 0.8]
+```
+
+### Phase 2: Integrate into Simple Mode
+
+**File:** `src/orchestrators/simple.py`
+
+```python
+# Add to __init__
+from src.services.research_memory import ResearchMemory
+
+class Orchestrator:
+    def __init__(self, ...):
+        ...
+        self._memory: ResearchMemory | None = None
+
+    async def run(self, query: str) -> AsyncGenerator[AgentEvent, None]:
+        # Initialize memory for this run
+        self._memory = ResearchMemory(query=query)
+
+        # In search phase:
+        new_ids = await self._memory.store_evidence(search_results.evidence)
+
+        # In judge phase:
+        relevant = await self._memory.get_relevant_evidence(n=30)
+        # ... existing judge logic, but now with memory context
+
+        # Track hypotheses from judge assessment
+        for h in assessment.details.drug_candidates:
+            self._memory.add_hypothesis(Hypothesis(
+                id=h,
+                statement=f"{h} identified as candidate",
+                status="proposed",
+                confidence=assessment.confidence,
+            ))
+```
+
+### Phase 3: Integrate into Advanced Mode
+
+**File:** `src/orchestrators/advanced.py`
+
+```python
+# Same pattern - inject ResearchMemory
+# Agents read/write to shared memory instead of chat history
+```
+
+### Phase 4: Remove God Mode from UI
+
+**File:** `src/app.py`
+
+```python
+# Before
+mode = gr.Radio(
+    choices=["simple", "magentic", "god"],
+    ...
+)
+
+# After
+mode = gr.Radio(
+    choices=["simple", "magentic"],
+    ...
+)
+# Memory is always enabled, not a mode choice
+```
+
+**File:** `src/orchestrators/factory.py`
+
+```python
+# Remove "god" and "langgraph" mode handling
+# Keep LangGraphOrchestrator code for reference/future use
+```
+
+---
+
+## 4. What Stays, What Goes
+
+| Component | Action |
+|-----------|--------|
+| `src/agents/graph/state.py` | ✅ KEEP - Hypothesis/Conflict models |
+| `src/agents/graph/nodes.py` | ⚠️ EXTRACT - Move memory logic to service |
+| `src/agents/graph/workflow.py` | 📦 ARCHIVE - LangGraph routing (optional) |
+| `src/orchestrators/langgraph_orchestrator.py` | 📦 ARCHIVE - Not needed if memory integrated |
+| `src/services/research_memory.py` | ✨ NEW - Shared memory service |
+
+---
+
+## 5. Files to Modify
+
+| File | Change |
+|------|--------|
+| `src/services/research_memory.py` | NEW - Extract from nodes.py |
+| `src/orchestrators/simple.py` | Add memory integration |
+| `src/orchestrators/advanced.py` | Add memory integration |
+| `src/orchestrators/factory.py` | Remove "god" mode |
+| `src/app.py` | Remove God Mode from dropdown |
+| `tests/unit/services/test_research_memory.py` | NEW - Test memory service |
+
+---
+
+## 6. Acceptance Criteria
+
+- [ ] `ResearchMemory` service extracted and tested
+- [ ] Simple mode uses `ResearchMemory` for evidence storage
+- [ ] Simple mode tracks hypotheses from judge assessments
+- [ ] Advanced mode uses `ResearchMemory` (shared state)
+- [ ] "God Mode" removed from UI
+- [ ] All existing tests pass
+- [ ] New tests for memory integration
+
+---
+
+## 7. Why This is the Right Pattern
+
+```
+Iterative Development:
+
+1. Build in isolation    ✅ (SPEC_07 - God Mode)
+   - Test without breaking existing code
+   - Verify the concept works
+
+2. Ship isolated feature ✅ (PR #72)
+   - Get it into main
+   - Real users can test it
+
+3. Integrate into stack  🔜 (This spec)
+   - Wire into existing modes
+   - Remove scaffolding
+
+4. Clean up              🔜
+   - Delete God Mode UI
+   - Archive LangGraph orchestrator
+```
+
+**You shipped the hard part. Now it's just plumbing.**
+
+---
+
+## 8. Time Estimate
+
+| Phase | Effort |
+|-------|--------|
+| Phase 1: Extract memory service | 2 hours |
+| Phase 2: Simple mode integration | 2 hours |
+| Phase 3: Advanced mode integration | 2 hours |
+| Phase 4: UI cleanup | 30 mins |
+| Testing | 1 hour |
+| **Total** | **~8 hours** |
+
+---
+
+## 9. References
+
+- SPEC_07: LangGraph Memory Architecture (implemented)
+- PR #72: God Mode implementation
+- Issue #73: Architectural refactor tracking

pyproject.toml

@@ -26,6 +26,14 @@ dependencies = [
     "requests>=2.32.5", # ClinicalTrials.gov (httpx blocked by WAF)
     "limits>=3.0", # Rate limiting
     "duckduckgo-search>=5.0", # Web search
+    # LangGraph deps - upper bounds prevent breaking changes from major versions
+    "langgraph>=0.2.50,<1.0",
+    "langchain>=0.3.9,<1.0",
+    "langchain-core>=0.3.21,<1.0",
+    "langchain-huggingface>=0.1.2,<1.0",
+    "langgraph-checkpoint-sqlite>=3.0.0,<4.0",  # 3.0.0 required for GHSA-wwqv-p2pp-99h5 fix
+    # Security: Pin urllib3 to fix GHSA-48p4-8xcf-vxj5 and GHSA-pq67-6m6q-mj2v
+    "urllib3>=2.5.0",
 ]
 
 [project.optional-dependencies]

src/agents/graph/__init__.py

@@ -0,0 +1 @@
+"""LangGraph agents package."""

src/agents/graph/nodes.py

@@ -0,0 +1,352 @@
+"""Graph node implementations for DeepBoner research."""
+
+from typing import Any, Literal
+
+import structlog
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent
+
+from src.agent_factory.judges import get_model
+from src.agents.graph.state import Hypothesis, ResearchState
+from src.prompts.hypothesis import SYSTEM_PROMPT as HYPOTHESIS_SYSTEM_PROMPT
+from src.prompts.hypothesis import format_hypothesis_prompt
+from src.prompts.report import SYSTEM_PROMPT as REPORT_SYSTEM_PROMPT
+from src.prompts.report import format_report_prompt
+from src.services.embeddings import EmbeddingService
+from src.tools.base import SearchTool
+from src.tools.clinicaltrials import ClinicalTrialsTool
+from src.tools.europepmc import EuropePMCTool
+from src.tools.pubmed import PubMedTool
+from src.tools.search_handler import SearchHandler
+from src.utils.citation_validator import validate_references
+from src.utils.models import (
+    Citation,
+    Evidence,
+    HypothesisAssessment,
+    MechanismHypothesis,
+    ResearchReport,
+)
+
+logger = structlog.get_logger()
+
+
+def _convert_hypothesis_to_mechanism(h: Hypothesis) -> MechanismHypothesis:
+    """Convert state Hypothesis to MechanismHypothesis for report generation.
+
+    The state Hypothesis stores the mechanism as a statement like:
+    "drug -> target -> pathway -> effect"
+
+    We parse this back into structured MechanismHypothesis fields.
+    """
+    # Parse statement format: "drug -> target -> pathway -> effect"
+    # Handle both " -> " (standard) and "->" (compact) separators
+    separator = " -> " if " -> " in h.statement else "->"
+    parts = [p.strip() for p in h.statement.split(separator)]
+
+    # Validate: exactly 4 non-empty parts
+    if len(parts) == 4 and all(parts):
+        drug, target, pathway, effect = parts
+    elif len(parts) > 4 and all(parts[:4]):
+        # More than 4 parts: join extras into effect
+        drug, target, pathway = parts[0], parts[1], parts[2]
+        effect = f"{separator}".join(parts[3:])
+        logger.debug(
+            "Hypothesis has extra parts, joined into effect",
+            hypothesis_id=h.id,
+            parts_count=len(parts),
+        )
+    else:
+        # Log parsing failure for debugging
+        logger.warning(
+            "Failed to parse hypothesis statement format",
+            hypothesis_id=h.id,
+            statement=h.statement[:100],  # Truncate for log safety
+            parts_count=len(parts),
+        )
+        # Use meaningful fallback values
+        drug = "Unknown"
+        target = "Unknown"
+        pathway = "Unknown"
+        effect = h.statement.strip() if h.statement else "Unknown effect"
+
+    return MechanismHypothesis(
+        drug=drug,
+        target=target,
+        pathway=pathway,
+        effect=effect,
+        confidence=h.confidence,
+        supporting_evidence=h.supporting_evidence_ids,
+        contradicting_evidence=h.contradicting_evidence_ids,
+    )
+
+
+# --- Supervisor Output Schema ---
+class SupervisorDecision(BaseModel):
+    """The decision made by the supervisor."""
+
+    next_step: Literal["search", "judge", "resolve", "synthesize", "finish"] = Field(
+        description="The next step to take in the research process."
+    )
+    reasoning: str = Field(description="Reasoning for this decision.")
+
+
+# --- Nodes ---
+
+
+async def search_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Execute search across all sources."""
+    query = state["query"]
+    logger.info("search_node: executing search", query=query)
+
+    # Initialize tools
+    tools: list[SearchTool] = [PubMedTool(), ClinicalTrialsTool(), EuropePMCTool()]
+    handler = SearchHandler(tools=tools)
+
+    # Execute search
+    result = await handler.execute(query)
+
+    new_evidence_count = 0
+    new_ids = []
+
+    if embedding_service and result.evidence:
+        # Deduplicate and store
+        unique_evidence = await embedding_service.deduplicate(result.evidence)
+
+        for ev in unique_evidence:
+            ev_id = ev.citation.url
+            await embedding_service.add_evidence(
+                evidence_id=ev_id,
+                content=ev.content,
+                metadata={
+                    "source": ev.citation.source,
+                    "title": ev.citation.title,
+                    "date": ev.citation.date,
+                    "authors": ",".join(ev.citation.authors or []),
+                    "url": ev.citation.url,
+                },
+            )
+            new_ids.append(ev_id)
+
+        new_evidence_count = len(unique_evidence)
+    else:
+        new_evidence_count = len(result.evidence)
+
+    message = (
+        f"Search completed. Found {result.total_found} total, "
+        f"{new_evidence_count} unique new papers."
+    )
+    if result.errors:
+        message += f" Errors: {'; '.join(result.errors)}"
+
+    return {
+        "evidence_ids": new_ids,
+        "messages": [AIMessage(content=message)],
+    }
+
+
+async def judge_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Evaluate evidence and update hypothesis confidence."""
+    logger.info("judge_node: evaluating evidence")
+
+    evidence_context: list[Evidence] = []
+    if embedding_service:
+        scored_points = await embedding_service.search_similar(state["query"], n_results=20)
+        for p in scored_points:
+            meta = p.get("metadata", {})
+            authors = meta.get("authors", "")
+            author_list = authors.split(",") if authors else []
+
+            evidence_context.append(
+                Evidence(
+                    content=p.get("content", ""),
+                    citation=Citation(
+                        url=p.get("id", ""),
+                        title=meta.get("title", "Unknown"),
+                        source=meta.get("source", "Unknown"),
+                        date=meta.get("date", ""),
+                        authors=author_list,
+                    ),
+                )
+            )
+
+    agent = Agent(
+        model=get_model(),
+        output_type=HypothesisAssessment,
+        system_prompt=HYPOTHESIS_SYSTEM_PROMPT,
+    )
+
+    prompt = await format_hypothesis_prompt(
+        query=state["query"], evidence=evidence_context, embeddings=embedding_service
+    )
+
+    try:
+        result = await agent.run(prompt)
+        assessment = result.output
+
+        new_hypotheses = []
+        for h in assessment.hypotheses:
+            new_hypotheses.append(
+                Hypothesis(
+                    id=h.drug,
+                    statement=f"{h.drug} -> {h.target} -> {h.pathway} -> {h.effect}",
+                    status="proposed",
+                    confidence=h.confidence,
+                    supporting_evidence_ids=[],
+                    contradicting_evidence_ids=[],
+                )
+            )
+
+        return {
+            "hypotheses": new_hypotheses,
+            "messages": [AIMessage(content=f"Judge: Generated {len(new_hypotheses)} hypotheses.")],
+            "next_step": "resolve",
+        }
+    except Exception as e:
+        logger.error("judge_node failed", error=str(e))
+        return {"messages": [AIMessage(content=f"Judge Error: {e!s}")], "next_step": "search"}
+
+
+async def resolve_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Handle open conflicts."""
+    messages = []
+
+    # Access attributes with dot notation because items are Pydantic models
+    high_conf = [h for h in state["hypotheses"] if h.confidence > 0.8]
+
+    if high_conf:
+        messages.append(
+            AIMessage(
+                content=(
+                    f"Resolver: Found {len(high_conf)} high confidence hypotheses. "
+                    "Conflicts resolved."
+                )
+            )
+        )
+    else:
+        messages.append(AIMessage(content="Resolver: No high confidence hypotheses yet."))
+
+    return {"messages": messages}
+
+
+async def synthesize_node(
+    state: ResearchState, embedding_service: EmbeddingService | None = None
+) -> dict[str, Any]:
+    """Generate final report."""
+    logger.info("synthesize_node: generating report")
+
+    evidence_context: list[Evidence] = []
+    if embedding_service:
+        scored_points = await embedding_service.search_similar(state["query"], n_results=50)
+        for p in scored_points:
+            meta = p.get("metadata", {})
+            authors = meta.get("authors", "")
+            author_list = authors.split(",") if authors else []
+
+            evidence_context.append(
+                Evidence(
+                    content=p.get("content", ""),
+                    citation=Citation(
+                        url=p.get("id", ""),
+                        title=meta.get("title", "Unknown"),
+                        source=meta.get("source", "Unknown"),
+                        date=meta.get("date", ""),
+                        authors=author_list,
+                    ),
+                )
+            )
+
+    agent = Agent(
+        model=get_model(),
+        output_type=ResearchReport,
+        system_prompt=REPORT_SYSTEM_PROMPT,
+    )
+
+    # Convert state hypotheses to MechanismHypothesis for report generation
+    mechanism_hypotheses = [_convert_hypothesis_to_mechanism(h) for h in state["hypotheses"]]
+
+    prompt = await format_report_prompt(
+        query=state["query"],
+        evidence=evidence_context,
+        hypotheses=mechanism_hypotheses,
+        assessment={},
+        metadata={"sources": list(set(e.citation.source for e in evidence_context))},
+        embeddings=embedding_service,
+    )
+
+    try:
+        result = await agent.run(prompt)
+        report = result.output
+        report = validate_references(report, evidence_context)
+
+        return {"messages": [AIMessage(content=report.to_markdown())], "next_step": "finish"}
+    except Exception as e:
+        logger.error("synthesize_node failed", error=str(e))
+        return {"messages": [AIMessage(content=f"Synthesis Error: {e!s}")], "next_step": "finish"}
+
+
+async def supervisor_node(state: ResearchState, llm: BaseChatModel | None = None) -> dict[str, Any]:
+    """Route to next node based on state using robust Pydantic parsing."""
+    if state["iteration_count"] >= state["max_iterations"]:
+        return {"next_step": "synthesize", "iteration_count": state["iteration_count"]}
+
+    if llm is None:
+        return {"next_step": "search", "iteration_count": state["iteration_count"] + 1}
+
+    parser = PydanticOutputParser(pydantic_object=SupervisorDecision)
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are the Research Supervisor. Manage the workflow.\n\n"
+                "State Summary:\n"
+                "- Query: {query}\n"
+                "- Hypotheses: {hypo_count}\n"
+                "- Conflicts: {conflict_count}\n"
+                "- Iteration: {iteration}/{max_iter}\n\n"
+                "Decide the next step based on this logic:\n"
+                "1. If there are open conflicts -> 'resolve'\n"
+                "2. If hypotheses are unverified or few -> 'search'\n"
+                "3. If new evidence needs evaluation -> 'judge'\n"
+                "4. If hypotheses are confirmed -> 'synthesize'\n\n"
+                "{format_instructions}",
+            ),
+            ("user", "What is the next step?"),
+        ]
+    )
+
+    chain = prompt | llm | parser
+
+    try:
+        # Note: state["conflicts"] contains Pydantic models, so use dot notation
+        decision: SupervisorDecision = await chain.ainvoke(
+            {
+                "query": state["query"],
+                "hypo_count": len(state["hypotheses"]),
+                "conflict_count": len([c for c in state["conflicts"] if c.status == "open"]),
+                "iteration": state["iteration_count"],
+                "max_iter": state["max_iterations"],
+                "format_instructions": parser.get_format_instructions(),
+            }
+        )
+        return {
+            "next_step": decision.next_step,
+            "iteration_count": state["iteration_count"] + 1,
+            "messages": [AIMessage(content=f"Supervisor: {decision.reasoning}")],
+        }
+    except Exception as e:
+        return {
+            "next_step": "synthesize",
+            "iteration_count": state["iteration_count"] + 1,
+            "messages": [AIMessage(content=f"Supervisor Error: {e!s}. Proceeding to synthesis.")],
+        }

src/agents/graph/state.py

@@ -0,0 +1,71 @@
+"""LangGraph state definitions for the research workflow."""
+
+import operator
+from typing import Annotated, Literal, TypedDict
+
+from langchain_core.messages import BaseMessage
+from pydantic import BaseModel, Field
+
+# --- Domain Models (Inner Objects) ---
+# We use Pydantic for strict validation of the data objects
+
+
+class Hypothesis(BaseModel):
+    """A research hypothesis with evidence tracking."""
+
+    id: str = Field(description="Unique identifier for the hypothesis")
+    statement: str = Field(description="The hypothesis statement")
+    status: Literal["proposed", "validating", "confirmed", "refuted"] = Field(
+        default="proposed", description="Current validation status"
+    )
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0, description="Confidence score (0.0-1.0)")
+    supporting_evidence_ids: list[str] = Field(default_factory=list)
+    contradicting_evidence_ids: list[str] = Field(default_factory=list)
+    reasoning: str | None = Field(default=None, description="Reasoning for current status")
+
+
+class Conflict(BaseModel):
+    """A detected contradiction between sources."""
+
+    id: str = Field(description="Unique identifier for the conflict")
+    description: str = Field(description="Description of the contradiction")
+    source_a_id: str = Field(description="ID of the first conflicting source")
+    source_b_id: str = Field(description="ID of the second conflicting source")
+    status: Literal["open", "resolved"] = Field(default="open")
+    resolution: str | None = Field(default=None, description="Resolution explanation if resolved")
+
+
+# --- Graph State (The Blackboard) ---
+# LangGraph requires TypedDict for the main state object to support
+# partial updates and reducers (operator.add).
+
+
+class ResearchState(TypedDict):
+    """The cognitive state shared across all graph nodes.
+
+    Fields with 'Annotated[..., operator.add]' are reducers:
+    returning a dict with these keys from a node will APPEND to the list
+    instead of overwriting it.
+    """
+
+    # Immutable context
+    query: str
+
+    # Cognitive state (The "Blackboard")
+    # Note: We store these as lists of Pydantic models.
+    # Nodes should be careful to update existing items by ID if needed,
+    # or we might need a custom reducer for merging by ID.
+    # For now, we'll append and let the synthesizer filter the latest.
+    hypotheses: Annotated[list[Hypothesis], operator.add]
+    conflicts: Annotated[list[Conflict], operator.add]
+
+    # Evidence links (actual content stored in ChromaDB)
+    evidence_ids: Annotated[list[str], operator.add]
+
+    # Chat history (for LLM context)
+    messages: Annotated[list[BaseMessage], operator.add]
+
+    # Control flow
+    next_step: Literal["search", "judge", "resolve", "synthesize", "finish"]
+    iteration_count: int
+    max_iterations: int

src/agents/graph/workflow.py

@@ -0,0 +1,95 @@
+"""DeepBoner research workflow definition using LangGraph."""
+
+from functools import partial
+from typing import Any
+
+from langchain_core.language_models.chat_models import BaseChatModel
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.graph import END, StateGraph
+from langgraph.graph.state import CompiledStateGraph
+
+from src.agents.graph.nodes import (
+    judge_node,
+    resolve_node,
+    search_node,
+    supervisor_node,
+    synthesize_node,
+)
+from src.agents.graph.state import ResearchState
+from src.services.embeddings import EmbeddingService
+
+
+def create_research_graph(
+    llm: BaseChatModel | None = None,
+    checkpointer: "BaseCheckpointSaver[Any]" | None = None,  # Generic type from langgraph
+    embedding_service: EmbeddingService | None = None,
+) -> "CompiledStateGraph[Any]":  # type: ignore[type-arg]
+    """Build the research state graph.
+
+    Args:
+        llm: The language model for the supervisor node.
+        checkpointer: Optional persistence layer.
+        embedding_service: Service for evidence storage and retrieval.
+    """
+    graph = StateGraph(ResearchState)
+
+    # --- Nodes ---
+    # Bind the LLM to the supervisor node using partial
+    bound_supervisor = partial(supervisor_node, llm=llm) if llm else supervisor_node
+
+    # Bind embedding service to worker nodes
+    # We use partial to inject the service dependency while keeping the node signature clean
+    bound_search = (
+        partial(search_node, embedding_service=embedding_service)
+        if embedding_service
+        else search_node
+    )
+    bound_judge = (
+        partial(judge_node, embedding_service=embedding_service)
+        if embedding_service
+        else judge_node
+    )
+    bound_resolve = (
+        partial(resolve_node, embedding_service=embedding_service)
+        if embedding_service
+        else resolve_node
+    )
+    bound_synthesize = (
+        partial(synthesize_node, embedding_service=embedding_service)
+        if embedding_service
+        else synthesize_node
+    )
+
+    graph.add_node("supervisor", bound_supervisor)
+    graph.add_node("search", bound_search)
+    graph.add_node("judge", bound_judge)
+    graph.add_node("resolve", bound_resolve)
+    graph.add_node("synthesize", bound_synthesize)
+
+    # --- Edges ---
+    # All worker nodes report back to supervisor
+    graph.add_edge("search", "supervisor")
+    graph.add_edge("judge", "supervisor")
+    graph.add_edge("resolve", "supervisor")
+
+    # Synthesis is the end
+    graph.add_edge("synthesize", END)
+
+    # --- Conditional Routing ---
+    # Supervisor decides where to go next based on state["next_step"]
+    graph.add_conditional_edges(
+        "supervisor",
+        lambda state: state["next_step"],
+        {
+            "search": "search",
+            "judge": "judge",
+            "resolve": "resolve",
+            "synthesize": "synthesize",
+            "finish": END,
+        },
+    )
+
+    # Entry Point
+    graph.set_entry_point("supervisor")
+
+    return graph.compile(checkpointer=checkpointer)

src/agents/retrieval_agent.py

@@ -32,9 +32,8 @@ async def search_web(query: str, max_results: int = 10) -> str:
         logger.info("Web search returned no results", query=query)
         return f"No web results found for: {query}"
 
-    # Update state
-    # We add *all* found results to state
-    new_count = state.add_evidence(results.evidence)
+    # Store evidence with deduplication and embedding (all handled by memory layer)
+    new_count = await state.add_evidence(results.evidence)
     logger.info(
         "Web search complete",
         query=query,
@@ -42,11 +41,6 @@ async def search_web(query: str, max_results: int = 10) -> str:
         new_evidence=new_count,
     )
 
-    # Use embedding service for deduplication/indexing if available
-    if state.embedding_service:
-        # This method also adds to vector DB as a side effect for unique items
-        await state.embedding_service.deduplicate(results.evidence)
-
     output = [f"Found {len(results.evidence)} web results ({new_count} new stored):\n"]
     for i, r in enumerate(results.evidence[:max_results], 1):
         output.append(f"{i}. **{r.citation.title}**")

src/agents/state.py

@@ -5,78 +5,70 @@ searching simultaneously via Gradio).
 """
 
 from contextvars import ContextVar
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
-from src.utils.models import Citation, Evidence
+from src.services.research_memory import ResearchMemory
 
 if TYPE_CHECKING:
     from src.services.embeddings import EmbeddingService
+    from src.utils.models import Evidence
 
 
 class MagenticState(BaseModel):
     """Mutable state for a Magentic workflow session."""
 
-    evidence: list[Evidence] = Field(default_factory=list)
-    # Type as Any to avoid circular imports/runtime resolution issues
-    # The actual object injected will be an EmbeddingService instance
-    embedding_service: Any = None
+    # We wrap ResearchMemory. Type as Any to avoid pydantic validation issues with complex objects
+    memory: Any = None  # Instance of ResearchMemory
 
     model_config = {"arbitrary_types_allowed": True}
 
-    def add_evidence(self, new_evidence: list[Evidence]) -> int:
-        """Add new evidence, deduplicating by URL.
+    # --- Proxy methods for backwards compatibility with retrieval_agent.py ---
+
+    async def add_evidence(self, evidence: list["Evidence"]) -> int:
+        """Add evidence to memory with deduplication and embedding storage.
+
+        This method delegates to ResearchMemory.store_evidence() which:
+        1. Performs semantic deduplication (threshold 0.9)
+        2. Stores unique evidence in the vector store
+        3. Caches evidence for retrieval
+
+        Args:
+            evidence: List of Evidence objects to store.
 
         Returns:
-            Number of *new* items added.
+            Number of new (non-duplicate) evidence items stored.
         """
-        existing_urls = {e.citation.url for e in self.evidence}
-        count = 0
-        for item in new_evidence:
-            if item.citation.url not in existing_urls:
-                self.evidence.append(item)
-                existing_urls.add(item.citation.url)
-                count += 1
-        return count
-
-    async def search_related(self, query: str, n_results: int = 5) -> list[Evidence]:
-        """Search for semantically related evidence using the embedding service."""
-        if not self.embedding_service:
-            return []
-
-        results = await self.embedding_service.search_similar(query, n_results=n_results)
-
-        # Convert dict results back to Evidence objects
-        evidence_list = []
-        for item in results:
-            meta = item.get("metadata", {})
-            authors_str = meta.get("authors", "")
-            authors = [a.strip() for a in authors_str.split(",") if a.strip()]
-
-            ev = Evidence(
-                content=item["content"],
-                citation=Citation(
-                    title=meta.get("title", "Related Evidence"),
-                    url=item["id"],
-                    source="pubmed",  # Defaulting to pubmed if unknown
-                    date=meta.get("date", "n.d."),
-                    authors=authors,
-                ),
-                relevance=max(0.0, 1.0 - item.get("distance", 0.5)),
-            )
-            evidence_list.append(ev)
-
-        return evidence_list
+        if self.memory is None:
+            return 0
+
+        memory: ResearchMemory = self.memory
+        initial_count = len(memory.evidence_ids)
+        await memory.store_evidence(evidence)
+        return len(memory.evidence_ids) - initial_count
+
+    @property
+    def embedding_service(self) -> "EmbeddingService | None":
+        """Get the embedding service from memory."""
+        if self.memory is None:
+            return None
+        # Cast needed because memory is typed as Any to avoid Pydantic issues
+        from src.services.embeddings import EmbeddingService as EmbeddingSvc
+
+        return cast(EmbeddingSvc | None, self.memory._embedding_service)
 
 
 # The ContextVar holds the MagenticState for the current execution context
 _magentic_state_var: ContextVar[MagenticState | None] = ContextVar("magentic_state", default=None)
 
 
-def init_magentic_state(embedding_service: "EmbeddingService | None" = None) -> MagenticState:
+def init_magentic_state(
+    query: str, embedding_service: "EmbeddingService | None" = None
+) -> MagenticState:
     """Initialize a new state for the current context."""
-    state = MagenticState(embedding_service=embedding_service)
+    memory = ResearchMemory(query=query, embedding_service=embedding_service)
+    state = MagenticState(memory=memory)
     _magentic_state_var.set(state)
     return state
 
@@ -85,6 +77,5 @@ def get_magentic_state() -> MagenticState:
     """Get the current state. Raises RuntimeError if not initialized."""
     state = _magentic_state_var.get()
     if state is None:
-        # Auto-initialize if missing (e.g. during tests or simple scripts)
-        return init_magentic_state()
+        raise RuntimeError("MagenticState not initialized. Call init_magentic_state() first.")
     return state

src/agents/tools.py

@@ -38,27 +38,29 @@ async def search_pubmed(query: str, max_results: int = 10) -> str:
     if not results:
         return f"No PubMed results found for: {query}"
 
-    # 2. Semantic Deduplication & Expansion (The "Digital Twin" Brain)
-    display_results = results
-    if state.embedding_service:
-        # Deduplicate against what we just found vs what's in the DB
-        unique_results = await state.embedding_service.deduplicate(results)
+    # 2. Store in Memory (handles dedup and persistence)
+    # ResearchMemory handles semantic deduplication and persistence
+    new_ids = await state.memory.store_evidence(results)
+    new_count = len(new_ids)
 
-        # Search for related context in the vector DB (previous searches)
-        related = await state.search_related(query, n_results=3)
+    # 3. Context Expansion (The "Digital Twin" Brain)
+    # Combine what we just found with what we already know is relevant
+    display_results = list(results)
 
-        # Combine unique new results + relevant historical results
-        display_results = unique_results + related
+    # Search for related context in the memory (previous searches)
+    related = await state.memory.get_relevant_evidence(n=3)
 
-    # 3. Update State (Persist for ReportAgent)
-    # We add *all* found results to state, not just the displayed ones
-    new_count = state.add_evidence(results)
+    # Add related items if they aren't already in the results
+    current_urls = {r.citation.url for r in display_results}
+    for item in related:
+        if item.citation.url not in current_urls:
+            display_results.append(item)
 
     # 4. Format Output for LLM
     output = [f"Found {len(results)} results ({new_count} new stored):\n"]
 
     # Limit display to avoid context window overflow, but state has everything
-    limit = min(len(display_results), max_results)
+    limit = min(len(display_results), max_results + 3)
 
     for i, r in enumerate(display_results[:limit], 1):
         title = r.citation.title
@@ -96,7 +98,8 @@ async def search_clinical_trials(query: str, max_results: int = 10) -> str:
         return f"No clinical trials found for: {query}"
 
     # Update state
-    new_count = state.add_evidence(results)
+    new_ids = await state.memory.store_evidence(results)
+    new_count = len(new_ids)
 
     output = [f"Found {len(results)} clinical trials ({new_count} new stored):\n"]
     for i, r in enumerate(results[:max_results], 1):
@@ -135,7 +138,8 @@ async def search_preprints(query: str, max_results: int = 10) -> str:
         return f"No papers found for: {query}"
 
     # Update state
-    new_count = state.add_evidence(results)
+    new_ids = await state.memory.store_evidence(results)
+    new_count = len(new_ids)
 
     output = [f"Found {len(results)} papers ({new_count} new stored):\n"]
     for i, r in enumerate(results[:max_results], 1):
@@ -164,11 +168,13 @@ async def get_bibliography() -> str:
         Formatted bibliography string.
     """
     state = get_magentic_state()
-    if not state.evidence:
+    all_evidence = state.memory.get_all_evidence()
+
+    if not all_evidence:
         return "No evidence collected."
 
     output = ["## References"]
-    for i, ev in enumerate(state.evidence, 1):
+    for i, ev in enumerate(all_evidence, 1):
         output.append(f"{i}. {ev.citation.formatted}")
         output.append(f"   URL: {ev.citation.url}")
 

src/app.py

@@ -269,7 +269,7 @@ def create_demo() -> tuple[gr.ChatInterface, gr.Accordion]:
                 choices=["simple", "advanced"],
                 value="simple",
                 label="Orchestrator Mode",
-                info="⚡ Simple: Free/OpenAI/Anthropic | 🔬 Advanced: OpenAI only",
+                info="⚡ Simple: Free/Any | 🔬 Advanced: OpenAI (Deep Research)",
             ),
             gr.Textbox(
                 label="🔑 API Key (Optional)",

src/orchestrators/advanced.py

@@ -152,7 +152,7 @@ class AdvancedOrchestrator(OrchestratorProtocol):
 
         # Initialize context state
         embedding_service = self._init_embedding_service()
-        init_magentic_state(embedding_service)
+        init_magentic_state(query, embedding_service)
 
         workflow = self._build_workflow()
 
@@ -355,6 +355,7 @@ def _create_deprecated_alias() -> type["AdvancedOrchestrator"]:
         """
 
         def __init__(self, *args: Any, **kwargs: Any) -> None:
+            """Initialize deprecated MagenticOrchestrator (use AdvancedOrchestrator)."""
             warnings.warn(
                 "MagenticOrchestrator is deprecated, use AdvancedOrchestrator instead. "
                 "The name 'magentic' was confusing with the 'magentic' PyPI package.",

src/orchestrators/factory.py

@@ -70,7 +70,7 @@ def create_orchestrator(
         search_handler: The search handler (required for simple mode)
         judge_handler: The judge handler (required for simple mode)
         config: Optional configuration (max_iterations, timeouts, etc.)
-        mode: "simple", "magentic", "advanced", "hierarchical" or None (auto-detect)
+        mode: "simple", "magentic", "advanced", or "hierarchical"
               Note: "magentic" is an alias for "advanced" (kept for backwards compatibility)
         api_key: Optional API key for advanced mode (OpenAI)
 

src/orchestrators/hierarchical.py

@@ -98,7 +98,7 @@ class HierarchicalOrchestrator(OrchestratorProtocol):
         logger.info("Starting hierarchical orchestrator", query=query)
 
         service = get_embedding_service_if_available()
-        init_magentic_state(service)
+        init_magentic_state(query, service)
 
         yield AgentEvent(type="started", message=f"Starting research: {query}")
 

src/orchestrators/langgraph_orchestrator.py

@@ -0,0 +1,135 @@
+"""LangGraph-based orchestrator implementation.
+
+NOTE: This orchestrator is deprecated in favor of the shared memory layer
+integrated into Simple and Advanced modes (SPEC-08). It remains as a reference
+implementation for LangGraph patterns.
+"""
+
+import os
+import uuid
+from collections.abc import AsyncGenerator, AsyncIterator
+from typing import Any, Literal
+
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+
+from src.agents.graph.state import ResearchState
+from src.agents.graph.workflow import create_research_graph
+from src.orchestrators.base import OrchestratorProtocol
+from src.services.embeddings import EmbeddingService
+from src.utils.config import settings
+from src.utils.models import AgentEvent
+
+
+class LangGraphOrchestrator(OrchestratorProtocol):
+    """State-driven research orchestrator using LangGraph.
+
+    DEPRECATED: Memory features are now integrated into Simple and Advanced modes.
+    This class is kept for reference and potential future use.
+    """
+
+    def __init__(
+        self,
+        max_iterations: int = 10,
+        checkpoint_path: str | None = None,
+    ):
+        self._max_iterations = max_iterations
+        self._checkpoint_path = checkpoint_path
+
+        # Initialize the LLM (Llama 3.1 via HF Inference)
+        # We use the serverless API by default
+        repo_id = "meta-llama/Llama-3.1-70B-Instruct"
+
+        # Ensure we have an API key
+        api_key = settings.hf_token
+        if not api_key:
+            raise ValueError(
+                "HF_TOKEN (Hugging Face API Token) is required for LangGraph orchestrator."
+            )
+
+        self.llm_endpoint = HuggingFaceEndpoint(  # type: ignore
+            repo_id=repo_id,
+            task="text-generation",
+            max_new_tokens=1024,
+            temperature=0.1,
+            huggingfacehub_api_token=api_key,
+        )
+        self.chat_model = ChatHuggingFace(llm=self.llm_endpoint)
+
+    async def run(self, query: str) -> AsyncGenerator[AgentEvent, None]:
+        """Execute research workflow with structured state."""
+        # Initialize embedding service for this specific run (ensures isolation)
+        embedding_service = EmbeddingService()
+
+        # Setup checkpointer (SQLite for dev)
+        if self._checkpoint_path:
+            # Ensure directory exists (handle paths without directory component)
+            dir_name = os.path.dirname(self._checkpoint_path)
+            if dir_name:
+                os.makedirs(dir_name, exist_ok=True)
+            saver = AsyncSqliteSaver.from_conn_string(self._checkpoint_path)
+        else:
+            saver = None
+
+        # Use a helper context manager to handle the optional saver
+        from contextlib import asynccontextmanager
+
+        @asynccontextmanager
+        async def get_graph_context(saver_instance: Any) -> AsyncIterator[Any]:
+            if saver_instance:
+                async with saver_instance as s:
+                    yield create_research_graph(
+                        llm=self.chat_model,
+                        checkpointer=s,
+                        embedding_service=embedding_service,
+                    )
+            else:
+                yield create_research_graph(
+                    llm=self.chat_model,
+                    checkpointer=None,
+                    embedding_service=embedding_service,
+                )
+
+        async with get_graph_context(saver) as graph:
+            # Initialize state
+            initial_state: ResearchState = {
+                "query": query,
+                "hypotheses": [],
+                "conflicts": [],
+                "evidence_ids": [],
+                "messages": [],
+                "next_step": "search",  # Start with search
+                "iteration_count": 0,
+                "max_iterations": self._max_iterations,
+            }
+
+            yield AgentEvent(type="started", message=f"Starting LangGraph research: {query}")
+
+            # Config for persistence (unique thread_id per run to avoid state conflicts)
+            thread_id = str(uuid.uuid4())
+            config = {"configurable": {"thread_id": thread_id}} if saver else {}
+
+            # Stream events
+            # We use astream to get updates from the graph
+            async for event in graph.astream(initial_state, config=config):
+                # Event is a dict of node_name -> state_update
+                for node_name, update in event.items():
+                    if update.get("messages"):
+                        last_msg = update["messages"][-1]
+                        event_type: Literal["progress", "thinking", "searching"] = "progress"
+                        if node_name == "supervisor":
+                            event_type = "thinking"
+                        elif node_name == "search":
+                            event_type = "searching"
+
+                        yield AgentEvent(
+                            type=event_type, message=str(last_msg.content), data={"node": node_name}
+                        )
+                    elif node_name == "supervisor":
+                        yield AgentEvent(
+                            type="thinking",
+                            message=f"Supervisor decided: {update.get('next_step')}",
+                            data={"node": node_name},
+                        )
+
+            yield AgentEvent(type="complete", message="Research complete.")

src/orchestrators/simple.py

@@ -93,36 +93,6 @@ class Orchestrator:
                 self._enable_analysis = False
         return self._analyzer
 
-    def _get_embeddings(self) -> EmbeddingService | None:
-        """Lazy initialization of EmbeddingService."""
-        if self._embeddings is None and self._enable_embeddings:
-            from src.utils.service_loader import get_embedding_service_if_available
-
-            self._embeddings = get_embedding_service_if_available()
-            if self._embeddings is None:
-                self._enable_embeddings = False
-        return self._embeddings
-
-    async def _deduplicate_and_rank(self, evidence: list[Evidence], query: str) -> list[Evidence]:
-        """Use embeddings to deduplicate and rank evidence by relevance."""
-        embeddings = self._get_embeddings()
-        if not embeddings or not evidence:
-            return evidence
-
-        try:
-            # Deduplicate using semantic similarity
-            unique_evidence: list[Evidence] = await embeddings.deduplicate(evidence, threshold=0.85)
-
-            logger.info(
-                "Deduplicated evidence",
-                before=len(evidence),
-                after=len(unique_evidence),
-            )
-            return unique_evidence
-        except Exception as e:
-            logger.warning("Deduplication failed, using original", error=str(e))
-            return evidence
-
     async def _run_analysis_phase(
         self, query: str, evidence: list[Evidence], iteration: int
     ) -> AsyncGenerator[AgentEvent, None]:
@@ -237,6 +207,10 @@ class Orchestrator:
         Yields:
             AgentEvent objects for each step of the process
         """
+        # Import here to avoid circular deps if any
+        from src.agents.graph.state import Hypothesis
+        from src.services.research_memory import ResearchMemory
+
         logger.info("Starting orchestrator", query=query)
 
         yield AgentEvent(
@@ -245,6 +219,9 @@ class Orchestrator:
             iteration=0,
         )
 
+        # Initialize Shared Memory
+        # We keep 'all_evidence' for local tracking/reporting, but use Memory for intelligence
+        memory = ResearchMemory(query=query)
         all_evidence: list[Evidence] = []
         current_queries = [query]
         iteration = 0
@@ -282,15 +259,14 @@ class Orchestrator:
                         # Should not happen with return_exceptions=True but safe fallback
                         errors.append(f"Unknown result type for '{q}': {type(result)}")
 
-                # Deduplicate evidence by URL (fast, basic)
-                seen_urls = {e.citation.url for e in all_evidence}
-                unique_new = [e for e in new_evidence if e.citation.url not in seen_urls]
+                # === MEMORY INTEGRATION: Store and Deduplicate ===
+                # ResearchMemory handles semantic deduplication and persistence
+                # It returns IDs of actual NEW evidence
+                new_ids = await memory.store_evidence(new_evidence)
 
-                # BUG FIX: Only dedup NEW evidence, not all_evidence
-                # Old evidence is already in the vector store - re-checking it
-                # would mark items as duplicates of themselves (distance ≈ 0)
-                if unique_new:
-                    unique_new = await self._deduplicate_and_rank(unique_new, query)
+                # Filter new_evidence to only keep what was actually new (based on IDs)
+                # Note: This assumes IDs are URLs, which match Citation.url
+                unique_new = [e for e in new_evidence if e.citation.url in new_ids]
 
                 all_evidence.extend(unique_new)
 
@@ -319,15 +295,35 @@ class Orchestrator:
             # === JUDGE PHASE ===
             yield AgentEvent(
                 type="judging",
-                message=f"Evaluating {len(all_evidence)} sources...",
+                message=f"Evaluating evidence (Memory: {len(memory.evidence_ids)} docs)...",
                 iteration=iteration,
             )
 
             try:
+                # Retrieve RELEVANT evidence from memory for the judge
+                # This keeps the context window manageable and focused
+                judge_context = await memory.get_relevant_evidence(n=30)
+
+                # Fallback if memory is empty (shouldn't happen if search worked)
+                if not judge_context and all_evidence:
+                    judge_context = all_evidence[-30:]
+
                 assessment = await self.judge.assess(
-                    query, all_evidence, iteration, self.config.max_iterations
+                    query, judge_context, iteration, self.config.max_iterations
                 )
 
+                # === MEMORY INTEGRATION: Track Hypotheses ===
+                # Convert loose strings to structured Hypotheses
+                for candidate in assessment.details.drug_candidates:
+                    h = Hypothesis(
+                        id=candidate.replace(" ", "_").lower(),
+                        statement=f"{candidate} is a potential candidate for {query}",
+                        status="proposed",
+                        confidence=assessment.confidence,
+                        reasoning=f" identified in iteration {iteration}",
+                    )
+                    memory.add_hypothesis(h)
+
                 yield AgentEvent(
                     type="judge_complete",
                     message=(
@@ -388,6 +384,7 @@ class Orchestrator:
                     )
 
                     # Generate final response
+                    # Use all gathered evidence for the final report
                     final_response = self._generate_synthesis(query, all_evidence, assessment)
 
                     yield AgentEvent(

src/services/research_memory.py

@@ -0,0 +1,133 @@
+"""Shared research memory layer for all orchestration modes."""
+
+from typing import Any
+
+import structlog
+
+from src.agents.graph.state import Conflict, Hypothesis
+from src.services.embeddings import EmbeddingService
+from src.utils.models import Citation, Evidence
+
+logger = structlog.get_logger()
+
+
+class ResearchMemory:
+    """Shared cognitive state for research workflows.
+
+    This is the memory layer that ALL modes use.
+    It mimics the LangGraph state management but for manual orchestration.
+    """
+
+    def __init__(self, query: str, embedding_service: EmbeddingService | None = None):
+        """Initialize ResearchMemory with a query and optional embedding service.
+
+        Args:
+            query: The research query to track evidence for.
+            embedding_service: Service for semantic search and deduplication.
+                             Creates a new instance if not provided.
+        """
+        self.query = query
+        self.hypotheses: list[Hypothesis] = []
+        self.conflicts: list[Conflict] = []
+        self.evidence_ids: list[str] = []
+        self._evidence_cache: dict[str, Evidence] = {}
+        self.iteration_count: int = 0
+
+        # Injected service
+        self._embedding_service = embedding_service or EmbeddingService()
+
+    async def store_evidence(self, evidence: list[Evidence]) -> list[str]:
+        """Store evidence and return new IDs (deduped)."""
+        if not self._embedding_service:
+            return []
+
+        unique = await self._embedding_service.deduplicate(evidence)
+        new_ids = []
+
+        for ev in unique:
+            ev_id = ev.citation.url
+            await self._embedding_service.add_evidence(
+                evidence_id=ev_id,
+                content=ev.content,
+                metadata={
+                    "source": ev.citation.source,
+                    "title": ev.citation.title,
+                    "date": ev.citation.date,
+                    "authors": ",".join(ev.citation.authors or []),
+                    "url": ev.citation.url,
+                },
+            )
+            new_ids.append(ev_id)
+            self._evidence_cache[ev_id] = ev
+
+        self.evidence_ids.extend(new_ids)
+        if new_ids:
+            logger.info("Stored new evidence", count=len(new_ids))
+        return new_ids
+
+    def get_all_evidence(self) -> list[Evidence]:
+        """Get all accumulated evidence objects."""
+        return list(self._evidence_cache.values())
+
+    async def get_relevant_evidence(self, n: int = 20) -> list[Evidence]:
+        """Retrieve relevant evidence for current query."""
+        if not self._embedding_service:
+            return []
+
+        results = await self._embedding_service.search_similar(self.query, n_results=n)
+        evidence_list = []
+
+        for r in results:
+            meta = r.get("metadata", {})
+            authors_str = meta.get("authors", "")
+            authors = authors_str.split(",") if authors_str else []
+
+            # Reconstruct Evidence object
+            source_raw = meta.get("source", "web")
+
+            # Basic validation/fallback for source
+            valid_sources = [
+                "pubmed",
+                "clinicaltrials",
+                "europepmc",
+                "preprint",
+                "openalex",
+                "web",
+            ]
+            source_name: Any = source_raw if source_raw in valid_sources else "web"
+
+            citation = Citation(
+                source=source_name,
+                title=meta.get("title", "Unknown"),
+                url=meta.get("url", r.get("id", "")),
+                date=meta.get("date", "Unknown"),
+                authors=authors,
+            )
+
+            evidence_list.append(
+                Evidence(
+                    content=r.get("content", ""),
+                    citation=citation,
+                    relevance=1.0 - r.get("distance", 0.5),  # Approx conversion
+                )
+            )
+
+        return evidence_list
+
+    def add_hypothesis(self, hypothesis: Hypothesis) -> None:
+        """Add a hypothesis to tracking."""
+        self.hypotheses.append(hypothesis)
+        logger.info("Added hypothesis", id=hypothesis.id, confidence=hypothesis.confidence)
+
+    def add_conflict(self, conflict: Conflict) -> None:
+        """Add a detected conflict."""
+        self.conflicts.append(conflict)
+        logger.info("Added conflict", id=conflict.id)
+
+    def get_open_conflicts(self) -> list[Conflict]:
+        """Get unresolved conflicts."""
+        return [c for c in self.conflicts if c.status == "open"]
+
+    def get_confirmed_hypotheses(self) -> list[Hypothesis]:
+        """Get high-confidence hypotheses."""
+        return [h for h in self.hypotheses if h.confidence > 0.8]

tests/integration/graph/test_workflow.py

@@ -0,0 +1,78 @@
+"""Integration tests for the research graph."""
+
+import pytest
+
+from src.agents.graph.workflow import create_research_graph
+
+
+@pytest.mark.asyncio
+async def test_graph_execution_flow(mocker):
+    """Test the graph runs from start to finish (simulated)."""
+    # Mock Agent.run to avoid API calls
+    mock_run = mocker.patch("pydantic_ai.Agent.run")
+    # Return dummy report/assessment
+    mock_result = mocker.Mock()
+    mock_result.output = mocker.Mock()  # generic output
+    # For judge: output.hypotheses = []
+    mock_result.output.hypotheses = []
+    # For report: validate_references needs specific structure?
+    # Actually validate_references expects a ResearchReport.
+    # Let's mock the return of validate_references too if needed, or make report valid.
+    # Or just mock the node logic? No, we want to test the graph wiring.
+
+    # Minimal valid report
+    from src.utils.models import ReportSection, ResearchReport
+
+    dummy_section = ReportSection(title="Dummy", content="Content")
+
+    mock_report = ResearchReport(
+        title="Test Report",
+        executive_summary="Summary " * 20,  # Ensure > 100 chars
+        research_question="Question",
+        methodology=dummy_section,
+        hypotheses_tested=[],
+        mechanistic_findings=dummy_section,
+        clinical_findings=dummy_section,
+        drug_candidates=[],
+        limitations=["None"],
+        conclusion="Conclusion",
+        references=[],
+        confidence_score=0.5,
+    )
+
+    # Since fallback supervisor skips Judge and goes Search -> Synthesize,
+    # Agent.run is only called once by SynthesizeNode.
+    # It expects a ResearchReport.
+    mock_result.output = mock_report
+    mock_run.return_value = mock_result
+
+    # Create graph without LLM (will use fallback supervisor logic -> search -> synthesize)
+    graph = create_research_graph(llm=None)
+
+    # Initial state
+    initial_state = {
+        "query": "test query",
+        "hypotheses": [],
+        "conflicts": [],
+        "evidence_ids": [],
+        "messages": [],
+        "next_step": "search",
+        "iteration_count": 0,
+        "max_iterations": 2,  # Short run
+    }
+
+    # Execute graph
+    events = []
+    async for event in graph.astream(initial_state):
+        events.append(event)
+
+    # Verify flow
+    # 1. Supervisor (start) -> decides search
+    # 2. Search node runs
+    # 3. Supervisor runs again -> max_iter reached -> synthesize
+    # 4. Synthesize runs
+    # 5. End
+
+    # Just check we hit synthesis
+    final_event = events[-1]
+    assert "synthesize" in final_event or "messages" in str(final_event)

tests/unit/graph/test_nodes.py

@@ -0,0 +1,93 @@
+"""Unit tests for graph nodes."""
+
+import pytest
+
+from src.agents.graph.nodes import judge_node, search_node, supervisor_node
+from src.agents.graph.state import ResearchState
+
+
+@pytest.mark.asyncio
+async def test_judge_node_initialization(mocker):
+    """Test judge creates initial hypothesis if none exist."""
+    # Mock get_model to avoid needing real API keys
+    mocker.patch("src.agents.graph.nodes.get_model", return_value=mocker.Mock())
+
+    # Create a mock assessment with attributes
+    mock_hypothesis = mocker.Mock()
+    mock_hypothesis.drug = "Caffeine"
+    mock_hypothesis.target = "Adenosine"
+    mock_hypothesis.pathway = "CNS"
+    mock_hypothesis.effect = "Alertness"
+    mock_hypothesis.confidence = 0.8
+
+    mock_assessment = mocker.Mock()
+    mock_assessment.hypotheses = [mock_hypothesis]
+
+    mock_result = mocker.Mock()
+    mock_result.output = mock_assessment
+
+    # Mock the Agent class entirely
+    mock_agent_instance = mocker.Mock()
+    mock_agent_instance.run = mocker.AsyncMock(return_value=mock_result)
+    mocker.patch("src.agents.graph.nodes.Agent", return_value=mock_agent_instance)
+
+    state: ResearchState = {
+        "query": "Does coffee cause cancer?",
+        "hypotheses": [],
+        "conflicts": [],
+        "evidence_ids": [],
+        "messages": [],
+        "next_step": "judge",
+        "iteration_count": 0,
+        "max_iterations": 10,
+    }
+
+    update = await judge_node(state)
+
+    assert "hypotheses" in update
+    assert len(update["hypotheses"]) == 1
+    assert update["hypotheses"][0].id == "Caffeine"
+    assert update["hypotheses"][0].status == "proposed"
+
+
+@pytest.mark.asyncio
+async def test_supervisor_termination():
+    """Test supervisor forces synthesis at max iterations."""
+    state: ResearchState = {
+        "query": "test",
+        "hypotheses": [],
+        "conflicts": [],
+        "evidence_ids": [],
+        "messages": [],
+        "next_step": "search",
+        "iteration_count": 10,  # Max reached
+        "max_iterations": 10,
+    }
+
+    update = await supervisor_node(state)
+    assert update["next_step"] == "synthesize"
+
+
+@pytest.mark.asyncio
+async def test_search_node_execution(mocker):
+    """Test search node calls tools (mocked)."""
+    # Mock the tools
+    mocker.patch("src.tools.pubmed.PubMedTool.search", return_value=[])
+    mocker.patch("src.tools.clinicaltrials.ClinicalTrialsTool.search", return_value=[])
+    mocker.patch("src.tools.europepmc.EuropePMCTool.search", return_value=[])
+
+    state: ResearchState = {
+        "query": "test",
+        "hypotheses": [],
+        "conflicts": [],
+        "evidence_ids": [],
+        "messages": [],
+        "next_step": "search",
+        "iteration_count": 0,
+        "max_iterations": 10,
+    }
+
+    update = await search_node(state)
+    assert "messages" in update
+    # Matches "Found 0 total, 0 unique new papers."
+    assert "0 unique new papers" in update["messages"][0].content

tests/unit/graph/test_state.py

@@ -0,0 +1,64 @@
+"""Unit tests for LangGraph state management."""
+
+import operator
+
+from langchain_core.messages import AIMessage, HumanMessage
+
+from src.agents.graph.state import Conflict, Hypothesis, ResearchState
+
+
+def test_state_schema_definition():
+    """Verify the ResearchState TypedDict structure."""
+    # Just checking we can instantiate it (it's a TypedDict, so it's just a dict at runtime)
+    state: ResearchState = {
+        "query": "test query",
+        "hypotheses": [],
+        "conflicts": [],
+        "evidence_ids": [],
+        "messages": [],
+        "next_step": "search",
+        "iteration_count": 0,
+        "max_iterations": 10,
+    }
+    assert state["query"] == "test query"
+    assert state["next_step"] == "search"
+
+
+def test_hypothesis_pydantic_model():
+    """Verify Hypothesis Pydantic model validation."""
+    hypo = Hypothesis(id="h1", statement="Test hypothesis", status="proposed", confidence=0.5)
+    assert hypo.id == "h1"
+    assert hypo.status == "proposed"
+    assert hypo.confidence == 0.5
+    # Test default lists
+    assert hypo.supporting_evidence_ids == []
+
+
+def test_state_reducers_simulation():
+    """Simulate how LangGraph reduces state updates (operator.add)."""
+    # Initial state
+    messages = [HumanMessage(content="Start")]
+
+    # Node 1 update (Search)
+    new_messages = [AIMessage(content="Found results")]
+
+    # Simulation of operator.add reducer
+    messages = operator.add(messages, new_messages)
+
+    assert len(messages) == 2
+    assert isinstance(messages[0], HumanMessage)
+    assert isinstance(messages[1], AIMessage)
+    assert messages[1].content == "Found results"
+
+
+def test_conflict_model():
+    """Verify Conflict model."""
+    conflict = Conflict(
+        id="c1",
+        description="Conflict A vs B",
+        source_a_id="doc1",
+        source_b_id="doc2",
+        status="open",
+    )
+    assert conflict.status == "open"
+    assert conflict.resolution is None

tests/unit/services/test_research_memory.py

@@ -0,0 +1,118 @@
+"""Tests for the shared ResearchMemory service."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from src.agents.graph.state import Conflict, Hypothesis
+from src.services.research_memory import ResearchMemory
+from src.utils.models import Citation, Evidence
+
+
+@pytest.fixture
+def mock_embedding_service():
+    service = MagicMock()
+    service.deduplicate = AsyncMock()
+    service.add_evidence = AsyncMock()
+    service.search_similar = AsyncMock()
+    return service
+
+
+@pytest.fixture
+def memory(mock_embedding_service):
+    return ResearchMemory(query="test query", embedding_service=mock_embedding_service)
+
+
+@pytest.mark.asyncio
+async def test_store_evidence(memory, mock_embedding_service):
+    # Setup
+    ev1 = Evidence(
+        content="content1",
+        citation=Citation(source="pubmed", title="t1", url="u1", date="2023", authors=["a1"]),
+    )
+    ev2 = Evidence(
+        content="content2",
+        citation=Citation(source="pubmed", title="t2", url="u2", date="2023", authors=["a2"]),
+    )
+
+    # deduplicate returns only ev1 (simulating ev2 is duplicate)
+    mock_embedding_service.deduplicate.return_value = [ev1]
+
+    # Execute
+    new_ids = await memory.store_evidence([ev1, ev2])
+
+    # Verify
+    assert new_ids == ["u1"]
+    assert memory.evidence_ids == ["u1"]
+
+    # deduplicate called with both
+    mock_embedding_service.deduplicate.assert_called_once_with([ev1, ev2])
+
+    # add_evidence called only for ev1
+    mock_embedding_service.add_evidence.assert_called_once()
+    args = mock_embedding_service.add_evidence.call_args[1]
+    assert args["evidence_id"] == "u1"
+    assert args["content"] == "content1"
+
+
+@pytest.mark.asyncio
+async def test_get_relevant_evidence(memory, mock_embedding_service):
+    # Setup mock return from ChromaDB format
+    mock_embedding_service.search_similar.return_value = [
+        {
+            "id": "u1",
+            "content": "content1",
+            "metadata": {
+                "source": "pubmed",
+                "title": "t1",
+                "date": "2023",
+                "authors": "a1,a2",
+                "url": "u1",
+            },
+            "distance": 0.1,
+        }
+    ]
+
+    # Execute
+    results = await memory.get_relevant_evidence(n=5)
+
+    # Verify
+    assert len(results) == 1
+    ev = results[0]
+    assert isinstance(ev, Evidence)
+    assert ev.content == "content1"
+    assert ev.citation.title == "t1"
+    assert ev.citation.authors == ["a1", "a2"]
+    assert ev.relevance > 0.8  # 1.0 - 0.1 = 0.9
+
+
+def test_hypothesis_tracking(memory):
+    h1 = Hypothesis(id="h1", statement="drug -> target", status="confirmed", confidence=0.9)
+    h2 = Hypothesis(id="h2", statement="drug -> unknown", status="proposed", confidence=0.5)
+
+    memory.add_hypothesis(h1)
+    memory.add_hypothesis(h2)
+
+    assert len(memory.hypotheses) == 2
+    confirmed = memory.get_confirmed_hypotheses()
+    assert len(confirmed) == 1
+    assert confirmed[0].id == "h1"
+
+
+def test_conflict_tracking(memory):
+    c1 = Conflict(id="c1", description="conflict", source_a_id="a", source_b_id="b", status="open")
+    c2 = Conflict(
+        id="c2",
+        description="resolved conflict",
+        source_a_id="a",
+        source_b_id="b",
+        status="resolved",
+    )
+
+    memory.add_conflict(c1)
+    memory.add_conflict(c2)
+
+    assert len(memory.conflicts) == 2
+    open_conflicts = memory.get_open_conflicts()
+    assert len(open_conflicts) == 1
+    assert open_conflicts[0].id == "c1"

tests/unit/test_ui_elements.py

@@ -6,17 +6,17 @@ from src.app import create_demo
 def test_examples_include_advanced_mode():
     """Verify that one example entry uses 'advanced' mode."""
     demo, _ = create_demo()
-    assert any("advanced" == example[1] for example in demo.examples), (
-        "Expected at least one example to be 'advanced' mode"
-    )
+    assert any(
+        example[1] == "advanced" for example in demo.examples
+    ), "Expected at least one example to be 'advanced' mode"
 
 
 def test_accordion_label_updated():
     """Verify the accordion label reflects the new, concise text."""
     _, accordion = create_demo()
-    assert accordion.label == "⚙️ Mode & API Key (Free tier works!)", (
-        "Accordion label not updated to '⚙️ Mode & API Key (Free tier works!)'"
-    )
+    assert (
+        accordion.label == "⚙️ Mode & API Key (Free tier works!)"
+    ), "Accordion label not updated to '⚙️ Mode & API Key (Free tier works!)'"
 
 
 def test_orchestrator_mode_info_text_updated():
@@ -24,10 +24,10 @@ def test_orchestrator_mode_info_text_updated():
     demo, _ = create_demo()
     # Assuming additional_inputs is a list and the Radio is the first element
     orchestrator_radio = demo.additional_inputs[0]
-    expected_info = "⚡ Simple: Free/OpenAI/Anthropic | 🔬 Advanced: OpenAI only"
-    assert isinstance(orchestrator_radio, gr.Radio), (
-        "Expected first additional input to be gr.Radio"
-    )
-    assert orchestrator_radio.info == expected_info, (
-        "Orchestrator Mode info text not updated correctly"
-    )
+    expected_info = "⚡ Simple: Free/Any | 🔬 Advanced: OpenAI (Deep Research)"
+    assert isinstance(
+        orchestrator_radio, gr.Radio
+    ), "Expected first additional input to be gr.Radio"
+    assert (
+        orchestrator_radio.info == expected_info
+    ), "Orchestrator Mode info text not updated correctly"

uv.lock

@@ -1124,6 +1124,11 @@ dependencies = [
     { name = "gradio", extra = ["mcp"] },
     { name = "httpx" },
     { name = "huggingface-hub" },
+    { name = "langchain" },
+    { name = "langchain-core" },
+    { name = "langchain-huggingface" },
+    { name = "langgraph" },
+    { name = "langgraph-checkpoint-sqlite" },
     { name = "limits" },
     { name = "openai" },
     { name = "pydantic" },
@@ -1133,6 +1138,7 @@ dependencies = [
     { name = "requests" },
     { name = "structlog" },
     { name = "tenacity" },
+    { name = "urllib3" },
     { name = "xmltodict" },
 ]
 
@@ -1179,6 +1185,11 @@ requires-dist = [
     { name = "gradio", extras = ["mcp"], specifier = ">=6.0.0" },
     { name = "httpx", specifier = ">=0.27" },
     { name = "huggingface-hub", specifier = ">=0.20.0" },
+    { name = "langchain", specifier = ">=0.3.9,<1.0" },
+    { name = "langchain-core", specifier = ">=0.3.21,<1.0" },
+    { name = "langchain-huggingface", specifier = ">=0.1.2,<1.0" },
+    { name = "langgraph", specifier = ">=0.2.50,<1.0" },
+    { name = "langgraph-checkpoint-sqlite", specifier = ">=3.0.0,<4.0" },
     { name = "limits", specifier = ">=3.0" },
     { name = "llama-index", marker = "extra == 'modal'", specifier = ">=0.11.0" },
     { name = "llama-index-embeddings-openai", marker = "extra == 'modal'" },
@@ -1205,6 +1216,7 @@ requires-dist = [
     { name = "structlog", specifier = ">=24.1" },
     { name = "tenacity", specifier = ">=8.2" },
     { name = "typer", marker = "extra == 'dev'", specifier = ">=0.9.0" },
+    { name = "urllib3", specifier = ">=2.5.0" },
     { name = "xmltodict", specifier = ">=0.13" },
 ]
 provides-extras = ["dev", "magentic", "embeddings", "modal"]
@@ -2257,6 +2269,27 @@ wheels = [
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 ]
 
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+version = "1.33"
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+]
+
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 name = "jsonschema"
 version = "4.25.1"
@@ -2319,12 +2352,13 @@ wheels = [
 
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 name = "kubernetes"
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+version = "33.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "certifi" },
     { name = "durationpy" },
     { name = "google-auth" },
+    { name = "oauthlib" },
     { name = "python-dateutil" },
     { name = "pyyaml" },
     { name = "requests" },
@@ -2333,9 +2367,159 @@ dependencies = [
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+    { name = "langchain-text-splitters" },
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