Architecture update

.env.example

@@ -56,9 +56,9 @@ SLACK_CLIENT_SECRET=...
 # Maximum reasoning iterations before responding
 MAX_REASONING_ITERATIONS=5
 
-# Confidence threshold for auto-reply (0.0 - 1.0)
+#  threshold for auto-reply (0.0 - 1.0)
 # Below this, agent will escalate or ask for clarification
-CONFIDENCE_THRESHOLD_FOR_AUTO_REPLY=0.75
+_THRESHOLD_FOR_AUTO_REPLY=0.75
 
 # Enable multi-iteration deep reasoning
 ENABLE_DEEP_REASONING=true

.github/workflows/claude-code-review.yml

@@ -0,0 +1,44 @@
+name: Claude Code Review
+
+on:
+  pull_request:
+    types: [opened, synchronize, ready_for_review, reopened]
+    # Optional: Only run on specific file changes
+    # paths:
+    #   - "src/**/*.ts"
+    #   - "src/**/*.tsx"
+    #   - "src/**/*.js"
+    #   - "src/**/*.jsx"
+
+jobs:
+  claude-review:
+    # Optional: Filter by PR author
+    # if: |
+    #   github.event.pull_request.user.login == 'external-contributor' ||
+    #   github.event.pull_request.user.login == 'new-developer' ||
+    #   github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR'
+
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: read
+      issues: read
+      id-token: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Run Claude Code Review
+        id: claude-review
+        uses: anthropics/claude-code-action@v1
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          plugin_marketplaces: 'https://github.com/anthropics/claude-code.git'
+          plugins: 'code-review@claude-code-plugins'
+          prompt: '/code-review:code-review ${{ github.repository }}/pull/${{ github.event.pull_request.number }}'
+          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
+          # or https://code.claude.com/docs/en/cli-reference for available options
+

.github/workflows/claude.yml

@@ -0,0 +1,36 @@
+name: Claude Code
+on:
+  issue_comment:
+    types: [created]
+  pull_request_review_comment:
+    types: [created]
+  issues:
+    types: [opened, assigned]
+  pull_request_review:
+    types: [submitted]
+
+jobs:
+  claude:
+    if: |
+      (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
+      (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: write
+      issues: write
+      id-token: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+      - name: Run Claude Code
+        uses: anthropics/claude-code-action@v1
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          model: stepfun/step-3.5-flash:free
+        env:
+          ANTHROPIC_BASE_URL: ${{ secrets.ANTHROPIC_BASE_URL }}

agent/cli_chat.py

@@ -41,7 +41,6 @@
 from agent.state import create_initial_state
 from agent.cli_graph import get_cli_agent_graph
 from agent.github_repo_tracker import GitHubRepoTracker
-from agent.codebase_search import CodebaseSearchEngine
 
 # Configure Rich console
 console = Console(width=120)
@@ -337,19 +336,17 @@ def print_files(self, files: List[Any]):
 
         for i, file in enumerate(files[:10], 1):  # Show top 10
             self.console.print(f"\n[bold cyan]{i}.[/bold cyan] [yellow]{file.path}[/yellow]")
-            self.console.print(f"   [dim]Source: {file.source} | Language: {file.language} | Score: {len(file.matches)}[/dim]")
+            self.console.print(f"   [dim]Matches: {len(file.matches)}[/dim]")
             self.console.print(f"   [dim]Why: {file.retrieval_reason}[/dim]")
 
             if file.matches:
                 self.console.print(f"   [dim]Matches:[/dim]")
-                # Show more matches for directory structure files
-                max_matches = 20 if file.language == "directory" else 3
-                for match in file.matches[:max_matches]:
+                for match in file.matches[:3]:
                     display_match = match[:100] if len(match) > 100 else match
                     self.console.print(f"     [gray]• {display_match}[/gray]")
-            
+
             # Show content preview for small files
-            if file.content and file.language != "directory" and len(file.content) < 2000:
+            if file.content and len(file.content) < 2000:
                 self.console.print(f"   [dim]Content preview:[/dim]")
                 self.console.print(f"     [gray]{file.content[:500]}[/gray]")
 
@@ -360,17 +357,15 @@ def print_research_summary(self, state: Dict[str, Any]):
         """Print research summary after agent processing."""
         thinking_log = state.get("thinking_log", [])
         research_files = state.get("research_files", [])
-        confidence = state.get("research_confidence", 0)
-
+
         # Print thinking log
         if thinking_log:
             self.console.print("\n")
             for entry in thinking_log:
-                # Extract the thinking part after "Iteration X:"
                 if ": " in entry:
                     thinking = entry.split(": ", 1)[1]
                     self.print_thinking(thinking)
-        
+
         # Print files
         if research_files:
             self.console.print("\n")
@@ -379,9 +374,8 @@ def print_research_summary(self, state: Dict[str, Any]):
         # Print summary
         self.console.print(f"\n[bold green]✓ Research Complete[/bold green]")
         self.console.print(
-            f"   [dim]Iterations: {state.get('research_iterations', 0)} | "
-            f"Files: {len(research_files)} | "
-            f"Confidence: {confidence:.1%}[/dim]\n"
+            f"   [dim]Iterations: {len(state.get('search_history') or [])} | "
+            f"Files: {len(research_files)}[/dim]\n"
         )
 
     def process_message(self, message: str) -> Optional[str]:
@@ -454,12 +448,10 @@ def make_display() -> Any:
 
         # Short summary; full thinking/files available via 'thinking' and 'files' commands
         research_files = result.get("research_files", [])
-        confidence = result.get("research_confidence", 0)
         self.console.print(f"\n[bold green]✓ Research complete[/bold green]")
         self.console.print(
-            f"   [dim]Iterations: {result.get('research_iterations', 0)} | "
-            f"Files: {len(research_files)} | "
-            f"Confidence: {confidence:.1%}[/dim]"
+            f"   [dim]Iterations: {len(result.get('search_history') or [])} | "
+            f"Files: {len(research_files)}[/dim]"
         )
         self.console.print(
             "[dim]Type [cyan]thinking[/cyan] or [cyan]files[/cyan] for full details.[/dim]\n"

agent/cli_graph.py

@@ -1,74 +1,62 @@
 """
 LangGraph workflow for CLI mode.
 
-Simplified topology without Slack dependencies:
-  cli_context_builder → olake_context_summariser → (needs_codebase_search? deep_researcher → relevance_filter : direct) → solution → END
+Topology:
+  cli_context_builder → gate_filter → deep_researcher → solution → END
+
+Note: gate_filter's Slack reply is silently skipped in CLI mode (no Slack credentials).
+Blocked/non-actionable messages still reach END without a solution node call.
 """
 
 from langgraph.graph import StateGraph, END
 from typing import Literal
 
 from agent.state import ConversationState
-from agent.nodes.olake_context_summariser import summarise_olake_context
+from agent.nodes.gate_filter import gate_filter
 from agent.nodes.deep_researcher import deep_researcher
-from agent.nodes.cli import (
-    build_cli_context,
-    cli_solution_provider,
-    cli_relevance_filter,
-)
+from agent.nodes.cli import build_cli_context, cli_solution_provider
 from agent.logger import get_logger
 
 
-def route_after_summariser(
+def route_after_gate(
     state: ConversationState,
-) -> Literal["deep_researcher", "solution"]:
-    """If question needs codebase search go to deep_researcher, else directly to solution."""
-    if state.get("needs_codebase_search", True):
-        return "deep_researcher"
-    return "solution"
+) -> Literal["deep_researcher", "__end__"]:
+    if state.get("is_harmful") or not state.get("is_relevant", True):
+        return "__end__"
+    if not state.get("is_actionable", True):
+        return "__end__"
+    return "deep_researcher"
 
 
 # ---------------------------------------------------------------------------
 # Graph factory
 # ---------------------------------------------------------------------------
 
 def create_cli_agent_graph() -> StateGraph:
-    """
-    Build and compile the LangGraph agent workflow for CLI mode.
-
-    Node sequence:
-      cli_context_builder → olake_context_summariser → (deep_researcher → relevance_filter) or direct → solution
-    """
     logger = get_logger()
     logger.logger.info("Creating CLI agent graph...")
 
     workflow = StateGraph(ConversationState)
 
-    # ── Nodes ────────────────────────────────────────────────────────────
     workflow.add_node("cli_context_builder", build_cli_context)
-    workflow.add_node("olake_context_summariser", summarise_olake_context)
-    workflow.add_node("deep_researcher", deep_researcher)
-    workflow.add_node("relevance_filter", cli_relevance_filter)
-    workflow.add_node("solution", cli_solution_provider)
+    workflow.add_node("gate_filter",         gate_filter)
+    workflow.add_node("deep_researcher",     deep_researcher)
+    workflow.add_node("solution",            cli_solution_provider)
 
-    # ── Entry ─────────────────────────────────────────────────────────────
     workflow.set_entry_point("cli_context_builder")
 
-    workflow.add_edge("cli_context_builder", "olake_context_summariser")
+    workflow.add_edge("cli_context_builder", "gate_filter")
     workflow.add_conditional_edges(
-        "olake_context_summariser",
-        route_after_summariser,
+        "gate_filter",
+        route_after_gate,
         {
             "deep_researcher": "deep_researcher",
-            "solution": "solution",
+            "__end__":         END,
         },
     )
-
-    # After research: filter relevance, then to solution
-    workflow.add_edge("deep_researcher", "relevance_filter")
-    workflow.add_edge("relevance_filter", "solution")
+    workflow.add_edge("deep_researcher", "solution")
     workflow.add_edge("solution", END)
-    
+
     compiled = workflow.compile()
     logger.logger.info("CLI agent graph created successfully")
     return compiled