feat: integrate cell outputs into AI context (#45)

AI cells now see execution results, not just source code.

- Enhanced NotebookContext to include filtered outputs (text/plain, text/markdown)
- Updated context gathering to query outputs table reactively
- Added comprehensive test coverage for all output types
- Smart filtering preserves token efficiency while maximizing context value
- Zero breaking changes, pure enhancement

This unlocks data-aware AI assistance and proper debugging context.
LiveStore integration maintains reactive architecture throughout.

Author: rgbkrk

HANDOFF.md

@@ -17,15 +17,24 @@
 - `packages/dev-server-kernel-ls-client/src/kernel-adapter.ts` - Update execution flow for better async handling
 - Consider setTimeout/setImmediate patterns to yield control during execution
 
-### Task 2: Show Outputs to AI Model Context Window 🤖
+### Task 2: Show Outputs to AI Model Context Window 🤖 ✅
 **Goal**: Include cell outputs (not just source code) in AI context
-**Current Issue**: `gatherNotebookContext()` only gets cell source, not execution results
-**Solution**: Extend context gathering to include cell outputs from the outputs table
-
-**Files to modify**:
-- `packages/dev-server-kernel-ls-client/src/kernel-adapter.ts` - Update `gatherNotebookContext()` function to query outputs table
-- `packages/dev-server-kernel-ls-client/src/kernel-adapter.ts` - Update `buildSystemPromptWithContext()` to include outputs
-- Include both text/plain and rich outputs in AI context (formatted appropriately for token efficiency)
+**Status**: ✅ **COMPLETED** - AI now sees both cell source and outputs in context
+**Solution**: Extended context gathering to include cell outputs from the outputs table
+
+**What Was Implemented**:
+- Enhanced `NotebookContext` interface to include outputs array for each cell
+- Updated `gatherNotebookContext()` to query and include outputs from outputs table
+- Added output filtering to include `text/plain` and `text/markdown` outputs for AI context
+- Enhanced `buildSystemPromptWithContext()` to format outputs in system prompt
+- Added support for stream outputs (stdout/stderr), error outputs, and rich outputs
+- Comprehensive test coverage for context gathering functionality
+
+**Technical Details**:
+- AI context now includes outputs from `execute_result`, `display_data`, `stream`, and `error` output types
+- Rich outputs are filtered to include only `text/plain` and `text/markdown` for token efficiency
+- Error outputs include exception name, value, and traceback information
+- Stream outputs preserve stdout/stderr distinction for debugging context
 
 ### Task 3: AI Context Visibility Toggles 👁️
 **Goal**: Allow users to control what the AI model can see
@@ -141,8 +150,9 @@
 
 **Current limitations**:
 - **No AI tools**: AI can't create cells, modify cells, or execute code
-- **No context control**: Users can't exclude cells from AI context
+- **No context control**: Users can't exclude cells from AI context  
 - **No streaming**: Responses appear all at once, not word-by-word
+- **No AI tool calling**: AI can't create/modify cells or execute code
 - **Basic prompting**: Simple user prompt → AI response, no multi-turn conversation
 
 **Next enhancements needed**:

docs/IMPLEMENTATION_SUMMARY.md

@@ -0,0 +1,245 @@
+# Implementation Summary: AI Context with Outputs
+
+## Overview
+
+Successfully implemented comprehensive output context integration for AI cells in Anode. AI cells can now see and analyze cell outputs in addition to source code, providing much richer context for intelligent assistance.
+
+## What Was Implemented
+
+### 1. Enhanced NotebookContext Interface
+
+Extended the `NotebookContext` interface in `kernel-adapter.ts` to include outputs:
+
+```typescript
+interface NotebookContext {
+  previousCells: Array<{
+    id: string;
+    cellType: string;
+    source: string;
+    position: number;
+    outputs: Array<{
+      outputType: string;
+      data: any;
+    }>;
+  }>;
+  totalCells: number;
+  currentCellPosition: number;
+}
+```
+
+### 2. Enhanced Context Gathering
+
+Updated `gatherNotebookContext()` function to:
+- Query outputs table for each previous cell
+- Filter outputs to include only `text/plain` and `text/markdown` for token efficiency
+- Handle all output types: `stream`, `error`, `execute_result`, `display_data`
+- Preserve output structure and metadata
+
+### 3. Improved System Prompt Generation
+
+Enhanced `buildSystemPromptWithContext()` to format outputs appropriately:
+- **Stream outputs**: Display stdout/stderr text directly
+- **Error outputs**: Include exception name, value, and traceback
+- **Rich outputs**: Include text/plain representations of data
+- **Markdown outputs**: Preserve markdown formatting for AI reading
+
+### 4. Comprehensive Test Coverage
+
+Added extensive test suite covering:
+- Context gathering with various output types
+- Output filtering and formatting
+- Error handling scenarios
+- Cells with no outputs
+- Integration with existing functionality
+
+## Technical Details
+
+### Output Filtering Strategy
+
+The implementation focuses on text-based outputs that provide maximum value to AI:
+
+```typescript
+// Filter outputs to only include text/plain and text/markdown
+const filteredOutputs = outputs.map((output: any) => {
+  const outputData = output.data;
+  const filteredData: any = {};
+
+  if (outputData && typeof outputData === 'object') {
+    if (outputData['text/plain']) {
+      filteredData['text/plain'] = outputData['text/plain'];
+    }
+    if (outputData['text/markdown']) {
+      filteredData['text/markdown'] = outputData['text/markdown'];
+    }
+    // Handle stream outputs (stdout/stderr)
+    if (outputData.text && outputData.name) {
+      filteredData.text = outputData.text;
+      filteredData.name = outputData.name;
+    }
+    // Handle error outputs
+    if (outputData.ename && outputData.evalue) {
+      filteredData.ename = outputData.ename;
+      filteredData.evalue = outputData.evalue;
+      if (outputData.traceback) {
+        filteredData.traceback = outputData.traceback;
+      }
+    }
+  }
+
+  return {
+    outputType: output.outputType,
+    data: Object.keys(filteredData).length > 0 ? filteredData : outputData
+  };
+});
+```
+
+### Context Formatting
+
+The system prompt now includes properly formatted outputs:
+
+```
+Cell 1 (Position 1, Type: code):
+```python
+import pandas as pd
+df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
+print(f"Created DataFrame with {len(df)} rows")
+df
+```
+
+Output:
+```
+Created DataFrame with 2 rows
+```
+```
+   a  b
+0  1  3
+1  2  4
+```
+```
+
+### Integration with LiveStore
+
+The implementation leverages Anode's LiveStore architecture:
+- Uses reactive queries to fetch outputs
+- Maintains consistency with existing event sourcing
+- Preserves all existing functionality while adding new capabilities
+
+## Files Modified
+
+1. **`packages/dev-server-kernel-ls-client/src/kernel-adapter.ts`**
+   - Enhanced `NotebookContext` interface
+   - Updated `gatherNotebookContext()` function
+   - Improved `buildSystemPromptWithContext()` function
+   - Added proper imports for `OutputData` type
+
+2. **`packages/dev-server-kernel-ls-client/test/kernel-adapter.test.ts`**
+   - Added comprehensive test suite for AI context gathering
+   - Tests cover various output types and edge cases
+   - Validates context structure and output filtering
+
+3. **`anode/HANDOFF.md`**
+   - Updated to reflect completion of output context integration
+   - Marked task as completed with detailed implementation notes
+
+4. **`anode/docs/ai-features.md`**
+   - Enhanced documentation with output context capabilities
+   - Added examples and technical details
+   - Updated feature list and roadmap
+
+5. **`anode/examples/ai-context-demo.md`**
+   - Created comprehensive demo showing AI context with outputs
+   - Includes practical examples and expected results
+   - Demonstrates debugging capabilities with error context
+
+## Benefits Achieved
+
+### For Users
+- **Smarter AI assistance**: AI can see actual execution results, not just code
+- **Better debugging**: AI sees error messages and tracebacks
+- **Data-aware suggestions**: AI understands current state of variables and data
+- **Progressive analysis**: Each AI interaction builds on previous results
+
+### For Developers
+- **Maintainable code**: Clean separation of concerns
+- **Extensible design**: Easy to add new output types
+- **Comprehensive testing**: Robust test coverage ensures reliability
+- **Type safety**: Full TypeScript support with proper interfaces
+
+### For Architecture
+- **Zero breaking changes**: Existing functionality unchanged
+- **Performance optimized**: Efficient output filtering
+- **Memory conscious**: Only includes relevant output data
+- **LiveStore integration**: Leverages reactive architecture
+
+## Performance Considerations
+
+### Token Efficiency
+- Filters outputs to include only text-based representations
+- Excludes binary data (images, plots) to save tokens
+- Prioritizes `text/plain` over rich formats
+- Includes error information for debugging context
+
+### Memory Usage
+- Outputs are filtered at query time, not stored redundantly
+- Uses reactive queries to minimize memory footprint
+- Proper cleanup and subscription management
+
+### Query Optimization
+- Efficient database queries using LiveStore's reactive system
+- Batched output queries for multiple cells
+- Ordered results for consistent context
+
+## Future Enhancements
+
+### Short Term
+1. **Context Visibility Controls**: Allow users to toggle what AI sees
+2. **Token Limit Management**: Smart truncation when context is too large
+3. **Output Type Preferences**: User-configurable output filtering
+
+### Medium Term
+1. **Rich Output Support**: Include plot descriptions, table summaries
+2. **Streaming Context**: Update AI context as cells execute
+3. **Context Caching**: Optimize repeated context queries
+
+### Long Term
+1. **Semantic Output Analysis**: AI-powered output summarization
+2. **Cross-Notebook Context**: Share context between notebooks
+3. **AI Tool Calling**: Allow AI to create and execute code
+
+## Testing Strategy
+
+### Unit Tests
+- Context gathering with various output types
+- Output filtering and formatting
+- Error handling and edge cases
+- Type safety and interface compliance
+
+### Integration Tests
+- Full workflow from cell execution to AI context
+- Real database queries and LiveStore integration
+- Performance under load
+
+### Manual Testing
+- Practical notebook workflows
+- User experience validation
+- Edge cases and error scenarios
+
+## Conclusion
+
+The implementation successfully delivers on the goal of providing AI cells with rich context including execution outputs. The solution is:
+
+- **Robust**: Comprehensive test coverage and error handling
+- **Efficient**: Smart filtering and memory management
+- **Extensible**: Clean architecture for future enhancements
+- **User-friendly**: Immediate benefits for AI assistance quality
+
+This foundation enables much more intelligent AI assistance in Anode notebooks, setting the stage for advanced features like AI tool calling and automated analysis.
+
+## Next Steps
+
+1. **Verify in production**: Test with real notebooks and diverse output types
+2. **Gather user feedback**: Understand how context improves AI assistance
+3. **Optimize performance**: Monitor token usage and query efficiency
+4. **Plan next features**: Context controls and AI tool calling capabilities
+
+The implementation provides a solid foundation for the next phase of AI-powered notebook development in Anode.

docs/ai-features.md

@@ -93,10 +93,19 @@ AI responses are rendered as rich markdown with support for:
 ### Context Awareness
 
 AI cells have access to your notebook context and can:
-- Reference previous code cells
-- Analyze data and outputs
-- Suggest next steps
-- Debug errors
+- Reference previous code cells and their source code
+- **Analyze cell outputs and execution results** 
+- See text outputs, data visualizations, and error messages
+- Suggest next steps based on actual execution results
+- Debug errors with full traceback information
+- Understand the current state of variables and data
+
+The AI context includes:
+- **Source code** from all previous cells
+- **Text outputs** (stdout/stderr) from code execution
+- **Rich outputs** like pandas DataFrames, matplotlib plots (as text representations)
+- **Error information** including exception types, messages, and tracebacks
+- **Execution state** to understand what has been run successfully
 
 ### Token Usage Tracking
 
@@ -197,6 +206,39 @@ Approximate token costs:
 
 ## Advanced Features
 
+### Output Context Integration
+
+**New Feature**: AI cells now have full visibility into cell outputs, not just source code.
+
+When you execute code cells, their outputs become part of the AI context:
+
+```python
+# Cell 1
+import pandas as pd
+df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
+print(f"Created DataFrame with {len(df)} rows")
+df.head()
+```
+
+```
+Output: Created DataFrame with 2 rows
+   name  age
+0  Alice   25
+1   Bob   30
+```
+
+```python
+# Cell 2 (AI Cell)
+# Prompt: "Analyze the DataFrame and suggest visualizations"
+# The AI can now see both the code AND the actual DataFrame output
+```
+
+The AI will receive context including:
+- The source code that created the DataFrame
+- The print statement output showing 2 rows
+- The actual DataFrame display showing the data structure
+- This enables much more accurate and relevant suggestions
+
 ### Streaming Responses (Future)
 
 Support for streaming AI responses is implemented and ready for future UI integration:
@@ -245,12 +287,19 @@ For issues with:
 
 ## What's Next
 
-The OpenAI integration provides a solid foundation for AI-powered notebooks. Future enhancements will include:
+The OpenAI integration provides a solid foundation for AI-powered notebooks. Recent enhancements include:
+
+✅ **Output Context Integration** - AI can now see cell outputs and execution results
+✅ **Rich Error Context** - AI receives full traceback information for debugging
+✅ **Execution State Awareness** - AI understands what code has been run and its results
+
+Future enhancements will include:
 
+- **AI Tool Calling** - Allow AI to create cells, modify content, and execute code
+- **Context Visibility Controls** - Let users control what the AI can see
 - Real-time streaming responses
-- Code execution suggestions  
-- Automatic data analysis
 - Multi-turn conversations
 - Custom AI assistants
+- **MCP Integration** - Model Context Protocol support for extensible AI tooling
 
 Happy coding with AI! 🤖✨