- Comprehensive analysis of potential improvements
- Recommended improvements without SDK migration:
  1. Message validation pipeline (HIGH priority)
  2. Automatic retry with exponential backoff (HIGH)
  3. Streaming idle watchdog (MEDIUM)
  4. Token counting and context management (MEDIUM)
  5. Cache token tracking (LOW)
- SDK migration analysis with pros/cons
- Recommendation: Don't migrate yet, implement quick wins first
- Hybrid approach evaluation for future consideration

- Claude API requires temperature: 1.0 when thinking is enabled
- Our Anthropic-Beta header includes interleaved-thinking-2025-05-14
- Sending temperature: 0.0 with thinking beta causes API errors
- Now only add temperature to payload if > 0

- Changed rate_limit from 0 to 5 seconds for Claude provider
- Changed rate_limit from 0 to 5 seconds for all Claude models
- This adds a minimum 5-second delay between requests to avoid
  hitting Anthropic's OAuth2 API rate limits

- Handle 'thinking' and 'redacted_thinking' in content_block_start events
- Handle 'thinking_delta' events to accumulate thinking content during streaming
- Handle 'signature_delta' events for thinking block signatures
- Log thinking block completion with character count
- Thinking content is accumulated but not emitted to client (stored for final response)
- Matches original Claude Code streaming thinking implementation

- Added detailed analysis of 3419-line claude.ts implementation
- Expanded streaming comparison with 30+ features from original source
- Updated message conversion comparison with normalizeMessagesForAPI details
- Added comprehensive feature comparison table for streaming implementations
- Documented advanced features: idle watchdog, stall detection, VCR support,
  cache break detection, cost tracking, memory cleanup, request ID tracking

Analysis of debug.log showed 429 rate limit errors during streaming
were not being caught by the retry logic because:
1. Streaming generators don't raise exceptions until consumed
2. Error message 'Claude API error (429): Error' didn't contain retry keywords

Changes:
1. Added _handle_streaming_request_with_retry() wrapper that catches
   rate limit errors and re-raises with proper keywords
2. Added _wrap_streaming_with_retry() method that consumes streaming
   generator and retries with fallback models on rate limit errors
3. Updated retry logic to check for '429' keyword in error messages
4. Added exponential backoff with jitter before retry attempts
5. Improved error messages to include rate limit context

This ensures that when streaming hits a 429 rate limit, the system
will automatically retry with fallback models instead of failing.

Add image content block handling to ClaudeProviderHandler:

1. Image Extraction (_extract_images_from_content):
   - Extract images from OpenAI message content format
   - Handle base64 data URLs (data:image/jpeg;base64,...)
   - Handle HTTP/HTTPS URL-based images
   - Convert to Anthropic image source format
   - Validate image size (5MB limit for base64)
   - Pass through existing Anthropic-format image blocks

2. Image Integration in Message Conversion:
   - Extract images from user message content blocks
   - Convert image_url blocks to Anthropic image source format
   - Add image blocks to anthropic_messages content array
   - Preserve text content alongside images

Reference: vendors/kilocode image handling + vendors/claude multimodal support

Add three robustness improvements to ClaudeProviderHandler:

1. Message Role Validation (_validate_messages):
   - Validate roles are one of: user, assistant, system, tool
   - Auto-fix unknown roles to 'user'
   - Ensure system messages only appear at start
   - Insert synthetic assistant messages between consecutive user messages
   - Merge consecutive assistant messages
   - Validate tool messages have tool_call_id
   - Reference: vendors/kilocode normalizeMessages() + ensure_alternating_roles()

2. Tool Result Size Validation (_truncate_tool_result):
   - Truncate oversized tool results with configurable limit (default 100k chars)
   - Add truncation notice with original length info
   - Reference: vendors/claude applyToolResultBudget

3. Model Fallback Support (handle_request refactoring):
   - Add _get_fallback_models() to read fallback list from config
   - Retry with fallback models on retryable errors (rate limit, overloaded)
   - Split into handle_request() (with retry) and _handle_request_with_model() (actual logic)
   - Log fallback attempts for debugging

All methods integrated into handle_request() for automatic application.

Add three key improvements to ClaudeProviderHandler:

1. Thinking Block Support (Phase 2.1):
   - Extract thinking/reasoning content from Claude API responses
   - Handle both 'thinking' and 'redacted_thinking' block types
   - Store thinking content in provider_options for downstream access
   - Reference: vendors/kilocode thinking support via AI SDK

2. Tool Call Streaming (Phase 2.2):
   - Parse content_block_start events for tool_use blocks
   - Stream tool call arguments via input_json_delta events
   - Emit tool calls in OpenAI streaming format on content_block_stop
   - Reference: fine-grained-tool-streaming-2025-05-14 beta feature

3. Detailed Usage Metadata (Phase 2.3):
   - Extract cache_read_input_tokens from API response
   - Extract cache_creation_input_tokens from API response
   - Add prompt_tokens_details and completion_tokens_details to usage
   - Log cache usage for analytics
   - Reference: vendors/kilocode session/index.ts usage extraction

All methods integrated into _convert_to_openai_format() and
_handle_streaming_request() for automatic application.

Add three key improvements to ClaudeProviderHandler based on comparison
with vendors/kilocode implementation:

1. Tool Call ID Sanitization (_sanitize_tool_call_id):
   - Replace invalid characters in tool call IDs with underscores
   - Claude API requires alphanumeric, underscore, hyphen only
   - Reference: vendors/kilocode normalizeMessages() sanitization

2. Empty Content Filtering (_filter_empty_content):
   - Filter out empty string messages and empty text parts
   - Claude API rejects messages with empty content
   - Reference: vendors/kilocode normalizeMessages() filtering

3. Prompt Caching (_apply_cache_control):
   - Apply ephemeral cache_control to last 2 messages
   - Enable Anthropic's prompt caching feature for cost savings
   - Reference: vendors/kilocode applyCaching()

All methods integrated into _convert_messages_to_anthropic() for
automatic application during message conversion.

Create docs/claude_provider_improvement_plan.md with detailed implementation
plan for AISBF ClaudeProviderHandler improvements identified in the provider
comparison analysis.

Plan includes 10 improvements across 4 phases:
- Phase 1 (Quick Wins): Tool call ID sanitization, empty content filtering,
  prompt caching
- Phase 2 (Core): Thinking block support, tool call streaming, usage metadata
- Phase 3 (Robustness): Message validation, tool result size limits, fallback
- Phase 4 (Advanced): Image/multimodal support

Each improvement includes: problem statement, reference implementation,
detailed implementation steps, files to modify, and effort estimate.

Total estimated effort: 24-37 hours across 4 weeks.

Document now correctly compares only the three Claude provider implementations:

- AISBF (aisbf/providers.py) - Direct HTTP with OAuth2
- vendors/kilocode (vendors/kilocode/packages/opencode/src/provider/) - AI SDK
- vendors/claude (vendors/claude/src/) - Original Claude Code

All tables and references now use these three sources exclusively.
Removed all Kiro Gateway content which was unrelated to Claude.

Kiro Gateway is an Amazon Q Developer implementation using AWS CodeWhisperer API,
not a Claude provider. The comparison now focuses on actual Claude implementations:

- AISBF Claude Provider (direct HTTP with OAuth2)
- Original Claude Code (TypeScript/React from Anthropic)
- KiloCode (TypeScript using @ai-sdk/anthropic)

Removed all Kiro-related sections including:
- Kiro Gateway architecture comparison
- Kiro message conversion and tool handling
- Kiro streaming (AWS Event Stream)
- Kiro model name normalization
- Kiro exclusive features (thinking injection, truncation recovery, etc.)

Document now cleanly compares three Claude provider implementations.

- Add KiloCode implementation analysis (vendors/kilocode/packages/opencode/src/provider/)
- Compare KiloCode's AI SDK approach (@ai-sdk/anthropic) vs direct HTTP
- Document KiloCode's features: automatic prompt caching, thinking support,
  message validation, reasoning variants, model management
- Add comparison tables for architecture, message conversion, streaming,
  headers, model resolution, reasoning/thinking support, prompt caching
- Document KiloCode exclusive features: empty content filtering, tool call ID
  sanitization, duplicate reasoning fix, provider option remapping,
  Gemini schema sanitization, unsupported part handling
- Update summary with KiloCode strengths and additional improvement areas

- Add comprehensive Kiro Gateway analysis alongside Claude Code comparison
- Document Kiro's unified intermediate message format approach
- Compare streaming implementations (SSE vs AWS Event Stream)
- Document Kiro's advanced features: thinking injection, tool content stripping,
  image extraction, truncation recovery, model name normalization
- Add comparison tables for architecture, message handling, tools, streaming
- Identify patterns from Kiro that could improve AISBF (unified format,
  message validation, multimodal support)

- Add comprehensive comparison of AISBF Claude provider vs original Claude Code source
- Document message conversion, tool handling, streaming, and response parsing differences
- Identify areas for improvement: thinking blocks, tool call streaming, usage metadata
- Include all other pending changes across the codebase

- Updated CHANGELOG.md with complete feature list including:
  * Claude OAuth2 provider with PKCE flow and automatic token refresh
  * Response caching with semantic deduplication (Memory/Redis/SQLite/MySQL)
  * Model embeddings cache with multiple backends
  * User-specific API endpoints and MCP enhancements
  * Adaptive rate limiting and token usage analytics
  * Smart request batching and streaming optimization
  * All performance features and bug fixes

- Enhanced README.md with:
  * Claude OAuth2 authentication section with setup guide
  * Response caching details with all backends and deduplication
  * Flexible caching system with Redis/MySQL/SQLite/File/Memory
  * Updated key features with expanded descriptions
  * Configuration examples for all caching systems

- Updated DOCUMENTATION.md with:
  * Claude Code provider in Provider Support section
  * Enhanced provider descriptions with caching capabilities
  * Reference to Claude OAuth2 setup documentation

- Enhanced CLAUDE_OAUTH2_SETUP.md with key features list

- Added clarifying comments to aisbf/claude_auth.py

All documentation now accurately reflects the codebase with complete
coverage of caching systems (response cache and model embeddings cache),
request deduplication via SHA256, and all implemented features.

- Document user-specific API endpoints: /api/user/models, /api/user/providers,
  /api/user/rotations, /api/user/autoselects, /api/user/chat/completions
- Document user MCP tools: list_user_models, list_user_providers, set_user_provider,
  delete_user_provider, list_user_rotations, set_user_rotation, delete_user_rotation,
  list_user_autoselects, set_user_autoselect, delete_user_autoselect, user_chat_completion
- Update user dashboard with clear endpoint documentation
- Add enhanced analytics for user token usage tracking
- Add database improvements for user token management

- Added /api/user/* endpoints for authenticated users to access their own configurations
- Admin users get access to global + user configs, regular users get user-only
- Global tokens from aisbf.json have full access to all configurations
- Enhanced MCP with user-specific tools for authenticated users
- Updated user dashboard with comprehensive API endpoint documentation
- Updated README.md, DOCUMENTATION.md with new endpoint documentation
- Updated CHANGELOG.md with new features
- Bumped version to 0.9.1

Add pricing extraction (rate_multiplier, rate_unit, prompt/completion tokens) and auto-configure rate limits on 429

- Parse rate_multiplier and rate_unit from nexlab API as pricing
- Parse promptTokenPrice and completionTokenPrice from AWS Q API
- Extract pricing from OpenRouter-style API responses for OpenAI provider
- Add _auto_configure_rate_limits to extract X-RateLimit-* headers
- Update parse_429_response to capture rate limit headers

Add model metadata fields (top_provider, pricing, description, supported_parameters, architecture) and dashboard Get Models button

- Update providers.py to extract all fields from provider API responses
- Add max_input_tokens support for Claude provider context size
- Add top_provider, pricing, description, supported_parameters, architecture fields
- Update cache functions to save/load new metadata fields
- Update handlers.py to expose new fields in model list response
- Add Get Models button to dashboard

- Added filter parameters to analytics route in main.py
- Updated get_model_performance() to support filtering by provider, model, rotation, and autoselect
- Added get_rotations_stats() and get_autoselects_stats() methods
- Added filter UI to analytics.html with dropdowns for filtering
- Updated Model Performance table to show type (Provider/Rotation/Autoselect)

- Added new dashboard template (templates/dashboard/users.html) for managing users
- Added routes in main.py: GET /dashboard/users, POST /dashboard/users/add,
  POST /dashboard/users/{id}/edit, POST /dashboard/users/{id}/toggle,
  POST /dashboard/users/{id}/delete
- Added 'Users' link to navigation menu (visible only for admin users)
- Added update_user method to database.py for editing user details

Features:
- Add new users with username, password, and role (user/admin)
- Edit existing user details
- Toggle user active/inactive status
- Delete users

- Add AdaptiveRateLimiter class in aisbf/providers.py for per-provider adaptive rate limiting
- Enhance 429 handling with exponential backoff and jitter
- Track 429 patterns per provider with configurable history window
- Implement dynamic rate limit adjustment that learns from 429 responses
- Add rate limit headroom (stays 10% below learned limits)
- Add gradual recovery after consecutive successful requests
- Add AdaptiveRateLimitingConfig in aisbf/config.py
- Add adaptive_rate_limiting configuration to config/aisbf.json
- Add dashboard UI at /dashboard/rate-limits
- Add dashboard API endpoints for stats and reset functionality
- Update TODO.md to mark item #8 as completed

- Add aisbf/analytics.py module with Analytics class for tracking token usage,
  request counts, latency, error rates, and cost estimation per provider
- Add templates/dashboard/analytics.html with comprehensive dashboard page
- Integrate analytics recording into RequestHandler, RotationHandler, and
  AutoselectHandler
- Add /dashboard/analytics route in main.py
- Add Analytics link to base.html navigation
- Update CHANGELOG.md with new feature documentation

Features:
- Token usage tracking with database persistence
- Real-time request counts and latency tracking
- Error rates and types tracking
- Cost estimation per provider (Anthropic, OpenAI, Google, Kiro, OpenRouter)
- Model performance comparison
- Token usage over time visualization (1h, 6h, 24h, 7d)
- Optimization recommendations
- Export functionality (JSON, CSV)
- Integration with all request handlers
- Support for rotation_id and autoselect_id tracking

- Add aisbf/streaming_optimization.py module with:
  - StreamingConfig: Configuration dataclass for optimization settings
  - ChunkPool: Memory-efficient chunk object reuse pool
  - BackpressureController: Flow control to prevent overwhelming consumers
  - StreamingOptimizer: Main coordinator combining all optimizations
  - KiroSSEParser: Optimized SSE parser for Kiro streaming
  - OptimizedTextAccumulator: Memory-efficient text accumulation
  - calculate_google_delta(): Incremental delta calculation

- Update aisbf/handlers.py to integrate streaming optimizations:
  - Use chunk pooling for Google streaming
  - Use OptimizedTextAccumulator for memory efficiency
  - Add delta-based streaming for Google provider
  - Integrate KiroSSEParser for Kiro provider

- Update setup.py to include streaming_optimization.py
- Update pyproject.toml with package data
- Update TODO.md with completed status
- Update README.md with new feature description
- Update CHANGELOG.md with streaming optimization details

Expected benefits:
- 10-20% memory reduction in streaming responses
- Better flow control with backpressure handling
- Optimized Google and Kiro streaming with delta calculation
- Configurable optimization via StreamingConfig

- Add aisbf/batching.py module with RequestBatcher class
- Implement time-based (100ms window) and size-based batching
- Add provider-specific batching configurations (OpenAI: 10, Anthropic: 5)
- Integrate batching with BaseProviderHandler
- Add batching configuration to config/aisbf.json
- Initialize batching system in main.py startup
- Update version to 0.8.0 in setup.py and pyproject.toml
- Add batching.py to setup.py data_files
- Update README.md and TODO.md documentation
- Expected benefit: 15-25% latency reduction

Features:
- Automatic batch formation and processing
- Response splitting and distribution
- Statistics tracking (batches formed, requests batched, avg batch size)
- Graceful error handling and fallback
- Non-blocking async queue management
- Streaming request bypass (batching disabled for streams)

- Optimized existing condensation methods (hierarchical, conversational, semantic, algorithmic)
- Added 4 new condensation methods (sliding_window, importance_based, entity_aware, code_aware)
- Fixed critical bugs in conversational and semantic methods (undefined variables)
- Added internal model warm-up functionality for faster first inference
- Implemented condensation analytics (effectiveness %, latency tracking)
- Added similarity detection in algorithmic method using difflib
- Support for condensation method chaining
- Per-model condensation thresholds
- Adaptive condensation based on context size
- Updated README, TODO, DOCUMENTATION, and CHANGELOG

- Add ResponseCache class with multiple backend support (memory, Redis, SQLite, MySQL)
- Implement LRU eviction for memory backend with configurable max size
- Add SHA256-based cache key generation for request deduplication
- Implement TTL-based expiration (default: 600 seconds)
- Add cache statistics tracking (hits, misses, hit rate, evictions)
- Integrate caching into RequestHandler, RotationHandler, and AutoselectHandler
- Add granular cache control at model, provider, rotation, and autoselect levels
- Implement hierarchical configuration: Model > Provider > Rotation > Autoselect > Global
- Add dashboard endpoints for cache statistics (/dashboard/response-cache/stats) and clearing (/dashboard/response-cache/clear)
- Add response cache initialization in main.py startup event
- Skip caching for streaming requests
- Add comprehensive test suite (test_response_cache.py) with 6 test scenarios
- Update configuration models with enable_response_cache fields
- Update TODO.md to mark Response Caching as completed
- Update CHANGELOG.md with response caching features

Files created:
- aisbf/response_cache.py (740+ lines)
- test_response_cache.py (comprehensive test suite)

Files modified:
- aisbf/handlers.py (cache integration and _should_cache_response helper)
- aisbf/config.py (ResponseCacheConfig and enable_response_cache fields)
- config/aisbf.json (response_cache configuration section)
- main.py (response cache initialization)
- TODO.md (mark task as completed)
- CHANGELOG.md (document new features)

- Implement Anthropic cache_control support for 50-70% cost reduction
- Add Google Context Caching API framework with TTL configuration
- Add provider-level caching configuration (enable_native_caching, cache_ttl, min_cacheable_tokens)
- Update dashboard UI with caching settings
- Update documentation with detailed caching guide and examples
- Mark system messages and conversation prefixes as cacheable automatically
- Test Python compilation and validate implementation

- Implement user-specific configuration isolation with SQLite database
- Add user management, authentication, and role-based access control
- Create user-specific providers, rotations, and autoselect configurations
- Add API token management and usage tracking per user
- Update handlers to support user-specific configs with fallback to global
- Add MCP support for user-specific configurations
- Update documentation and README with multi-user features
- Add user dashboard templates for configuration management

- Integrate existing SQLite database module with full functionality
- Add persistent token usage tracking across application restarts
- Implement context dimension tracking and effective context updates
- Add automatic database cleanup on startup (7+ day old records)
- Implement multi-user authentication with role-based access control
- Add user management with isolated configurations (providers, rotations, autoselects)
- Enable user-specific API token management and usage tracking
- Update dashboard with role-based access (admin vs user dashboards)
- Add database-first authentication with config admin fallback
- Update README, TODO, and documentation with database features
- Cache model embeddings for semantic classification performance

- Add NSFW/privacy boolean fields to models (providers.json, rotations.json, autoselect.json)
- Implement content classification using last 3 messages for performance
- Add semantic classification with hybrid BM25 + sentence-transformer re-ranking
- Update autoselect handler to support classify_semantic flag
- Add new semantic_classifier.py module with hybrid search capabilities
- Update dashboard templates to manage new configuration fields
- Update documentation (README.md, DOCUMENTATION.md) with new features
- Bump version to 0.6.0 in pyproject.toml and setup.py
- Add new dependencies: sentence-transformers, rank-bm25
- Update package configuration for PyPI distribution

- Added Kiro AWS Event Stream parsing and converters
- Added TOR hidden service support
- Added MCP server endpoint
- Added credential validation for kiro/kiro-cli
- Added various Python 3.13 compatibility fixes
- Added intelligent 429 rate limit handling
- Updated venv handling and auto-update features

- Added Unreleased section for OpenRouter fields implementation
- Added web dashboard documentation updates
- Documented Model class fixes for API compatibility

- Added screenshot.png reference at top of README
- Added Web Dashboard section describing features
- Includes dashboard access information and default credentials

- Added description, context_length, architecture, pricing, top_provider, supported_parameters, and default_parameters fields
- Fixes crash when returning model data through API with OpenRouter metadata
- Aligns Model class with ProviderModelConfig, RotationConfig, and AutoselectConfig

- Add aisbf/kiro_parsers.py: AWS Event Stream parser for Kiro API responses
- Update kiro_converters_openai.py: Add build_kiro_payload_from_dict function
- Update kiro_converters.py: Minor fixes
- Update kiro_auth.py: Add AWS SSO OIDC authentication support
- Update handlers.py: Enhance streaming and error handling
- Update main.py: Add proxy headers middleware and configuration
- Update setup.py: Add version bump
- Add TODO.md: Comprehensive roadmap for caching and performance improvements

Features:
- Kiro AWS Event Stream parsing for non-streaming responses
- OpenAI-to-Kiro payload conversion
- AWS SSO OIDC authentication for Kiro
- Proxy headers middleware for reverse proxy support
- TODO roadmap with prioritized items for future development