Changelog

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[2.11.8] - 2026-02-08

Added

• Portkey provider: OpenAI-compatible gateway with config-based routing (env: PORTKEY_API_KEY, PORTKEY_CONFIG; optional PORTKEY_BASE_URL).
• Tests: Portkey provider payload adaptation, reasoning model restrictions, explicit-None handling, and base URL validation.

Changed

• Portkey payload hygiene: forward optional generation parameters only when explicitly set.
• Token parameter mapping: use max_completion_tokens for OpenAI reasoning families (gpt-5/o1); keep legacy max_tokens for other backends.
• Reasoning model compatibility: drop unsupported parameters (temperature/top_p/penalties) with structured logging.
• Error diagnostics: base URL validation and improved DNS/connectivity hints.
• Server logging: route Python warnings through structured logging; avoid raw stderr warnings at default ERROR verbosity.
• Server UX: print internal/external access URLs outside logging on startup.
• OpenAPI schema: normalize request examples to prevent /openapi.json validation failures.

Fixed

• Config CLI: interactive vision fallback now accepts any provider/model and uses provider-agnostic guidance.
• Config CLI: interactive console logging default now uses ERROR to match package defaults.

Documentation

• Portkey usage guidance added across core docs.
• Media docs: clarified vision fallback examples as provider-agnostic.
• Server docs: moved interactive API docs links to the top of the page.

[2.11.6] - 2026-02-06

Added

• Config CLI: video defaults (--set-video-*) and --config alias for interactive setup.

Changed

• Faster CLI startup by lazily importing optional web parsing deps in abstractcore.tools.common_tools.
• Docs: clarified requirements and configuration for image/video/audio fallbacks (including abstractcore --config).

[2.11.5] - 2026-02-06

Changed

• STT fallback when abstractvoice is installed
• faster utils.cli with lazy loading of the providers

[2.11.3] - 2026-02-04

Changed

• Updated the timeout settings (abstractcore config 3600s)

[2.11.2] - 2026-02-04

Added

• Skim tool benchmarks: added examples/skim_tools_benchmark.py to measure output footprint and latency for skim_websearch/web_search and skim_url/fetch_url.
• Import-safety test: added a test to ensure import abstractcore does not eagerly import optional deps (requests, bs4, sentence_transformers, pymupdf*, ...).

Changed

• Skim outputs stay compact: skim_websearch now truncates long titles/snippets to keep tool outputs prompt-friendly by default.
• Tool guidance for prompted models: tool prompts now render short when_to_use hints for small tool sets and a few high-impact tools (edit/write/execute + web triage tools).
• Tool examples: globally-capped examples now include skim_websearch/skim_url earlier so models learn the token-efficient web triage workflow.
• Native tool payload compatibility: native tool schemas no longer include non-standard metadata keys (tags, when_to_use, examples) to avoid strict provider schema validation failures.
• Docs accuracy: clarified fetch_url behavior for PDFs/binaries and documented the recommended skim_* → fetch_* workflow in the docs entry points.

[2.11.1] - 2026-02-04

Added

• Security policy: added SECURITY.md with responsible disclosure guidance.
• API overview doc: added docs/api.md as a user-facing map of the public Python API.
• FAQ: added docs/faq.md and linked it from the docs entry points.
• Events + logging docs: added docs/events.md and docs/structured-logging.md.
• Skim tools: added skim_url (fast URL triage) and skim_websearch (compact/filtered search) to keep agent prompts smaller when you only need “what is this about?”.

Changed

• Install composition (default stays small): docs and packaging emphasize a lightweight core install, with heavy features enabled via explicit extras (tools, media, embeddings, server, provider SDKs).
• Dependency compatibility: relaxed abstractcore[huggingface] transformers upper bound to <6 so it can co-install with abstractcore[mlx] (as mlx-lm currently pins transformers==5.0.0rc*).
• Documentation polish: refreshed wording and navigation for external users; ensured internal links/anchors resolve across docs.
• Skim output footprint: tuned skim_url defaults (smaller preview/headings) and made skim_websearch JSON compact so tool outputs are more token-efficient by default.
• Web search URLs: web_search now unwraps DuckDuckGo redirect URLs (more readable links; smaller tool outputs).

Fixed

• Docs accuracy: aligned event fields and examples with the current codebase (events, telemetry, and usage data).
• Optional imports: made Telegram Bot API tools import-safe when requests is not installed (returns a clear abstractcore[tools] install hint when used).
• HTML extraction edge cases: improved main-content selection/pruning so fetch_url/skim_url previews don’t get wiped by over-aggressive boilerplate removal on some pages.

[2.11.0] - 2026-01-28

Added

• MLX throughput benchmarking: examples/mlx_concurrency_benchmark.py to sweep concurrency with continuous batching (mlx-lm) and generate summary CSVs + PNG plots.

Changed

• MLX install extras: refreshed/clarified mlx + mlx-bench optional dependencies for Apple Silicon throughput benchmarking.

Fixed

• Embedding model detection: treat model_type: "embedding" as the canonical signal; add nomic-embed-text-v1.5 (incl. LMStudio alias text-embedding-nomic-embed-text-v1.5@q6_k) to assets/model_capabilities.json.
• MLX model discovery: MLXProvider.list_available_models() now also scans LM Studio's local cache (~/.lmstudio/models) (including lmstudio-community/* and mlx-community/*) and loads from those local directories when present.
• GPT-OSS (Harmony) on MLX: improved prompt formatting (prefers tokenizer chat templates), extracts Harmony transcripts into clean content (stores reasoning in metadata.reasoning), and propagates correct finish_reason (stop/length) for truncation handling.

Documentation

• Concurrency guide: added MLX concurrency benchmarking notes and tracked benchmark plots/CSVs under docs/assets/ so docs don't depend on the ignored test_results/ folder.

[2.10.1] - 2026-01-11

Fixed

• Config CLI parity: implemented missing ConfigurationManager methods used by abstractcore config commands (streaming defaults, embeddings config, cache dirs, logging controls, vision fallback chain).
• OpenAI-compatible auth: openai-compatible provider now reads OPENAI_COMPATIBLE_API_KEY when set.
• CLI provider selection: abstractcore.utils.cli now exposes openrouter, openai-compatible, and vllm in --provider choices (and updates usage examples).
• CLI token controls: abstractcore.utils.cli now supports --max-output-tokens and interactive /max-tokens + /max-output-tokens.

Documentation

• Updated provider/config/CLI/server docs to reflect OpenAI-compatible consolidation, OpenRouter usage, current Claude model naming, and base_url usage for OpenAI-compatible endpoints.

[2.10.0] - 2026-01-10

Added

• OpenRouter provider: create_llm("openrouter", ...) via the OpenAI-compatible API (https://openrouter.ai/api/v1), with config support for OPENROUTER_API_KEY.

Changed

• OpenAI-compatible consolidation: refactored OpenAICompatibleProvider into the shared implementation and made LMStudioProvider / VLLMProvider thin subclasses.
• Config: added api_keys.openrouter support and wiring for abstractcore --set-api-key openrouter ....
• Defaults: updated Anthropic default model to claude-haiku-4-5.

Fixed

• Test stability: live-network and local-server provider tests are consistently opt-in via env flags; tracing tests no longer require a running Ollama server.
• Media validation: AnthropicMediaHandler.validate_media_for_model() now relies on centralized vision capability detection for newer Claude naming (e.g. claude-haiku-4-5).

[2.9.1] - 2026-01-07

Fixed

• Packaging / installability: pip install abstractcore now includes beautifulsoup4 so import abstractcore does not fail due to ModuleNotFoundError: bs4.

[2.9.0] - 2025-01-06

Added

• MCP (Model Context Protocol) Integration: First-class support for MCP servers
• New abstractcore.mcp package with HTTP and stdio client implementations
• McpClient for HTTP-based MCP servers with session management
• McpStdioClient for local stdio-based MCP server processes
• McpToolSource for automatic tool discovery and schema normalization
• Tool namespacing (mcp:server_name:tool_name) to prevent collisions

• Comprehensive test coverage for MCP integration

• Model Support: Added 5 new models to capabilities database

• claude-haiku-4-5: Claude Haiku 4.5 with 64K max output, 200K context
• claude-opus-4-5: Claude Opus 4.5 with 64K max output, 200K context
• glm-4.7: GLM-4.7 358B MoE with enhanced coding and reasoning (32K output, 128K context)
• minimax-m2.1: MiniMax M2.1 229B MoE optimized for coding (128K output, 200K context)

• nemotron-3-nano-30b-a3b: NVIDIA Nemotron 30B hybrid MoE (23 Mamba-2 + 6 Attention layers, 256K context)

• Architecture Support: Added nemotron_hybrid_moe architecture in architecture_formats.json for hybrid Mamba-2/Attention models

• Model Name Resolution: Enhanced architecture detection to strip provider prefixes (nvidia, azure, bedrock, fireworks, gemini, google, groq, together, etc.) from model names for capability lookups (e.g., lmstudio/qwen/qwen3-next-80b → qwen3-next-80b)

• Tools Infrastructure:

• Filesystem ignore policy (abstractcore.tools.abstractignore) with .abstractignore support and default patterns for *.d/ runtime directories
• Argument canonicalization (arg_canonicalizer.py) for flexible parameter naming (e.g., file_path/filepath/path)
• JSON-ish parser (abstractcore.utils.jsonish) for robust LLM-generated JSON parsing

• Tool schema now includes required_args field in ToolDefinition.to_dict()

• Documentation:

• GLM-4.6V tool format troubleshooting guide (docs/misc/glm-4.6v-tool-format-inconsistency.md)
• Enhanced docs/tool-calling.md with best practices
• Backlog organization with docs/backlog/README.md and completed items moved to subdirectory

Changed

• Tool Output Format (Breaking): Core tools now return structured JSON
• execute_command: Returns {success, return_code, stdout, stderr, rendered} dict
• fetch_url: Returns {rendered, raw_text, normalized_text, ...} dict
• Maintains rendered field for human-readable output

• Tool Registry supports structured failure reporting

• Provider Enhancements:

• max_tokens parameter (if provided without max_output_tokens) is automatically mapped to max_output_tokens for backward compatibility with callers using legacy terminology. Within AbstractCore, max_output_tokens remains the first-class citizen alongside max_input_tokens and max_tokens (context window)
• Centralized timeout configuration from abstractcore/config
• Server endpoint /v1/chat/completions accepts timeout_s request field
• Refactored tool prompt handling for better model-specific format support

• Enhanced performance tracking with detailed timing metrics

• File Operations:

• read_file max lines increased from 600 to 1000
• list_files now includes directories and uses relative paths
• edit_file enhanced with idempotent insertion behavior, better error messages, diff observability

Fixed

• Provider Fixes:
• Anthropic: Unknown claude* models default to native tool calling; claude-haiku-4-5 and claude-opus-4-5 properly recognized; role="tool" messages converted to tool_result content blocks
• OpenAI-Compatible: Fixed tool call normalization for wrapped tool names (e.g., "{function-name: write_file}")
• Ollama: Added metadata._provider_request for provider-wire observability
• VLLM: Enhanced tool call handling
• LMStudio: Improved timeout handling

• All: Normalized timeout errors, enhanced metadata handling, better architecture detection

• Tool Fixes:

• Web Search: Prefer ddgs with fallback to duckduckgo_search; bounded retries with query cleaning; region fallback; relevance scoring
• File Operations: write_file now requires content parameter; edit_file improved diagnostics; enhanced search_files and read_file context handling

• Code Analysis: Enhanced analyze_code documentation

• Tool Calling Infrastructure:

• Parser handles doubled tags, broken closing tags, unescaped control characters
• Bracket prefix support for alternative formats
• Better Nemotron XMLish format handling
• Wrapped tool name mapping in BaseProvider

• Enhanced tag rewriting and normalization

• Model Capabilities:

• Caching for default capabilities warnings (reduces log noise)
• Updated multiple models to "native" tool support (including qwen3-next-80b-a3b)

• Proper max output token clamping with better error messages

• Testing: Added 30+ new test files for MCP, tool calling, providers, filesystem policy, streaming, and packaging

Migration Notes

• Tool Outputs: Update code parsing execute_command or fetch_url outputs to handle dicts with rendered field
• File Operations: Explicitly provide content parameter to write_file (use content="" for empty files)
• Claude Models: Review tool support settings for Claude 4.5 models (now default to native)

Statistics

• 43 commits improving tools, providers, MCP integration, and infrastructure
• 120 files changed: 8,738 insertions, 12,472 deletions
• 5 new models added to capabilities database (135 total models)
• 30+ new test files for comprehensive coverage
• 21,385 total lines changed across the codebase

[2.8.1 - 2025-12-21

Added

Add workflow event types: Introduce new event types for workflow progress tracking

• Added EVENT_TYPE constants for workflow steps: WORKFLOW_STEP_STARTED, WORKFLOW_STEP_COMPLETED, WORKFLOW_STEP_WAITING, and WORKFLOW_STEP_FAILED.
• Enhances event tracking capabilities for durable execution processes.

[2.8.0] - 2025-12-18

Added

• Model Support: Added 15+ new models including GLM-4.6V, Qwen3-VL series, Devstral, GPT-OSS, MiniMax-M2, and Granite-4.0-H
• Vision models with enhanced OCR (32 languages) and visual agent capabilities
• MoE models with detailed expert configurations and quantization specs
• Coding models optimized for agentic workflows
• Architecture Support: Added 8 new architectures (glm4v_moe, mistral3, ministral3, granitemoehybrid, gpt_oss, qwen3_vl, qwen3_vl_moe, minimax_m2, harmony)
• Compression Modes: Added CompressionMode enum for chat history summarization (LIGHT/STANDARD/HEAVY)
• Trace Metadata: Added HTTP header extraction for distributed tracing support
• Token Budget Control: BasicSummarizer now supports AUTO mode for token management
• max_tokens=-1 (AUTO): Uses model's full context window capability
• max_tokens=N: Hard limit for deployment constraints (GPU/RAM)
• Same logic applies to max_output_tokens
• CLI supports --max-tokens auto or specific values

Enhanced

• Tool Call Parsing: Improved robustness with sanitization for malformed LLM output
• Handles doubled tags, broken closing tags, and unescaped control characters
• String-aware JSON escaping preserves structural whitespace
• Summarization: Smart token budget management prevents OOM while optimizing performance
• AUTO mode uses model's full capability
• Hard limits respect deployment constraints (GPU memory)
• Reduces API calls on large-context models (up to 12x improvement)
• Fallback parsing when structured output fails
• File Editing: Added flexible whitespace matching and unified diff support to edit_file
• Matches patterns ignoring indentation differences
• Preserves file's original indentation style
• Error Handling: Added fallback strategies throughout for improved reliability

Fixed

• Async Trace Capture: Improved reliability of trace capture in agenerate() for async LLM calls

Technical Details

• All changes maintain backward compatibility
• Default changed to max_tokens=-1 (AUTO) for optimal performance
• Token limits prevent OOM in memory-constrained environments
• Added deprecation warnings for execute_tools parameter

[2.6.7] - 2025-12-13

Fixed

• Made PIL/Pillow a required core dependency
• Providers need media handling, so PIL cannot be optional
• Fixes import errors when using abstractcore without explicit media installation
• Modified files: pyproject.toml, abstractcore/media/utils/image_scaler.py, abstractcore/utils/vlm_token_calculator.py

[2.6.6] - 2025-12-13

Fixed

• Fixed NameError: name 'Image' is not defined when importing tools module without PIL/Pillow installed
• image_scaler.py used PIL types in annotations but imported conditionally, causing NameError instead of ImportError
• Changed to direct imports with clear error messages
• Core functionality (tools, create_llm) now works without PIL installed

• Modified files: abstractcore/media/utils/image_scaler.py, abstractcore/utils/vlm_token_calculator.py

• Fixed compression installation group to depend on media (includes Pillow)

• Added missing installation groups: all-non-mlx, all-providers-non-mlx, local-providers-non-mlx

[2.6.5] - 2025-12-10

Added

• Dynamic Base URL Support for Server Endpoint: POST parameter for runtime base_url configuration
• New Parameter: base_url field in /v1/chat/completions request body
• Use Case: Connect to custom OpenAI-compatible endpoints without environment variables
• Example: {"model": "openai-compatible/model-name", "base_url": "http://localhost:1234/v1", ...}
• Integration: Works with openai-compatible provider and any provider supporting base_url
• Logging: Custom base URLs logged with 🔗 emoji for easy debugging
• Priority: POST parameter > environment variable > provider default
• Zero Breaking Changes: Optional parameter, existing code unchanged

Fixed

• OpenAI-Compatible Provider Model Listing: Fixed /v1/models?provider=openai-compatible endpoint
• Root Cause: Provider validation rejected "default" placeholder model used by registry for model discovery
• Solution: Skip model validation when model == "default" (registry placeholder)
• Impact: /v1/models endpoint now correctly lists all 27 models from LMStudio/llama.cpp servers
• Verified: Works with environment variable (OPENAI_COMPATIBLE_BASE_URL) configuration
• Model Prefix: All models returned with correct openai-compatible/ prefix

Enhanced

• Provider Registry: Added openai-compatible to instance-based model listing
• Previous: Attempted static method call, failed with openai-compatible
• Fixed: Added "openai-compatible" to instance-based providers list alongside ollama, lmstudio, anthropic
• Benefit: Proper model discovery with base_url injection from environment variables

Technical Details

• Files Modified:
• abstractcore/server/app.py (added base_url field to ChatCompletionRequest, ~18 lines)
• abstractcore/providers/openai_compatible_provider.py (skip validation for "default" model, ~3 lines)
• abstractcore/providers/registry.py (added openai-compatible to instance providers, 1 line)
• abstractcore/utils/version.py (version bump to 2.6.5)
• Architecture: Clean parameter injection pattern, minimal code changes
• Testing: Validated with LMStudio server on localhost:1234 (qwen/qwen3-next-80b model)

Usage Examples

# POST with dynamic base_url parameter (NEW in v2.6.5)
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai-compatible/qwen/qwen3-next-80b",
    "messages": [{"role": "user", "content": "Hello"}],
    "base_url": "http://localhost:1234/v1"
  }'

# List models with environment variable (FIXED in v2.6.5)
export OPENAI_COMPATIBLE_BASE_URL="http://localhost:1234/v1"
curl http://localhost:8080/v1/models?provider=openai-compatible
# Returns all 27 models with openai-compatible/ prefix

[2.6.4] - 2025-12-10

Added

• vLLM Provider: Dedicated provider for high-throughput GPU inference on NVIDIA CUDA hardware
• Native vLLM Features: Exposes guided decoding, Multi-LoRA, and beam search capabilities
• Guided Decoding: guided_regex, guided_json, guided_grammar parameters for 100% syntax-safe code generation
• Multi-LoRA Support: load_adapter(), unload_adapter(), list_adapters() for dynamic adapter management
• Beam Search: best_of, use_beam_search parameters for higher accuracy on complex tasks
• Full Async Support: Native async implementation with lazy-loaded httpx.AsyncClient
• OpenAI-Compatible: Uses /v1/chat/completions endpoint while exposing vLLM extensions via extra_body
• Shared Cache: Automatically shares HuggingFace cache with HF/MLX providers via HF_HOME
• Environment Variables: VLLM_BASE_URL (default: http://localhost:8000/v1), VLLM_API_KEY (optional)
• Default Model: Qwen/Qwen3-Coder-30B-A3B-Instruct (or use Qwen2.5-Coder-7B-Instruct for testing)
• Registry Integration: Listed in get_all_providers_status() alongside other 6 providers
• Implementation: 823 lines of provider code, 371 lines of tests, comprehensive GPU testing guide

• Use Cases: Production GPU deployments, multi-GPU tensor parallelism, specialized AI agents with LoRA adapters

• OpenAI-Compatible Generic Provider: Universal provider for any OpenAI-compatible API endpoint

• Maximum Compatibility: Works with llama.cpp, text-generation-webui, LocalAI, FastChat, Aphrodite, SGLang, proxies
• Optional Authentication: API key support (optional, many local servers don't require it)
• Feature Parity: Chat completions, streaming, async, embeddings, structured output, prompted tools
• Environment Variables: OPENAI_COMPATIBLE_BASE_URL (default: http://localhost:8080/v1), OPENAI_COMPATIBLE_API_KEY (optional)
• Default Model: "default" (server-dependent)
• 8 Providers Total: Completes provider ecosystem alongside OpenAI, Anthropic, Ollama, LMStudio, MLX, HuggingFace, vLLM
• Implementation: 764 lines of provider code, 328 lines of tests
• Architecture: Inherits from BaseProvider, uses httpx for HTTP communication
• Use Cases: llama.cpp local servers, text-generation-webui deployments, OpenAI-compatible proxies, custom endpoints
• Future Enhancement: Planned refactoring to create base class for vLLM/LMStudio to reduce code duplication (see docs/backlog/)

Documentation

• Hardware Requirements: Updated README.md and docs/prerequisites.md with hardware compatibility warnings
• Added "Hardware" column to provider table (MLX: Apple Silicon only, vLLM: NVIDIA CUDA only)
• Clear installation guidance per hardware platform
• Multi-GPU Setup: Complete guide for tensor parallelism on 4x NVIDIA L4 GPUs
• Startup commands for single GPU, multi-GPU, production with LoRA
• Key parameters documentation (--tensor-parallel-size, --gpu-memory-utilization, --max-num-seqs)
• OOM troubleshooting based on real deployment experience
• Testing Infrastructure: GPU test scripts for quick verification and comprehensive integration testing
• test-repl-gpu.py: Interactive REPL for direct vLLM provider testing
• test-gpu.py: Full stack test with AbstractCore server + curl examples
• FastDoc UI available at http://localhost:8080/docs when server running

Deployment Experience

• Validated on 4x NVIDIA L4 GPUs (23GB VRAM each, Scaleway Paris)
• Successfully resolved multi-GPU tensor parallelism requirements
• Fixed sampler warm-up OOM by reducing --max-num-seqs from 256 to 128
• Documented Triton kernel compilation issues with MoE models (recommend 7B models for reliability)

Technical Details

• Files Created:
• abstractcore/providers/vllm_provider.py (823 lines)
• abstractcore/providers/openai_compatible_provider.py (764 lines)
• tests/providers/test_vllm_provider.py (371 lines)
• tests/providers/test_openai_compatible_provider.py (328 lines)
• Files Modified:
• abstractcore/providers/registry.py (added 2 provider registrations)
• abstractcore/providers/__init__.py (exported 2 new providers)
• README.md (hardware requirements)
• docs/prerequisites.md (multi-GPU setup guide)
• Architecture: Both providers inherit from BaseProvider (not OpenAIProvider) for clean httpx implementation
• Pattern: vLLM uses extra_body for vLLM-specific params; OpenAI-compatible is pure OpenAI-compatible
• Branch: vllm-provider (pending merge to main)

[2.6.3] - 2025-12-10

Changed

• More Stringent Assessment Scoring: BasicJudge now applies rigorous, context-aware scoring to prevent grade inflation (2025-12-10)
• Anti-Grade-Inflation: Explicit guidance to avoid defaulting to high scores (3-4) for adequate work
• Context-Aware Criteria: Scores criteria based on task type (e.g., innovation=1-2 for routine calculations, not 3)
• Task-Appropriate Expectations: Different rubrics for routine tasks vs creative work vs complex problem-solving
• New Evaluation Step: "Assess if each criterion meaningfully applies to this task (if not, score 1-2)"
• Impact: More accurate and fair assessments that distinguish between routine competence and genuine excellence
• Example: Basic arithmetic now correctly scores innovation=1-2 (routine formula), not 3 (adequate innovation)
• Zero Breaking Changes: Assessment API unchanged, only internal scoring logic improved

Added

• Complete Score Visibility: session.generate_assessment() now returns all predefined criterion scores in structured format
• New Field: scores dict containing clarity, simplicity, actionability, soundness, innovation, effectiveness, relevance, completeness, coherence
• Before: Only overall_score, custom_scores, and text feedback visible
• After: Full transparency with individual scores for both predefined and custom criteria
• Impact: Users can now see exactly how each criterion was scored, not just overall and custom scores
• Backward Compatible: New scores field added to assessment result without breaking existing code

Technical Details

• Files Modified: abstractcore/processing/basic_judge.py (scoring principles), abstractcore/core/session.py (score extraction)
• Prompt Enhancement: Added "SCORING PRINCIPLES - CRITICAL" section with 6 explicit guidelines
• Implementation: ~15 lines added to scoring rubric, ~10 lines to session assessment storage

[2.6.2] - 2025-12-01

Added

• Programmatic Provider Configuration: Runtime configuration API for provider settings without environment variables (2025-12-01)
• Simple API: configure_provider(), get_provider_config(), clear_provider_config() functions
• Runtime Configuration: Set provider base URLs and other settings programmatically
• Automatic Application: All future create_llm() calls automatically use configured settings
• Provider Discovery: get_all_providers_with_models() automatically uses runtime configuration
• Use Cases:
  • Web UI settings pages: Configure providers through user interfaces
  • Docker startup scripts: Read from custom env vars and configure programmatically
  • Integration testing: Set mock server URLs without environment variables
  • Multi-tenant deployments: Configure different base URLs per tenant
• Priority System: Constructor parameter > Runtime configuration > Environment variable > Default value
• Implementation: ~65 lines across 3 files (config/manager.py, config/init.py, providers/registry.py)
• Testing: 9/9 tests passing with real implementations (no mocking)
• Zero Breaking Changes: Optional runtime configuration, all existing code works unchanged
• Feature Request: Extension of Digital Article team's base URL configuration request

Documentation

• README.md: Added Programmatic Configuration section with use cases and priority system
• llms.txt: Added feature line for v2.6.2
• llms-full.txt: Added comprehensive section with Web UI, Docker, testing, and multi-tenant examples
• FEATURE_REQUEST_RESPONSE_ENV_VARS.md: Updated with programmatic API examples

Technical Details

• Architecture: Runtime-only (in-memory), not persisted to config JSON file
• Injection Point: ProviderRegistry.create_provider_instance() merges runtime config into kwargs
• Pattern: merged_kwargs = {**runtime_config, **kwargs} ensures user kwargs take precedence
• Backward Compatibility: All 6 providers work automatically via registry injection
• Test Coverage: Unit tests for config methods, provider creation, precedence, and registry integration

[2.6.1] - 2025-12-01

Added

• Environment Variable Support for Provider Base URLs: Ollama and LMStudio providers now respect environment variables for custom base URLs (2025-12-01)
• Ollama Provider: Supports OLLAMA_BASE_URL and OLLAMA_HOST environment variables
• LMStudio Provider: Supports LMSTUDIO_BASE_URL environment variable
• Provider Discovery: get_all_providers_with_models() automatically respects environment variables when checking provider availability
• Use Cases:
  • Remote Ollama servers (e.g., GPU server on http://192.168.1.100:11434)
  • Docker/Kubernetes deployments with custom networking
  • Non-standard ports for multi-instance deployments (e.g., :11435, :1235)
  • Accurate provider availability detection in distributed environments
• Priority System: Programmatic base_url parameter > Environment variable > Default value
• Implementation: ~30 lines across 2 providers, follows existing OpenAI/Anthropic pattern
• Testing: 12/12 tests passing with real implementations (no mocking)
• Zero Breaking Changes: Optional environment variables, defaults unchanged, fully backward compatible
• Feature Request: Submitted by Digital Article team for computational notebook deployment

Documentation

• README.md: Added Environment Variables section with examples for all providers
• llms.txt: Added feature line for v2.6.1
• llms-full.txt: Added comprehensive Environment Variables section with use cases and code examples

Technical Details

• Architecture: Consistent with OpenAI/Anthropic providers (implemented in v2.6.0)
• Pattern: base_url or os.getenv("PROVIDER_BASE_URL") or default_value
• Providers Updated: ollama_provider.py, lmstudio_provider.py
• Test Coverage: Unit tests for env var reading, precedence, defaults, and integration with provider registry

[2.6.0] - 2025-12-01

Added

• Model Download API: Provider-agnostic async model download with progress reporting (2025-12-01)
• Top-Level Function: from abstractcore import download_model - simple, discoverable API
• Async Progress Reporting: Real-time status updates via async generator pattern
• Provider Support:
  • ✅ Ollama: Full progress with percent and bytes via /api/pull streaming NDJSON
  • ✅ HuggingFace: Start/complete messages via huggingface_hub.snapshot_download
  • ✅ MLX: Same as HuggingFace (uses HF Hub internally)
• Progress Information: DownloadProgress dataclass with status, message, percent, downloaded_bytes, total_bytes
• Error Handling: Clear error messages for connection failures, missing models, and gated repositories
• Use Cases: Docker deployments, automated setup, web UIs with SSE streaming, batch downloads
• Implementation: ~240 lines in abstractcore/download.py, 11/11 tests passing with real implementations

• Zero Breaking Changes: New functionality only, fully backward compatible

• Custom Base URL Support: Configure custom API endpoints for OpenAI and Anthropic providers (2025-12-01)

• OpenAI Provider: base_url parameter + OPENAI_BASE_URL environment variable
• Anthropic Provider: base_url parameter + ANTHROPIC_BASE_URL environment variable
• Use Cases:
  • OpenAI-compatible proxies (Portkey, etc.) for observability, caching, cost management
  • Local OpenAI-compatible servers
  • Enterprise gateways for security and compliance
  • Custom endpoints for testing and development
• Configuration Methods: Programmatic parameter (recommended) or environment variables
• Implementation: ~30 lines across 2 providers, follows Ollama/LMStudio pattern
• Testing: 8/10 tests passing, 2 appropriately skipped (OpenAI model validation with test keys)
• Zero Breaking Changes: Optional parameter with None default, fully backward compatible

• Note: Azure OpenAI NOT supported (requires AzureOpenAI SDK class)

• Production-Ready Native Async Support: Complete async/await implementation with validated 6-7.5x performance improvement (2025-11-30)

• Native Async Providers: Ollama, LMStudio, OpenAI, Anthropic now use native async clients (httpx.AsyncClient, AsyncOpenAI, AsyncAnthropic)
• Performance Validated:
  • Ollama: 7.5x faster for concurrent requests
  • LMStudio: 6.5x faster for concurrent requests
  • OpenAI: 6.0x faster for concurrent requests
  • Anthropic: 7.4x faster for concurrent requests
• Fallback Providers: MLX and HuggingFace use asyncio.to_thread() (industry standard for non-async libraries)
• Implementation Time: 15-16 hours (vs 80-120 hours originally planned) - simplified approach
• Code Changes: ~529 lines across 4 provider files (Ollama, LMStudio native implementations)
• Zero Breaking Changes: All sync APIs unchanged, async purely additive

• Testing: Comprehensive validation with real models (no mocking), 100% success rate

• Structured Logging Standardization: Completed migration of 14 core modules to structured logging (2025-12-01)

• 100% Migration Rate: 14/14 target files successfully migrated to get_logger() from abstractcore.utils.structured_logging
• Modules Migrated: tools/ (6 files), architectures/, core/, embeddings/, media/, providers/, utils/
• Simplified Approach: 2 hours implementation (vs 6-12 hours originally planned) - 5-6x more efficient
• SOTA Compliance: Follows PEP 282, Django, FastAPI, and cloud-native patterns
• Zero Breaking Changes: Fully backward compatible, all tests passing
• Benefits: Consistent structured logs, JSON output support, cloud-native ready, improved observability

Enhanced

• Async Documentation:
• Updated README.md with performance data and provider-specific details
• Educational async CLI demo with 8 core async/await patterns
• Created comprehensive async guide in docs/async-guide.md

• Backlog documents: async-mlx-hf.md (investigation), batching.md (future enhancement)

• Observability: Consistent structured logging across all critical infrastructure

• Module-level loggers using get_logger(__name__) pattern
• Structured fields support for machine-readable logs (ELK/Datadog/Splunk)
• Cloud-native JSON output ready
• No file dependencies (stdout/stderr only)

Technical Details

• Architecture:
• BaseProvider._agenerate_internal() as extension point for native async
• Lazy-loaded async clients (zero overhead for sync-only users)
• Proper async cleanup in unload() methods
• Pattern follows SOTA from LangChain, LiteLLM, Pydantic-AI
• Why MLX/HF use fallback: Libraries don't expose async APIs, direct function calls (no HTTP layer)
• SOTA Validation: Research confirmed approach matches industry best practices

Performance

• Average Speedup: ~7x faster for concurrent requests across all providers
• Real Concurrency: True async I/O overlap for network providers (HTTP client/server architecture)
• Fallback Efficiency: MLX/HF keep event loop responsive for mixing with async I/O operations

Documentation

• Async/Await Support - Updated usage examples
• Async Guide - Comprehensive examples and patterns
• Async CLI Demo - Educational reference for learning

[2.5.4] - 2025-11-27

Added

• Async/Await Support: Native async API for concurrent LLM requests with 3-10x performance improvement
• agenerate() Method: Async version of generate() works with all 6 providers (OpenAI, Anthropic, Ollama, LMStudio, MLX, HuggingFace)
• Concurrent Execution: Use asyncio.gather() for parallel requests with proven 3.52x speedup on real workloads
• Async Streaming: Full streaming support with AsyncIterator for real-time token generation
• Session Async: BasicSession.agenerate() maintains conversation history in async workflows
• Zero Breaking Changes: All sync APIs continue to work unchanged - async is purely additive
• FastAPI Compatible: Works seamlessly with async web frameworks and non-blocking applications
• Real Concurrency Verified: Benchmark tests confirm true async concurrency, not fake async wrappers
• Implementation: ~90 lines in 2 files using asyncio.to_thread() for thread-pool async execution
• Files Modified: abstractcore/providers/base.py, abstractcore/core/session.py

• Tests: Comprehensive test suite with real provider implementations (no mocking) in tests/async/

• Cross-Platform Installation Options: New installation extras for Linux/Windows users

• abstractcore[all-non-mlx] - Complete installation without MLX (for Linux/Windows)
• abstractcore[all-providers-non-mlx] - All providers except MLX
• abstractcore[local-providers-non-mlx] - Ollama and LMStudio without MLX
• Fixes installation failures when trying to install MLX on non-macOS systems
• Comprehensive installation guide: docs/installation-guide.md
• Updated README with platform-specific installation instructions

Enhanced

• Async Documentation: Comprehensive documentation updates across all guides
• README.md: Added async to Key Features and dedicated Async/Await section with examples
• docs/getting-started.md: New Section 6 covering async patterns and use cases
• docs/api-reference.md: Complete API documentation for agenerate() methods
• docs/README.md: Added async to Essential Guides navigation
• llms.txt: Added async code examples and capabilities for AI consumption
• llms-full.txt: Comprehensive async section with 4 subsections (basic, streaming, session, multi-provider)

Fixed

• Platform Compatibility: pip install abstractcore[all] no longer fails on Linux/Windows
• Previously, abstractcore[all] would fail on non-macOS systems due to MLX dependencies
• Users should now use abstractcore[all-non-mlx] on Linux/Windows for complete installation

Technical

• Async Implementation Details:
• Uses asyncio.to_thread() to run sync methods in thread pool without blocking event loop
• Proper AsyncIterator protocol for streaming responses
• Works with all existing provider implementations automatically via BaseProvider
• Full parameter passthrough for all generation options
• Tested with real LLM calls across all providers

Performance

• Verified Speedup: Benchmark testing shows 3.52x improvement for concurrent requests
• Sequential: 0.93s for 3 requests
• Concurrent: 0.26s for 3 requests with asyncio.gather()
• Real async concurrency confirmed (not fake async wrappers)

Use Cases

• Batch document processing
• Multi-provider consensus/comparison
• Non-blocking web applications (FastAPI, async frameworks)
• Parallel data extraction tasks
• High-throughput API endpoints

[2.5.3] - 2025-11-10

Added

• Added programmatic interaction tracing to capture complete LLM interaction history, enabling debugging, compliance, and performance analysis.
• Introduced provider-level and session-level tracing with customizable metadata and automatic trace collection.
• Implemented trace retrieval and export utilities for JSONL, JSON, and Markdown formats.
• Enhanced documentation and examples for interaction tracing usage and benefits.

• Comprehensive test coverage added for tracing functionality, ensuring reliability and correctness.

• MiniMax M2 Model Support: Added comprehensive detection for MiniMax M2 Mixture-of-Experts model

• Model Specs: 230B total parameters with 10B active (MoE architecture)
• Capabilities: Native tool calling, structured outputs, interleaved thinking with <think> tags
• Context Window: 204K tokens (industry-leading), optimized for coding and agentic workflows
• Variant Detection: Supports all distribution formats:
  • minimax-m2 (canonical name)
  • MiniMaxAI/MiniMax-M2 (HuggingFace official)
  • mlx-community/minimax-m2 (MLX quantized)
  • unsloth/MiniMax-M2-GGUF (GGUF format)
• Case-Insensitive: All variants detected regardless of case (e.g., MiniMax-M2, MINIMAX-m2)
• Source: Official MiniMax documentation (minimax-m2.org, HuggingFace, GitHub)
• License: Apache-2.0 with no commercial restrictions

• Note: Added single entry in model_capabilities.json with comprehensive aliases for automatic detection across all distribution formats

• [EXPERIMENTAL] Glyph Visual-Text Compression: Renders long text as optimized images for VLM processing

• ⚠️ Vision Model Requirement: ONLY works with vision-capable models (gpt-4o, claude-3-5-sonnet, llama3.2-vision, etc.)
• ⚠️ Error Handling: glyph_compression="always" raises UnsupportedFeatureError if model lacks vision support
• ⚠️ Auto Mode: glyph_compression="auto" (default) logs warning and falls back to text processing for non-vision models
• PIL-based text rendering with custom font support and proper DPI scaling
• Markdown-like formatting with hierarchical headers, bold/italic text, and smart newline handling
• Multi-column layout support with configurable spacing and margins
• Special OCRB font family support with separate regular/italic variants and stroke-based bold effect
• Font customization via --font (by name) and --font-path (by file) parameters
• Research-based VLM token calculator with provider-specific formulas
• Thread-safe caching system in ~/.abstractcore/glyph_cache/
• Optional dependencies: pip install abstractcore[compression] (removed ReportLab dependency)
• Vision capability validation in AutoMediaHandler._should_apply_compression()

Enhanced

• Model Capability Filtering: Clean, type-safe system for filtering models by input/output capabilities
• Input Capabilities: Filter by what models can analyze (TEXT, IMAGE, AUDIO, VIDEO)
• Output Capabilities: Filter by what models generate (TEXT, EMBEDDINGS)
• Python API: list_available_models(input_capabilities=[...], output_capabilities=[...])
• HTTP API: /v1/models?input_type=image&output_type=text

• All Providers: Works consistently across OpenAI, Anthropic, Ollama, LMStudio, MLX, HuggingFace

• Text File Support: Media module now supports 90+ text-based file extensions with intelligent content detection

• Expanded Mappings: Added support for programming languages (.py, .js, .r, .R, .rs, .go, .jl, etc.), notebooks (.ipynb, .rmd), config files (.yaml, .toml, .ini), web files (.css, .vue, .svelte), build scripts (.sh, .dockerfile), and more
• Smart Detection: Unknown extensions are analyzed via content sampling (UTF-8, Latin-1, etc.) to automatically detect text files
• Programmatic Access: New get_all_supported_extensions() and get_supported_extensions_by_type() functions for querying supported formats
• CLI Enhancement: @filepath syntax now works with ANY text-based file (R scripts, Jupyter notebooks, SQL files, etc.)
• Fallback Processing: TextProcessor handles all text files via plain text fallback, ensuring universal support
• Model Capabilities: Added 50+ VLM models (Mistral Small 3.1/3.2, LLaMA 4, Qwen3-VL, Granite Vision)
• Detection System: All model queries go through detection.py with structured logging
• Token Calculation: Accurate image tokenization using model-specific parameters
• Offline-First Architecture: AbstractCore now enforces offline-first operation by default
• Added centralized offline configuration in config/manager.py
• HuggingFace provider loads models directly from local cache when offline
• Environment variables (TRANSFORMERS_OFFLINE, HF_HUB_OFFLINE) set automatically
• Uses centralized cache directory configuration
• Designed primarily for open source LLMs with full offline capability
• HuggingFace Provider: Added vision model support for GLM4V architecture (Glyph, GLM-4.1V)
• Upgraded transformers requirement to >=4.57.1 for GLM4V architecture support
• Added _is_vision_model() detection for AutoModelForImageTextToText models
• Added _load_vision_model() and _generate_vision_model() methods
• Proper multimodal message handling with AutoProcessor
• Suppressed progress bars and processor warnings during model loading
• Vision Compression: Enhanced test script with exact token counting from API responses
• Added --detail parameter for Qwen3-VL token optimization (low, high, auto, custom)
• Added --target-tokens parameter for precise token control per image
• Improved compression ratio calculation using actual vs estimated tokens
• Added model-specific context window validation and warnings
• Media Handler Architecture: Clarified OpenAI vs Local handler usage patterns
• LMStudio uses OpenAIMediaHandler for vision models (API compatibility)
• Ollama uses LocalMediaHandler with custom image array format
• Added comprehensive architecture documentation and diagrams

Fixed

• Cache Creation: Automatic directory creation with proper error handling
• Dependency Validation: Structured logging for missing libraries
• Compression Pipeline: Fixed parameter passing and quality threshold bypass
• GLM4V Architecture: Fixed KeyError: 'glm4v' when loading Glyph and GLM-4.1V models
• Text Formatting Performance: Fixed infinite loop in inline formatting parser for large files
• Text Pagination: Implemented proper multi-image splitting for long texts
• Literal Newline Handling: Fixed \\n sequences not being converted to actual newlines
• Token Estimation: Added model-specific visual token calculations and context overflow protection
• Media Path Logging: Fixed media output paths not showing in INFO logs
• Qwen3-VL Context Management: Auto-adjusts detail level to prevent memory allocation errors
• LMStudio GLM-4.1V Compatibility: Documented LMStudio's internal vision config limitations
• HuggingFace GLM4V Support: Added proper error handling for transformers version requirements
• Requires vision-capable models (llama3.2-vision, qwen2.5vl, gpt-4o, claude-3-5-sonnet, zai-org/Glyph)
• System dependency on poppler-utils may require manual installation on some systems
• Quality assessment heuristics may be overly conservative for some document types

[2.5.2] - 2025-10-26

Added

• Native Structured Output Support for HuggingFace GGUF Models: HuggingFace provider now supports server-side schema enforcement for GGUF models via llama-cpp-python's response_format parameter
• GGUF models loaded through HuggingFace provider automatically get native structured output support
• Uses the same OpenAI-compatible response_format parameter as LMStudio
• Server-side schema enforcement validates output against the provided schema
• Transformers models continue to use prompted approach as fallback
• Provider registry updated to advertise structured output capability
• Native Structured Output via Outlines for HuggingFace Transformers: HuggingFace Transformers models now support native structured output via optional Outlines integration
• Constrained decoding ensures 100% schema compliance without validation retries
• Optional dependency - only installed with pip install abstractcore[huggingface]
• Automatic detection and activation when Outlines is available
• Graceful fallback to prompted approach if Outlines not installed
• Works with any transformers-compatible model
• Server-side logit filtering guarantees valid token selection
• Native Structured Output via Outlines for MLX: MLX models now support native structured output via optional Outlines integration
• Constrained decoding on Apple Silicon with 100% schema compliance
• Optional dependency - only installed with pip install abstractcore[mlx]
• Automatic detection and activation when Outlines is available
• Graceful fallback to prompted approach if Outlines not installed
• Optimized for Apple M-series processors
• Zero validation retries required

Changed

• StructuredOutputHandler: Enhanced provider detection to identify HuggingFace GGUF models, Transformers with Outlines, and MLX with Outlines as having native support
• Checks for model_type == "gguf" to determine GGUF native support
• Checks for model_type == "transformers" with Outlines availability for Transformers native support
• Checks for Outlines availability for MLX native support
• GGUF models benefit from llama-cpp-python's constrained sampling
• Transformers and MLX models benefit from Outlines constrained decoding when available
• Automatic fallback to prompted strategy if Outlines not installed
• Structured Output Control: Added structured_output_method parameter to HuggingFace and MLX providers for explicit control
• "auto" (default): Use Outlines if available, fallback to prompted
• "native_outlines": Force Outlines usage (error if unavailable)
• "prompted": Always use prompted fallback (recommended - fastest, 100% success)
• Allows users to optimize for performance vs theoretical guarantees
• Model Capabilities: Verified and documented native structured output support for Ollama and LMStudio providers
• Ollama: Confirmed correct implementation using format parameter with full JSON schema
• LMStudio: Documented existing OpenAI-compatible response_format implementation
• Both providers leverage server-side schema enforcement for schema compliance
• Dependencies: Added Outlines as optional dependency for HuggingFace and MLX providers
• pip install abstractcore[huggingface] now includes Outlines for native structured output
• pip install abstractcore[mlx] now includes Outlines for native structured output
• Base installation remains lightweight - Outlines only installed when needed

Fixed

• HuggingFace Provider: Added missing response_model parameter propagation through internal generation methods
• Fixed _generate_internal() to pass response_model to both GGUF and transformers backends
• Both _generate_gguf() and _generate_transformers() now accept and handle response_model parameter
• Provider Registry: Added "structured_output" to supported features for Ollama, LMStudio, HuggingFace, and MLX providers
• Ensures accurate capability reporting for structured output functionality

Performance Notes

Surprising Findings from Comprehensive Testing (October 26, 2025):

Extensive testing on Apple Silicon M4 Max revealed unexpected performance characteristics:

MLX Provider (mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit): - Prompted fallback: 745-4,193ms, 100% success rate - Outlines native: 2,031-9,840ms, 100% success rate - Overhead: 173-409% slower with Outlines constrained generation - Conclusion: Both approaches achieve 100% schema compliance, but prompted is 2-5x faster

Key Insight: The prompted approach (client-side validation) achieves identical 100% success rate at significantly better performance than Outlines' server-side constrained generation. This is contrary to typical expectations where server-side constraints should be more reliable.

Recommendation: - Default to structured_output_method="prompted" for best performance with proven reliability - Use structured_output_method="native_outlines" only when theoretical guarantees are required despite performance cost - The "auto" setting uses Outlines if installed, which may impact performance without improving reliability

This finding suggests that for these specific models and use cases, the overhead of constrained decoding outweighs its benefits when client-side validation already achieves 100% success.

[2.5.1] - 2025-10-24

Added

• New intent CLI application for analyzing conversation intents and detecting deception patterns
• /intent command in interactive CLI to analyze participant motivations in real-time conversations
• Support for multi-participant conversation analysis with focus on specific participants
• Native Structured Output Support: LMStudio provider now supports server-side schema enforcement via OpenAI-compatible response_format parameter
• Structured outputs are now guaranteed to match the provided schema without retry logic
• Works seamlessly with Pydantic models through the existing response_model parameter
• Provider registry updated to advertise structured output capability

Changed

• Renamed "Internal CLI" to "AbstractCore CLI" throughout documentation
• File renamed: docs/internal-cli.md → docs/acore-cli.md
• Model Capabilities: Updated 50+ Ollama-compatible models to report native structured output support (Llama, Qwen, Gemma, Mistral, Phi families)
• This reflects the actual server-side schema enforcement capabilities these models have when used with Ollama
• Provider Registry: Added "structured_output" to supported features for both Ollama and LMStudio providers

Fixed

• Updated all documentation cross-references to use new CLI naming
• Ollama Provider: Improved documentation of native structured output implementation (was already correct, now better documented)
• StructuredOutputHandler: Enhanced provider detection logic to correctly identify Ollama and LMStudio as having native support regardless of configuration

[2.4.9] - 2025-10-21

Fixed

• Configuration System: Fixed missing configuration module that caused 'NoneType' object is not callable error
• Renamed abstractcore/cli to abstractcore/config to match expected import path
• Added complete configuration manager implementation with vision, embeddings, and app defaults
• Fixed abstractcore --set-vision-provider and all other configuration commands

[2.4.7] - 2025-10-21

Fixed

• Tools Dependencies: Added missing requests dependency to core requirements and created tools optional extra for enhanced functionality

Added

Consistent Token Terminology

• Unified Token Naming: Standardized token terminology across AbstractCore to match input parameter naming
• GeneratedResponse now provides input_tokens, output_tokens, total_tokens properties
• Maintains backward compatibility with legacy prompt_tokens and completion_tokens keys
• All providers now use consistent terminology in usage dictionaries
• Token counts sourced from: Provider APIs (OpenAI, Anthropic, LMStudio) or AbstractCore's token_utils.py (MLX, HuggingFace)

Token Count Source Transparency

• Provider-Specific Token Handling: Clear documentation of token count sources
• From Provider APIs: OpenAI, Anthropic, LMStudio (native API token counts)
• From AbstractCore: MLX, HuggingFace providers (calculated using token_utils.py)
• Mixed Sources: Ollama (combination of provider and calculated tokens)
• Consistent Interface: All providers normalized through unified GeneratedResponse.usage structure

Generation Time Tracking

• Universal Timing: Added gen_time property to GeneratedResponse across all providers (in milliseconds)
• Precise Measurement: Tracks actual API call duration for network-based providers (OpenAI, Anthropic, LMStudio, Ollama)
• Local Processing Time: Measures inference time for local providers (MLX, HuggingFace)
• Simulated Timing: Local providers include realistic timing simulation
• Precision: Rounded to 1 decimal place for clean, readable output
• Performance Insights: Enables performance monitoring, optimization, and comparative analysis across providers
• Summary Integration: Generation time automatically included in response.get_summary() output

[2.4.6] - 2025-10-21

Added

Enhanced fetch_url Tool Performance

• Optimized HTML Parsing: Added lxml parser support for 2-3x faster HTML processing (with html.parser fallback)
• Session-Based Connection Reuse: Improved network performance through connection pooling
• Enhanced Encoding Detection: Multiple encoding fallback strategies for better text decoding reliability
• Improved Content Extraction: Better main content detection, removes navigation/footer/sidebar elements
• Smart Download Chunking: Optimized chunk sizes based on content type (32KB for binary, 16KB for text)
• Better JSON Formatting: Smart truncation at logical boundaries for improved readability

Universal SEED and Temperature Control

• Unified Parameter Support: Added comprehensive seed and temperature parameter support across all 6 providers
• Provider-Level: All providers now accept seed and temperature parameters in constructor and generate() calls
• Session-Level: BasicSession now supports persistent temperature and seed parameters across conversation
• Parameter Inheritance: Session parameters are used as defaults, can be overridden per generate() call
• Consistent Interface: Same API works across OpenAI, Anthropic, HuggingFace, Ollama, LMStudio, and MLX providers

Provider-Specific SEED Implementation

• OpenAI: Native seed parameter support for deterministic outputs (except reasoning models like o1)
• Anthropic: Graceful fallback with debug logging (Claude API doesn't support seed natively)
• HuggingFace: Full seed support for both transformers (torch.manual_seed()) and GGUF models (llama-cpp-python)
• Ollama: Native seed parameter support via options
• LMStudio: OpenAI-compatible seed parameter support
• MLX: Graceful fallback with debug logging (MLX-LM has limited seed support)

Enhanced Temperature Control

• Consistent Handling: Improved temperature parameter consistency across all providers
• Session Persistence: Temperature can be set at session level and persists across generate() calls
• Provider Defaults: Each provider maintains its own default temperature (0.7) when not specified

Enhanced

Architectural Improvements (Post-Implementation Review)

• Interface-Level Parameter Declaration: Moved temperature and seed to AbstractCoreInterface for consistent contract
• Eliminated Code Duplication: Removed redundant parameter initialization from all 6 providers (DRY principle)
• Centralized Parameter Logic: Added _extract_generation_params() helper method for consistent parameter extraction
• Cleaner Provider Code: Providers now focus only on their specific configuration, inheriting common parameters
• Robust Fallback Hierarchy: kwargs → instance variables → interface defaults with elegant one-liner implementation

Session Management

• Parameter Persistence: Session-level temperature and seed are maintained across conversation
• Flexible Override: Per-call parameters override session defaults without changing session state
• Enhanced Documentation: Updated session docstrings with parameter descriptions

Technical Details

Implementation Strategy & Architecture Review

• Non-Breaking: All changes are backward compatible - existing code continues to work
• Provider-Agnostic: Same seed/temperature API works regardless of underlying provider capabilities
• Graceful Degradation: Providers that don't support seed log debug messages instead of failing
• Clean Architecture: Leveraged existing parameter inheritance system in BaseProvider

Code Quality Improvements (Independent Review)

• Eliminated Duplication: Removed 12 lines of identical parameter initialization across 6 providers
• Interface Contract: Parameters now declared at interface level, ensuring consistent API contract
• Centralized Logic: Single _extract_generation_params() method replaces scattered parameter handling
• Simplified Providers: Each provider reduced by 2-4 lines, focusing only on provider-specific concerns
• Maintainability: Future parameter additions only require interface-level changes, not per-provider updates

Usage Examples

# Provider-level parameters
llm = create_llm("openai", model="gpt-4", temperature=0.3, seed=42)
response = llm.generate("Hello", temperature=0.8)  # Override temperature for this call

# Session-level parameters
session = BasicSession(provider=llm, temperature=0.5, seed=123)
response1 = session.generate("First message")  # Uses session temperature=0.5, seed=123
response2 = session.generate("Second message", temperature=0.9)  # Override temperature, keep seed

Architecture Review Summary

After independent analysis, the implementation was refactored for maximum elegance and maintainability:

Original Issues Identified

• Code duplication across 6 providers (12 identical lines)
• Inconsistent parameter handling patterns
• Missing interface-level parameter contract
• Scattered parameter extraction logic

Architectural Improvements Applied

• Interface-Level Declaration: Parameters moved to AbstractCoreInterface for consistent contract
• DRY Principle: Eliminated all parameter duplication across providers
• Centralized Logic: Single _extract_generation_params() method for consistent behavior
• Cleaner Providers: Each provider reduced by 2-4 lines, focusing only on provider-specific concerns
• Future-Proof: New parameters require only interface-level changes, not per-provider updates

Quality Metrics

• Lines Reduced: 12 lines of duplication eliminated
• Maintainability: 83% reduction in parameter-related code across providers
• Consistency: 100% uniform parameter handling across all 6 providers
• Extensibility: New parameters can be added with 2 lines instead of 12

See Generation Parameters Architecture for detailed technical analysis.

Testing & Verification

Comprehensive Test Suite

• Basic Parameter Tests: tests/test_seed_temperature_basic.py - CI/CD compatible parameter handling tests
• Determinism Tests: tests/test_seed_determinism.py - Real-world determinism verification across providers
• Manual Verification: tests/manual_seed_verification.py - Interactive script for testing actual determinism
• Test Documentation: tests/README_SEED_TESTING.md - Complete testing guide and troubleshooting

Provider Support Verification

• OpenAI: ✅ Native seed support (verified deterministic)
• Anthropic: ❌ No seed support (issues UserWarning when seed provided)
• HuggingFace: ✅ Full support for transformers and GGUF models
• Ollama: ✅ Native seed support via options
• LMStudio: ✅ OpenAI-compatible seed support
• MLX: ✅ Native seed support via mx.random.seed() (corrected implementation)

Real-World Testing & Verification ✅

Empirically Verified: All providers except Anthropic achieve true determinism with seed + temperature=0:

# Verified deterministic behavior (100% success rate):
✅ OpenAI (gpt-3.5-turbo): Same seed → Identical outputs
✅ Ollama (gemma3:1b): Same seed → Identical outputs  
✅ MLX (Qwen3-4B): Same seed → Identical outputs
⚠️ Anthropic (claude-3-haiku): temperature=0 → Consistent outputs (no seed support)

Test Commands:

# Test all available providers
python tests/manual_seed_verification.py

# Test specific provider determinism
python tests/manual_seed_verification.py --provider openai --prompt "Count to 5"

[2.4.5] - 2025-10-21

Fixed

Critical Package Distribution Bug

• Missing Media Subpackages: Fixed critical package installation bug where media subpackages were not included in distribution
• Issue: pyproject.toml only listed abstractcore.media parent package but not its subpackages
• Impact: Import from abstractcore import create_llm failed with ModuleNotFoundError: No module named 'abstractcore.media.processors'
• Missing Packages:
  • abstractcore.media.processors (ImageProcessor, PDFProcessor, OfficeProcessor, TextProcessor)
  • abstractcore.media.handlers (OpenAIMediaHandler, AnthropicMediaHandler, LocalMediaHandler)
  • abstractcore.media.utils (image_scaler utilities)
• Solution: Explicitly added all media subpackages to packages list in pyproject.toml
• Root Cause: When explicitly listing packages in pyproject.toml, setuptools does NOT auto-discover subpackages
• Workaround for 2.4.4: Use from abstractcore.core.factory import create_llm instead of from abstractcore import create_llm
• Credit: Bug discovered and reported during production deployment testing

Missing CLI Package

• Missing abstractcore.cli Module: Fixed missing abstractcore.cli package from distribution
• Issue: CLI entry point abstractcore command referenced abstractcore.cli.main:main but module was not included in package
• Impact: Configuration CLI commands would fail after installation from PyPI
• Solution: Added abstractcore.cli to packages list in pyproject.toml

Added

CLI Entry Point Improvements

• New Entry Points: Added convenient aliases to clarify CLI purpose and improve user experience
• abstractcore-config: Alias for abstractcore command (configuration CLI for settings, API keys, models)
• abstractcore-chat: New entry point for interactive REPL (abstractcore.utils.cli → LLM interaction)
• Purpose: Distinguish between configuration CLI (manage settings) and interactive chat CLI (talk to LLMs)
• Backwards Compatible: All existing commands continue to work (abstractcore, python -m abstractcore.utils.cli)

Technical

Package Configuration

• Updated packages list in pyproject.toml to include all required modules: toml packages = [ # ... existing packages ... "abstractcore.media", "abstractcore.media.processors", # ✅ Added "abstractcore.media.handlers", # ✅ Added "abstractcore.media.utils", # ✅ Added "abstractcore.cli" # ✅ Added ]
• Verification: All 19 packages now properly included in distribution
• Testing: Recommended to always test pip install from built wheel before PyPI release

Benefits

• Installation Works: Users can now successfully pip install abstractcore[all] or pip install abstractcore[media]
• Complete Media System: All media processing capabilities (images, PDFs, Office docs) now accessible after installation
• Clear CLI Commands: Users have obvious entry points for different CLI purposes
• Production Ready: Package installation thoroughly tested and verified

Migration Guide

No migration needed - this is a pure bug fix release. If you experienced installation issues with 2.4.4:

1. Upgrade: pip install --upgrade abstractcore
2. Verify: python -c "from abstractcore import create_llm; print('✅ Works!')"
3. Use new CLI aliases (optional): - abstractcore-config --status instead of abstractcore --status - abstractcore-chat instead of python -m abstractcore.utils.cli

[2.4.4] - 2025-10-21

Added

Provider Health Check System

• NEW .health() Method: Unified health check interface for all providers
• Structured Response: Consistent health status format across all providers
• Connectivity Testing: Uses list_available_models() as implicit connectivity test
• Smart Timeout Management: Configurable timeout (default: 5.0s) with automatic restoration
• Never Throws: Errors captured in response structure, never raises exceptions
• Rich Information: Returns status, provider name, model list, model count, error message, and latency
• Universal Compatibility: Works with all provider types (API, local, server-based)
• Override-able: Providers can customize health check logic if needed

Health Check Response Structure

{
    "status": bool,              # True if provider is healthy/online
    "provider": str,             # Provider class name (e.g., "OllamaProvider")
    "models": List[str] | None,  # Available models if online, None if offline
    "model_count": int,          # Number of models available (0 if offline)
    "error": str | None,         # Error message if offline, None if healthy
    "latency_ms": float          # Health check duration in milliseconds
}

Fixed

HuggingFace Token Counting Consistency

• Centralized Token Counter: Fixed HuggingFace provider to use centralized TokenUtils for consistency
• Problem: HuggingFace was the only provider using provider-specific tokenizer.encode() for token counting
• Solution: Added _calculate_usage() method matching MLX provider pattern using TokenUtils.estimate_tokens()
• Impact: All local providers now consistently use centralized token counting infrastructure
• Benefits:
  • ✅ Consistency across all providers (MLX, HuggingFace)
  • ✅ Robustness when tokenizer unavailable (GGUF models)
  • ✅ Content-type detection for better accuracy (code vs text vs JSON)
  • ✅ Model-family adjustments (qwen, llama, mistral tokenization patterns)

Enhanced

Token Usage Tracking

• Comprehensive Token Capture: All providers consistently capture THREE token metrics
• prompt_tokens: Input/context tokens (system prompt + history + current prompt)
• completion_tokens: Generated/output tokens (model's response)
• total_tokens: Sum of prompt + completion (used for billing/quotas)
• API Providers: OpenAI, Anthropic, Ollama, LMStudio use exact API-provided counts
• Local Providers: MLX, HuggingFace use centralized TokenUtils estimation

Technical

Token Counting Implementation

• Centralized Infrastructure: Located at abstractcore/utils/token_utils.py
• TokenUtils.estimate_tokens(text, model): Fast estimation with content-type detection
• TokenUtils.count_tokens(text, model, method): Flexible counting (auto/precise/fast)
• TokenUtils.count_tokens_precise(text, model): Accurate counting with tiktoken when available
• Multi-tiered strategy: tiktoken (precise) → provider tokenizer → model-aware heuristics → fast fallback

Files Modified

• abstractcore/providers/base.py: Added health() method (lines 870-965)
• abstractcore/providers/huggingface_provider.py:
• Added _calculate_usage() method using centralized TokenUtils (lines 890-902)
• Updated _single_generate_transformers() to use centralized token counting (lines 867-868)

Benefits

• Health Monitoring: Simple interface to check provider connectivity and availability
• Consistency: Unified token counting across all providers with same methodology
• Production Ready: Built-in timeout management prevents hanging health checks
• Developer Experience: Rich health information enables better error handling and monitoring
• Maintainability: Single centralized token counter to update/improve

Migration Guide

For Health Check Users

New .health() method available on all providers:

from abstractcore.core.factory import create_llm

# Check single provider
provider = create_llm("ollama", model="llama2")
health = provider.health(timeout=3.0)

if health["status"]:
    print(f"✅ {health['provider']} is healthy!")
    print(f"   📦 {health['model_count']} models available")
    print(f"   ⏱️  {health['latency_ms']}ms response time")
else:
    print(f"❌ {health['provider']} is offline")
    print(f"   Error: {health['error']}")

For Token Counting

No changes required - all existing code continues to work. HuggingFace provider now uses the same centralized token counting infrastructure as other local providers, improving consistency and accuracy.

[2.4.3] - 2025-10-20

Major Features

OpenAI Responses API Compatibility

• NEW /v1/responses Endpoint: 100% compatible with OpenAI's Responses API format
• input_file Support: Native support for {"type": "input_file", "file_url": "..."} in content arrays
• Backward Compatible: Existing messages format continues to work alongside new input format
• Automatic Format Detection: Server automatically detects and converts between OpenAI and legacy formats
• Streaming Support: Optional streaming with "stream": true for real-time responses (defaults to false)
• Universal File Processing: Works with all file types (PDF, DOCX, XLSX, CSV, images) across all providers

Enhanced File Attachment System

• type="file" Support: New content type alongside "text" and "image_url" for explicit file attachments
• Unified Format: {"type": "file", "file_url": {"url": "..."}} works consistently across all endpoints
• Multiple Sources: Supports HTTP(S) URLs, local file paths, and base64 data URLs
• Content-Type Detection: Intelligent file type detection from headers and URL extensions
• Generic Downloader: Replaces image-only downloader with universal file download supporting 15+ file types

Production-Grade PDF Processing

• Complete Text Extraction: Full PDF content extraction using PyMuPDF4LLM with formatting preservation
• 40,000+ Character Support: Successfully tested with large documents (Berkshire Hathaway annual letter)
• LLM-Optimized Output: Markdown formatting with preserved tables, headers, and structure
• Automatic Installation: Added PyMuPDF4LLM, PyMuPDF, and Pillow to dependencies
• Graceful Fallbacks: Multi-level fallback ensures content extraction even if advanced processing fails

Centralized Configuration System

• Global Configuration Management: Unified configuration at ~/.abstractcore/config/abstractcore.json
• App-Specific Defaults: Set different models for CLI, summarizer, extractor, and judge apps
• Global Fallbacks: Configure fallback models when app-specific settings aren't available
• API Key Management: Centralized API key storage for all providers
• Cache Configuration: Configurable cache directories for HuggingFace, local models, and general cache
• Logging Control: Console and file logging levels with enable/disable commands
• Streaming Defaults: Configure default streaming behavior for CLI applications

Comprehensive Media Handling System

• Universal Media API: Same media=[] parameter works across all providers with automatic format conversion
• Image Processing: Automatic resolution optimization for each model's maximum capability (GPT-4o: 4096px, Claude 3.5: 1568px, qwen2.5vl: 3584px)
• Document Processing: Full support for PDF, DOCX, XLSX, PPTX with complete content extraction
• Data Files: CSV, TSV, JSON, XML with intelligent parsing and analysis
• Provider-Specific Formatting: Automatic conversion to OpenAI JSON, Anthropic Messages API, or local text embedding
• Error Handling: Multi-level fallback strategy ensures users always get meaningful results

Vision Capabilities and Fallback System

• Vision Fallback for Text-Only Models: Transparent two-stage pipeline enables image processing for any model
• Automatic Detection: Identifies when text-only models receive images and activates fallback
• One-Command Setup: abstractcore --download-vision-model downloads and configures BLIP vision model
• Flexible Configuration: Supports local models (BLIP, ViT-GPT2, GIT), Ollama, LMStudio, and cloud APIs
• Transparent Operation: Users don't need to change code - system handles vision fallback automatically

Server Enhancements

Enhanced Debug and Logging

• Command-Line Arguments: Added --debug, --host, and --port flags for flexible server startup
• Debug Mode: --debug enables comprehensive request/response logging with timing metrics
• Custom Binding: --host and --port allow custom server addresses (default: 127.0.0.1:8000)

• Environment Integration: Follows centralized config patterns with ABSTRACTCORE_DEBUG variable

• Comprehensive Error Reporting: Enhanced 422 validation error handling with actionable diagnostics

• Field-Level Details: Shows exact field path, validation message, and problematic input
• Request Body Capture: In debug mode, logs full request body for troubleshooting
• Structured Logging: JSON-formatted logs with client IP, timing, and error context
• Before vs After: "422 Unprocessable Entity" now shows detailed field validation errors

Media Processing Integration

• OpenAI Vision API Format: Full support for image_url objects with base64 data URLs and HTTP(S) URLs
• File Processing Pipeline: Automatic media extraction, validation, and cleanup with request-specific prefixes
• Size Limits: 10MB per file, 32MB total per request with comprehensive validation
• Cleanup Logic: Automatic temporary file cleanup for abstractcore_img_*, abstractcore_file_*, and abstractcore_b64_* prefixes
• Prompt Adaptation: Intelligent prompt adaptation based on file types to avoid confusion

Fixed

Critical Runtime Issues

• Time Module Scoping: Removed redundant local import time statements causing "cannot access local variable" errors
• Fixed in lines 1995-1996 and 2123-2124 of abstractcore/server/app.py

• Now uses global time import consistently throughout server

• Boolean Syntax: Corrected JavaScript boolean syntax (false/true) to Python syntax (False/True)

• Fixed in lines 625, 813, 824, 1170, 1181, 1214 across request examples and defaults

• Streaming Default: Changed /v1/responses endpoint default from stream=True to stream=False

• Aligns with OpenAI API standard behavior (streaming opt-in, not opt-out)
• Line 361 in OpenAIResponsesRequest model

Swagger UI Integration

• Payload Input Issue: Fixed /v1/responses endpoint not showing request body in Swagger "Try it out"
• Replaced raw Request parameter with proper FastAPI Body(...) annotation
• Added comprehensive examples for OpenAI format, legacy format, file analysis, and streaming
• Lines 1148-1220 now properly expose request schema to OpenAPI documentation

Media Processing Reliability

• PDF Download Failures: Created generic file downloader replacing image-only version
• Added proper Accept: */* headers instead of image-specific headers
• Comprehensive content-type mapping for PDF, DOCX, XLSX, CSV, and 10+ other types
• URL extension fallback when content-type header missing
• Lines 1502-1627 in abstractcore/server/app.py

Enhanced

CLI Applications

• Centralized Configuration Integration: All CLI apps (summarizer, extractor, judge) now use centralized config
• Apps respect abstractcore --set-app-default configuration
• Fallback to global defaults when app-specific config not set

• Enhanced --debug mode for all applications

• Vision Configuration CLI: New abstractcore/cli/vision_config.py for vision fallback setup

• Interactive configuration wizard
• Model download commands
• Status checking and validation

Documentation

• Centralized Configuration: Created docs/centralized-config.md with complete configuration system documentation
• All available commands with examples
• Configuration file format and priority system

• Troubleshooting guide and common tasks

• Media Handling System: Comprehensive docs/media-handling-system.md with production-tested examples

• "How It Works Behind the Scenes" section explaining multi-layer architecture
• Provider-specific formatting documentation (OpenAI JSON, Anthropic Messages API)
• Real-world CLI usage examples with verified working commands

• Model compatibility matrix and resolution limits

• Server Documentation: Updated docs/server.md with /v1/responses endpoint details

• OpenAI Responses API format examples
• File attachment workflows
• Streaming configuration
• Media processing capabilities

Technical

Architecture Improvements

• Provider Registry Enhancement: Leverages centralized provider registry for model discovery
• /providers endpoint returns complete provider metadata
• No hardcoded provider lists - all dynamic discovery

• Registry version 2.0 indicators in API responses

• Message Preprocessing: New MessagePreprocessor for @filename syntax in CLI

• Extracts file attachments from text
• Validates file existence

• Cleans text for LLM processing

• Media Type Detection: Intelligent file type detection and processor selection

• AutoMediaHandler coordinates specialized processors
• ImageProcessor, PDFProcessor, OfficeProcessor, TextProcessor
• Graceful fallback ensures processing never fails completely

Test Coverage

• Media Examples: Added comprehensive test assets in tests/media_examples/
• PDF reports, Office documents, spreadsheets, presentations
• CSV/TSV data files with various encodings

• Image examples with metadata

• Server Testing: Enhanced test suite for media processing and OpenAI compatibility

• Real file processing tests (not mocked)
• Cross-provider media handling verification
• Streaming with media attachments

Breaking Changes

None. All changes maintain full backward compatibility with version 2.4.x.

Migration Guide

For Server Users

The /v1/responses endpoint now accepts both OpenAI's input format and our legacy messages format:

OpenAI Responses API Format (Recommended):

{
  "model": "gpt-4o",
  "input": [
    {
      "role": "user",
      "content": [
        {"type": "input_text", "text": "Analyze this document"},
        {"type": "input_file", "file_url": "https://example.com/doc.pdf"}
      ]
    }
  ],
  "stream": false
}

Legacy Format (Still Supported):

{
  "model": "openai/gpt-4",
  "messages": [
    {"role": "user", "content": "Tell me a story"}
  ],
  "stream": false
}

Note: Streaming is now opt-in (set "stream": true) instead of automatic, matching OpenAI's behavior.

For Configuration Users

New centralized configuration system available:

# Set global default model
abstractcore --set-global-default ollama/llama3:8b

# Set app-specific defaults
abstractcore --set-app-default summarizer openai gpt-4o-mini
abstractcore --set-app-default extractor ollama qwen3:4b-instruct

# Configure logging
abstractcore --set-console-log-level WARNING
abstractcore --enable-file-logging

# Check current configuration
abstractcore --status

Configuration is stored in ~/.abstractcore/config/abstractcore.json and respects priority: 1. Explicit parameters (highest priority) 2. App-specific configuration 3. Global configuration 4. Hardcoded defaults (lowest priority)

For Media Processing Users

Media processing now supports explicit file types:

CLI (Using @filename syntax):

python -m abstractcore.utils.cli --prompt "Analyze @report.pdf and @chart.png"

Python API:

response = llm.generate(
    "Analyze these documents",
    media=["report.pdf", "chart.png", "data.xlsx"]
)

Server API (New type="file"):

{
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "Analyze this file"},
        {"type": "file", "file_url": {"url": "https://example.com/doc.pdf"}}
      ]
    }
  ]
}

All formats work identically across all providers with automatic format conversion.

Dependencies Added

• pymupdf4llm (0.0.27): LLM-optimized PDF text extraction
• pymupdf (1.26.5): Core PDF processing library
• pydantic (2.12.3): Request validation and serialization
• fastapi: Enhanced with latest features
• pillow (12.0.0): Image processing support

Benefits

• Users: Seamless file attachment across all providers with @filename CLI syntax and media=[] API
• Developers: OpenAI-compatible server endpoints with comprehensive media processing
• Production: Robust error handling, detailed logging, and graceful degradation
• Configuration: Single source of truth for all package-wide preferences and defaults

[2.4.3] - 2025-10-19

Fixed

• Media System Critical Fixes: Resolved implementation issues preventing full media processing functionality
• PDF Processing: Fixed output_format parameter conflict in PDFProcessor._create_media_content() call (line 128) causing "got multiple values for keyword argument" error
• Office Document Processing: Fixed element iteration errors in OfficeProcessor by replacing convert_to_dict() approach with direct element processing for DOCX, XLSX, and PPTX files
• Unstructured Library Integration: Updated office processor to work correctly with current unstructured library API, eliminating "'NarrativeText' object is not iterable" and "'Table' object is not iterable" errors

Enhanced

• Production-Ready Media System: All file types now working perfectly with comprehensive content extraction
• PDF Files: Full text extraction with formatting preservation using PyMuPDF4LLM
• Word Documents: Complete document analysis with structure preservation (DOCX)
• Excel Spreadsheets: Sheet-by-sheet content extraction with intelligent data analysis (XLSX)
• PowerPoint Presentations: Slide content extraction with comprehensive presentation analysis (PPTX)
• CSV/TSV Files: Intelligent data parsing with quality assessment and recommendations

• Images: Seamless vision model integration with existing test infrastructure

• Server Debug Support: Comprehensive debug mode for troubleshooting API issues

• Command Line Interface: Added --debug, --host, and --port arguments to server startup with comprehensive help
• Enhanced Error Logging: Detailed 422 validation error reporting with field-level diagnostics and request body capture
• Request/Response Tracking: Full HTTP request logging with client information, timing metrics, and structured JSON output
• Centralized Configuration Integration: Follows centralized config system patterns with environment variable support
• Before vs After: Uninformative "422 Unprocessable Entity" messages now provide actionable field validation details

Verified

• CLI Integration: Confirmed @filename syntax works flawlessly across all file types
• Tested with real files: PDF reports, Office documents, spreadsheets, presentations, data files, and images
• Cross-provider compatibility verified with OpenAI, Anthropic, and LMStudio providers
• All examples documented in docs/media-handling-system.md are production-tested and working

Documentation

• Comprehensive Media System Documentation: Completely rewrote docs/media-handling-system.md to reflect actual implementation
• Added detailed "How It Works Behind the Scenes" section explaining the multi-layer architecture
• Documented provider-specific formatting (OpenAI JSON, Anthropic Messages API, local text embedding)
• Added real-world CLI usage examples with verified working commands
• Included cross-provider workflow diagrams and error handling strategies
• Architecture Documentation: Updated docs/architecture.md with comprehensive media system architecture section
• Added media processing workflow diagrams and component descriptions
• Documented graceful fallback strategy and provider-specific formatting
• Included unified media API documentation and CLI integration details

Technical

• Robust Error Handling: Multi-level fallback strategy ensures users always get meaningful results
• Advanced processing with specialized libraries (PyMuPDF4LLM, Unstructured)
• Basic processing fallbacks for text extraction
• Metadata-only fallbacks when all else fails
• System never crashes or fails completely
• Test Infrastructure: Leveraged existing tests/vision_examples/ with production-quality test assets
• 5 high-quality images with comprehensive JSON metadata for validation
• Real-world testing with actual provider APIs and file processing

Benefits

• Users: Can immediately attach any file type using @filename syntax with excellent analysis results
• Developers: Universal media=[] parameter works identically across all providers
• Production: Reliable media processing with comprehensive error handling and graceful degradation
• CLI: Simple file attachment workflow that works with all supported file formats

[2.4.2] - 2025-10-16

Added

• Centralized Provider Registry System: Unified provider discovery and metadata management
• Single Source of Truth: Created abstractcore/providers/registry.py with ProviderRegistry class for centralized provider management
• Package-wide Discovery Function: get_all_providers_with_models() provides unified access to ALL providers with complete metadata
• Complete Model Lists: Fixed truncation issue - now returns all models without "... and X more" truncation
• Rich Metadata: Installation instructions, features, authentication requirements, supported capabilities automatically available
• HTTP API Integration: Server /providers endpoint now uses centralized registry (registry_version: "2.0")
• Dynamic Discovery: Automatically discovers providers without hardcoding, eliminating manual synchronization

Enhanced

• Factory System: Simplified create_llm() from 70+ line if/elif chain to single registry call while maintaining full backward compatibility
• Server Endpoints: Enhanced /providers endpoint with comprehensive metadata including model counts, features, and installation instructions
• Documentation: Added "Provider Discovery" section to both llms.txt and llms-full.txt with Python API and HTTP API examples
• Error Messages: Improved error messages with dynamic provider lists from registry

Fixed

• Manual Provider Synchronization: Eliminated need to manually update provider lists across factory.py, server/app.py, and documentation
• Model List Truncation: Fixed "... and X more" truncation - now returns complete model lists for all providers
• Provider Metadata Inconsistency: Centralized all provider information including features, authentication requirements, and installation extras

Technical

• Comprehensive Test Suite: Added 50 tests in tests/provider_registry/ covering core functionality, server integration, and factory integration
• Lazy Loading: Provider classes loaded on-demand for better performance and memory usage
• Backward Compatibility: All existing code continues to work unchanged - no breaking changes
• Extensible Architecture: Easy to add new providers by registering them in the centralized registry

Benefits

• Developers: Single function to discover all providers programmatically
• Server Users: Enhanced /providers endpoint with rich metadata
• Maintainers: No more manual provider list synchronization across multiple files
• Documentation: Always up-to-date provider information in docs

[2.4.1] - 2025-10-16

Fixed

• Critical Package Distribution Fix: Fixed ModuleNotFoundError: No module named 'abstractcore.exceptions' that occurred when installing from PyPI
• Added missing abstractcore.exceptions and abstractcore.media packages to the setuptools configuration in pyproject.toml
• This issue was introduced during the refactoring process when these modules were not included in the package distribution list
• Users can now successfully import from abstractcore import create_llm after installing from PyPI
• Verified fix by building and testing the wheel package with the corrected configuration

[2.4.0] - 2025-10-15

Breaking Changes

• Complete Rebranding: Comprehensive rename from "AbstractLLM" to "AbstractCore" throughout the entire project
• Package Name: Internal package abstractllm/ → abstractcore/ to align with published package name
• Product Name: "AbstractLLM Core" → "AbstractCore" in all documentation and branding
• Import statements: All from abstractcore import ... must become from abstractcore import ...
• Console scripts: Entry points changed from abstractllm.apps.* to abstractcore.apps.*
• Interface names: AbstractLLMInterface → AbstractCoreInterface, AbstractLLMError → AbstractCoreError
• Environment variables: ABSTRACTLLM_* → ABSTRACTCORE_* (e.g., ABSTRACTCORE_ONNX_VERBOSE)
• Cache directories: ~/.abstractllm/ → ~/.abstractcore/
• Log files: abstractllm_*.log → abstractcore_*.log
• Module paths: All absolute imports updated throughout codebase
• Impact: This affects all users - complete migration required from AbstractLLM to AbstractCore branding

Migration Guide

To migrate from 2.3.x to 2.4.0, update all references to AbstractLLM:

1. Import Statements:

# Before (2.3.x)
from abstractcore import create_llm
from abstractllm.processing import BasicSummarizer
from abstractllm.embeddings import EmbeddingManager

# After (2.4.0+)
from abstractcore import create_llm
from abstractcore.processing import BasicSummarizer  
from abstractcore.embeddings import EmbeddingManager

2. Interface Names:

# Before (2.3.x) 
from abstractllm.core.interface import AbstractLLMInterface

# After (2.4.0+)
from abstractcore.core.interface import AbstractCoreInterface

3. Environment Variables:

# Before (2.3.x)
export ABSTRACTLLM_ONNX_VERBOSE=1

# After (2.4.0+)
export ABSTRACTCORE_ONNX_VERBOSE=1

4. Console Scripts: Console scripts remain the same (both summarizer and abstractcore-summarizer work), but internal module paths have changed to abstractcore.apps.*.

Technical

• Directory Structure: Renamed main package directory from abstractllm/ to abstractcore/
• Configuration Updates: Updated pyproject.toml with new package names, console scripts, and version paths
• Build System: Cleaned and regenerated all build artifacts with correct package structure
• Documentation: Updated all code examples, CLI usage, and module references across documentation
• Examples: Updated all example files with new import statements
• Tests: Updated all test imports and references throughout test suite

[2.3.9] - 2025-10-25

Fixed

• Timeout Handling: Comprehensive timeout parameter handling across all providers
• All providers now properly handle timeout=None (infinity) as the default
• HuggingFace Provider: Issues warning when non-None timeout is provided (local models don't support timeouts)
• MLX Provider: Issues warning when non-None timeout is provided (local models don't support timeouts)
• Local Providers: Accept timeout parameters appropriately
• API Providers (OpenAI, Anthropic, Ollama, LMStudio): Properly pass timeout to HTTP clients
• Added _update_http_client_timeout() method for providers that need to update client timeouts
• Setting timeout default to None (infinity)

[2.3.8] - 2025-10-25

Fixed

• Issue with the version

[2.3.7] - 2025-10-25

Fixed

• Syntax Warning: Fixed invalid escape sequence \( in common_tools.py docstring example
• CLI Enhancement: Added optional focus parameter to /compact command for targeted conversation summarization
• Usage: /compact [focus] where focus can be "technical details", "key decisions", etc.
• Leverages existing BasicSummarizer focus functionality for more precise compaction
• Maintains backward compatibility (no focus = default behavior)

[2.3.6] - 2025-10-14

Added

• Vector Embeddings: SOTA open-source models with EmbeddingGemma as default, ONNX optimization, multi-provider support (HuggingFace, Ollama, LMStudio)
• Processing Applications: BasicSummarizer, BasicExtractor, BasicJudge with CLI tools and structured output
• GitHub Pages Website: Professional documentation site with responsive design and provider showcase
• Unified Streaming Architecture: Real-time tool call detection and execution across all providers
• Memory Management: Provider unload() methods for resource management in constrained environments
• Session Management: Complete serialization with analytics (summary, assessment, facts)
• CLI Enhancements: Interactive REPL with tool integration, session persistence, and comprehensive help system

Fixed

• Critical Tool Compatibility: Tools + structured output now work together with sequential execution pattern
• Ollama Endpoint Selection: Fixed verbose responses by using correct /api/chat endpoint
• Streaming Tool Execution: Consistent formatting between streaming and non-streaming modes
• Architecture Detection: Corrected Qwen3-Next models and universal tool call parsing
• Session Serialization: Fixed parameter consistency and tool result integration
• Timeout Configuration: Unified timeout management across all components (default: 5 minutes)
• Package Dependencies: Made processing module core dependency, fixed installation extras

Enhanced

• Multi-Provider Embedding: Unified API across HuggingFace, Ollama, LMStudio with caching and optimization
• Tool Call Syntax Rewriting: Server-side format conversion for agentic CLI compatibility
• Documentation: Consolidated and professional tone, comprehensive tool calling guide
• Token Management: Helper methods and validation with provider-specific recommendations
• Test Coverage: 346+ tests with real models, comprehensive provider testing

Technical

• Event System: Real-time monitoring and observability with OpenTelemetry compatibility
• Circuit Breakers: Netflix Hystrix pattern with exponential backoff retry strategy
• FastAPI Server: OpenAI-compatible endpoints with comprehensive parameter support
• Model Discovery: Heuristic-based filtering and provider-specific routing

[2.3.5] - 2025-10-14

Fixed

CRITICAL: Tools + Structured Output Compatibility

• Problem: AbstractCore's tools and response_model parameters were mutually exclusive, preventing users from combining function calling with structured output validation
• Root Cause: StructuredOutputHandler bypassed normal tool execution flow and tried to validate tool call JSON against Pydantic model
• Solution: Implemented sequential execution pattern - tools execute first, then structured output uses results as context
• Impact: Enables sophisticated LLM applications requiring both function calling and structured output validation
• Usage: llm.generate(tools=[func], response_model=Model, execute_tools=True) now works seamlessly
• Limitation: Streaming not supported in hybrid mode (clear error message provided)

Enhanced BaseProvider Interface

• Added: generate() method to BaseProvider implementing AbstractCoreInterface
• Fixed: Proper delegation from generate() to generate_with_telemetry() with full parameter passthrough
• Impact: Ensures consistent API behavior across all provider implementations

Technical

Implementation Details

• Added _handle_tools_with_structured_output() method with sequential execution strategy
• Modified generate_with_telemetry() to detect and route hybrid requests appropriately
• Enhanced prompt engineering to inject tool execution results into structured output context
• Maintained full backward compatibility for single-mode usage (tools-only or structured-only)

Files Modified

• abstractcore/providers/base.py: Added hybrid handling logic and generate() method implementation
• Sequential execution: Tool execution → Context enhancement → Structured output generation
• Clean error handling with descriptive messages for unsupported combinations

Test Results

✅ Tools-only mode: Works correctly
✅ Structured output-only mode: Works correctly
✅ NEW: Hybrid mode (tools + structured output): Now works correctly
✅ Backward compatibility: All existing functionality preserved
✅ Error handling: Clear messages for unsupported streaming + hybrid combination

[2.3.4] - 2025-10-14

Added

State-of-the-Art GitHub Pages Website

• Professional Website: Created comprehensive GitHub Pages website at https://lpalbou.github.io/AbstractCore/
• Modern UI/UX: Responsive design with dark/light theme toggle, smooth animations, and mobile-first approach
• Interactive Features: Code block copy functionality, smooth scrolling navigation, and dynamic theme switching
• Provider Showcase: Visual display of all supported LLM providers (OpenAI, Anthropic, Ollama, MLX, LMStudio, HuggingFace)
• SEO Optimization: Complete sitemap.xml, robots.txt, and meta tags for search engine visibility
• LLM Integration: Added llms.txt and llms-full.txt files for enhanced LLM compatibility and content discovery

Comprehensive Tool Calling Documentation

• New Documentation: Created docs/tool-calling.md with complete coverage of the tool calling system
• Rich Decorator Examples: Documented the full capabilities of the @tool decorator including metadata injection
• Architecture-Aware Formatting: Explained how tool definitions adapt to different model architectures (Qwen, LLaMA, Gemma)
• Tool Syntax Rewriting: Integrated comprehensive documentation of Tag Rewriter and Syntax Rewriter systems
• Real-World Examples: Showcased actual tools from common_tools.py with full metadata and system prompt integration

Enhanced

Documentation Consolidation and Cleanup

• Professional Tone: Removed pretentious language, excessive emojis, and marketing hype from all documentation
• Consolidated Content: Merged tool-syntax-rewriting.md into comprehensive tool-calling.md documentation
• Fixed Cross-References: Updated all internal links in README.md, docs/README.md, and getting-started.md
• Consistent Styling: Standardized documentation format and removed redundant content
• HTML Documentation: Created HTML versions of all documentation for the GitHub Pages website

Website Architecture

• Static Site Generation: Pure HTML/CSS/JavaScript implementation for maximum performance and compatibility
• Asset Organization: Structured asset directory with optimized SVG logos and provider icons
• GitHub Pages Optimization: Added .nojekyll file and proper CNAME configuration for custom domains
• Documentation Integration: Seamless integration between website and documentation with consistent navigation

Technical

Files Added

• index.html: Main landing page with hero section, features showcase, and provider display
• assets/css/main.css: Comprehensive styling with CSS variables for theming and responsive design
• assets/js/main.js: Interactive functionality including theme switching and mobile navigation
• llms.txt: Concise LLM-friendly project overview with key documentation links
• llms-full.txt: Complete documentation content aggregated for LLM consumption
• docs/tool-calling.html: HTML version of comprehensive tool calling documentation
• robots.txt and sitemap.xml: SEO optimization files for search engine discovery

Documentation Updates

• Enhanced docs/tool-calling.md with complete @tool decorator capabilities and real-world examples
• Updated README.md, docs/README.md, and docs/getting-started.md with professional tone and correct links
• Removed redundant docs/tool-syntax-rewriting.md after content integration
• Fixed all cross-references and internal navigation links

GitHub Pages Deployment

• Created clean gh-pages branch with optimized website content
• Implemented proper GitHub Pages configuration with SEO optimization
• Added comprehensive LLM compatibility files for enhanced discoverability
• Structured deployment ready for custom domain configuration

Impact

• Enhanced Developer Experience: Professional website provides clear project overview and easy navigation
• Improved Documentation Quality: Consolidated, professional documentation without redundancy or pretentious language
• Better LLM Integration: Structured llms.txt files enable better LLM understanding and interaction with the project
• Increased Discoverability: SEO-optimized website improves project visibility and accessibility
• Comprehensive Tool Documentation: Complete coverage of tool calling system with practical examples and architecture details

[2.3.3] - 2025-10-14

Fixed

ONNX Runtime Warning Suppression

• Problem: ONNX Runtime displayed verbose CoreML execution provider warnings on macOS during embedding model initialization
• Root Cause: ONNX Runtime logs informational messages about CoreML partitioning and node assignment directly to stderr, bypassing Python's warning system
• Solution: Added ONNX Runtime log level configuration in _suppress_onnx_warnings() to suppress harmless informational messages
• Impact: Cleaner console output during embedding operations while preserving debugging capability via ABSTRACTLLM_ONNX_VERBOSE=1 environment variable
• Technical: Set onnxruntime.set_default_logger_severity(3) to suppress warnings that don't affect performance or quality

[2.3.2] - 2025-10-14

Fixed

Critical Ollama Endpoint Selection Bug

• Problem: Ollama provider was generating excessively verbose responses (1000+ characters for simple questions like "What is 2+2?")
• Root Cause: Provider incorrectly used /api/generate endpoint for all requests, including tool-enabled conversations
• Solution: Updated endpoint selection logic to use /api/chat by default, following Ollama's API design recommendations
• Impact: Reduced response length from 977+ characters to 15 characters for simple queries, eliminated "infinite text" generation issue
• Technical: Modified _generate_internal() method to use use_chat_format = tools is not None or messages is not None or True for proper endpoint routing

Session Serialization Parameter Consistency

• Problem: Inconsistent parameter naming between session.add_message() using name and session.generate() using username
• Root Cause: Parameter standardization was incomplete during metadata redesign
• Solution: Standardized both methods to use name parameter, aligning with session_schema.json specification
• Impact: Consistent API across session methods, improved developer experience

Tool Execution Results in Live Sessions

• Problem: Tool execution results were missing from chat history during live CLI sessions but appeared after session reload
• Root Cause: Tool results were not being added to session message history during execution
• Solution: Modified _execute_tool_calls() in CLI to explicitly add role="tool" messages with execution metadata
• Impact: Tool results now immediately available to assistant during conversation, consistent behavior between live and serialized sessions

Common Tools Defensive Programming

• Problem: list_files and search_files tools failed with type errors when head_limit parameter was passed as string
• Root Cause: LLM-generated tool calls sometimes provided numeric parameters as strings
• Solution: Added defensive type conversion with fallback to default values on ValueError
• Impact: Improved tool reliability and error handling

Enhanced

Comprehensive Session Management System

• Session Serialization: Complete session state preservation including provider, model, parameters, system prompt, tool registry, and conversation history
• Optional Analytics: Added generate_summary(), generate_assessment(), and extract_facts() methods for session-level insights
• Versioned Schema: Implemented session-archive/v1 format with JSON schema validation in abstractcore/assets/session_schema.json
• CLI Integration: Added /save <file> [--summary] [--assessment] [--facts] and /load <file> commands with optional analytics generation
• Backward Compatibility: Graceful handling of legacy session formats during load operations

Enhanced CLI User Experience

• Improved Help System: Comprehensive, aesthetically pleasing help text with detailed command documentation and usage examples
• Tool Integration: Added search_files tool to CLI with full documentation and status reporting
• Better Banner: Informative startup banner with quick commands and available tools overview
• Parameter Documentation: Clear documentation of /save command options and usage patterns

Metadata System Redesign

• Extensible Metadata: Moved name field into metadata dictionary for better extensibility
• Location Support: Added location property backed by metadata['location'] for geographical context
• Property-Based Access: Clean API with message.name and message.location properties while maintaining metadata flexibility
• Backward Compatibility: Automatic migration of legacy name field to metadata['name'] during deserialization

Technical

Files Modified

• abstractcore/providers/ollama_provider.py: Fixed endpoint selection logic to use /api/chat by default
• abstractcore/core/session.py: Enhanced serialization, standardized parameter naming, added analytics methods
• abstractcore/core/types.py: Redesigned metadata system with property-based access
• abstractcore/utils/cli.py: Improved help system, added tool integration, enhanced save/load commands
• abstractcore/tools/common_tools.py: Added defensive programming for parameter type handling
• abstractcore/assets/session_schema.json: Created comprehensive JSON schema for session validation
• docs/session.md: New documentation explaining session management and serialization benefits

Test Results

✅ Ollama responses now concise (15 chars vs 977+ chars previously)
✅ Session serialization preserves complete state including analytics
✅ Tool execution results properly integrated into live chat history
✅ Parameter consistency across all session methods
✅ Defensive tool parameter handling prevents type errors
✅ Backward compatibility maintained for existing session files

[2.3.0] - 2025-10-12

Major Changes

Server Simplification and Enhancement

• Simplified server implementation in abstractcore/server/app.py (reduced from ~4000 to ~1500 lines)
• Removed complex model discovery in favor of direct provider queries
• Added comprehensive endpoint documentation with OpenAI-style descriptions
• Enhanced request/response models with detailed parameter descriptions and examples

Multi-Provider Embedding Support

• EmbeddingManager now supports three providers: HuggingFace, Ollama, and LMStudio
• Unified embedding API across all providers with automatic format conversion
• Provider-specific caching for isolation and performance
• Backward compatible with existing HuggingFace-only code (default provider)

Tool Call Syntax Rewriting

• Added syntax_rewriter.py for server-side tool call format conversion
• Supports multiple formats: OpenAI, Codex, Qwen3, LLaMA3, Gemma, XML
• Automatic format detection based on headers, user-agent, and model name
• Enables seamless integration with agentic CLIs (Codex, Crush, Gemini CLI)

Model Discovery and Filtering

• Added /v1/models?type=text-embedding endpoint for filtering embedding models
• Heuristic-based model type detection (embedding vs text-generation)
• Embedding patterns: "embed", "all-minilm", "bert-", "-bert", "bge-", "gte-", etc.
• Provider-specific model filtering via query parameters

Server Enhancements

API Endpoints

• Enhanced /v1/embeddings endpoint with multi-provider support
• Added type parameter to /v1/models for model type filtering (text-generation/text-embedding)
• Improved /v1/chat/completions with comprehensive parameter documentation
• Added /{provider}/v1/chat/completions for provider-specific requests
• Enhanced /v1/responses endpoint for agentic CLI compatibility
• Updated /providers endpoint with detailed provider information

Request/Response Models

• Added detailed field descriptions and examples to all Pydantic models
• EmbeddingRequest: Comprehensive parameter explanations using OpenAI reference style
• ChatCompletionRequest: Enhanced with field-level documentation and examples
• ChatMessage: Detailed role and content descriptions with use cases
• Default examples updated to use working models

Format Conversion

• Automatic tool call format conversion for different agentic CLIs
• Support for custom tool call tags via agent_format parameter
• Configurable tool execution (server-side vs client-side)
• Environment variable configuration for default formats

Core Library Improvements

Embeddings

• Provider parameter added to EmbeddingManager.__init__() (default: "huggingface")
• embed() and embed_batch() methods now delegate to provider-specific implementations
• Ollama provider: Added embed() method using /api/embeddings endpoint
• LMStudio provider: Added embed() method using /v1/embeddings endpoint
• Cache naming includes provider for proper isolation

Providers

• Enhanced provider base classes with improved error handling
• Better streaming support across all providers
• Consistent timeout handling and retry logic
• Improved tool call detection and parsing

Exception Handling

• Added UnsupportedProviderError for better error messages
• Enhanced exception types for embedding-specific errors
• Improved error context and debugging information

Documentation Overhaul

Consolidated Documentation

• Merged common-mistakes.md into troubleshooting.md with cross-references
• Merged server-api-reference.md into simplified server.md (1006 → 479 lines)
• Created comprehensive docs/README.md as navigation hub
• Removed redundant documentation files (8 files consolidated)

New Documentation

• Created tool-syntax-rewriting.md covering both tag and syntax rewriters
• Enhanced embeddings.md with multi-provider support and examples
• Updated architecture.md with server architecture and present-tense language
• Improved getting-started.md with comprehensive tool documentation

Documentation Organization

• Moved basic-*.md files to docs/apps/ subdirectory
• Created docs/archive/ for superseded documentation
• Added docs/archive/README.md explaining archived content
• Updated all cross-references across documentation

Documentation Style

• Removed historical/refactoring language ("replaced", "improved", "before/after")
• Converted all documentation to present tense
• Focused on current capabilities and actionable content
• Simplified language for clarity and accessibility

Root README Updates

• Added clearer distinction between core library and optional server
• Enhanced documentation section with better organization
• Added "Architecture & Advanced" section
• Improved Quick Links with comprehensive navigation

Technical Improvements

Code Quality

• Removed unused simple_model_discovery.py module
• Cleaned up temporary debug files and scripts
• Removed integration.py tool module (functionality moved to providers)
• Better separation of concerns between core and server

Testing

• Added comprehensive tests for embedding providers
• Enhanced server endpoint testing
• Improved tool call syntax rewriting tests
• Better test coverage for multi-provider scenarios

Breaking Changes

None. All changes are backward compatible with version 2.2.x.

Migration Guide

For Embedding Users

If you were using embeddings, no changes needed. The default behavior remains HuggingFace.

To use other providers:

from abstractcore.embeddings import EmbeddingManager

# HuggingFace (default, unchanged)
embedder = EmbeddingManager(model="sentence-transformers/all-MiniLM-L6-v2")

# Ollama (new)
embedder = EmbeddingManager(model="granite-embedding:278m", provider="ollama")

# LMStudio (new)
embedder = EmbeddingManager(model="text-embedding-all-minilm-l6-v2-embedding", provider="lmstudio")

For Server Users

Server API endpoints remain compatible. New features: - Use ?type=text-embedding to filter embedding models - Use agent_format parameter for custom tool call formats - Environment variables for default configuration

For Documentation Users

• Use docs/server.md instead of server-api-reference.md
• Use docs/troubleshooting.md for all troubleshooting (includes common mistakes)
• Use docs/README.md as navigation hub
• Reference prerequisites.md instead of deleted providers.md

[2.2.4] - 2025-10-10

Fixed

• ONNX Optimization and Warning Management: Improved embedding performance and user experience
• Smart ONNX Model Selection: EmbeddingManager now automatically selects optimized model_O3.onnx for better performance
• Warning Suppression: Eliminated harmless warnings from PyTorch 2.8+ and sentence-transformers during model loading
• Graceful Fallbacks: Multiple fallback layers ensure reliability (optimized ONNX → basic ONNX → PyTorch)
• Performance Improvement: ONNX optimization provides significant speedup for batch embedding operations
• Clean Implementation: Conservative approach with minimal code changes (40 lines) for maintainability

Technical

• Added _suppress_onnx_warnings() context manager to handle known harmless warnings
• Added _get_optimal_onnx_model() function for intelligent ONNX variant selection
• Enhanced _load_model() with multi-layer fallback strategy and clear logging
• Zero breaking changes - all improvements are additive with sensible defaults

[2.2.3] - 2025-10-10

Fixed

• Installation Package [all] Extra: Fixed pip install abstractcore[all] to truly install ALL modules
• Issue: The [all] extra was missing development dependencies (dev, test, docs)
• Solution: Updated [all] extra to include complete dependency set (12 total extras)
• Coverage: Now includes all providers, features, and development tools
  • All Providers (6): openai, anthropic, ollama, lmstudio, huggingface, mlx
  • All Features (3): embeddings, processing, server
  • All Development (3): dev, test, docs
• Impact: Users can now confidently use abstractcore[all] for complete installation without missing dependencies

Technical

• Comprehensive Installation: pip install abstractcore[all] now installs 12 dependency groups
• Development Ready: Includes all testing frameworks (pytest-cov, responses), code tools (black, mypy, ruff), and documentation tools (mkdocs)
• Verified Configuration: All referenced extras exist and are properly defined with no circular dependencies

[2.2.2] - 2025-10-10

Added

• LLM-as-a-Judge: Production-ready objective evaluation with structured assessments
• BasicJudge class for critical assessment with constructive skepticism
• Multiple file support with sequential processing to avoid context overflow
• Global assessment synthesis for multi-file evaluations (appears first, followed by individual file results)
• Enhanced assessment structure with judge summary, source reference, and optional criteria details
• 9 evaluation criteria: clarity, simplicity, actionability, soundness, innovation, effectiveness, relevance, completeness, coherence
• CLI with simple command: judge file1.py file2.py --context="code review" (console script entry point)
• Flexible output formats: JSON, plain text, YAML with structured scoring (1-5 scale)
• Optional global assessment control: --exclude-global flag for original list behavior

Enhanced

• Built-in Applications: BasicJudge added to production-ready application suite
• Structured output integration with Pydantic validation and FeedbackRetry for validation error recovery
• Chain-of-thought reasoning for transparent evaluation with low temperature (0.1) for consistency
• Custom criteria support and reference-based evaluation for specialized assessment needs
• Comprehensive error handling with graceful fallbacks and detailed diagnostics

Documentation

• Complete BasicJudge documentation: Enhanced docs/basic-judge.md with API reference, examples, and best practices
• Real-world examples: Code review, documentation assessment, academic writing evaluation, multiple file scenarios
• CLI parameter documentation with practical usage patterns and advanced options
• Global assessment examples showing synthesis of multiple file evaluations
• Updated README.md: Added BasicJudge to built-in applications with 30-second examples
• Internal CLI integration: Added /judge command for conversation quality evaluation with detailed feedback

Technical

• Context overflow prevention: Optimized global assessment prompts to work within model context limits
• Production-grade architecture: Proper Pydantic integration, sequential file processing, backward compatibility
• Console script integration: Simple judge command available after package installation (matches extractor, summarizer)
• Full backward compatibility: All existing functionality preserved, optional features clearly marked

[2.2.1] - 2025-10-10

Enhanced

• Timeout Configuration: Unified timeout management across all components
• Updated default HTTP timeout from 180s to 300s (5 minutes) for better reliability with large models
• All providers now consistently inherit timeout from base configuration
• Server endpoints updated to use unified 5-minute default

• Improved handling of large language models (36B+ parameters) that require longer processing time

• Extractor CLI Improvements: Enhanced command-line interface for knowledge graph extraction

• Added --timeout parameter with proper validation (30s minimum, 2 hours maximum)
• Users can now configure timeout for large documents and models: --timeout 3600 for 60 minutes
• Improved error messages for timeout validation
• Better support for processing large documents with resource-intensive models

Fixed

• BasicExtractor JSON-LD Consistency: Resolved structural inconsistencies in knowledge graph output
• Fixed JSON-LD reference normalization where some providers generated string references instead of proper object format
• Corrected refinement prompt to match initial extraction format exactly (@type: "s:Relationship" vs @type: "r:provides")
• Added missing s:name and strength fields in relationship refinement

• All providers now generate consistent, properly structured JSON-LD output

• Cross-Provider Compatibility: Improved extraction reliability across different LLM providers

• LMStudio models now generate proper JSON-LD object references through automatic normalization
• Reduced warning noise by converting normalization messages to debug level
• Enhanced iterative refinement to follow exact same structure rules as initial extraction

Technical

• Centralized Timeout Management: All timeout configuration now emanates from base.py
• Providers inherit timeout via self._timeout from BaseProvider class
• Factory system properly propagates timeout parameters through **kwargs
• No hardcoded timeout values remain in provider implementations
• Consistent 300-second default across HTTP clients, tool execution, and embeddings

Documentation

• Updated Model References: Modernized documentation to use current recommended models
• Updated docs/getting-started.md to use qwen3:4b-instruct-2507-q4_K_M (default) and qwen3-coder:30b (premium)
• Replaced outdated qwen2.5-coder:7b references throughout getting started guide
• Added proper cross-references to reorganized documentation (server.md, acore-cli.md)

• Enhanced "What's Next?" section with links to universal API server and CLI documentation

• Cross-Reference Validation: Verified all documentation links and anchors

• Confirmed docs/prerequisites.md section anchors match README.md references
• Validated provider setup links point to correct sections (#openai-setup, #anthropic-setup, etc.)
• Ensured consistent documentation structure across all guides

Previous Versions

Previous version history is available in the git commit log.