Claude/sync main gamma f r2 y1 #37
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Compare multi-agent orchestration pattern (QuantCoder Gamma) vs event-driven TUI pattern (OpenCode) including: - Technology stacks (Python vs Go) - Agent architectures and execution models - Tool systems and MCP integration - LLM provider strategies - State management approaches - Self-improvement capabilities
Add Mistral Vibe CLI as the third architecture in the comparison, noting that QuantCoder Gamma's CLI was explicitly "inspired by Mistral Vibe CLI" (see quantcoder/cli.py:1). Key additions: - Mistral Vibe CLI architecture (minimal single-agent design) - Three-tier permission model (always/ask/disabled) - Project-aware context scanning - Devstral model requirements and capabilities - Lineage diagram showing inspiration flow - Expanded tool, config, and UI comparisons
…g Operator Based on successful gamma branch testing (15/15 tests passing), design adaptations of the multi-agent architecture for two new use cases: 1. Research Assistant: - Search, Paper, Patent, Web agents - Synthesis and Report agents - Tools for academic search, PDF parsing, citation management 2. Trading Operator: - Position, Risk, Execution, Reporting agents - Broker adapters (IB, Alpaca, QC, Binance) - Real-time P&L tracking and risk management Both reuse core gamma components: - Multi-agent orchestration pattern - Parallel execution framework - LLM provider abstraction - Tool system base classes - Learning database for self-improvement
Document the application's architecture including: - High-level system architecture diagram - CLI entry points and command flow - Article search and PDF download flows - Article processing pipeline with NLP stages - Code generation and refinement loop - GUI workflow and window layout - Data/entity relationships - File structure reference with line numbers
Replace previous architecture docs with comprehensive gamma branch analysis: - Multi-agent orchestration system (Coordinator, Universe, Alpha, Risk, Strategy) - Tool-based architecture inspired by Mistral Vibe pattern - Autonomous self-improving pipeline with error learning - Library builder system for comprehensive strategy generation - LLM provider abstraction (OpenAI, Anthropic, Mistral, DeepSeek) - Parallel execution framework with AsyncIO - Interactive and programmatic chat interfaces - Learning database for pattern extraction and prompt refinement
- VERSIONS.md: Comprehensive guide for v1.0, v1.1, and v2.0 - Feature comparison table - Installation instructions per version - Upgrade path recommendations - Use case guidance - CHANGELOG.md: Detailed changelog following Keep a Changelog format - v1.0: Legacy features (OpenAI v0.28, Tkinter GUI) - v1.1: LLM client abstraction, QC static validator - v2.0: Multi-agent architecture, autonomous pipeline (unreleased) - Migration notes between versions
- Comprehensive 20-line project description - Production readiness matrix scoring 30/50 (60%) - Identified critical gaps: no testing, legacy OpenAI SDK, security concerns - Documented strengths and recommendations - Classified as NOT production-ready without hardening
- Assessed claude/alphaevolve-cli-evaluation-No5Bx (most advanced branch) - Document new evolver module (+1,595 lines of code) - Detail AlphaEvolve-inspired evolution architecture - Score remains 30/50 (60%) - NOT production ready - Key gaps: no tests, legacy OpenAI SDK, sequential backtests - Note significant improvement from v0.3: full QuantConnect API integration
- Complete platform evolution: quantcli → quantcoder - Score: 88% (44/50) - NEARLY PRODUCTION READY - Key features: Multi-agent architecture, 4 LLM providers, autonomous mode - Modern stack: OpenAI v1.0+, pytest, CI/CD, async execution - 8,000+ lines across 35+ modules vs 1,500 in legacy - Remaining: expand test coverage, battle-test MCP integration
Document findings from code review including: - Overall score: 7.5/10 - 4 critical issues (bare except, plain-text API keys, low test coverage, print statements) - Metrics summary and prioritized remediation plan
- Evolve branch = gamma + evolver module - Updated score: 90% (45/50) - Production Ready - Used same scoring criteria as gamma assessment - Added branch comparison summary
- Fix syntax error in llm/providers.py:258: "Mistral Provider" -> "MistralProvider" This prevented the entire LLM providers module from being imported. - Fix bare exception handling in coordinator_agent.py:135 Changed `except:` to `except (json.JSONDecodeError, ValueError):` to properly catch only JSON parsing errors instead of all exceptions. These fixes were identified during comprehensive quality assessment of the gamma branch and are required for the code to function properly.
- Fix version inconsistencies (2.0.0 → 2.1.0 across all files) - Add missing dependencies to pyproject.toml (anthropic, mistralai, aiohttp) - Remove 12 redundant documentation files (cleanup summaries, branch guides, assessments) - Move ARCHITECTURE.md and VERSIONS.md to docs/ - Delete obsolete files (requirements-legacy.txt, reorganize-branches.sh) - Update README.md with cleaner header and correct links
- Add OllamaProvider class with async aiohttp client - Support OLLAMA_BASE_URL env var (default: localhost:11434) - Default model: llama3.2 - Register in LLMFactory with 'ollama' provider name - Add 'local' task type recommendation - Fix typo: 'Mistral Provider' -> 'MistralProvider'
Add OllamaProvider for local LLM support
Claude/cleanup repository x7a pr
Claude/merge tests to gamma yvvvl
Update LICENSE file to Apache License 2.0 and update README to reflect the license change in both the badge and license section.
Switch project license from MIT to Apache 2.0
- Changed version from 2.1.0-alpha.1 to 2.0.0 - Added warning that this version has not been systematically tested - This is a complete architectural rewrite from legacy v1.x
Update version to 2.0.0 and add testing warning
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