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…osystem - Set up Python package `marketing_organism` and `requirements.txt`. - Implemented `event_bus` subsystem with asynchronous Publish/Subscribe capability. - Implemented `agents` subsystem representing a Perception-Decision-Action loop. - Implemented `evolution` subsystem with strategy genome representation, mutation, and selection rules. - Implemented `llm` subsystem containing a mock FastAPI reasoning service and task chainer. - Implemented `knowledge` (graph db abstraction) and `tool_forge` (capability gap synthesis). - Created comprehensive `pytest` test suite covering the entire architecture and verified correctness. Co-authored-by: 77svene <201388040+77svene@users.noreply.github.com>
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- Added exponential backoff and configurable memory eviction to `BaseAgent`. - Implemented a Dead-Letter Queue (DLQ) in the `EventBus` to handle callback failures. - Upgraded Evolutionary Selection to factor in a structural diversity score, preventing premature convergence. - Secured `KnowledgeGraph` with `asyncio.Lock` for thread-safe concurrent operations. - Enhanced `ToolGenerator` with static AST analysis to block unsafe Python imports (`os`, `subprocess`, `sys`). - Expanded test suite to cover all new resilience and security features, ensuring 100% pass rate. Co-authored-by: 77svene <201388040+77svene@users.noreply.github.com>
- Implemented cryptographic SHA-256 tamper-evident hashing for `BaseEvent` and `StrategyGenome` objects. - Integrated real local LLM connectivity via `httpx` (Ollama/Qwen compatible) with fallback handling in `llm/reasoning.py` and `llm/service.py`. - Upgraded `ToolGenerator` to dynamically prompt the LLM to write capability gap resolution code instead of using hardcoded mock scripts. - Migrated `KnowledgeGraph` from basic JSON to a robust, asynchronous `sqlite3` backing store for true ACID-compliant local-first data persistence. - Created central `main.py` Ecosystem Orchestrator to instantiate the EventBus, KnowledgeGraph, AgentManager, EvolutionarySelector, and spawn the initial baseline agent. - Expanded `pytest` coverage to validate database transactions, cryptographic hash integrity, and dynamic LLM tool generation logic. Co-authored-by: 77svene <201388040+77svene@users.noreply.github.com>
- Standardized codebase using Google-style Python docstrings and comprehensive typing annotations. - Implemented a robust custom exception hierarchy (`OrganismError`, `AgentExecutionError`, `EventBusError`, etc.) in `exceptions.py`. - Upgraded the `KnowledgeGraph` to use `BaseKnowledgeGraph` Abstract Base Class, enforcing dependency injection and interface decoupling. - Migrated all `print` statements to structured `logging` modules for production observability. - Refined test suite (`test_llm.py`, `test_knowledge_toolforge.py`) to employ proper `unittest.mock` patching, guaranteeing isolated execution. - Ensured thread-safe connection pooling for asynchronous SQLite transactions.
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Provides the foundational skeletal implementation of the requested Autonomous Adaptive Marketing Ecosystem Architecture according to the design specification. The solution includes the Event-Driven Nervous System, Agent Architecture, Continuous Strategy Evolution Engine, Core Intelligence Integration, Persistent Knowledge Substrate, and Tool Forge, built primarily as a Python package. Test coverage is provided via
pytestfor all major subsystems.PR created automatically by Jules for task 12774292245363928056 started by @77svene