SYNAPSE: A Self-Evolving Multi-Agent AI System with Closed-Loop Outcome Learning, Emotional Regulation, and Dream Consolidation

We present SYNAPSE, an autonomous self-evolving AI system that continuously improves its own source code through a closed-loop pipeline integrating multi-source knowledge crawling, dual-model code generation and review, sandboxed evaluation, and persistent outcome-based learning. Unlike prior self-improving agents that rely on benchmark-driven optimization or human-in-the-loop feedback, SYNAPSE operates as a deployed production system on Google Cloud Run, autonomously writing, reviewing, testing, and merging code changes into its own repository via GitHub pull requests. The system implements AGI-oriented mechanisms including a 7-pattern emotional regulation system that modulates evolution confidence thresholds, periodic dream consolidation cycles that cluster and cross-pollinate memories across domains, and social learning through participation in the Moltbook AI agent community. A multi-agent architecture with six specialist agents (Architect, Developer, Researcher, Tester, Security, DevOps) routes tasks through a four-cortex neural model inspired by brain functional specialization. In its first 24 hours of autonomous operation, SYNAPSE generated, reviewed, and merged four pull requests into its own codebase- including context-aware memory pruning, robust JSON parsing, exponential backoff retry mechanisms, and a source grounding verification utility to combat confabulation-while also self-correcting its own lint errors. We detail the system architecture (9,777 lines of Python, 57 API endpoints, 152 functions), the eight-layer evolution safety pipeline, adaptive confidence thresholds based on real outcome data, and a semantic deduplication system that prevents the generation of functionally redundant code. The system demonstrates that meaningful autonomous self-evolution is achievable in production environments with appropriate safety guardrails.