Smart-AI-Bridge

# Smart AI Bridge - Future Scope & Roadmap ## Project Status **Current Version:** v2.0.0 **Architecture:** Modular (61 source files in src/) **Status:** Production-Ready **Last Updated:** February 2026 --- ## Completed in v2.0.0 (Previously Planned) The following items from the v1.0.0 roadmap have been implemented: - **Modular architecture** -- Monolithic server replaced with 61-file src/ layout (handlers, backends, intelligence, utils, config, monitoring, tools) - **Handler registry pattern** -- HandlerFactory with 17 handler classes, base-handler inheritance, dynamic dispatch via tool-definitions mapping - **Backend adapter pattern** -- BackendAdapter base class with 6 concrete adapters (local, nvidia_deepseek, nvidia_qwen, gemini, openai, groq) - **Config-driven backends** -- src/config/backends.json defines all backend properties, priorities, timeouts, and models declaratively - **Intelligent routing** -- 4-tier routing (forced, learning, rules, fallback) in MultiAIRouter - **Learning engine** -- In-memory learning engine, compound learning, pattern RAG store for routing optimization - **Intelligence layer** -- Dual-iterate executor, diff-context optimizer, enhanced self-review, playbook system, background analysis queue - **Expanded tool set** -- 20 tools (up from 11): explore, generate_file, refactor, write_files_atomic, validate_changes, backup_restore, batch_analyze, check_backend_health, read, plus original tools - **New backends** -- OpenAI (GPT-5.2) and Groq (Llama 3.3 70B) added alongside local, NVIDIA DeepSeek, NVIDIA Qwen, Gemini - **Real-time monitoring dashboard** -- Express-based dashboard with health monitoring and spawn metrics - **Advanced content analysis** -- Smart context system, capability matcher, model discovery - **Custom routing rules** -- Complexity thresholds, backend priorities, per-tool backend forcing - **Council and multi-agent** -- Council handler, parallel agents, subagent spawning with role templates - **Gemini rate limiter** -- Built-in rate limiting for Gemini backend - **Circuit breaker** -- Configurable circuit breaker threshold and reset in fallback policy - **Quality gates** -- Quality gates system for output validation - **Conversation threading** -- Thread-aware context management --- ## Roadmap ### Tier 1: High Impact, Medium Effort #### 1. TypeScript Migration Migrate the entire src/ codebase from JavaScript to TypeScript. - Add strict type definitions for all handler interfaces, backend adapters, and router contracts - Generate type declarations for external consumers - Enable incremental adoption (start with core interfaces, expand outward) - Catch routing bugs at compile time (e.g., invalid backend names, malformed tool args) **Effort:** 2-3 weeks **Impact:** Eliminates a class of runtime errors, improves IDE support, makes handler/backend contracts explicit #### 2. Persistent Learning Engine The current learning engine (src/intelligence/learning-engine.js) and pattern RAG store are in-memory only. Data is lost on server restart. - Add SQLite or JSON-file persistence for routing decisions, pattern scores, and compound learning data - Implement import/export for learning data (backup, transfer between environments) - Add decay/pruning for stale patterns - Track per-backend success rates over time with persistent counters **Effort:** 1-2 weeks **Impact:** Routing intelligence survives restarts and accumulates across sessions #### 3. Test Coverage Expansion Current test infrastructure is minimal (validate-hybrid-server.js, feature tests). - Add unit tests for every handler class (18 handlers) - Add integration tests for each backend adapter with mock responses - Add router tests covering all 4 tiers (forced, learning, rules, fallback) - Add intelligence layer tests (learning engine, diff-context optimizer, self-review) - Target 80%+ line coverage - Integrate with CI (GitHub Actions or similar) **Effort:** 2-3 weeks **Impact:** Enables confident refactoring and feature development #### 4. Streaming Responses Currently all tool calls return complete responses. Large outputs (code generation, batch analysis) would benefit from streaming. - Implement SSE or chunked response support in the MCP transport layer - Add streaming option to ask, review, generate_file, and dual_iterate handlers - Stream partial results during long-running council and parallel_agents operations - Maintain backward compatibility for non-streaming clients **Effort:** 2-3 weeks **Impact:** Better UX for long-running operations, reduced perceived latency --- ### Tier 2: High Impact, High Effort #### 5. WebSocket Transport The server currently supports stdio transport only. WebSocket transport enables remote and multi-client usage. - Add WebSocket server alongside stdio in src/server.js - Support multiple concurrent client connections with session isolation - Implement authentication for WebSocket connections (API key or token-based) - Maintain stdio as the default for Claude Code integration **Effort:** 2-3 weeks **Impact:** Enables remote access, web dashboard live interaction, multi-client scenarios #### 6. Benchmarking Suite Systematic performance measurement across backends, tools, and routing decisions. - Automated benchmark harness that runs each tool against each backend with standardized prompts - Latency, throughput, and token-efficiency metrics per backend per tool - Regression detection (compare current run against baseline) - Generate benchmark reports (Markdown or HTML) - Integrate with CI for performance regression gates **Effort:** 2-3 weeks **Impact:** Data-driven backend selection, performance regression prevention #### 7. Multi-Tenant Support Isolate routing rules, learning data, and backend access per user or organization. - Tenant context passed through handler pipeline - Per-tenant backend configuration overrides - Per-tenant learning engine instances - Rate limiting and quota management per tenant - Tenant-scoped dashboard views **Effort:** 4-6 weeks **Impact:** Enables shared deployments across teams or organizations --- ### Tier 3: Specialized / Long-Term #### 8. Dashboard Real-Time Monitoring Improvements The current Express dashboard provides basic health and metrics. Expand it significantly. - WebSocket-based live metric streaming (requests/sec, backend latency, error rates) - Per-tool and per-backend breakdown charts - Learning engine visualization (routing decision history, pattern confidence scores) - Circuit breaker state dashboard (open/closed/half-open per backend) - Alert configuration (threshold-based notifications) **Effort:** 3-4 weeks **Impact:** Operational visibility for production deployments #### 9. Database-Backed Analytics Move beyond in-memory metrics to persistent analytics. - Store all tool invocations with timing, backend used, success/failure, token counts - Query interface for usage patterns (most-used tools, busiest backends, error trends) - Cost estimation per backend per time period - Data retention policies and archival **Effort:** 3-4 weeks **Impact:** Long-term operational intelligence and cost management #### 10. Container Deployment Package Smart AI Bridge for containerized deployment. - Dockerfile with multi-stage build - Docker Compose for server + dashboard + local LLM backend - Kubernetes Helm chart for production deployments - Health check endpoints compatible with container orchestration probes **Effort:** 1-2 weeks **Impact:** Simplified deployment, reproducible environments #### 11. OpenAPI / Schema Generation Auto-generate API documentation from tool definitions. - Generate OpenAPI 3.1 spec from CORE_TOOL_DEFINITIONS - Include request/response examples from actual tool usage - Serve interactive docs from the dashboard - Keep spec synchronized with tool-definitions.js via CI check **Effort:** 1-2 weeks **Impact:** Improved developer onboarding, external tool integration --- ## Architecture Considerations - The HandlerFactory and BackendAdapter patterns are stable extension points. New features should integrate through these rather than modifying server.js. - The intelligence layer (11 modules) is the most complex subsystem. Persistence and testing should be prioritized before adding new intelligence features. - The 4-tier routing system is powerful but currently lacks observability. Any routing changes should include logging and dashboard integration. - Backend configuration via backends.json is clean and declarative. New backends should follow this pattern. --- ## Technology Stack Evolution | Area | Current | Proposed | |------|---------|----------| | Language | JavaScript (ESM) | TypeScript | | Persistence | In-memory | SQLite or LevelDB | | Transport | stdio only | stdio + WebSocket | | Testing | Ad-hoc scripts | Jest/Vitest with 80%+ coverage | | Deployment | Manual node start | Docker + Helm | | Docs | Static Markdown | Auto-generated OpenAPI + Markdown | --- ## Decision Framework When prioritizing future work, evaluate against: 1. **Does it reduce production incidents?** (Testing, persistence, monitoring) 2. **Does it enable new use cases?** (WebSocket, plugins, multi-tenant) 3. **Does it improve developer velocity?** (TypeScript, benchmarks, docs) 4. **Does it compound over time?** (Persistent learning, analytics) Items that score high on multiple criteria should be prioritized first. --- *Last Updated: February 2026* *System Version: v2.0.0*