OpenJarvis/docs/development/roadmap.md

Roadmap

OpenJarvis development follows a phased approach, with each version adding a major pillar or cross-cutting capability to the framework.

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Development Phases

Version	Phase	Status	Delivers
v0.1	Phase 0 -- Scaffolding	:material-check-circle:{ .green } Complete	Project scaffolding, registry system (RegistryBase[T]), core types (Message, ModelSpec, Conversation, ToolResult), configuration loader with hardware detection, Click CLI skeleton
v0.2	Phase 1 -- Intelligence + Inference	:material-check-circle:{ .green } Complete	Intelligence pillar (model catalog, heuristic router), inference engines (Ollama, vLLM, llama.cpp), engine discovery and health probing, jarvis ask command working end-to-end
v0.3	Phase 2 -- Memory	:material-check-circle:{ .green } Complete	Memory backends (SQLite/FTS5, FAISS, ColBERTv2, BM25, Hybrid/RRF), document chunking and ingestion pipeline, context injection with source attribution, jarvis memory commands
v0.4	Phase 3 -- Agents + Tools + Server	:material-check-circle:{ .green } Complete	Agent system (SimpleAgent, OrchestratorAgent, OpenClawAgent, CustomAgent), tool system (Calculator, Think, Retrieval, LLM, FileRead), ToolExecutor dispatch engine, OpenAI-compatible API server (jarvis serve)
v0.5	Phase 4 -- Learning + Telemetry	:material-check-circle:{ .green } Complete	Learning system (HeuristicRouter policy, TraceDrivenPolicy, GRPO stub), reward functions, telemetry aggregation (per-model/engine stats, export), --router CLI flag, jarvis telemetry commands
v1.0	Phase 5 -- SDK + Production	:material-check-circle:{ .green } Complete	Python SDK (Jarvis class, MemoryHandle), OpenClaw agent infrastructure (protocol, transports, plugins), benchmarking framework (latency, throughput), Docker deployment (CPU + GPU), MkDocs documentation site
v1.1	Phase 6 -- Traces + Learning	:material-progress-clock:{ .amber } In Progress	Trace system (TraceStore, TraceCollector, TraceAnalyzer), trace-driven learning, pluggable agent architectures (ReAct, OpenHands), MCP integration layer

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Current Status

OpenJarvis v1.0 is complete. The framework provides:

• Four core abstractions -- Intelligence, Engine, Agentic Logic, Memory -- each with an ABC interface and registry-based discovery
• Five inference engines -- Ollama, vLLM, llama.cpp, SGLang, Cloud (OpenAI/Anthropic/Google)
• Five memory backends -- SQLite/FTS5, FAISS, ColBERTv2, BM25, Hybrid (RRF fusion)
• Multiple agent types -- Simple, Orchestrator, Custom, OpenClaw, ReAct, OpenHands
• Seven built-in tools -- Calculator, Think, Retrieval, LLM, FileRead, WebSearch, CodeInterpreter
• Python SDK -- Jarvis class for programmatic use
• OpenAI-compatible API server -- POST /v1/chat/completions, GET /v1/models
• Benchmarking framework -- Latency and throughput measurements
• Telemetry and traces -- SQLite-backed recording and aggregation
• Docker deployment -- CPU and GPU images with docker-compose

Phase 6 is actively in progress, adding the trace system and trace-driven learning capabilities.

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Phase 6 Details

Phase 6 focuses on closing the loop between execution and learning:

Trace System

• TraceStore -- Persists complete Trace objects to SQLite, capturing the full sequence of steps (route, retrieve, generate, tool_call, respond) with timing, inputs, outputs, and outcomes
• TraceCollector -- Wraps any BaseAgent to automatically record traces during execution via EventBus subscription
• TraceAnalyzer -- Read-only query layer providing aggregated statistics (per-route, per-tool, by query type, time-range filtering)

Trace-Driven Learning

• TraceDrivenPolicy -- A router policy that learns from historical trace outcomes to improve model selection over time
• Query classification groups traces by type (code, math, short, long, general)
• Per-model scoring combines success rate and user feedback
• Online updates via observe() for incremental learning

Pluggable Agents

• ReActAgent -- Reasoning + Acting pattern for systematic tool use
• OpenHands -- Integration with the OpenHands agent framework

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Future Directions

Beyond Phase 6, areas of ongoing exploration include:

• GRPO training -- Reinforcement learning from trace data to train the routing policy, moving beyond heuristics and simple statistics
• Streaming telemetry -- Real-time performance dashboards and alerting
• Multi-model orchestration -- Coordinating multiple models within a single query pipeline (e.g., small model for classification, large model for generation)
• Federated memory -- Memory backends that synchronize across devices
• Plugin ecosystem -- Community-contributed engines, tools, and agents distributed as Python packages
• Energy-aware routing -- Using power consumption data from telemetry to optimize for energy efficiency alongside latency and quality