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Roadmap

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

Development Phases

Version Phase Status Delivers
v0.1 Phase 0 -- Scaffolding 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 Complete Intelligence primitive (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 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 Complete Agent system (SimpleAgent, OrchestratorAgent), tool system (Calculator, Think, Retrieval, LLM, FileRead), ToolExecutor dispatch engine, OpenAI-compatible API server (jarvis serve)
v0.5 Phase 4 -- Learning + Telemetry 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 Complete Python SDK (Jarvis class, MemoryHandle), multi-platform channel system (Telegram, Discord, Slack, WhatsApp, etc.), benchmarking framework (latency, throughput), Docker deployment (CPU + GPU), MkDocs documentation site
v1.1 Phase 6 -- Traces + Learning Complete Trace system (TraceStore, TraceCollector, TraceAnalyzer), trace-driven learning, MCP integration layer
v1.5 Phase 10 -- Agent Restructuring Complete BaseAgent helpers, ToolUsingAgent intermediate base, NativeReActAgent, NativeOpenHandsAgent, RLMAgent, OpenHandsAgent (SDK), accepts_tools introspection, backward-compat shims, CustomAgent removed

Current Status

OpenJarvis v1.5 (Phase 10) 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)
  • Seven agent types -- Simple, Orchestrator, NativeReAct, NativeOpenHands, RLM, Operative, MonitorOperative
  • 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 10 (Agent Restructuring) is complete. The agent hierarchy has been refactored with BaseAgent helpers, ToolUsingAgent intermediate base, and four new agent types (NativeReActAgent, NativeOpenHandsAgent, RLMAgent, OpenHandsAgent SDK).

Phase 10 Details

Phase 10 refactored the agent hierarchy for composability and extensibility:

BaseAgent Helpers

  • _emit_turn_start / _emit_turn_end -- Event bus integration without boilerplate
  • _build_messages -- System prompt + context + input assembly
  • _generate -- Engine call with stored defaults
  • _max_turns_result -- Standard max-turns-exceeded result
  • _strip_think_tags -- Remove <think> blocks from model output

ToolUsingAgent Intermediate Base

  • Sets accepts_tools = True for CLI/SDK introspection
  • Initializes ToolExecutor from provided tools
  • Configurable max_turns loop limit

New Agent Types

  • NativeReActAgent (native_react, alias react) -- Thought-Action-Observation loop
  • NativeOpenHandsAgent (native_openhands) -- CodeAct-style code execution with URL pre-fetching
  • RLMAgent (rlm) -- Recursive LM with persistent REPL and sub-LM calls
  • OpenHandsAgent (openhands) -- Thin wrapper for real openhands-sdk

Future Directions

Beyond Phase 10, 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