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8 Commits
Author SHA1 Message Date
Jon Saad-FalconandClaude Opus 4.6 eb9b481510 Subsume NanoClaw into OpenJarvis (Phase 11) 
Add four major components that bring NanoClaw's capabilities into the
OpenJarvis framework as native, config-driven modules:

- ClaudeCodeAgent: wraps @anthropic-ai/claude-code SDK via Node.js subprocess
- WhatsAppBaileysChannel: bidirectional WhatsApp messaging via Baileys protocol
- ContainerRunner/SandboxedAgent: Docker sandbox with mount security enforcement
- TaskScheduler: cron/interval/once scheduling with SQLite persistence, MCP tools, CLI

New config sections: [sandbox], [scheduler], [channel.whatsapp_baileys].
New CLI: jarvis scheduler create|list|pause|resume|cancel|logs|start.
46 files changed, ~5,867 lines. 2078 tests pass (36 skipped).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-25 03:47:07 +00:00
Jon Saad-FalconandClaude Opus 4.6 68091dd90b Refactor agent hierarchy: extract BaseAgent/ToolUsingAgent helpers, add native ReAct/OpenHands 
- Pull shared boilerplate (event emission, message building, generation,
  think-tag stripping) into BaseAgent concrete helpers and ToolUsingAgent
  intermediate base class with tool-call loop
- Add NativeReActAgent and NativeOpenHandsAgent as clean implementations
  built on the new base classes
- Simplify SimpleAgent, OrchestratorAgent, ReActAgent, OpenHandsAgent,
  and RLMAgent to use inherited helpers instead of duplicated logic
- Remove CustomAgent (superseded by BaseAgent subclassing)
- Add backward-compat tests, base agent tests, native agent tests

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-24 03:59:24 +00:00
Jon Saad-FalconandClaude Opus 4.6 323d7ff032 Add TOML config system for eval suites, pillar-aligned config, and documentation 
- Eval config: TOML-based suite configs defining models x benchmarks matrix,
  loaded via --config flag. Includes load_eval_config(), expand_suite(),
  7 config dataclasses, 3 example configs, and 61 new tests.
- Pillar-aligned config: generation params in IntelligenceConfig, nested
  engine/learning configs, agent objective/system_prompt/context_from_memory,
  structured learning sub-policies, TOML migration layer.
- Documentation: evaluations user guide, evals API reference, updated
  mkdocs.yml navigation, updated architecture docs.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-24 03:34:05 +00:00
Jon Saad-FalconandClaude Opus 4.6 c775d620af Simplify model resolution and refactor tool discovery via MCPServer 
- Remove --router CLI flag from `jarvis ask`; use config fallback chain
  (default_model -> first engine model -> fallback_model) instead
- Simplify SDK _resolve_model() to match the same fallback chain
- Reclassify OrchestratorSFTPolicy and OrchestratorGRPOPolicy as
  IntelligenceLearningPolicy (they fine-tune LM weights, not agent logic)
- Add MCPServer.get_tools() for SystemBuilder integration
- Refactor SystemBuilder._resolve_tools() to discover tools via MCPServer
  with dependency injection, replacing manual per-tool-name imports
- Update CLI router tests to test the new fallback chain

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-23 18:41:07 +00:00
Jon Saad-FalconandClaude Opus 4.6 8d538cd1b0 Add orchestrator training, channels, LiteLLM engine, and simplify learning taxonomy 
Major changes across parallel sessions:

- Add orchestrator SFT & GRPO training subpackage (learning/orchestrator/)
  with episode types, multi-objective reward, prompt registry, policy model,
  RL environment, and registered learning policies
- Add structured THOUGHT/TOOL/INPUT/FINAL_ANSWER mode to OrchestratorAgent
- Add 15 channel backends (Discord, Slack, Telegram, Email, Webhook, IRC,
  Matrix, Teams, WhatsApp, Signal, Mattermost, BlueBubbles, Feishu,
  Google Chat, Webchat) with channel tools and config
- Add LiteLLM engine backend for unified LLM provider access
- Add RLM agent and REPL tool
- Remove ToolLearningPolicy — learning taxonomy now only targets
  Intelligence (LM weights/routing) and Agents (logic/ICL/tool strategies)
- Rename SFTPolicy to SFTRouterPolicy (backward-compat alias kept)
- Remove OpenClaw agent infrastructure (openclaw*.py, openclaw_bridge.py)
- Fix async streaming tests (asyncio.run vs deprecated get_event_loop)
- Fix server channel route tests (pytest.importorskip for optional fastapi)
- Track uv.lock for reproducibility
- Update CLAUDE.md and docs to reflect all changes

1676 tests pass, 37 skipped.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-23 18:32:32 +00:00
Jon Saad-FalconandClaude Opus 4.6 a433740809 Add 30 tests for security/channels coverage gaps and 6 new docs pages 
Tests: GuardrailsEngine.stream() async tests, OpenClawChannelBridge listener_loop
tests, channel CLI command tests, FileReadTool sensitive file blocking, ingest_path
sensitive file filtering, SecurityConfig/ChannelConfig config tests. Documentation:
new user guides, architecture pages, and API references for Security and Channels
modules; updated CLAUDE.md, configuration docs, CLI reference, and mkdocs.yml nav.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-22 03:31:19 +00:00
Jon Saad-FalconandDevin 2faf58f3f7 Fix mock patch targets + add tool fallback for vLLM 
- Use importlib.import_module + patch.object to fix 19 broken tests
  (module shadowing issue in cli/__init__.py)
- Add 400 retry without tools in _openai_compat.py for vLLM servers
  without --enable-auto-tool-choice
- Update NOTES.md with session 2 findings

🤖 Generated with [Devin](https://cli.devin.ai/docs)

Co-Authored-By: Devin <noreply@cognition.ai>
 2026-02-17 04:37:39 +00:00
Jon Saad-FalconandClaude Opus 4.6 301e9cd2d4 Implement OpenJarvis v1.0 — all five pillars, SDK, benchmarks, Docker 
Complete implementation across six development phases (v0.1 through v1.0):

- Core: Registry system, config, event bus, types (Phase 0)
- Intelligence + Inference: Model routing, Ollama/vLLM/llama.cpp/Cloud engines (Phase 1)
- Memory: SQLite/FAISS/ColBERT/BM25/Hybrid backends, document ingest, context injection (Phase 2)
- Agents: Simple/Orchestrator/Custom/OpenClaw agents, tool system (Phase 3)
- Learning: HeuristicRouter, reward functions, GRPO stub, telemetry aggregation (Phase 4)
- SDK: Jarvis class, OpenClaw protocol/transport, benchmarks, Docker deployment (Phase 5)

520 tests passing, 8 skipped (optional deps). Ruff lint clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-17 00:52:48 +00:00