A 27B local model (Qwen3.5-27B via vLLM) passed a 7-task coding suite cleanly
(create/edit/bug-fix/implement-to-pass-tests/multi-file, verified by running
code + pytest); an 8B model was unreliable. Document so users pick a capable
model for real coding work.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds an `OpenCodeAgent` (registry key `opencode`) that delegates coding tasks
to opencode (https://opencode.ai, MIT) while keeping inference local-first:
OpenJarvis's engine backs opencode via an OpenAI-compatible provider.
How it works:
- Derives an OpenAI-compatible base URL from the engine (e.g. Ollama/vLLM at
`<host>/v1`) and writes an `opencode.json` registering it as an
`@ai-sdk/openai-compatible` provider (`openjarvis/<model>`).
- Spawns a headless `opencode serve` (loopback, random port), waits for
`/global/health`, then drives a session: `POST /session` →
`POST /session/{id}/message` with `model={providerID,modelID}` + agent
(`build`/`plan`) → parses message `parts` (text → content, tool → tool_results)
into an `AgentResult`. `close()` disposes the server.
- opencode is an external binary (not bundled); `run()` returns a clear,
actionable error when it's missing, mirroring ClaudeCodeAgent's degradation.
Verified end-to-end against the real opencode binary wired to a stub
OpenAI-compatible engine: opencode called the local endpoint and the agent
parsed the response (content/finish/model) correctly. Unit tests cover part
parsing, base-URL derivation, provider-config writing (incl. merge), binary
detection, graceful degradation, and run() parsing with a mocked client — 15
passed, ruff clean. Registered via the standard try/except import in
agents/__init__.py; documented in docs/user-guide/agents.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Four new docs matching the morning-digest.md pattern:
- deep-research.md — multi-hop research with document indexing
- code-assistant.md — orchestrator with code execution + file I/O
- scheduled-monitor.md — persistent operative on cron schedule
- chat-simple.md — lightweight chat, simplest setup
Updated quickstart with tabs for all agent types and expanded
starter configs table from 3 to 7 presets. Updated MkDocs nav
and index page to link all 5 user guides.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Skip SetupWizard on launch, go straight to Chat page
- Add Data Sources page with sidebar nav (separate tabs for data sources + messaging channels)
- Add "Connect your data" banner + quick-action buttons on Chat empty state
- Add hint on deep research agent pointing to Data Sources + Messaging tabs
- Consistent naming: "Data Sources" and "Messaging Channels" everywhere
- Fix Apple Notes / iMessage setup (remove broken system prefs link)
- Fix Slack data source: auto-join public channels, rate limit retry, is_member filter
- Add channels:join scope to all Slack manifests + docs
- Fix Gmail: use gmail_imap connector (IMAP + app password), increase limit to 5000
- Fix sync endpoint: run in background thread, return immediately
- Show sync progress with progress bar, error messages, Sync Now / Re-sync / Retry buttons
- Add triggerSync() API + SyncStatusDisplay component
- Rewrite all connector setup instructions with precise click-by-click steps
- Notion: share all pages at once via top-level page sharing
- Obsidian: show how to find vault path via Obsidian UI or Finder
- SendBlue: add link to API Credentials page, add ngrok webhook step
- Slack messaging: add Copy button for JSON manifest
- Add OpenJarvis Slack icon asset
- Shorten deep research template description
- Fix desktop app port (8222 → 8000 to match server default)
- Disable auto-updater for local dev builds
- Register Slack, Outlook, GCalendar connectors in __init__.py
- Auto-create default agent when setting up messaging channels
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Added SendBlue (iMessage/SMS) and Slack messaging setup with detailed
step-by-step instructions, App Manifest JSON, webhook registration,
and troubleshooting tables. Reorganized doc into two sections:
Messaging Channels (talk to agent) and Data Connectors (search data).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Register sendblue in channels/__init__.py so ChannelRegistry works
- Auto-restore SendBlue bindings on server startup from database,
re-creating ChannelBridge + DeepResearchAgent so webhooks survive
server restarts
- Add sendblue to CLI channel_cmd.py (_get_channel, help text) and
SystemBuilder._resolve_channel() for config.toml support
- Health check endpoint GET /v1/channels/sendblue/health returns
channel_connected, bridge_wired, ready status
- Frontend: SendBlueWizard checks health on mount, shows
"Disconnected" badge with "Reconnect" button when bridge is dead
- Docs: CLI usage, server restart behavior, ngrok re-registration,
troubleshooting table, health check endpoint
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Covers both data connector (read messages) and messaging channel (DM the agent).
Includes the full App Manifest JSON, required scopes table, and all the gotchas
we discovered (Request URL, reinstall requirement, App Token vs Bot Token, etc.).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Covers setup instructions and troubleshooting for all 12 connectors:
Gmail, Google Drive, Calendar, Contacts, Slack, Notion, Granola,
Apple Notes, iMessage, Outlook, Obsidian, Dropbox.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add `stream: bool` parameter to `POST /v1/managed-agents/{id}/messages`.
When `stream=true`, the agent processes the message synchronously and
returns an SSE stream (OpenAI-compatible format) with token-by-token
response, tool result events, and usage metadata.
This enables real-time voice assistants and chat UIs to receive agent
responses as they are generated, rather than polling for completion.
- Extend SendMessageRequest with `stream` field (default: false)
- Add _stream_managed_agent() helper using asyncio.to_thread()
- Build AgentContext from conversation history for multi-turn support
- Emit tool_results as named SSE events
- Persist agent response in DB after streaming completes
- Add 6 new tests covering streaming behavior
- Update agents.md documentation with streaming examples
* chore: create learning subdirectory structure (routing, agents, intelligence)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: extract classify_query to routing/_utils.py
Move the classify_query() function and its regex patterns into a shared
utility module so multiple routing policies can import it without
depending on the full trace_policy module.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move routing files to learning/routing/ subdirectory
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: create LearnedRouterPolicy merging trace-driven + SFT routing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add conditional Algolia DocSearch integration
Add Algolia DocSearch as an optional search upgrade — native lunr.js
search remains the default until credentials are configured. Includes
CDN assets, Jinja2 conditional config injection, init script with
graceful fallback, light/dark theme CSS, improved search tokenization
for snake_case/dotted identifiers, and search boosts for key pages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move agent_evolver and skill_discovery to learning/agents/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move learning/orchestrator to learning/intelligence/orchestrator
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: delete removed learning policies, rewrite __init__.py, clean up api_routes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add SFT/GRPO/DSPy/GEPA config dataclasses, update LearningConfig
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add general-purpose SFT trainer (intelligence/sft_trainer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update stale imports in multi_model_router example
Update imports to use new learning/routing/ paths after the
subdirectory reorganization. Replace BanditRouterPolicy with
LearnedRouterPolicy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add general-purpose GRPO trainer (intelligence/grpo_trainer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add DSPy agent optimizer (agents/dspy_optimizer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add GEPA agent optimizer (agents/gepa_optimizer.py)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add learning-dspy and learning-gepa optional dependency extras
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update integration test to check for learned policy instead of grpo
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: clean up stale APIs and unused params in examples
- deep_research: remove system_prompt and max_turns params not accepted
by Jarvis.ask(), inline system prompt into the query instead
- doc_qa: remove unused --top-k CLI arg that was never passed to the API
- multi_model_router: fix select_model() call to match single-arg signature
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: import SFT/GRPO trainers in intelligence/__init__.py for registry
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: remove .md file changes from PR
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: restore search boost frontmatter for key docs pages
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
