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>
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
