- Empty message fix: skip pushing assistant msg to history when both
content and tool_calls are absent (GLM-5 mid-reasoning token exhaustion).
Also filter preexisting empty assistant messages from returned history.
- System prompt now correctly injects knowledgeContext from opts into the
Tier-B system message (was missing in the loop's buildMessages).
- GITEA_API_TOKEN updated externally in Coolify (old token was invalid).
Made-with: Cursor
The VM's metadata server doesn't grant cloud-platform scope by default.
Read GOOGLE_APPLICATION_CREDENTIALS_JSON env var (service account key JSON)
and pass it directly to GoogleAuth. Falls back to metadata server if unset.
This restores GLM-5 access via Vertex AI.
Made-with: Cursor
gcloud is not available inside the Docker container. Use google-auth-library
instead, which reads credentials from the GCP metadata server (works on any
GCP VM) or GOOGLE_APPLICATION_CREDENTIALS env var. Also rebuilds dist/.
Made-with: Cursor
src/llm.ts was never committed — this caused the Docker build to fail
with "Cannot find module './llm'". Also commit updated agent-runner.ts,
agents.ts, and .env.example that reference the new LLM client.
Made-with: Cursor
- Dockerfile now runs tsc during build so committed dist/ is never stale
- ChatResult interface was missing history[] and memoryUpdates[] fields
- Re-add missing MemoryUpdate import in orchestrator.ts
- Rebuild dist/ with all new fields included
Made-with: Cursor
- ToolContext gets memoryUpdates[] — accumulated by save_memory calls
- orchestratorChat accepts preloadedHistory and knowledgeContext opts
- History trimmed to last 40 messages per turn (cost control)
- Knowledge items injected into system prompt as ## Project Memory
- ChatResult returns history[] and memoryUpdates[] for frontend persistence
- server.ts accepts history/knowledge_context from POST body
- save_memory tool: lets AI persist facts (key, type, value) to long-term memory
Made-with: Cursor
