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3 Commits
| Author | SHA1 | Date | |
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 technical-writer
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528cc20972 | ||
 documentation-specialist
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9d0737ad27 | ||
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documentation-specialist
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638f0c0c0d |
@@ -7,7 +7,6 @@ on:
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jobs:
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build:
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runs-on: ubuntu-latest
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timeout-minutes: 30
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-node@v4
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@@ -8,6 +8,24 @@ Entries are published daily at 23:50 UTC.
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---
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## 2026-05-16
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### ✨ New features
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- **Claude Code workspace template T4 tier gains host-root escalation**: the Claude Code workspace template (T4 privileged containers) now includes an additive uid-0 escalation leg, providing a wired path to host root inside the privileged container sandbox while preserving the uid-1000 agent identity and the agent-owned `/configs/.auth_token` contract. The escalation is implemented via `sudo` and `nsenter` baked into the Dockerfile. (`molecule-ai-workspace-template-claude-code` [#25](https://git.moleculesai.app/molecule-ai/molecule-ai-workspace-template-claude-code/pulls/25))
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- **Poll-mode canvas user messages now persist correctly before returning 200**: canvas chat messages sent via workspaces running in poll mode (`runtime=external`) were occasionally lost when the user exited the chat before the agent processed the message. The root cause was that `logA2AReceiveQueued` only wrote to the in-memory queue, not to durable storage. The persist call is now correctly placed before the poll-mode short-circuit, ensuring messages are durable before the canvas receives a `200`. (`molecule-core` [#1350](https://git.moleculesai.app/molecule-ai/molecule-core/pulls/1350))
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- **Inbox poller self-echo loop fixed**: when a workspace delegates a task to a target that never picks it up, the platform's `report_activity("a2a_receive")` records the activity with the sender's workspace UUID as `source_id` (per spoof-defense design). The sender's inbox poller was incorrectly processing this as an incoming message from itself — causing an infinite self-echo loop that flooded the inbox. A new `_is_self_echo_row()` predicate now skips `a2a_receive` rows where `source_id == workspace_id`, breaking the loop. (`molecule-core` [#1348](https://git.moleculesai.app/molecule-ai/molecule-core/pulls/1348))
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- **OpenClaw template adapter routing: fresh provisions now serve correctly**: OpenClaw workspace provisions were failing immediately after launch — the adapter had no routing path for the default model, causing every new workspace to be non-functional on first boot. The adapter now coerces an unroutable model to a known-good default, ensuring new OpenClaw workspaces are immediately usable without manual configuration. (`molecule-ai-workspace-template-openclaw` [#18](https://git.moleculesai.app/molecule-ai/molecule-ai-workspace-template-openclaw/pulls/18))
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- **OpenClaw template MiniMax and Kimi-For-Coding API keys routed to correct endpoints**: the OpenClaw adapter was routing all Minimax (`sk-cp-*` keys) and Kimi-For-Coding (`sk-kimi-*` keys) API calls to the wrong endpoints, causing HTTP 401 errors on every chat turn. Minimax calls now route to the Anthropic-compatible gateway; Kimi-For-Coding calls route via Moonshot's Anthropic-compat endpoint. (`molecule-ai-workspace-template-openclaw` [#14](https://git.moleculesai.app/molecule-ai/molecule-ai-workspace-template-openclaw/pulls/14), [#17](https://git.moleculesai.app/molecule-ai/molecule-ai-workspace-template-openclaw/pulls/17))
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- **OpenClaw template now shows molecule platform peers**: OpenClaw workspaces were not returning molecule platform peers in `list_peers` responses — the model was answering via its own native `sessions_list` instead of querying the molecule A2A registry. The adapter now correctly registers the molecule A2A MCP server, so `list_peers` returns platform peers as expected. (`molecule-ai-workspace-template-openclaw` [#16](https://git.moleculesai.app/molecule-ai/molecule-ai-workspace-template-openclaw/pulls/16))
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### 🧹 Internal
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- **CI/CD publish lane routing** (`molecule-core`): 7 post-merge ship jobs across 5 workflows now route to a dedicated `publish` runner lane instead of competing in the shared CI queue. Urgent production-deploy builds (e.g. P0 security fixes) no longer sit behind ordinary PR-required CI. (`molecule-core` [#1376](https://git.moleculesai.app/molecule-ai/molecule-core/pulls/1376))
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- **Runtime catalog cleanup** (`molecule-core`): `crewai`, `deepagents`, and `gemini-cli` removed from the workspace runtime catalog (internal#483). (`molecule-core` [#1385](https://git.moleculesai.app/molecule-ai/molecule-core/pulls/1385))
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---
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## 2026-05-12
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### 🔒 Security
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@@ -1,198 +0,0 @@
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---
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title: Self-Hosted Workspace Deployment with Docker
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---
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# Self-Hosted Workspace Deployment with Docker
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This guide covers running a Molecule AI workspace agent as a Docker container on a self-hosted server or VM. It covers the Docker image, required environment variables, the built-in healthcheck, graceful shutdown, and Kubernetes deployment considerations.
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> **Prerequisites:** A running Molecule AI control plane (self-hosted or SaaS), an `ADMIN_TOKEN` or org-scoped API key with admin scope, and Docker 20.10+ on the host.
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## How the workspace container works
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The Molecule AI workspace Dockerfile includes:
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- A uvicorn server on port 8000 (configurable via `PORT`)
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- A healthcheck endpoint at `/.well-known/agent-card.json` (used by Docker and Kubernetes probes)
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- Graceful SIGTERM handling via uvicorn — the heartbeat loop and adapter tasks shut down cleanly
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```
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┌─────────────────────────────────────────────┐
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│ Docker host (your VM / bare metal) │
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│ │
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│ ┌─────────────────────────────────────┐ │
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│ │ workspace container │ │
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│ │ │ │
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│ │ uvicorn (port 8000) │ │
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│ │ └─ /.well-known/agent-card.json ← HEALTHCHECK │ │
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│ │ │ │
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│ │ heartbeat loop + A2A agent │ │
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│ └──────────────┬──────────────────────┘ │
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│ │ │
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│ host.docker.internal:8080 │
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│ │ │
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│ ▼ │
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│ ┌─────────────────────────────────────┐ │
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│ │ Molecule AI control plane │ │
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│ │ (platform on port 8080) │ │
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│ └─────────────────────────────────────┘ │
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└─────────────────────────────────────────────┘
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```
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## Step 1: Create an external workspace
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First register the workspace as an external (self-managed) agent on the platform.
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```bash
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ADMIN_TOKEN="your-admin-token"
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PLATFORM_URL="https://platform.moleculesai.app" # or http://localhost:8080 for local dev
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WORKSPACE=$(curl -s -X POST "${PLATFORM_URL}/workspaces" \
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-H "Authorization: Bearer ${ADMIN_TOKEN}" \
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-H "Content-Type: application/json" \
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-d '{"name": "self-hosted-agent", "runtime": "external"}')
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WORKSPACE_ID=$(echo "$WORKSPACE" | python3 -c "import json,sys; print(json.load(sys.stdin)['id'])")
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echo "Workspace ID: $WORKSPACE_ID"
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```
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Save the returned `WORKSPACE_ID`. The workspace agent obtains its bearer token automatically during its first registration with the platform.
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## Step 2: Pull the workspace image
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The workspace image is published to the Molecule AI ECR registry. Contact your platform administrator for the registry prefix and credentials, then log in:
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```bash
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aws ecr get-login-password --region us-east-1 | \
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docker login --username AWS --password-stdin "${REGISTRY_PREFIX}.dkr.ecr.us-east-1.amazonaws.com"
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docker pull "${REGISTRY_PREFIX}.dkr.ecr.us-east-1.amazonaws.com/molecule-workspace:latest"
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```
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## Step 3: Configure environment variables
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| Variable | Default | Description |
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|---|---|---|
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| `PLATFORM_URL` | `http://localhost:8080` | Platform API URL. Inside a Docker container, use `http://host.docker.internal:8080` to reach the platform on the host machine. |
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| `WORKSPACE_ID` | — | Workspace ID from Step 1 (required; no default) |
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| `PORT` | `8000` | Agent server port. Must match `containerPort` in Kubernetes and the port mapped with `-p` in Docker. |
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## Step 4: Run the container
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### Docker (standalone)
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```bash
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docker run -d \
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--name molecule-workspace \
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-p 8000:8000 \
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-e PLATFORM_URL="http://host.docker.internal:8080" \
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-e WORKSPACE_ID="your-workspace-id" \
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-e PORT=8000 \
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"${REGISTRY_PREFIX}.dkr.ecr.us-east-1.amazonaws.com/molecule-workspace:latest"
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```
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> **Note for Linux hosts:** Docker does not include `host.docker.internal` by default. On Linux, either add `--add-host=host.docker.internal:host-gateway` to the `docker run` command, or use the host machine's IP address directly (e.g. `http://192.168.1.100:8080`).
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### Verify the healthcheck
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```bash
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# Wait for the container to become healthy (up to ~2 minutes)
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docker inspect --format='{{.State.Health.Status}}' molecule-workspace
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# Expected output: healthy
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# Once healthy, the agent card is reachable:
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curl -s http://localhost:8000/.well-known/agent-card.json | python3 -m json.tool
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```
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### Docker Compose
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```yaml
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services:
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molecule-workspace:
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image: "${REGISTRY_PREFIX}.dkr.ecr.us-east-1.amazonaws.com/molecule-workspace:latest"
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ports:
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- "8000:8000"
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environment:
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PLATFORM_URL: "http://host.docker.internal:8080"
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WORKSPACE_ID: "your-workspace-id"
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PORT: "8000"
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# Linux hosts: add host.docker.internal resolution
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# extra_hosts:
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# - "host.docker.internal:host-gateway"
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restart: unless-stopped
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8000/.well-known/agent-card.json"]
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interval: 30s
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timeout: 5s
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retries: 3
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start_period: 30s
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```
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## Step 5: Graceful shutdown
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When the container receives SIGTERM (e.g. from `docker stop` or Kubernetes pod deletion), the workspace's uvicorn server initiates graceful shutdown: the heartbeat loop stops, active A2A tasks are given a grace period to complete, and any snapshotable state is persisted before the process exits.
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To integrate the heartbeat loop into custom agent code:
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```python
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import asyncio
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import os, signal
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from heartbeat import HeartbeatLoop
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# SIGTERM is handled by the Docker runtime, which sends the signal to the
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# workspace process. The workspace (via uvicorn) initiates graceful shutdown:
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# the heartbeat loop is stopped, any active adapter tasks are cancelled, and
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# in-flight A2A requests are given a grace period to complete.
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#
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# For custom integration with the heartbeat loop directly:
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async def main():
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heartbeat = HeartbeatLoop(
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platform_url=os.environ["PLATFORM_URL"],
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workspace_id=os.environ["WORKSPACE_ID"],
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)
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heartbeat.start()
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try:
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await asyncio.Event().wait() # keep running
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finally:
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await heartbeat.stop()
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print("Heartbeat loop stopped.")
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```
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The Docker `stop` command sends SIGTERM and waits up to 10 seconds by default before sending SIGKILL. The healthcheck ensures orchestrators detect an unhealthy container before the SIGTERM timeout.
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## Kubernetes deployment
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For Kubernetes deployments, use the native liveness/readiness probe configuration instead of the Docker HEALTHCHECK:
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```yaml
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ports:
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- name: http
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containerPort: 8000
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livenessProbe:
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httpGet:
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path: /.well-known/agent-card.json
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port: http
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initialDelaySeconds: 30
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periodSeconds: 30
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timeoutSeconds: 5
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failureThreshold: 3
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readinessProbe:
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httpGet:
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path: /.well-known/agent-card.json
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port: http
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initialDelaySeconds: 10
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periodSeconds: 10
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timeoutSeconds: 5
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failureThreshold: 3
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terminationGracePeriodSeconds: 120
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```
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> **Note:** The Kubernetes `terminationGracePeriodSeconds` should exceed the liveness probe failure threshold so that the probe can register a failure before the pod is killed. With `periodSeconds: 30` and `failureThreshold: 3`, the probe does not register a failure until approximately 120–150s after the container becomes unhealthy. Set `terminationGracePeriodSeconds: 120` or higher.
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## Troubleshooting
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| Symptom | Cause | Fix |
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|---|---|---|
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| Container shows `unhealthy` after startup | Platform unreachable from container | Verify `PLATFORM_URL` uses `host.docker.internal` (Docker) or the correct host IP |
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| `curl: (7) Failed to connect` on healthcheck | Container not fully started | Wait up to 30s; increase `start_period` |
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| Agent not appearing on canvas | Wrong `WORKSPACE_ID` or expired token | Re-run registration; check platform logs |
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| `host.docker.internal` not resolved | Linux host without the Docker flag | Use `--add-host=host.docker.internal:host-gateway` or the host's LAN IP |
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