Initial research: OpenClaw on AgentCore architecture
- Architecture comparison (OpenClaw daemon vs AgentCore serverless) - Component compatibility analysis - Fargate analysis - AgentCore rebuild plan (Telegram, zero always-on compute) - Memory strategy: AgentCore Memory + factbase as structured KB - Serverless relay patterns per channel - All open questions resolved - OpenClaw feature delta March→May 2026 - Build phases and cost estimates
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daniel
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open-questions-resolved.md
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open-questions-resolved.md
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# Open Questions — Final Research Findings
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*Updated 2026-05-04 after research pass*
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---
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## Q1: Direct Code Deployment vs Container — ✅ RESOLVED
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**CodeZip is the default and recommended path. No Docker needed.**
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The AgentCore CLI scaffolds CodeZip by default:
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```bash
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agentcore create --name MyAgent --framework Strands --model-provider Bedrock --build CodeZip
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agentcore deploy # AWS CodeBuild packages it; no local Docker required
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```
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Container mode is opt-in (`--build Container`). Q4 (ARM64 Dockerfile) is moot for initial build.
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---
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## Q2: Secrets in the Container — ✅ RESOLVED (with known limitation)
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AgentCore Runtime env vars are **plaintext only** today. GitHub issue #396 (filed ~April 2026) requests ECS-style `valueFrom` Secrets Manager references — not yet implemented.
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**Recommended pattern: IAM role + SDK fetch at startup**
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```python
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import boto3, os
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def load_secrets():
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sm = boto3.client('secretsmanager')
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secret = sm.get_secret_value(SecretId='openclaw/agent/keys')
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os.environ['BRAVE_API_KEY'] = secret['SecretString']
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# etc.
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# Call once at module load → cached for the 6-8hr warm session
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load_secrets()
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```
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The container's IAM execution role grants Secrets Manager access. Runs once per session start — negligible cost. Don't pass secrets through the invocation payload.
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---
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## Q3: AgentCore Memory Pricing — ✅ RESOLVED (low risk for personal scale)
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**Pricing structure confirmed:**
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- Long-term retrieval: billed **per retrieve request**
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- Built-in strategy model costs (extraction + consolidation): **included in Memory pricing** (confirmed by AWS re:Post)
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- Storage: per GB
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Exact per-event and per-GB rates not yet clearly published (still preview pricing). At personal assistant scale (~100 turns/day), cost will be pennies. Validate after first test deployment.
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---
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## Q4: ARM64 Container Build — ✅ RESOLVED (moot, but documented)
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Superseded by CodeZip (Q1). If container mode ever needed:
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```dockerfile
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FROM --platform=linux/arm64 ghcr.io/astral-sh/uv:python3.11-bookworm-slim
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WORKDIR /app
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COPY pyproject.toml uv.lock ./
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RUN uv sync --frozen --no-cache
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COPY agent.py ./
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EXPOSE 8080
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CMD ["uv", "run", "uvicorn", "agent:app", "--host", "0.0.0.0", "--port", "8080"]
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```
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Build: `docker buildx build --platform linux/arm64 -t <ecr-uri>:latest --push .`
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⚠️ Hard requirement: ARM64 only. x86 image → `ValidationException: Architecture incompatible` on CreateAgentRuntime.
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---
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## Q5: Region + Model — ✅ RESOLVED
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**Region: us-east-1** (broadest service availability, aligns with existing AWS work)
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**Models (Bedrock cross-region inference, `us.` prefix):**
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| Use | Model ID | Notes |
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|---|---|---|
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| Main agent | `us.anthropic.claude-3-7-sonnet-20250219-v1:0` | Primary workhorse |
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| Heartbeats | `us.anthropic.claude-3-5-haiku-20241022-v1:0` | Fast, cheap |
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| Experiment | `us.anthropic.claude-sonnet-4-*` | Sonnet 4 now on Bedrock (1M ctx preview) |
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Strands defaults to Bedrock + Sonnet when AWS creds are present. No extra config needed for basic setup.
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---
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## Q6: Self-Managed Memory Strategy — ⚠️ NOT SUPPORTED YET
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**Finding:** AgentCore CLI issue #677 (March 26, 2026): *"AgentCore memory does not currently support self-managed strategies."* Docs describe it; CLI doesn't implement it.
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**Impact:** The "bring your own Lambda extraction pipeline" pattern is blocked via CLI.
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**What still works:**
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- ✅ Built-in strategies: SUMMARIZATION, USER_PREFERENCE, SEMANTIC — fully supported, automatic
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- ✅ Strands `AgentCoreMemorySessionManager` — auto-stores turns, handles extraction
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- ✅ `BatchCreateMemoryRecords` API directly — works for explicit writes, bypasses CLI
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**Recommended mitigation:**
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- Use built-in strategies for automatic extraction (covers ~90% of MEMORY.md value)
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- Add `write_memory_record` as an agent tool that calls `BatchCreateMemoryRecords` directly
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- This gives explicit "remember this" control without the self-managed strategy pipeline
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```python
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@tool
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def write_memory_record(content: str, namespace: str = "/curated/daniel/") -> str:
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"""Explicitly write an important fact or lesson to long-term memory."""
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memory_client.batch_create_memory_records(
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memoryId=MEMORY_ID,
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memoryRecords=[{"content": {"text": content}, "namespace": namespace}]
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)
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return f"Written to memory: {content[:50]}..."
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```
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---
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## Summary
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| # | Question | Status | Decision |
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|---|---|---|---|
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| 1 | Direct code deploy vs container | ✅ | Use CodeZip — no Docker |
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| 2 | Secrets in container | ✅ | IAM role + SDK fetch at startup |
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| 3 | Memory pricing | ✅ | Unknown exact rates, low risk at personal scale |
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| 4 | ARM64 Dockerfile | ✅ | Moot (CodeZip), documented for reference |
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| 5 | Region + model | ✅ | us-east-1, Claude Sonnet (cross-region) |
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| 6 | Self-managed memory trigger | ✅ | Use built-in + BatchCreateMemoryRecords as tool |
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**All open questions resolved. Ready for Phase 0 spike.**
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