0369a74ac1
- 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
111 lines
6.5 KiB
Markdown
111 lines
6.5 KiB
Markdown
# OpenClaw Feature Delta: March 2026 → May 2026
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*Covers releases 2026.3.7 through 2026.5.3 (current)*
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*Research baseline was 2026.3.2*
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---
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## Features That Change the AgentCore Comparison
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### 🔴 New features we'd need to replicate — not trivial
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**Agents/Commitments** (2026.4.29)
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New opt-in system where the agent infers follow-up commitments from conversations and delivers them via heartbeat. Config: `commitments.enabled`, `commitments.maxPerDay`. Commitments are extracted in a background sub-agent, stored with due times, and surfaced at the next appropriate heartbeat.
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This is NEW OpenClaw behavior that our AgentCore build doesn't account for. We'd need:
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- A "commitment extractor" pass after each conversation (similar to long-term memory extraction)
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- Storage for commitments with due times (DynamoDB)
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- Heartbeat Lambda checks due commitments and delivers them
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**Memory → People Wiki** (2026.4.29)
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OpenClaw's memory system has grown significantly: canonical people aliases, person cards, relationship graphs, privacy/provenance reports, evidence-kind drilldown. This is well beyond what we analyzed as "MEMORY.md replacement." The agent now has a structured knowledge graph about people and relationships.
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AgentCore Memory's built-in strategies (SUMMARIZATION, USER_PREFERENCE, SEMANTIC) don't have a direct equivalent. This is a gap — OpenClaw's memory is now substantially more sophisticated.
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**REM Dreaming** (2026.4.29)
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Referenced in the `doctor.memory.remHarness` RPC — OpenClaw now has a "REM" background consolidation pass that synthesizes and reorganizes memories asynchronously. Similar to our planned "self-managed memory strategy" but built-in and richer.
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No direct AgentCore Memory equivalent. Would need custom Lambda + BatchCreateMemoryRecords.
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**Active Memory / Partial Recall** (2026.4.29)
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- Per-conversation `allowedChatIds`/`deniedChatIds` filters for memory recall
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- Returns partial recall results on timeout instead of failing closed
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AgentCore Memory doesn't have conversation-scoped recall filters. Retrieving memories without a filter would return everything, not just conversation-relevant context.
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---
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### 🟡 Design decisions our build should incorporate
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**Streaming Progress Drafts** (2026.5.3)
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`streaming.mode: "progress"` — live draft messages across all channels (Discord, Telegram, Matrix, Slack, Teams) that update in-place as the agent processes. In Telegram, this means an editing message that shows progress without spamming new messages.
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**Directly relevant to our Telegram build.** Instead of the "typing indicator" hack (which expires in 5s), we can send an initial "thinking..." message and edit it in-place with progress, then finalize. Much cleaner UX. We should implement this from day one.
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**`sessions_yield`** (2026.3.12)
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New tool: orchestrators can end the current turn immediately and carry a hidden payload into the next session turn. Enables cleaner multi-agent hand-offs.
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For our AgentCore build: this would be handled differently (sub-agent via A2A), but the pattern is worth knowing.
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**Queue Mode: steer is now default** (2026.4.29)
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Active-run queueing now defaults to `steer` with 500ms debounce. This means multiple rapid messages don't queue sequentially — later messages "steer" the current run. The SQS batching approach we designed (bundle all queued messages at once) is aligned with this.
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**`/steer` command** (2026.5.3)
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Explicit steering of active session runs without starting a new turn when idle. Our architecture handles this naturally through SQS FIFO batching.
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**ACP: resumeSessionId** (2026.3.11)
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`sessions_spawn` with `runtime: "acp"` can now resume existing sessions. Relevant if the AgentCore build eventually spawns coding agents.
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---
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### 🟢 Improvements to existing features (no gap impact)
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**Multimodal memory indexing** (2026.3.11)
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Image and audio indexing for memory search with Gemini embedding. OpenClaw-only feature (uses local embedding + Gemini). AgentCore Memory handles multimodal separately via AgentCore Browser/Code Interpreter context.
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**Dashboard v2** (2026.3.12)
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Control UI overhaul: modular overview, chat, config, agent, and session views, command palette, mobile bottom tabs. Not relevant to AgentCore build (we won't have the OpenClaw Control UI).
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**SQLite plugin state store** (2026.4.29)
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Restart-safe keyed registries with TTL for plugins. Analogous to DynamoDB in our AgentCore build.
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**Bedrock Opus 4.7 thinking** (2026.4.29)
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Now available. Update model selection in the build plan — Opus 4.7 with thinking is an option.
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**Telegram improvements** (2026.3.12, 2026.4.29, 2026.5.3)
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- Model picker via inline buttons
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- Chunking improvements
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- Proxy/webhook/polling resilience
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These are bug fixes in OpenClaw's Telegram channel; our build avoids these by using the simpler Telegram Bot API directly.
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---
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## Updated Gap Assessment
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| Feature | OpenClaw (now) | AgentCore Build Plan | Gap |
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|---|---|---|---|
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| Streaming progress | ✅ Live draft edits | ❌ Not planned | Add Telegram edit-in-place |
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| Commitments | ✅ Background extraction + heartbeat | ❌ Not designed | Medium effort to add |
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| People wiki | ✅ Structured knowledge graph | ❌ Flat memories only | Significant gap |
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| REM dreaming | ✅ Background consolidation | ❌ Built-in strategies only | Partial via built-ins |
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| Memory filters | ✅ Per-conversation recall | ❌ Global retrieval | Small gap |
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| Multimodal memory | ✅ Images + audio | ❌ N/A | Lower priority |
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| Queue/steer default | ✅ Built-in | ✅ SQS FIFO batching | Equivalent |
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| sessions_yield | ✅ Built-in | ✅ A2A equivalent | Equivalent |
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---
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## Recommendations for Build Plan Updates
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1. **Add streaming progress to Phase 1** — don't defer this. In Telegram: send initial "⏳ thinking..." message, edit it with progress updates, replace with final answer. Better UX than typing indicator. OpenClaw ships this by default now.
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2. **Add commitments to Phase 3 scope** — extract from conversations, store in DynamoDB with due_at, check in heartbeat Lambda, deliver if overdue. Simple but valuable.
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3. **Acknowledge the memory gap** — OpenClaw's memory is now a full people-knowledge-graph system. AgentCore Memory's built-in strategies (SUMMARIZATION, USER_PREFERENCE, SEMANTIC) cover the basics but not the people wiki or REM dreaming depth. This is a real differentiation. The AgentCore build starts simpler — that's OK for a personal assistant, but worth calling out.
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4. **Update model options** — Bedrock Opus 4.7 with thinking is now available. Consider for complex tasks.
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---
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*Updated 2026-05-04. Covers delta from v2026.3.2 → v2026.5.3.*
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