Files
agent-claw/openclaw-feature-delta.md
daniel 0369a74ac1 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
2026-05-04 08:28:52 -05:00

6.5 KiB

OpenClaw Feature Delta: March 2026 → May 2026

Covers releases 2026.3.7 through 2026.5.3 (current) Research baseline was 2026.3.2


Features That Change the AgentCore Comparison

🔴 New features we'd need to replicate — not trivial

Agents/Commitments (2026.4.29) 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.

This is NEW OpenClaw behavior that our AgentCore build doesn't account for. We'd need:

  • A "commitment extractor" pass after each conversation (similar to long-term memory extraction)
  • Storage for commitments with due times (DynamoDB)
  • Heartbeat Lambda checks due commitments and delivers them

Memory → People Wiki (2026.4.29) 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.

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.

REM Dreaming (2026.4.29) 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.

No direct AgentCore Memory equivalent. Would need custom Lambda + BatchCreateMemoryRecords.

Active Memory / Partial Recall (2026.4.29)

  • Per-conversation allowedChatIds/deniedChatIds filters for memory recall
  • Returns partial recall results on timeout instead of failing closed

AgentCore Memory doesn't have conversation-scoped recall filters. Retrieving memories without a filter would return everything, not just conversation-relevant context.


🟡 Design decisions our build should incorporate

Streaming Progress Drafts (2026.5.3) 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.

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.

sessions_yield (2026.3.12) 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.

For our AgentCore build: this would be handled differently (sub-agent via A2A), but the pattern is worth knowing.

Queue Mode: steer is now default (2026.4.29) 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.

/steer command (2026.5.3) Explicit steering of active session runs without starting a new turn when idle. Our architecture handles this naturally through SQS FIFO batching.

ACP: resumeSessionId (2026.3.11) sessions_spawn with runtime: "acp" can now resume existing sessions. Relevant if the AgentCore build eventually spawns coding agents.


🟢 Improvements to existing features (no gap impact)

Multimodal memory indexing (2026.3.11) 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.

Dashboard v2 (2026.3.12) 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).

SQLite plugin state store (2026.4.29) Restart-safe keyed registries with TTL for plugins. Analogous to DynamoDB in our AgentCore build.

Bedrock Opus 4.7 thinking (2026.4.29) Now available. Update model selection in the build plan — Opus 4.7 with thinking is an option.

Telegram improvements (2026.3.12, 2026.4.29, 2026.5.3)

  • Model picker via inline buttons
  • Chunking improvements
  • Proxy/webhook/polling resilience These are bug fixes in OpenClaw's Telegram channel; our build avoids these by using the simpler Telegram Bot API directly.

Updated Gap Assessment

Feature OpenClaw (now) AgentCore Build Plan Gap
Streaming progress Live draft edits Not planned Add Telegram edit-in-place
Commitments Background extraction + heartbeat Not designed Medium effort to add
People wiki Structured knowledge graph Flat memories only Significant gap
REM dreaming Background consolidation Built-in strategies only Partial via built-ins
Memory filters Per-conversation recall Global retrieval Small gap
Multimodal memory Images + audio N/A Lower priority
Queue/steer default Built-in SQS FIFO batching Equivalent
sessions_yield Built-in A2A equivalent Equivalent

Recommendations for Build Plan Updates

  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.

  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.

  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.

  4. Update model options — Bedrock Opus 4.7 with thinking is now available. Consider for complex tasks.


Updated 2026-05-04. Covers delta from v2026.3.2 → v2026.5.3.