OpenClaw v2026.4.7 — Update Now
Tony Slavin 905-767-0101 www.openclaw911.com
Three major feature unlocks, eight quality-of-life improvements, and one clear recommendation: this release is worth the upgrade. Here's everything your team needs to evaluate the change.
View Release Notes
Top Priority
Why This Release Matters
OpenClaw v2026.4.7 isn't a routine patch — it directly addresses pain points your team has been hitting in production. Lost session context during long ASE and debugging runs, a memory system that needed structural depth, and manual automation bottlenecks are all on the fix list. Three headline features lead the charge.
3
Major Features
Compaction Checkpoints, Memory Wiki, Webhook Ingress
8
Notable Improvements
CLI tools, model support, fallback logic, and more
1
Clear Verdict
Update recommended — no caveats
Biggest Win #1
Compaction Checkpoints
If your team has lost session context mid-way through a long ASE run or deep debugging session, this is the fix you've been waiting for. Session history that was previously discarded during compaction can now be inspected and fully restored directly from the Sessions UI — no workarounds, no manual reconstruction.
Compaction has always been a necessary evil: it keeps sessions performant but nukes the historical thread. Checkpoints change that contract entirely. You get the performance benefits of compaction and the ability to rewind. For long-running engineering sessions where accumulated context is the whole point, this alone justifies the upgrade.
Biggest Win #2
Memory Wiki — Fully Restored
The full memory-wiki stack is back — and it's more capable than before. This isn't just a bug fix; it's a structured knowledge layer that can meaningfully complement your existing Palace memory system.
The stack now includes structured claims with evidence fields, active contradiction detection, staleness dashboards for monitoring knowledge freshness, and freshness-weighted search that surfaces the most relevant and current knowledge first. Together, these features make the memory layer far more trustworthy and auditable than a flat note store.
Memory Wiki Stack
  • Structured claims with evidence fields
  • Contradiction detection
  • Staleness dashboards
  • Freshness-weighted search
Biggest Win #3
Webhook Ingress Plugin
External automation can now drive TaskFlows directly via shared-secret webhook endpoints. This is a significant unlock for teams running client site automation — any external system that can fire a webhook can now trigger workflows in OpenClaw without requiring a human in the loop.
The shared-secret model keeps the surface area simple and auditable. Client site pipelines, CI/CD triggers, monitoring alerts — anything that can POST to an endpoint is now a potential automation driver. For teams already thinking about no-touch automation, this is the integration primitive you've been missing.
Notable Changes
CLI & Inference Improvements
The openclaw infer CLI introduces a first-class inference hub that consolidates model, media, web, and embedding tasks into a single, unified command surface. No more juggling separate entry points for different modality tasks.
openclaw infer CLI
Unified hub for model, media, web, and embedding inference tasks
Anthropic Claude CLI
Restored as the preferred local path for Claude-based workflows
Pluggable Compaction
Plugins can now replace the built-in summarization pipeline entirely
Notable Changes
Model Support & Media Resilience
New Model Support
Google's Gemma 4 is now fully supported, expanding your local and cloud model roster. On the local side, Ollama vision detection now auto-detects vision-capable models — no manual configuration required. If your Ollama instance supports vision, OpenClaw will find it and use it.
Media Generation Auto-Fallback
Image, music, and video generation tasks now automatically switch providers when the primary provider fails. This significantly improves reliability for any workflow that depends on media generation — dropped requests due to provider outages become a problem of the past.
Notable Changes
Heartbeat & Dreaming Refinements
Heartbeat Prompt-Section Controls
Heartbeat can now run without injecting instructions into every turn. This produces significantly cleaner session transcripts and reduces noise in contexts where Heartbeat's background activity was cluttering the interaction log. A small change with a noticeable quality-of-life improvement for teams running Heartbeat continuously.
Dreaming Corpus Improvement
Session transcripts are now ingested into the dreaming corpus with per-day notes attached. This means the dreaming process has richer, more temporally structured source material to work with — resulting in better knowledge consolidation and more coherent long-term memory formation over time.
Full Change Summary
Everything in v2026.4.7 at a glance — from headline features to supporting improvements.
Recommendation: Update
Compaction checkpoints alone are worth it — no more lost context mid-session. The memory wiki stack could meaningfully complement our Palace system too.
This is a low-risk, high-reward upgrade. The three headline features address real, documented pain points your team has been working around. The supporting changes add resilience and capability without introducing breaking changes to existing workflows.
Start with the compaction checkpoint behavior in your next long ASE session. Evaluate the memory wiki stack against your Palace architecture as a second step. Webhook ingress can be scoped to a client site pilot before broader rollout.