How do I enable the Model Auth status card in OpenClaw?

You don't need to enable it manually. The card appears automatically in Control UI/Overview when you upgrade to OpenClaw 2026.4.15-beta.1 or later. Run `claw control` to launch the web interface, then navigate to the Overview tab. If you run headless, use `claw status --watch` to see auth data in your terminal. Ensure your user has `read:auth` scope permissions configured in your authentication settings. The feature requires no additional configuration beyond your existing provider credentials. If you don't see the card, verify you've restarted the OpenClaw daemon after upgrading to load the new gateway module.

Does the models.authStatus gateway store my OAuth tokens?

No. The gateway never stores tokens in the cache or response payload. It validates tokens against provider endpoints in real-time, then returns only metadata like expiration dates and rate-limit headers. The credential stripping happens at the gateway layer before any data reaches the UI or API consumers. Your tokens remain in your configured secret stores or environment variables exactly where you placed them. The 60-second cache only stores the validation result and metadata, never the token itself. This design prevents credential leakage even if the OpenClaw process becomes compromised.

Can I adjust the 60-second cache duration for auth status checks?

Yes, but it requires editing your `claw.config.yaml`. Set `models.authStatus.cacheTTL` to your preferred value in seconds. Be aware that values below 30 seconds may trigger provider rate limits on validation endpoints. For high-security environments where you need real-time data, you can disable caching entirely by setting `models.authStatus.cache: false`, though this increases API quota consumption significantly. Most production deployments should stick with the default 60 seconds to balance freshness with API conservation.

What happens when a token shows as expiring in the Control UI?

The UI displays a yellow warning badge with hours remaining until expiration. You can click the rotation button to trigger `claw auth refresh` immediately, or configure automatic webhook alerts to notify your team. Agents continue functioning normally until the expiration timestamp passes. Once expired, the status turns red and agents using that provider will fail with authentication errors until you rotate the credentials. You can customize the warning threshold via `models.authStatus.warnThreshold` to match your internal rotation schedules.

Does this release affect how I configure GitHub Copilot authentication?

Indirectly. While the Model Auth card displays Copilot token status if configured, the separate Copilot embedding provider introduced in this release uses its own authentication flow. You configure it via `embedding.provider: "github-copilot"` in your memory settings. The Copilot transport helper handles token refresh automatically, and these tokens appear in the Model Auth status card alongside your other providers for unified monitoring. No changes are required to existing Copilot configurations, though you should verify the new provider appears in your status overview after upgrading.

OpenClaw 2026.4.15-beta.1 Released: Model Auth Status and Rate-Limit Monitoring Explained

OpenClaw 2026.4.15-beta.1 shipped yesterday with a critical visibility upgrade for production agent deployments. The release introduces a Model Auth status card in the Control UI/Overview that surfaces real-time OAuth token health and provider rate-limit pressure, backed by the new models.authStatus gateway method. You no longer need to dig through logs or wait for 401 errors to discover that your OpenAI or Anthropic tokens expired three hours ago. The system now strips credentials from responses, caches auth state for 60 seconds, and throws attention callouts when tokens approach expiration. This changes how you monitor authenticated model connections in high-throughput agent clusters.

What Changed in OpenClaw 2026.4.15-beta.1?

Yesterday’s beta drops seven major changes plus security fixes, but the headliner is the Model Auth status card. You get a new Control UI panel that displays OAuth token health across all configured providers alongside rate-limit pressure indicators. The feature rides on models.authStatus, a fresh gateway method that queries auth state without exposing credentials. Memory/LanceDB now supports cloud storage for durable indexes, letting you run vector databases on S3 instead of local SSDs. GitHub Copilot gets a dedicated embedding provider for memory search with automatic token refresh handling. Experimental localModelLean mode strips heavyweight tools like browser and cron for weaker local models, cutting prompt sizes by 40%. Packaging improvements isolate plugin dependencies, trimming core build sizes by 200MB. Matrix QA splits into source-linked runners for better CI isolation. CLI config writes now re-read persisted hashes to prevent stale-config races. Security fixes include redaction of secrets in exec approval prompts, preventing credential leaks during inline review workflows.

The Model Auth Status Card: A Technical Overview

The new card lives in Control UI/Overview and consumes the models.authStatus endpoint. It renders three critical data points: token validity status, expiration timestamps, and provider rate-limit headers. Each provider row shows a health indicator (green/yellow/red) based on token time-to-live and recent API response codes. Yellow triggers when expiration falls within your configured warning threshold, typically 24 hours. Red appears on expiration or validation failure. Rate-limit pressure displays as a percentage of remaining quota or requests-per-minute velocity. The UI updates every 60 seconds to match the gateway cache TTL, keeping the dashboard responsive without hammering provider APIs. You can click any row to expand raw metadata including token scopes and last validation timestamp. The interface uses Server-Sent Events for live updates, ensuring you see auth state changes immediately when they occur without manual page refreshes. Mobile responsiveness ensures these indicators remain readable on tablet devices used for operations monitoring.

How models.authStatus Strips Credentials Securely

The models.authStatus gateway method handles the sensitive work. When queried, it inspects your configured model providers, validates OAuth tokens against issuer endpoints, and returns a sanitized response. The method explicitly redacts access tokens, refresh tokens, and client secrets from the JSON payload before it hits the UI. It returns only metadata: expiration dates, scopes, validation timestamps, and rate-limit headers. The implementation uses a 60-second in-memory cache to prevent repeated validation requests that could trigger provider rate limits or token rotation policies. This architecture ensures that even if the Control UI process gets compromised, credential material never traverses the internal API surface exposed to the browser or CLI inspection tools. The gateway runs these checks in isolated worker threads to prevent blocking the main event loop during slow provider responses. All validation traffic routes through OpenClaw’s existing HTTP agent with certificate pinning enabled for additional transport security.

OAuth Token Health: From Invisible to Visible

Before this release, you discovered expired tokens when agents started failing with authentication errors. Now you see degradation coming. The status card polls models.authStatus to render token lifetimes as progress bars or countdown timers depending on your UI theme. For OpenAI, it checks against https://api.openai.com/v1/models with a HEAD request. For Anthropic, it validates against their token info endpoint. The system tracks refresh token rotation and flags when a provider returns 401 or 403 responses. You can configure health check intervals via models.authCheckInterval in your config, though the 60-second default works for most production loads.

Understanding Rate-Limit Monitoring in OpenClaw

Beyond token validity, the Model Auth status card provides crucial insights into your current rate-limit pressure. This feature is particularly valuable for organizations operating at scale, where exceeding API quotas can lead to service disruptions and increased operational costs. The models.authStatus gateway actively parses X-Ratelimit-* headers from API responses, such as X-Ratelimit-Limit, X-Ratelimit-Remaining, and X-Ratelimit-Reset. These values are then normalized and presented in the UI as a percentage of remaining requests or time until reset. This allows operators to proactively scale out their agent deployments, adjust token usage, or even switch to alternative providers before hitting hard limits.

For example, if your OpenAI X-Ratelimit-Remaining drops below 10%, the UI will display a yellow warning, turning red if it approaches zero. This real-time feedback loop is essential for maintaining uninterrupted service for your AI agents. The system can even be configured to trigger automated alerts via webhooks when certain thresholds are breached, enabling integration with existing incident management systems. This proactive approach significantly reduces the likelihood of unexpected service interruptions due to quota exhaustion, a common challenge in large-scale AI deployments.

Configuring Custom Thresholds and Alerting

OpenClaw 2026.4.15-beta.1 allows for extensive customization of warning and critical thresholds for both token expiration and rate-limit pressure. In your claw.config.yaml, you can define these parameters globally or per provider. For instance, you might want a 48-hour warning for your primary OpenAI tokens but only a 12-hour warning for a less critical internal model.

models:
  authStatus:
    warnThreshold: 48h # Global token expiration warning at 48 hours
    criticalThreshold: 6h # Global token expiration critical at 6 hours
    rateLimit:
      warnPercentage: 0.20 # Warn when remaining quota is below 20%
      criticalPercentage: 0.05 # Critical when remaining quota is below 5%
    providers:
      openai:
        warnThreshold: 24h # OpenAI specific override
        rateLimit:
          warnPercentage: 0.15 # OpenAI specific rate limit warning
      anthropic:
        warnThreshold: 72h # Anthropic specific override

These configurable thresholds empower operations teams to tailor monitoring to their specific organizational needs and provider agreements. Furthermore, OpenClaw supports webhook integrations for all status changes. When a token status shifts from green to yellow, or a rate-limit crosses a critical threshold, a configurable webhook payload can be dispatched to a specified URL. This allows for seamless integration with tools like Slack, PagerDuty, or custom alert systems, ensuring that the right personnel are notified immediately when proactive action is required. The webhook payload includes detailed information about the event, such as the provider name, the specific token affected, the new status, and relevant metrics like remaining time or quota. This level of detail facilitates rapid diagnosis and resolution of potential issues without manual intervention.

Cloud Storage for Vector Indexes via Memory/LanceDB

A significant enhancement in this release is the expanded support for cloud storage in Memory/LanceDB. Previously, LanceDB deployments were often tied to local SSDs, which presented challenges for scalability, data durability, and disaster recovery in distributed environments. With 2026.4.15-beta.1, OpenClaw agents can now leverage object storage services like Amazon S3, Google Cloud Storage, or Azure Blob Storage for their vector indexes.

This fundamental shift allows for greater flexibility and resilience in your vector database infrastructure. Storing indexes on S3 means that your vector data is automatically replicated, highly available, and durable, mitigating the risk of data loss due due to local disk failures. It also simplifies the deployment of ephemeral agent clusters, where agents can spin up, connect to a shared, persistent vector index in the cloud, perform their tasks, and then spin down without needing to manage local storage or complex data synchronization mechanisms. This capability is especially beneficial for large-scale, burstable AI workloads where agents might be dynamically provisioned across various compute instances. Configuration is straightforward, requiring only the specification of an S3 bucket or equivalent cloud storage path in your LanceDB settings within claw.config.yaml. This greatly streamlines the operational overhead of managing vector databases, transforming them into a more cloud-native, scalable component of your AI agent architecture.

Dedicated GitHub Copilot Embedding Provider

OpenClaw 2026.4.15-beta.1 introduces a dedicated embedding provider for GitHub Copilot. This integration allows OpenClaw agents to leverage Copilot’s powerful code embeddings for tasks like code search, semantic understanding, and context retrieval within development workflows. While Copilot has been accessible through other means, this dedicated provider streamlines its use, offering automatic token refresh and improved performance.

The key benefit is the seamless integration into OpenClaw’s memory system. By configuring embedding.provider: "github-copilot" in your memory settings, agents can generate and store embeddings from code snippets using Copilot’s models. This enables more intelligent code-aware agents capable of, for example, identifying similar code patterns, suggesting relevant documentation based on code context, or even performing automated code reviews with a deeper understanding of the codebase. The automatic token refresh mechanism ensures that your Copilot access remains uninterrupted, reducing administrative burden. The status of your Copilot tokens will also appear in the new Model Auth status card, providing a unified view of all your authentication statuses. This feature unlocks new possibilities for AI agents operating in software development and engineering environments, allowing them to interact with and understand code more effectively.

Experimental `localModelLean` Mode for Resource Optimization

For users deploying OpenClaw agents on resource-constrained local models, the experimental localModelLean mode offers a significant advantage. This mode is designed to reduce the overall prompt size and complexity by selectively disabling or stripping heavyweight tools and capabilities that might overwhelm smaller, less capable local language models (LLMs). By setting agent.localModelLean: true in your configuration, OpenClaw agents will operate with a streamlined set of tools, primarily focusing on core reasoning and text generation.

Tools such as browser (for web browsing), cron (for scheduled tasks), and certain advanced data processing utilities are automatically omitted when localModelLean is active. This reduction in the available toolset directly translates to shorter, more focused prompts sent to the local LLM, which can dramatically improve inference speed, reduce memory consumption, and enhance the reliability of responses from models that struggle with extensive context windows or complex tool instructions. Initial benchmarks show a prompt size reduction of up to 40% in typical scenarios. This makes OpenClaw more accessible and performant for edge deployments, personal AI assistants running on consumer hardware, or in environments where network access to external services is restricted or costly. While experimental, this mode represents a step towards optimizing OpenClaw for a broader spectrum of deployment environments, balancing capability with efficiency.

Packaging Improvements and CI Isolation

OpenClaw 2026.4.15-beta.1 also brings substantial improvements to the core packaging and continuous integration (CI) infrastructure. These changes might not be immediately visible to end-users but contribute significantly to the stability, security, and maintainability of the OpenClaw platform. The core build sizes have been trimmed by 200MB through better isolation of plugin dependencies. This means that individual plugins now carry their own specific dependencies, preventing dependency conflicts and reducing the overall footprint of the OpenClaw core distribution. Smaller build sizes translate to faster downloads, quicker deployments, and reduced storage requirements, which is particularly beneficial for containerized environments and CI/CD pipelines.

Furthermore, the Matrix QA system has been refactored to split into source-linked runners. This enhancement improves CI isolation, meaning that different test suites and quality assurance checks now run in more isolated environments. This minimizes interference between tests, increases the reliability of CI results, and makes it easier to diagnose and fix build failures. Developers will experience faster and more accurate feedback from the CI system, leading to a more robust development cycle. These internal improvements reflect an ongoing commitment to engineering excellence and ensure that OpenClaw remains a reliable and efficient platform as it continues to evolve.

CLI Config Writes and Stale-Config Race Prevention

A subtle yet important improvement in this release addresses a potential race condition with CLI configuration writes. Previously, when modifying configuration via the command-line interface, there was a theoretical possibility for a stale configuration to be re-read and overwrite recent changes if multiple processes or users were interacting with the config file simultaneously. OpenClaw 2026.4.15-beta.1 introduces a mechanism where CLI config writes now re-read persisted hashes of the configuration file before committing changes.

This process ensures that the CLI always operates on the most up-to-date version of the configuration. If a hash mismatch is detected, indicating that the file has been modified by another process since it was initially read, the CLI will prompt the user or attempt a merge, preventing unintended data loss or configuration inconsistencies. This enhancement significantly improves the robustness of configuration management, especially in collaborative or automated deployment environments where multiple agents or scripts might be updating OpenClaw settings. It provides an additional layer of safety, ensuring that your claw.config.yaml remains consistent and reliable, even under concurrent modification scenarios.

Enhanced Security: Redaction in Exec Approval Prompts

Security remains a paramount concern for OpenClaw, and this release includes a critical security enhancement: the redaction of secrets in exec approval prompts. In previous versions, if an agent’s execution plan included commands that directly exposed sensitive credentials or API keys (e.g., curl -H "Authorization: Bearer $SOME_API_KEY" ...), these secrets could potentially be visible in the interactive approval prompts presented to the user or in logs.

With 2026.4.15-beta.1, OpenClaw now intelligently identifies and redacts common patterns of secrets (like API keys, tokens, and passwords) within the execution approval prompts. This ensures that even during inline review workflows, sensitive information is never inadvertently displayed or logged in an unredacted format. The redaction engine uses a combination of pattern matching and context analysis to identify potential secrets, replacing them with masked placeholders (e.g., *****). This feature significantly reduces the risk of credential leakage during operational oversight and improves the overall security posture of OpenClaw deployments, especially in environments where human oversight is a part of the agent’s workflow. This proactive redaction mechanism applies across various output channels, including the Control UI, CLI logs, and any external logging integrations, ensuring a consistent level of security.

Future Outlook for OpenClaw Authentication and Monitoring

The release of OpenClaw 2026.4.15-beta.1 marks a significant milestone in providing robust authentication and monitoring capabilities for AI agents. The models.authStatus gateway and the accompanying UI card are foundational elements upon which future enhancements will be built. Looking ahead, the OpenClaw team plans to expand these features even further.

One key area of development will be more sophisticated predictive analytics for rate limits. Instead of just displaying current usage, future versions might incorporate historical data and agent activity patterns to forecast when rate limits are likely to be hit, allowing for even earlier proactive intervention. This could involve machine learning models analyzing past consumption and agent task queues to provide more accurate projections.

Another planned enhancement involves deeper integration with enterprise identity providers. While OAuth is well-supported, future releases may offer more direct integrations with systems like Okta, Azure AD, or custom SAML providers, simplifying credential management in large organizations. This would include features like automated token rotation policies managed directly by the identity provider, further reducing the operational burden on OpenClaw administrators.

Furthermore, expect to see more granular control over notification channels for authentication and rate-limit events. This could include direct integration with popular ITSM (IT Service Management) platforms, allowing for automated ticket creation and incident assignment when critical issues arise. The goal is to evolve OpenClaw’s monitoring capabilities from reactive alerts to a fully integrated, intelligent system that anticipates and helps prevent service disruptions, solidifying its role as a reliable platform for mission-critical AI agent deployments. The continuous feedback loop from the community will also play a crucial role in shaping these future developments, ensuring that OpenClaw addresses the most pressing needs of its users in the ever-evolving landscape of AI operations.

Conclusion

OpenClaw 2026.4.15-beta.1 introduces Model Auth status monitoring with OAuth token health, rate-limit pressure tracking, and secure credential handling via models.authStatus gateway.