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[Release] openclaw/openclaw v2026.4.23-beta.6: openclaw 2026.4.23-beta.6
OpenClaw v2026.4.23-beta.6 introduces significant enhancements to image generation across multiple providers (OpenAI, OpenRouter), adds optional forked context for subagents, implements per-call timeout support for generation tools, and includes configurable local embedding context sizes. The release also delivers numerous fixes for Codex harness routing, media handling, provider authentication, and UI persistence, improving reliability across chat platforms including Slack, Telegram, WhatsApp, and WebChat.
- Enable image generation without API keys using Codex OAuth for OpenAI and OpenRouter image models
- Add optional forked context for subagent sessions to inherit requester transcripts while maintaining isolated default sessions
- Implement per-call `timeoutMs` support for image, video, music, and TTS generation tools for flexible provider request timeouts
- +7 more key points...
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Agent Teams
See all 6 →I Built an AI That Searches the Web 🤯 | AutoGen Tool Calling (Python + Groq)
This tutorial demonstrates how to build an AI agent using AutoGen and Groq that can search the web and answer user queries. The project leverages tool calling capabilities to enable the agent to perform web searches dynamically. The implementation uses Python and integrates with Groq's language model for efficient processing. The complete code is available on GitHub for reference and further development.
Nidhi Chouhan
114,000 Stars In 57 Days: The OpenClaw Killer #Shorts
Hermes Agent achieved 114,000 GitHub stars in just 57 days after its February 25, 2026 launch, making it the fastest-growing AI agent framework to date—surpassing the combined growth rates of LangChain and AutoGen. This viral success demonstrates exceptional market demand for efficient, developer-friendly agent solutions. The rapid adoption suggests Hermes Agent introduced significant innovations or improvements over existing frameworks.
Mayank | Eager With AI
OpenClaw AI Agent Observability Demo
This demo showcases OpenClaw AI Agent Observability using PuppyGraph to provide visibility into AI agent behavior and operations. The presentation explores the challenges of understanding what AI agents are actually doing during execution and demonstrates how observability tools can help track, monitor, and debug agent activities in real-time.
PuppyGraph
Memory Systems
See all 4 →[Release] openclaw/openclaw v2026.4.23-beta.5: OpenClaw 2026.4.23 beta 5
OpenClaw v2026.4.23-beta.5 introduces significant enhancements to image generation across multiple providers (OpenAI, OpenRouter), adds optional forked context for subagents, implements per-call timeout support for generation tools, and includes configurable local embedding context sizes. The release fixes numerous issues across Codex harness routing, media handling, provider authentication, and UI persistence, while updating Pi dependencies to v0.70.0 and improving debug logging for harness selection decisions.
steipete
[Release] openclaw/openclaw v2026.4.23-beta.4: openclaw 2026.4.23-beta.4
OpenClaw v2026.4.23-beta.4 introduces significant enhancements to image generation across multiple providers (OpenAI, OpenRouter), adds optional forked context for subagents, implements per-call timeout support for generation tools, and includes configurable local embedding context sizes. The release also delivers numerous fixes for Codex harness routing, media handling across platforms (WhatsApp, Telegram, Slack), transcript replay behavior, and provider authentication flows to improve reliability and user experience.
steipete
[Release] openclaw/openclaw v2026.4.23: openclaw 2026.4.23
OpenClaw v2026.4.23 introduces significant enhancements to image generation across multiple providers (OpenAI, OpenRouter), adds optional forked context for subagents, implements per-call timeout support for generation tools, and includes configurable local embedding context sizes. The release also fixes numerous issues related to Codex harness routing, media handling, provider authentication, and streaming behavior across multiple platforms including WhatsApp, Slack, and Telegram.
steipete
MCP Servers
[Release] langchain-ai/langchain langchain-fireworks==1.2.0: langchain-fireworks==1.2.0
langchain-fireworks version 1.2.0 introduces several improvements including proper handling of `max_retries`, population of `usage_metadata` on streaming responses, and enhanced model profile data. The release includes dependency updates (langsmith, pillow, pytest), security patches (pygments CVE-2026-4539), and standardization of integration tests across the partners ecosystem. Key fixes address tool binding with strict mode, reasoning content handling, and model naming schema consistency.
github-actions[bot]
[Release] langchain-ai/langchain langchain-core==1.3.2: langchain-core==1.3.2
LangChain Core v1.3.2 introduces content-block-centric streaming (v2), a new streaming architecture that improves how content blocks are handled during streaming operations. This release builds on v1.3.1 with enhanced streaming capabilities for better performance and developer experience. The update focuses on refining the core streaming mechanisms used throughout the LangChain framework.
github-actions[bot]
Security
[Release] anthropics/claude-code v2.1.119: v2.1.119
Claude Code v2.1.119 introduces persistent settings management, enhanced PR/merge-request support across multiple platforms, improved hook functionality with execution timing, parallel MCP server connections, and numerous bug fixes. Key improvements include better permission handling for PowerShell, refined Vim mode behavior, enhanced slash command UI, and fixes for clipboard handling, OAuth flows, and plugin management. The release also adds environment variables for customization and improves OpenTelemetry observability with additional event metadata.
ashwin-ant
The Risky Reality of AI Agents. #AIAgents #OpenClaw #CyberSecurity #AIRisk
This content highlights critical security vulnerabilities in AI agents, presenting real-world incidents where autonomous agents have caused significant harm—including a data breach affecting 100 million people from government agencies and unauthorized cryptocurrency mining. The discussion emphasizes the risks of deploying AI agents without adequate safeguards and control mechanisms. These incidents underscore the urgent need for robust security frameworks, monitoring systems, and containment strategies before AI agents are widely deployed in sensitive environments.
Mading Thok
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