MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild

Large language model (LLM) agents have rapidly emerged as powerful assistants for complex, multi-step tasks, yet agents deployed in the wild remain largely static, trained once and served unchanged regardless of how user needs evolve. This creates a fundamental tension: they must serve users continuously without interruption, yet their capabilities grow stale as the task distribution drifts with real-world usage. On platforms such as OpenClaw, where a single agent connects to 20+ messaging channels and handles diverse, evolving workloads, existing approaches either store raw trajectories without distilling transferable behavioral knowledge, maintain static skill libraries disconnected from weight optimization, or incur service downtime during retraining. We present MetaClaw, a continual meta-learning framework that jointly maintains a base LLM policy and an evolving skill library of reusable behavioral instructions, improving both through two complementary mechanisms. Skill-driven fast adaptation analyzes failure trajectories and synthesizes new skills via an LLM evolver, taking effect immediately with zero service downtime. Opportunistic policy optimization performs gradient-based weight updates via cloud LoRA fine-tuning using RL with a process reward model, triggered only during user-inactive windows by the Opportunistic Meta-Learning Scheduler (OMLS), which monitors configurable sleep hours, system keyboard inactivity, and Google Calendar occupancy. The two mechanisms are mutually reinforcing: a better policy produces more informative failures for skill synthesis, and richer skills yield higher-reward trajectories for policy optimization. To prevent stale reward contamination, a skill generation versioning mechanism strictly separates support data (failure trajectories consumed by skill evolution) from query data (post-adaptation trajectories used for RL updates). Built on a proxy-based architecture, MetaClaw scales to production-size LLMs without a local GPU. Experiments on MetaClaw-Bench (934 questions, 44 simulated workdays) and AutoResearchClaw (23-stage autonomous research pipeline) demonstrate consistent improvements: skill-driven adaptation improves accuracy by up to 32% relative; the full pipeline advances Kimi-K2.5 from 21.4% to 40.6% accuracy (vs. GPT-5.2 baseline 41.1%) with an 8.25x gain in end-to-end task completion; and skill injection alone improves AutoResearchClaw composite robustness by 18.3%.