Deep Research and Development

OpenClaw is best understood as a self-hosted gateway plus agent runtime system , not as a framework where every user-facing agent is a Python class or long-running daemon. The Gateway receives messages from channels, resolves them to an agent and session, runs one serialized agent turn, records transcript state, executes tools, and delivers the answer back through the originating channel. The official agent-loop docs summarize the core path as “intake → context assembly → model inference → tool execution → streaming replies → persistence,” with one serialized run per session.  That matters when building something like a deterministic financial, like our DBOT - analyst that must compute DCFs, query financial facts, rank companies, and never hallucinate numbers. In OpenClaw, the right design is usually: Agent = prompt/config/session/runtime scope Skill = markdown guidance for when/how to act Tool = typed callable capability MCP server = cross-language tool server, ideal for Python/p...