OpenClaw Conversation Q&A
1) What is OpenClaw’s event-driven architecture?
Q: Can you explain the OpenClaw processing chain in a technical but concise way?
A: OpenClaw is an event-driven agent runtime. Inputs enter through channels or triggers, get normalized, pass through access control, bind to a session, build context, call an LLM, execute tools when needed, and then return a response while preserving logs and memory.
2) How does the event flow work end to end?
Q: What is the event-driven loop, and how does it handle incoming events from channels, webhooks, cron jobs, or MCP triggers?
A: The loop is: incoming event -> Gateway -> channel parsing -> access control -> session resolution -> context assembly -> LLM call -> tool execution -> response. “Incoming event” is a broad term that includes channel messages, webhooks, scheduled jobs, and internal triggers.
3) What does Gateway-to-channel parsing mean?
Q: What happens when the Gateway receives an event and routes it to a channel adapter?
A: The Gateway decides which channel the raw event belongs to and hands it off to the correct adapter. The adapter converts platform-specific payloads into a common internal event format and enriches it with metadata like chat ID, reply-to ID, and attachments.
4) What happens after parsing?
Q: Once the event is normalized, what comes next in the processing flow?
A: After parsing, the next stages are access control, session resolution, context assembly, the LLM call, tool execution if needed, and finally the response.
5) How does access control and session resolution fit in?
Q: Why are access control and session resolution separate stages?
A: Access control acts as a gatekeeper, checking whether the sender, channel, or signature is allowed to proceed. Session resolution then maps the event to a stable conversation boundary so one user’s state does not leak into another’s.
6) How does OpenClaw handle state on disk?
Q: How are session logs and memory stored and used?
A: Session logs are append-only JSONL transcripts containing messages, tool calls, and tool outputs. Memory is separate and stored as Markdown files such as MEMORY.md and memory/YYYY-MM-DD.md. The session log is replayable history, while memory is curated long-term knowledge.
7) Why is the summary written back to the session log?
Q: Why does the summarized LLM output go into the session log if the log is “just conversation”?
A: Because the summary is still an assistant message. The session log is the full transcript of observable session events, so the summary belongs there for replay, debugging, and later memory extraction.
8) How are session logs updated after summarization?
Q: What does a session log look like after a multi-tool run is summarized?
A: The log usually contains the user message, tool calls, tool results, and then a final assistant summary. That final summary is simply another transcript entry, not a separate memory artifact.
9) How does OpenClaw handle context window limits?
Q: What does OpenClaw do when the context gets too large?
A: It budgets tokens, trims oversized inputs, summarizes older history, and injects only the most relevant memory snippets. The goal is to preserve meaning while staying within the model’s window.
10) Who vectorizes the memory files?
Q: Who performs vectorization for semantic search in OpenClaw?
A: The memory layer does. Markdown memory files are chunked, embedded, and stored in a vector index so semantic search can retrieve the most relevant snippets for the prompt.
11) Is vectorization automatic?
Q: Does OpenClaw automatically vectorize memory files?
A: Yes, once memory search is enabled. The system chunks and embeds memory files automatically and keeps the vector index updated as memory changes.
12) Is the session log vectorized too?
Q: Are raw session logs vectorized by default?
A: No. Session logs are used mainly for replay and history. Memory files are the default source of vectorized content unless you build a custom retrieval layer on top of session logs.
13) How should the article be structured?
Q: How can this topic be turned into a good Medium-style article?
A: A strong article should follow the actual processing chain: ingress, Gateway, adapters, access control, session resolution, context assembly, memory search, LLM call, tool execution, session logs vs memory, context limits, and the end-to-end mental model.
14) What should be included for clarity?
Q: Where do diagrams and mind maps help most?
A: Use ASCII diagrams at the main transitions: ingress flow, Gateway to adapter handoff, access control and session resolution, context assembly, memory search, and the LLM/tool loop.
15) What is the clearest mental model?
Q: What is the simplest way to remember OpenClaw’s flow?
A: Ingress -> Normalize -> Govern -> Session -> Context -> Model -> Tools -> Response, with logs and memory preserving what matters for replay and recall.