clawdbot/docs/concepts/agent-loop.md

summary, read_when

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Agent loop lifecycle, streams, and wait semantics	You need an exact walkthrough of the agent loop or lifecycle events

Agent Loop (Moltbot)

An agentic loop is the full “real” run of an agent: intake → context assembly → model inference → tool execution → streaming replies → persistence. It’s the authoritative path that turns a message into actions and a final reply, while keeping session state consistent.

In Moltbot, a loop is a single, serialized run per session that emits lifecycle and stream events as the model thinks, calls tools, and streams output. This doc explains how that authentic loop is wired end-to-end.

Entry points

• Gateway RPC: agent and agent.wait.
• CLI: agent command.

How it works (high-level)

1. agent RPC validates params, resolves session (sessionKey/sessionId), persists session metadata, returns { runId, acceptedAt } immediately.
2. agentCommand runs the agent:
   • resolves model + thinking/verbose defaults
   • loads skills snapshot
   • calls runEmbeddedPiAgent (pi-agent-core runtime)
   • emits lifecycle end/error if the embedded loop does not emit one
3. runEmbeddedPiAgent:
   • serializes runs via per-session + global queues
   • resolves model + auth profile and builds the pi session
   • subscribes to pi events and streams assistant/tool deltas
   • enforces timeout -> aborts run if exceeded
   • returns payloads + usage metadata
4. subscribeEmbeddedPiSession bridges pi-agent-core events to Moltbot agent stream:
   • tool events => stream: "tool"
   • assistant deltas => stream: "assistant"
   • lifecycle events => stream: "lifecycle" (phase: "start" | "end" | "error")
5. agent.wait uses waitForAgentJob:
   • waits for lifecycle end/error for runId
   • returns { status: ok|error|timeout, startedAt, endedAt, error? }

Queueing + concurrency

• Runs are serialized per session key (session lane) and optionally through a global lane.
• This prevents tool/session races and keeps session history consistent.
• Messaging channels can choose queue modes (collect/steer/followup) that feed this lane system. See Command Queue.

Session + workspace preparation

• Workspace is resolved and created; sandboxed runs may redirect to a sandbox workspace root.
• Skills are loaded (or reused from a snapshot) and injected into env and prompt.
• Bootstrap/context files are resolved and injected into the system prompt report.
• A session write lock is acquired; SessionManager is opened and prepared before streaming.

Prompt assembly + system prompt

• System prompt is built from Moltbot’s base prompt, skills prompt, bootstrap context, and per-run overrides.
• Model-specific limits and compaction reserve tokens are enforced.
• See System prompt for what the model sees.

Hook points (where you can intercept)

Moltbot has two hook systems:

• Internal hooks (Gateway hooks): event-driven scripts for commands and lifecycle events.
• Plugin hooks: extension points inside the agent/tool lifecycle and gateway pipeline.

Internal hooks (Gateway hooks)

• agent:bootstrap: runs while building bootstrap files before the system prompt is finalized. Use this to add/remove bootstrap context files.
• Command hooks: /new, /reset, /stop, and other command events (see Hooks doc).

See Hooks for setup and examples.

Plugin hooks (agent + gateway lifecycle)

These run inside the agent loop or gateway pipeline:

• before_agent_start: inject context or override system prompt before the run starts.
• agent_end: inspect the final message list and run metadata after completion.
• before_compaction / after_compaction: observe or annotate compaction cycles.
• before_tool_call / after_tool_call: intercept tool params/results.
• tool_result_persist: synchronously transform tool results before they are written to the session transcript.
• message_received / message_sending / message_sent: inbound + outbound message hooks.
• session_start / session_end: session lifecycle boundaries.
• gateway_start / gateway_stop: gateway lifecycle events.

See Plugins for the hook API and registration details.

Streaming + partial replies

• Assistant deltas are streamed from pi-agent-core and emitted as assistant events.
• Block streaming can emit partial replies either on text_end or message_end.
• Reasoning streaming can be emitted as a separate stream or as block replies.
• See Streaming for chunking and block reply behavior.

Tool execution + messaging tools

• Tool start/update/end events are emitted on the tool stream.
• Tool results are sanitized for size and image payloads before logging/emitting.
• Messaging tool sends are tracked to suppress duplicate assistant confirmations.

Reply shaping + suppression

• Final payloads are assembled from:
  • assistant text (and optional reasoning)
  • inline tool summaries (when verbose + allowed)
  • assistant error text when the model errors
• NO_REPLY is treated as a silent token and filtered from outgoing payloads.
• Messaging tool duplicates are removed from the final payload list.
• If no renderable payloads remain and a tool errored, a fallback tool error reply is emitted (unless a messaging tool already sent a user-visible reply).

Compaction + retries

• Auto-compaction emits compaction stream events and can trigger a retry.
• On retry, in-memory buffers and tool summaries are reset to avoid duplicate output.
• See Compaction for the compaction pipeline.

Event streams (today)

• lifecycle: emitted by subscribeEmbeddedPiSession (and as a fallback by agentCommand)
• assistant: streamed deltas from pi-agent-core
• tool: streamed tool events from pi-agent-core

Chat channel handling

• Assistant deltas are buffered into chat delta messages.
• A chat final is emitted on lifecycle end/error.

Timeouts

• agent.wait default: 30s (just the wait). timeoutMs param overrides.
• Agent runtime: agents.defaults.timeoutSeconds default 600s; enforced in runEmbeddedPiAgent abort timer.

Where things can end early

• Agent timeout (abort)
• AbortSignal (cancel)
• Gateway disconnect or RPC timeout
• agent.wait timeout (wait-only, does not stop agent)