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https://github.com/langbot-app/LangBot.git
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feat(agent-runner): integrate AgentRunner Protocol v1 with plugin system
Phase 0 integration complete - verified minimal loop with local-agent stub runner. Changes: - Add AgentRunOrchestrator for plugin-based agent execution - Add AgentResultNormalizer for Protocol v1 result conversion - Add AgentRunnerDescriptor for runner ID parsing (plugin:author/name/runner) - Update chat handler to use new orchestrator instead of direct runner lookup - Add plugin handler methods for list_agent_runners and run_agent - Add connector methods for AgentRunner protocol forwarding - Update pipeline API to include runner options in metadata - Add integration docs and implementation plan Integration verified: - Runner: plugin:langbot/local-agent/default - Input: "你好" - Output: [stub] Echo: 你好 - Date: 2026-05-10 10:09 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
huanghuoguoguo
180
src/langbot/pkg/agent/runner/result_normalizer.py
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180
src/langbot/pkg/agent/runner/result_normalizer.py
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"""Agent result normalizer for converting SDK v1 AgentRunResult to Pipeline messages."""
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from __future__ import annotations
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import typing
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from langbot_plugin.api.entities.builtin.provider import message as provider_message
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from ...core import app
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from .descriptor import AgentRunnerDescriptor
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from .errors import RunnerExecutionError, RunnerProtocolError
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# Maximum size for a single result payload (prevent memory exhaustion)
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MAX_RESULT_SIZE_BYTES = 1024 * 1024 # 1 MB
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class AgentResultNormalizer:
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"""Normalizer for converting SDK v1 AgentRunResult to Pipeline messages.
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Responsibilities:
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- Accept only SDK v1 result types (message.delta, message.completed, etc.)
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- Map message.delta -> MessageChunk
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- Map message.completed -> Message
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- Map run.completed (with message) -> Message
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- Handle run.failed as controlled error
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- Ignore unknown types with warning
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- Validate result size
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- Validate message schema
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Per PROTOCOL_V1.md, accepted types:
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- message.delta
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- message.completed
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- tool.call.started
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- tool.call.completed
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- state.updated
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- run.completed
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- run.failed
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- action.requested (log only, don't execute)
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"""
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ap: app.Application
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def __init__(self, ap: app.Application):
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self.ap = ap
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async def normalize(
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self,
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result_dict: dict[str, typing.Any],
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descriptor: AgentRunnerDescriptor,
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) -> provider_message.Message | provider_message.MessageChunk | None:
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"""Normalize AgentRunResult to Message or MessageChunk.
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Args:
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result_dict: Raw result dict from plugin runtime
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descriptor: Runner descriptor for error context
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Returns:
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Message, MessageChunk, or None (for non-message events)
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Raises:
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RunnerExecutionError: On run.failed
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RunnerProtocolError: On invalid result format
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"""
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# Validate result type
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result_type = result_dict.get('type')
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if not result_type:
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raise RunnerProtocolError(descriptor.id, 'Missing result type')
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# Validate result size
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try:
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import json
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result_json = json.dumps(result_dict)
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if len(result_json) > MAX_RESULT_SIZE_BYTES:
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self.ap.logger.warning(
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f'Runner {descriptor.id} result too large ({len(result_json)} bytes), truncating'
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)
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# Truncate content if possible
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data = result_dict.get('data', {})
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if 'chunk' in data or 'message' in data:
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content = data.get('chunk', {}).get('content', '') or data.get('message', {}).get('content', '')
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if isinstance(content, str) and len(content) > 10000:
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# Keep reasonable length
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data['chunk'] = {'role': 'assistant', 'content': content[:10000] + '...[truncated]'}
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except Exception:
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pass
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# Handle each result type
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data = result_dict.get('data', {})
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if result_type == 'message.delta':
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return self._normalize_message_delta(data, descriptor)
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elif result_type == 'message.completed':
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return self._normalize_message_completed(data, descriptor)
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elif result_type == 'tool.call.started':
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# Log only, don't yield to pipeline
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self.ap.logger.debug(
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f'Runner {descriptor.id} tool call started: {data.get("tool_name", "unknown")}'
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)
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return None
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elif result_type == 'tool.call.completed':
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# Log only, don't yield to pipeline
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self.ap.logger.debug(
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f'Runner {descriptor.id} tool call completed: {data.get("tool_name", "unknown")}'
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)
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return None
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elif result_type == 'state.updated':
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# Log for telemetry, don't yield
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self.ap.logger.debug(
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f'Runner {descriptor.id} state updated: {data.get("key", "unknown")}={data.get("value", "...")}'
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)
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return None
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elif result_type == 'run.completed':
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# May include final message
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if 'message' in data:
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return self._normalize_message_completed(data, descriptor)
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# If no message, it's just completion signal
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return None
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elif result_type == 'run.failed':
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error_msg = data.get('error', 'Unknown error')
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error_code = data.get('code', 'unknown')
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retryable = data.get('retryable', False)
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raise RunnerExecutionError(
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descriptor.id,
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f'{error_msg} (code: {error_code})',
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retryable=retryable,
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)
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elif result_type == 'action.requested':
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# Reserved for EBA - log only, don't execute
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self.ap.logger.info(
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f'Runner {descriptor.id} requested action (not executed in current phase): '
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f'{data.get("action", "unknown")}'
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)
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return None
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else:
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# Unknown type - warn and ignore (SDK v1 only)
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self.ap.logger.warning(
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f'Runner {descriptor.id} returned unknown result type: {result_type}. '
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f'Expected SDK v1 types (message.delta, message.completed, run.completed, run.failed, etc.)'
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)
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return None
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def _normalize_message_delta(
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self,
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data: dict[str, typing.Any],
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descriptor: AgentRunnerDescriptor,
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) -> provider_message.MessageChunk:
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"""Normalize message.delta to MessageChunk."""
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chunk_data = data.get('chunk', {})
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if not chunk_data:
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raise RunnerProtocolError(descriptor.id, 'message.delta missing chunk data')
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try:
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chunk = provider_message.MessageChunk.model_validate(chunk_data)
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return chunk
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except Exception as e:
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raise RunnerProtocolError(descriptor.id, f'Invalid chunk schema: {e}')
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def _normalize_message_completed(
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self,
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data: dict[str, typing.Any],
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descriptor: AgentRunnerDescriptor,
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) -> provider_message.Message:
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"""Normalize message.completed to Message."""
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message_data = data.get('message', {})
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if not message_data:
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raise RunnerProtocolError(descriptor.id, 'message.completed missing message data')
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try:
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msg = provider_message.Message.model_validate(message_data)
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return msg
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except Exception as e:
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raise RunnerProtocolError(descriptor.id, f'Invalid message schema: {e}')
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