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feat(agent-runner): integrate AgentRunner Protocol v1 with plugin system

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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
2026-05-10 10:11:54 +08:00
parent 98bbdf268f
commit 3812bd97eb
29 changed files with 3960 additions and 289 deletions
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src/langbot/pkg/pipeline/preproc/preproc.py
+40 -16
View File
@@ -9,6 +9,12 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.platform.events as platform_events
from ...agent.runner.config_migration import ConfigMigration
# Official local-agent runner ID
LOCAL_AGENT_RUNNER_ID = 'plugin:langbot/local-agent/default'
@stage.stage_class('PreProcessor')
class PreProcessor(stage.PipelineStage):
@@ -31,19 +37,28 @@ class PreProcessor(stage.PipelineStage):
stage_inst_name: str,
) -> entities.StageProcessResult:
"""Process"""
selected_runner = query.pipeline_config['ai']['runner']['runner']
include_skill_authoring = (
selected_runner == 'local-agent' and getattr(self.ap, 'skill_service', None) is not None
)
# Resolve runner ID using ConfigMigration (supports both new and old formats)
runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
# Get runner config (from new ai.runner_config or old ai.<runner-name>)
runner_config = ConfigMigration.resolve_runner_config(query.pipeline_config, runner_id) if runner_id else {}
session = await self.ap.sess_mgr.get_session(query)
# Determine if this is a local-agent runner (built-in LLM capabilities)
# Check by runner_id OR by legacy runner field for backward compatibility
is_local_agent = runner_id == LOCAL_AGENT_RUNNER_ID or (
runner_id is None and
query.pipeline_config.get('ai', {}).get('runner', {}).get('runner') == 'local-agent'
)
include_skill_authoring = is_local_agent and getattr(self.ap, 'skill_service', None) is not None
# When not local-agent, llm_model is None
llm_model = None
if selected_runner == 'local-agent':
if is_local_agent:
# Read model config — new format is { primary: str, fallbacks: [str] },
# but handle legacy plain string for backward compatibility
model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
model_config = runner_config.get('model', {})
if isinstance(model_config, str):
# Legacy format: plain UUID string
primary_uuid = model_config
@@ -70,10 +85,17 @@ class PreProcessor(stage.PipelineStage):
if valid_fallbacks:
query.variables['_fallback_model_uuids'] = valid_fallbacks
# Get prompt config - for local-agent, use runner_config; for others, use default prompt
prompt_config = runner_config.get('prompt', [
{'role': 'system', 'content': 'You are a helpful assistant.'}
]) if is_local_agent else [
{'role': 'system', 'content': 'You are a helpful assistant.'}
]
conversation = await self.ap.sess_mgr.get_conversation(
query,
session,
query.pipeline_config['ai']['local-agent']['prompt'],
prompt_config,
query.pipeline_uuid,
query.bot_uuid,
)
@@ -82,7 +104,7 @@ class PreProcessor(stage.PipelineStage):
# been idle for longer than the configured conversation expire time.
# The idle window is measured from the last preprocess/update time, not
# from the conversation creation time.
conversation_expire_time = query.pipeline_config.get('ai', {}).get('runner', {}).get('expire-time', None)
conversation_expire_time = ConfigMigration.get_expire_time(query.pipeline_config)
now = datetime.datetime.now()
if conversation_expire_time is not None and conversation_expire_time > 0:
last_update_time = getattr(conversation, 'update_time', None) or getattr(conversation, 'create_time', None)
@@ -104,7 +126,7 @@ class PreProcessor(stage.PipelineStage):
query.prompt = conversation.prompt.copy()
query.messages = conversation.messages.copy()
if selected_runner == 'local-agent':
if is_local_agent:
query.use_funcs = []
if llm_model:
query.use_llm_model_uuid = llm_model.model_entity.uuid
@@ -160,7 +182,7 @@ class PreProcessor(stage.PipelineStage):
# Check if this model supports vision, if not, remove all images
# TODO this checking should be performed in runner, and in this stage, the image should be reserved
if (
selected_runner == 'local-agent'
is_local_agent
and llm_model
and not llm_model.model_entity.abilities.__contains__('vision')
):
@@ -173,14 +195,15 @@ class PreProcessor(stage.PipelineStage):
content_list: list[provider_message.ContentElement] = []
plain_text = ''
quote_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')
quote_msg = query.pipeline_config['trigger'].get('misc', {}).get('combine-quote-message', False)
for me in query.message_chain:
if isinstance(me, platform_message.Plain):
content_list.append(provider_message.ContentElement.from_text(me.text))
plain_text += me.text
elif isinstance(me, platform_message.Image):
if selected_runner != 'local-agent' or (
# Allow images for non-local-agent runners or if local-agent has vision
if not is_local_agent or (
llm_model and llm_model.model_entity.abilities.__contains__('vision')
):
if me.base64 is not None:
@@ -201,7 +224,7 @@ class PreProcessor(stage.PipelineStage):
if isinstance(msg, platform_message.Plain):
content_list.append(provider_message.ContentElement.from_text(msg.text))
elif isinstance(msg, platform_message.Image):
if selected_runner != 'local-agent' or (
if not is_local_agent or (
llm_model and llm_model.model_entity.abilities.__contains__('vision')
):
if msg.base64 is not None:
@@ -225,9 +248,10 @@ class PreProcessor(stage.PipelineStage):
# Extract knowledge base UUIDs into query variables so plugins can modify them
# during PromptPreProcessing before the runner performs retrieval.
kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
# Only for local-agent runner
kb_uuids = runner_config.get('knowledge-bases', []) if is_local_agent else []
if not kb_uuids:
old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
old_kb_uuid = runner_config.get('knowledge-base', '') if is_local_agent else ''
if old_kb_uuid and old_kb_uuid != '__none__':
kb_uuids = [old_kb_uuid]
query.variables['_knowledge_base_uuids'] = list(kb_uuids)
@@ -260,7 +284,7 @@ class PreProcessor(stage.PipelineStage):
# only) into the system prompt. The contributor's original PR
# relied on this injection; without it the LLM never discovers
# the skills are there and just calls native tools instead.
if selected_runner == 'local-agent' and self.ap.skill_mgr:
if is_local_agent and self.ap.skill_mgr:
pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid)
extensions_prefs = (pipeline_data or {}).get('extensions_preferences', {})
enable_all_skills = extensions_prefs.get('enable_all_skills', True)