24fec62d7f
Forked clean from public hackathon repo (Starfire-AgentTeam, BSL 1.1) with full rebrand to Molecule AI under github.com/Molecule-AI/molecule-monorepo. Brand: Starfire → Molecule AI. Slug: starfire / agent-molecule → molecule. Env vars: STARFIRE_* → MOLECULE_*. Go module: github.com/agent-molecule/platform → github.com/Molecule-AI/molecule-monorepo/platform. Python packages: starfire_plugin → molecule_plugin, starfire_agent → molecule_agent. DB: agentmolecule → molecule. History truncated; see public repo for prior commits and contributor attribution. Verified green: go test -race ./... (platform), pytest (workspace-template 1129 + sdk 132), vitest (canvas 352), build (mcp). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
"""LangGraph adapter — Python-based ReAct agent with skills, tools, and plugins."""
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import os
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import logging
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from adapters.base import BaseAdapter, AdapterConfig
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from a2a.server.agent_execution import AgentExecutor
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logger = logging.getLogger(__name__)
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class LangGraphAdapter(BaseAdapter):
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@staticmethod
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def name() -> str:
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return "langgraph"
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@staticmethod
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def display_name() -> str:
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return "LangGraph"
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@staticmethod
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def description() -> str:
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return "LangGraph ReAct agent — Python-based with skills, tools, plugins, and peer coordination"
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@staticmethod
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def get_config_schema() -> dict:
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return {
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"model": {"type": "string", "description": "LangChain model string (e.g. openrouter:google/gemini-2.5-flash)"},
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"skills": {"type": "array", "items": {"type": "string"}, "description": "Skill folder names to load"},
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"tools": {"type": "array", "items": {"type": "string"}, "description": "Built-in tools (web_search, filesystem, etc.)"},
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}
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def __init__(self):
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self.loaded_skills = []
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self.all_tools = []
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self.system_prompt = None
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async def setup(self, config: AdapterConfig) -> None:
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result = await self._common_setup(config)
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self.loaded_skills = result.loaded_skills
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self.all_tools = result.langchain_tools
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self.system_prompt = result.system_prompt
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async def create_executor(self, config: AdapterConfig) -> AgentExecutor:
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from agent import create_agent
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from a2a_executor import LangGraphA2AExecutor
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agent = create_agent(config.model, self.all_tools, self.system_prompt)
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return LangGraphA2AExecutor(agent, heartbeat=config.heartbeat, model=config.model)
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