feat: add adapter code + Dockerfile for standalone deployment
Adapters extracted from molecule-monorepo/workspace-template. Uses molecule-ai-workspace-runtime PyPI package for shared infrastructure. - adapter.py — runtime-specific adapter class - requirements.txt — runtime-specific deps + molecule-ai-workspace-runtime - Dockerfile — FROM python:3.11-slim, pip install, COPY adapter, molecule-runtime entrypoint - ADAPTER_MODULE=adapter tells the runtime to load this repo's Adapter class Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Hongming Wang
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Dockerfile
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Dockerfile
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FROM python:3.11-slim
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl gosu ca-certificates \
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&& rm -rf /var/lib/apt/lists/*
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RUN useradd -u 1000 -m -s /bin/bash agent
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY adapter.py .
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COPY __init__.py .
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ENV ADAPTER_MODULE=adapter
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ENTRYPOINT ["molecule-runtime"]
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__init__.py
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__init__.py
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from .adapter import DeepAgentsAdapter
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Adapter = DeepAgentsAdapter
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adapter.py
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adapter.py
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"""DeepAgents adapter — fully utilizing the DeepAgents SDK.
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Uses create_deep_agent() with:
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- FilesystemBackend(/workspace) — persistent file access across messages
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- MemorySaver checkpointer — session continuity
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- Memory files — CLAUDE.md loaded natively
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- Filesystem permissions — restrict writes to /workspace and /configs
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- InMemoryCache — avoid repeat API calls
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- All built-in tools: write_todos, read_file, write_file, edit_file,
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ls, glob, grep, execute, task
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Supports: anthropic, openai, openrouter, groq, cerebras, google_genai, ollama.
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"""
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import os
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import glob as globmod
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import logging
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from molecule_runtime.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 DeepAgentsAdapter(BaseAdapter):
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def __init__(self):
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self.agent = None
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self._checkpointer = None
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@staticmethod
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def name() -> str:
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return "deepagents"
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@staticmethod
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def display_name() -> str:
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return "DeepAgents"
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@staticmethod
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def description() -> str:
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return "LangChain DeepAgents — planning, filesystem, sub-agents, shell execution, session persistence"
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@staticmethod
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def get_config_schema() -> dict:
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return {
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"model": {
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"type": "string",
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"description": "provider:model (e.g. google_genai:gemini-2.5-flash, groq:llama-3.3-70b-versatile)",
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"default": "google_genai:gemini-2.5-flash",
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},
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"skills": {"type": "array", "items": {"type": "string"}},
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"tools": {"type": "array", "items": {"type": "string"}},
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}
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def _create_llm(self, model_str: str):
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"""Create a LangChain LLM from a provider:model string."""
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if ":" in model_str:
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provider, model_name = model_str.split(":", 1)
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else:
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provider, model_name = "anthropic", model_str
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if provider == "openai":
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from langchain_openai import ChatOpenAI
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kwargs = {"model": model_name}
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base_url = os.environ.get("OPENAI_BASE_URL", "")
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if base_url:
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kwargs["openai_api_base"] = base_url
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return ChatOpenAI(**kwargs)
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elif provider == "openrouter":
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from langchain_openai import ChatOpenAI
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return ChatOpenAI(
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model=model_name,
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openai_api_key=os.environ.get("OPENROUTER_API_KEY", os.environ.get("OPENAI_API_KEY", "")),
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openai_api_base="https://openrouter.ai/api/v1",
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max_tokens=int(os.environ.get("MAX_TOKENS", "2048")),
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)
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elif provider == "groq":
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from langchain_openai import ChatOpenAI
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return ChatOpenAI(
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model=model_name,
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openai_api_key=os.environ.get("GROQ_API_KEY", ""),
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openai_api_base="https://api.groq.com/openai/v1",
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)
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elif provider == "cerebras":
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from langchain_openai import ChatOpenAI
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return ChatOpenAI(
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model=model_name,
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openai_api_key=os.environ.get("CEREBRAS_API_KEY", ""),
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openai_api_base="https://api.cerebras.ai/v1",
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)
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elif provider == "qianfan":
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from langchain_openai import ChatOpenAI
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return ChatOpenAI(
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model=model_name,
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openai_api_key=os.environ.get("QIANFAN_API_KEY", os.environ.get("AISTUDIO_API_KEY", "")),
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openai_api_base="https://qianfan.baidubce.com/v2",
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)
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elif provider == "anthropic":
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from langchain_anthropic import ChatAnthropic
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kwargs = {"model": model_name}
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base_url = os.environ.get("ANTHROPIC_BASE_URL", "")
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if base_url:
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kwargs["anthropic_api_url"] = base_url
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return ChatAnthropic(**kwargs)
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elif provider == "google_genai":
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from langchain_google_genai import ChatGoogleGenerativeAI
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return ChatGoogleGenerativeAI(model=model_name)
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elif provider == "ollama":
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from langchain_ollama import ChatOllama
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return ChatOllama(model=model_name)
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else:
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raise ValueError(f"Unsupported model provider: {provider}")
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async def setup(self, config: AdapterConfig) -> None:
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try:
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from deepagents import create_deep_agent, FilesystemPermission
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from deepagents.backends import FilesystemBackend
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from langgraph.checkpoint.memory import MemorySaver
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from langchain_core.caches import InMemoryCache
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except ImportError as e:
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raise RuntimeError(f"deepagents not installed: {e}")
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result = await self._common_setup(config)
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logger.info("DeepAgents platform tools: %s", [t.name for t in result.langchain_tools])
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llm = self._create_llm(config.model)
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# FilesystemBackend — persistent file access
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workspace_dir = "/workspace" if os.path.isdir("/workspace") else "/configs"
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# virtual_mode=False: read/write the real bind-mounted filesystem so
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# read_file/ls/write_file/edit_file match what `bash` sees. With
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# virtual_mode=True agents operate on an in-memory snapshot and
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# report real files as "missing" (and writes don't persist across
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# restarts). Permissions below still scope access to /workspace + /configs.
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backend = FilesystemBackend(root_dir=workspace_dir, virtual_mode=False)
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# MemorySaver — session continuity
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self._checkpointer = MemorySaver()
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# Memory — load CLAUDE.md natively
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memory_files = []
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claude_md = os.path.join(config.config_path, "CLAUDE.md")
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if os.path.exists(claude_md):
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memory_files.append(claude_md)
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# Filesystem permissions
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permissions = [
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FilesystemPermission(operations=["read", "write"], paths=["/workspace/**"], mode="allow"),
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FilesystemPermission(operations=["read", "write"], paths=["/configs/**"], mode="allow"),
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]
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# Native skills from /configs/skills/*.py
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deepagent_skills = []
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skills_dir = os.path.join(config.config_path, "skills")
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if os.path.isdir(skills_dir):
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deepagent_skills = globmod.glob(os.path.join(skills_dir, "**", "*.py"), recursive=True)
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# LLM cache
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cache = InMemoryCache()
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self.agent = create_deep_agent(
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model=llm,
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tools=result.langchain_tools,
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system_prompt=result.system_prompt,
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backend=backend,
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checkpointer=self._checkpointer,
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memory=memory_files if memory_files else None,
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permissions=permissions,
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skills=deepagent_skills if deepagent_skills else None,
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cache=cache,
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)
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logger.info(
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"DeepAgents: %d tools, backend=%s, checkpointer=MemorySaver, "
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"cache=InMemoryCache, memory=%d, permissions=%d, skills=%d",
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len(result.langchain_tools), type(backend).__name__,
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len(memory_files), len(permissions), len(deepagent_skills),
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)
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async def create_executor(self, config: AdapterConfig) -> AgentExecutor:
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if self.agent is None:
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raise RuntimeError("setup() must be called before create_executor()")
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from a2a_executor import LangGraphA2AExecutor
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return LangGraphA2AExecutor(self.agent, heartbeat=config.heartbeat, model=config.model)
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8
requirements.txt
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8
requirements.txt
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# Molecule AI workspace runtime — shared infrastructure
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molecule-ai-workspace-runtime>=0.1.0
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# DeepAgents adapter specific deps
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deepagents>=0.5.0
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langchain-openai>=1.0.0
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langchain-google-genai>=2.1.0
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langchain-anthropic>=1.4.0
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