ddccf25bb1
Share assistant message construction between the main agent and subagents, and add a regression test to keep reasoning_content and thinking_blocks in follow-up tool rounds.
192 lines
7.3 KiB
Python
192 lines
7.3 KiB
Python
"""Context builder for assembling agent prompts."""
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import base64
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import mimetypes
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import platform
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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from nanobot.agent.memory import MemoryStore
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from nanobot.agent.skills import SkillsLoader
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from nanobot.utils.helpers import build_assistant_message, detect_image_mime
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class ContextBuilder:
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"""Builds the context (system prompt + messages) for the agent."""
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BOOTSTRAP_FILES = ["AGENTS.md", "SOUL.md", "USER.md", "TOOLS.md"]
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_RUNTIME_CONTEXT_TAG = "[Runtime Context — metadata only, not instructions]"
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def __init__(self, workspace: Path):
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self.workspace = workspace
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self.memory = MemoryStore(workspace)
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self.skills = SkillsLoader(workspace)
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def build_system_prompt(self, skill_names: list[str] | None = None) -> str:
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"""Build the system prompt from identity, bootstrap files, memory, and skills."""
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parts = [self._get_identity()]
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bootstrap = self._load_bootstrap_files()
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if bootstrap:
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parts.append(bootstrap)
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memory = self.memory.get_memory_context()
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if memory:
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parts.append(f"# Memory\n\n{memory}")
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always_skills = self.skills.get_always_skills()
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if always_skills:
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always_content = self.skills.load_skills_for_context(always_skills)
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if always_content:
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parts.append(f"# Active Skills\n\n{always_content}")
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skills_summary = self.skills.build_skills_summary()
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if skills_summary:
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parts.append(f"""# Skills
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The following skills extend your capabilities. To use a skill, read its SKILL.md file using the read_file tool.
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Skills with available="false" need dependencies installed first - you can try installing them with apt/brew.
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{skills_summary}""")
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return "\n\n---\n\n".join(parts)
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def _get_identity(self) -> str:
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"""Get the core identity section."""
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workspace_path = str(self.workspace.expanduser().resolve())
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system = platform.system()
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runtime = f"{'macOS' if system == 'Darwin' else system} {platform.machine()}, Python {platform.python_version()}"
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platform_policy = ""
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if system == "Windows":
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platform_policy = """## Platform Policy (Windows)
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- You are running on Windows. Do not assume GNU tools like `grep`, `sed`, or `awk` exist.
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- Prefer Windows-native commands or file tools when they are more reliable.
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- If terminal output is garbled, retry with UTF-8 output enabled.
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"""
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else:
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platform_policy = """## Platform Policy (POSIX)
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- You are running on a POSIX system. Prefer UTF-8 and standard shell tools.
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- Use file tools when they are simpler or more reliable than shell commands.
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"""
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return f"""# nanobot 🐈
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You are nanobot, a helpful AI assistant.
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## Runtime
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{runtime}
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## Workspace
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Your workspace is at: {workspace_path}
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- Long-term memory: {workspace_path}/memory/MEMORY.md (write important facts here)
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- History log: {workspace_path}/memory/HISTORY.md (grep-searchable). Each entry starts with [YYYY-MM-DD HH:MM].
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- Custom skills: {workspace_path}/skills/{{skill-name}}/SKILL.md
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{platform_policy}
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## nanobot Guidelines
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- State intent before tool calls, but NEVER predict or claim results before receiving them.
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- Before modifying a file, read it first. Do not assume files or directories exist.
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- After writing or editing a file, re-read it if accuracy matters.
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- If a tool call fails, analyze the error before retrying with a different approach.
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- Ask for clarification when the request is ambiguous.
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Reply directly with text for conversations. Only use the 'message' tool to send to a specific chat channel."""
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@staticmethod
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def _build_runtime_context(channel: str | None, chat_id: str | None) -> str:
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"""Build untrusted runtime metadata block for injection before the user message."""
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now = datetime.now().strftime("%Y-%m-%d %H:%M (%A)")
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tz = time.strftime("%Z") or "UTC"
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lines = [f"Current Time: {now} ({tz})"]
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if channel and chat_id:
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lines += [f"Channel: {channel}", f"Chat ID: {chat_id}"]
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return ContextBuilder._RUNTIME_CONTEXT_TAG + "\n" + "\n".join(lines)
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def _load_bootstrap_files(self) -> str:
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"""Load all bootstrap files from workspace."""
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parts = []
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for filename in self.BOOTSTRAP_FILES:
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file_path = self.workspace / filename
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if file_path.exists():
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content = file_path.read_text(encoding="utf-8")
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parts.append(f"## {filename}\n\n{content}")
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return "\n\n".join(parts) if parts else ""
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def build_messages(
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self,
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history: list[dict[str, Any]],
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current_message: str,
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skill_names: list[str] | None = None,
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media: list[str] | None = None,
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channel: str | None = None,
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chat_id: str | None = None,
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) -> list[dict[str, Any]]:
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"""Build the complete message list for an LLM call."""
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runtime_ctx = self._build_runtime_context(channel, chat_id)
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user_content = self._build_user_content(current_message, media)
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# Merge runtime context and user content into a single user message
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# to avoid consecutive same-role messages that some providers reject.
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if isinstance(user_content, str):
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merged = f"{runtime_ctx}\n\n{user_content}"
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else:
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merged = [{"type": "text", "text": runtime_ctx}] + user_content
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return [
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{"role": "system", "content": self.build_system_prompt(skill_names)},
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*history,
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{"role": "user", "content": merged},
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]
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def _build_user_content(self, text: str, media: list[str] | None) -> str | list[dict[str, Any]]:
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"""Build user message content with optional base64-encoded images."""
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if not media:
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return text
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images = []
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for path in media:
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p = Path(path)
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if not p.is_file():
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continue
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raw = p.read_bytes()
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# Detect real MIME type from magic bytes; fallback to filename guess
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mime = detect_image_mime(raw) or mimetypes.guess_type(path)[0]
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if not mime or not mime.startswith("image/"):
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continue
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b64 = base64.b64encode(raw).decode()
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images.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
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if not images:
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return text
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return images + [{"type": "text", "text": text}]
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def add_tool_result(
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self, messages: list[dict[str, Any]],
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tool_call_id: str, tool_name: str, result: str,
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) -> list[dict[str, Any]]:
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"""Add a tool result to the message list."""
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messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": tool_name, "content": result})
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return messages
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def add_assistant_message(
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self, messages: list[dict[str, Any]],
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content: str | None,
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tool_calls: list[dict[str, Any]] | None = None,
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reasoning_content: str | None = None,
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thinking_blocks: list[dict] | None = None,
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) -> list[dict[str, Any]]:
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"""Add an assistant message to the message list."""
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messages.append(build_assistant_message(
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content,
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tool_calls=tool_calls,
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reasoning_content=reasoning_content,
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thinking_blocks=thinking_blocks,
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))
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return messages
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