Hans Heinemann
45e3b7663e
--dry-run was crashing with ModuleNotFoundError because: 1. adapters/llm/anthropic.py imported 'anthropic' at module level 2. TeamRunner.__init__ always built the LLM adapter regardless of dry_run flag Fixes: - Move 'import anthropic' inside AnthropicAdapter.__init__ (lazy import) so the module loads cleanly without the SDK installed - In TeamRunner.__init__, wrap _build_llm in a try/except when dry_run=True so missing adapter deps are logged as warnings, not fatal errors dry-run now works with no third-party packages installed.
140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
"""
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adapters/llm/anthropic.py
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Anthropic Claude LLM adapter — Phase 2 implementation.
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Uses the ``anthropic`` SDK to call Claude models. Model selection is driven
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by the capability_map in team.yaml so the adapter stays provider-agnostic in
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configuration.
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"""
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from __future__ import annotations
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import os
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from adapters.base.llm import LLMAdapter
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class AnthropicAdapter(LLMAdapter):
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"""
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LLM adapter for Anthropic Claude models.
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Reads model configuration from the loaded team.yaml config dict::
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models:
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default_max_tokens: 4096 # fallback max_tokens for all calls
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default_temperature: 0 # fallback temperature for all calls
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capability_map:
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reasoning-heavy:
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anthropic: claude-opus-4-6
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capable:
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anthropic: claude-sonnet-4-6
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fast-cheap:
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anthropic: claude-haiku-3-5
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The provider key used when looking up ``capability_map`` is hardcoded to
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``"anthropic"`` — the adapter knows its own provider; there is no need for
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a separate ``models.provider`` config field.
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Both ``default_max_tokens`` and ``default_temperature`` can be overridden
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per-call via the ``context`` dict passed to :meth:`complete`.
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Environment variables
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---------------------
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ANTHROPIC_API_KEY : Required. Authenticates with the Anthropic API.
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"""
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def __init__(self, config: dict) -> None:
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"""
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Initialise the Anthropic adapter.
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Parameters
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----------
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config : Loaded team.yaml config dict.
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Raises
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------
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ValueError
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If ANTHROPIC_API_KEY is not set in the environment.
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"""
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try:
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import anthropic as _anthropic
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except ModuleNotFoundError as exc:
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raise ImportError(
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"The 'anthropic' package is required for AnthropicAdapter. "
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"Install it with: pip install anthropic"
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) from exc
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self._config = config
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api_key = os.environ.get("ANTHROPIC_API_KEY")
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if not api_key:
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raise ValueError(
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"ANTHROPIC_API_KEY environment variable is not set. "
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"Export it before running the-agency."
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)
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self._client = _anthropic.Anthropic(api_key=api_key)
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self._models_cfg: dict = config.get("models", {})
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self._default_max_tokens: int = self._models_cfg.get("default_max_tokens", 4096)
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self._default_temperature: float = self._models_cfg.get("default_temperature", 0)
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def complete(self, prompt: str, capability: str, context: dict) -> str:
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"""
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Send a prompt to a Claude model and return the text response.
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Parameters
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----------
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prompt : User-role prompt content.
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capability : One of "reasoning-heavy" | "capable" | "fast-cheap".
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context : Optional per-call overrides:
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system_prompt (str) — prepended as the system turn.
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max_tokens (int) — defaults to models.default_max_tokens in team.yaml.
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temperature (float) — defaults to models.default_temperature in team.yaml.
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Returns
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-------
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The model's text completion as a plain string.
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"""
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model = self.resolve_model(capability)
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max_tokens: int = context.get("max_tokens", self._default_max_tokens)
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temperature: float = context.get("temperature", self._default_temperature)
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system_prompt: str = context.get("system_prompt", "")
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create_kwargs: dict = {
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"model": model,
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"max_tokens": max_tokens,
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"messages": [{"role": "user", "content": prompt}],
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}
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if system_prompt:
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create_kwargs["system"] = system_prompt
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if temperature != 0.0:
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create_kwargs["temperature"] = temperature
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response = self._client.messages.create(**create_kwargs)
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return response.content[0].text
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def resolve_model(self, capability: str) -> str:
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"""
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Map a capability string to the Anthropic model identifier.
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Looks up ``config.models.capability_map[capability][provider]``.
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Falls back to the "capable" tier model if the capability is unknown.
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Parameters
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----------
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capability : One of "reasoning-heavy" | "capable" | "fast-cheap".
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Returns
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-------
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Anthropic model identifier (e.g. "claude-opus-4-6").
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"""
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# The adapter knows its own provider — no need to read it from config.
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cap_map: dict = self._models_cfg.get("capability_map", {})
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if capability in cap_map and "anthropic" in cap_map[capability]:
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return cap_map[capability]["anthropic"]
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# Fall back to "capable" tier
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if "capable" in cap_map and "anthropic" in cap_map["capable"]:
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return cap_map["capable"]["anthropic"]
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# Hard-coded last resort
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return "claude-sonnet-4-6"
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