feat(llm): add LLM profiles #1843
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,132 @@ | ||
| """Create and use an LLM profile with :class:`LLMRegistry`. | ||
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| Run with:: | ||
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| uv run python examples/01_standalone_sdk/36_llm_profiles.py | ||
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| Profiles are stored under ``$LLM_PROFILES_DIR/<name>.json`` when the env var is | ||
| set, otherwise ``~/.openhands/llm-profiles/<name>.json``. | ||
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| Set ``LLM_PROFILE_NAME`` to choose which profile file to load. | ||
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| Notes on credentials: | ||
| - New profiles include API keys by default when saved | ||
| - To omit secrets on disk, pass include_secrets=False to LLMRegistry.save_profile | ||
| """ | ||
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| import json | ||
| import os | ||
| from pathlib import Path | ||
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| from pydantic import SecretStr | ||
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| from openhands.sdk import ( | ||
| LLM, | ||
| Agent, | ||
| Conversation, | ||
| LLMRegistry, | ||
| Tool, | ||
| ) | ||
| from openhands.tools.terminal import TerminalTool | ||
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| PROFILE_NAME = os.getenv("LLM_PROFILE_NAME", "gpt-5-mini") | ||
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| def ensure_profile_exists(registry: LLMRegistry, name: str) -> None: | ||
| """Create a starter profile in the default directory when missing.""" | ||
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| if name in registry.list_profiles(): | ||
| return | ||
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| model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929") | ||
| base_url = os.getenv("LLM_BASE_URL") | ||
| api_key = os.getenv("LLM_API_KEY") | ||
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| profile_defaults = LLM( | ||
| usage_id="agent", | ||
| model=model, | ||
| base_url=base_url, | ||
| api_key=SecretStr(api_key) if api_key else None, | ||
| temperature=0.2, | ||
| max_output_tokens=4096, | ||
| ) | ||
| path = registry.save_profile(name, profile_defaults) | ||
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| print(f"Created profile '{name}' at {path}") | ||
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| def load_profile(registry: LLMRegistry, name: str) -> LLM: | ||
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| """Load profile and merge credentials from environment if needed. | ||
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| Note: Profiles should be saved without secrets (include_secrets=False) | ||
| and credentials provided via environment variables for better security. | ||
| """ | ||
| llm = registry.load_profile(name) | ||
| # If profile was saved without secrets, allow providing API key via env var | ||
| if llm.api_key is None: | ||
| api_key = os.getenv("LLM_API_KEY") | ||
| if api_key: | ||
| llm = llm.model_copy(update={"api_key": SecretStr(api_key)}) | ||
| return llm | ||
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| registry = LLMRegistry() | ||
| ensure_profile_exists(registry, PROFILE_NAME) | ||
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| llm = load_profile(registry, PROFILE_NAME) | ||
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| tools = [Tool(name=TerminalTool.name)] | ||
| agent = Agent(llm=llm, tools=tools) | ||
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| workspace_dir = Path(os.getcwd()) | ||
| summary_path = workspace_dir / "summary_readme.md" | ||
| if summary_path.exists(): | ||
| summary_path.unlink() | ||
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| persistence_root = workspace_dir / ".conversations_llm_profiles" | ||
| conversation = Conversation( | ||
| agent=agent, | ||
| workspace=str(workspace_dir), | ||
| persistence_dir=str(persistence_root), | ||
| visualizer=None, | ||
| ) | ||
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| conversation.send_message( | ||
| "Read README.md in this workspace, create a concise summary in " | ||
| "summary_readme.md (overwrite it if it exists), and respond with " | ||
| "SUMMARY_READY when the file is written." | ||
| ) | ||
| conversation.run() | ||
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| if summary_path.exists(): | ||
| print(f"summary_readme.md written to {summary_path}") | ||
| else: | ||
| print("summary_readme.md not found after first run") | ||
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| conversation.send_message( | ||
| "Thanks! Delete summary_readme.md from the workspace and respond with " | ||
| "SUMMARY_REMOVED once it is gone." | ||
| ) | ||
| conversation.run() | ||
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| if summary_path.exists(): | ||
| print("summary_readme.md still present after deletion request") | ||
| else: | ||
| print("summary_readme.md removed") | ||
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| persistence_dir = conversation.state.persistence_dir | ||
| if persistence_dir is None: | ||
| raise RuntimeError("Conversation did not persist base state to disk") | ||
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| base_state_path = Path(persistence_dir) / "base_state.json" | ||
| state_payload = json.loads(base_state_path.read_text()) | ||
| llm_entry = state_payload.get("agent", {}).get("llm", {}) | ||
| profile_in_state = llm_entry.get("profile_id") | ||
| kind_in_state = llm_entry.get("kind") | ||
| print(f"Profile recorded in base_state.json: {kind_in_state} / {profile_in_state}") | ||
| if kind_in_state != "profile_ref" or profile_in_state != PROFILE_NAME: | ||
| print( | ||
| "Warning: base_state.json did not persist the expected profile_ref payload." | ||
| " This likely means your runtime LLM did not have profile_id set," | ||
| " or persistence was configured differently." | ||
| ) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| { | ||
| "model": "litellm_proxy/openai/gpt-5-mini", | ||
| "base_url": "https://llm-proxy.eval.all-hands.dev", | ||
| "temperature": 0.2, | ||
| "max_output_tokens": 4096, | ||
| "usage_id": "agent" | ||
| } |
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🔴 Critical: This documentation is misleading and dangerous. The current default behavior (
include_secrets=True) could lead users to accidentally commit API keys.Uh oh!
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Same as below