@samitouri / QOSami_Agents_Zero-2 / commits / 154b92cf

browser-use downgrade to 0.2.5

frdel committed Aug 27, 2025 at 21:43 UTC 154b92cfd52950547556c675517f4f3c23d5f19b

3 files changed +53 -219

models.py

+50 -215

@@ -23,7 +23,6 @@ from python.helpers.dotenv import load_dotenv

23 from python.helpers.providers import get_provider_config

24 from python.helpers.rate_limiter import RateLimiter

25 from python.helpers.tokens import approximate_tokens

26 -from python.helpers import dirty_json

27 from langchain_core.language_models.chat_models import SimpleChatModel

28 from langchain_core.outputs.chat_generation import ChatGenerationChunk

@@ -91,7 +90,6 @@ class ChatChunk(TypedDict):

90 rate_limiters: dict[str, RateLimiter] = {}

91 api_keys_round_robin: dict[str, int] = {}

94 -

93 def get_api_key(service: str) -> str:

94 # get api key for the service

95 key = (

@@ -118,14 +116,7 @@ def get_rate_limiter(

116 limiter.limits["output"] = output or 0

117 return limiter

118

121 -

122 -async def apply_rate_limiter(

123 - model_config: ModelConfig | None,

124 - input_text: str,

125 - rate_limiter_callback: (

126 - Callable[[str, str, int, int], Awaitable[bool]] | None

127 - ) = None,

128 -):

119 +async def apply_rate_limiter(model_config: ModelConfig|None, input_text: str, rate_limiter_callback: Callable[[str, str, int, int], Awaitable[bool]] | None = None):

120 if not model_config:

121 return

122 limiter = get_rate_limiter(

@@ -140,41 +131,25 @@ async def apply_rate_limiter(

131 await limiter.wait(rate_limiter_callback)

132 return limiter

133

143 -

144 -def apply_rate_limiter_sync(

145 - model_config: ModelConfig | None,

146 - input_text: str,

147 - rate_limiter_callback: (

148 - Callable[[str, str, int, int], Awaitable[bool]] | None

149 - ) = None,

150 -):

134 +def apply_rate_limiter_sync(model_config: ModelConfig|None, input_text: str, rate_limiter_callback: Callable[[str, str, int, int], Awaitable[bool]] | None = None):

135 if not model_config:

136 return

137 import asyncio, nest_asyncio

154 -

138 nest_asyncio.apply()

156 - return asyncio.run(

157 - apply_rate_limiter(model_config, input_text, rate_limiter_callback)

158 - )

139 + return asyncio.run(apply_rate_limiter(model_config, input_text, rate_limiter_callback))

140

141

142 class LiteLLMChatWrapper(SimpleChatModel):

143 model_name: str

144 provider: str

145 kwargs: dict = {}

165 -

146 +

147 class Config:

148 arbitrary_types_allowed = True

149 extra = "allow" # Allow extra attributes

150 validate_assignment = False # Don't validate on assignment

151

171 - def __init__(

172 - self,

173 - model: str,

174 - provider: str,

175 - model_config: Optional[ModelConfig] = None,

176 - **kwargs: Any,

177 - ):

152 + def __init__(self, model: str, provider: str, model_config: Optional[ModelConfig] = None, **kwargs: Any):

153 model_value = f"{provider}/{model}"

154 super().__init__(model_name=model_value, provider=provider, kwargs=kwargs) # type: ignore

155 # Set A0 model config as instance attribute after parent init

@@ -183,7 +158,7 @@ class LiteLLMChatWrapper(SimpleChatModel):

158 @property

159 def _llm_type(self) -> str:

160 return "litellm-chat"

186 -

161 +

162 def _convert_messages(self, messages: List[BaseMessage]) -> List[dict]:

163 result = []

164 # Map LangChain message types to LiteLLM roles

@@ -194,9 +169,7 @@ class LiteLLMChatWrapper(SimpleChatModel):

169 "tool": "tool",

170 }

171 for m in messages:

197 - m_type = getattr(m, "type", getattr(m, "role", ""))

198 - role = role_mapping.get(m_type, m_type)

199 - content = getattr(m, "content", getattr(m, "text", ""))

172 + role = role_mapping.get(m.type, m.type)

173 message_dict = {"role": role, "content": m.content}

174

175 # Handle tool calls for AI messages

@@ -242,12 +215,12 @@ class LiteLLMChatWrapper(SimpleChatModel):

215 **kwargs: Any,

216 ) -> str:

217 import asyncio

245 -

218 +

219 msgs = self._convert_messages(messages)

247 -

220 +

221 # Apply rate limiting if configured

222 apply_rate_limiter_sync(self.a0_model_conf, str(msgs))

250 -

223 +

224 # Call the model

225 resp = completion(

226 model=self.model_name, messages=msgs, stop=stop, **{**self.kwargs, **kwargs}

@@ -265,12 +238,12 @@ class LiteLLMChatWrapper(SimpleChatModel):

238 **kwargs: Any,

239 ) -> Iterator[ChatGenerationChunk]:

240 import asyncio

268 -

241 +

242 msgs = self._convert_messages(messages)

270 -

243 +

244 # Apply rate limiting if configured

245 apply_rate_limiter_sync(self.a0_model_conf, str(msgs))

273 -

246 +

247 for chunk in completion(

248 model=self.model_name,

249 messages=msgs,

@@ -293,10 +266,11 @@ class LiteLLMChatWrapper(SimpleChatModel):

266 **kwargs: Any,

267 ) -> AsyncIterator[ChatGenerationChunk]:

268 msgs = self._convert_messages(messages)

296 -

269 +

270 # Apply rate limiting if configured

271 await apply_rate_limiter(self.a0_model_conf, str(msgs))

299 -

272 +

273 +

274 response = await acompletion(

275 model=self.model_name,

276 messages=msgs,

@@ -320,9 +294,7 @@ class LiteLLMChatWrapper(SimpleChatModel):

294 response_callback: Callable[[str, str], Awaitable[None]] | None = None,

295 reasoning_callback: Callable[[str, str], Awaitable[None]] | None = None,

296 tokens_callback: Callable[[str, int], Awaitable[None]] | None = None,

323 - rate_limiter_callback: (

324 - Callable[[str, str, int, int], Awaitable[bool]] | None

325 - ) = None,

297 + rate_limiter_callback: Callable[[str, str, int, int], Awaitable[bool]] | None = None,

298 **kwargs: Any,

299 ) -> Tuple[str, str]:

300

@@ -340,9 +312,7 @@ class LiteLLMChatWrapper(SimpleChatModel):

312 msgs_conv = self._convert_messages(messages)

313

314 # Apply rate limiting if configured

343 - limiter = await apply_rate_limiter(

344 - self.a0_model_conf, str(msgs_conv), rate_limiter_callback

345 - )

315 + limiter = await apply_rate_limiter(self.a0_model_conf, str(msgs_conv), rate_limiter_callback)

316

317 # call model

318 _completion = await acompletion(

@@ -390,27 +360,7 @@ class LiteLLMChatWrapper(SimpleChatModel):

360 return response, reasoning

361

362

393 -class AsyncAIChatReplacement:

394 - class _Completions:

395 - def __init__(self, wrapper):

396 - self._wrapper = wrapper

397 -

398 - async def create(self, *args, **kwargs):

399 - # call the async _acall method on the wrapper

400 - return await self._wrapper._acall(*args, **kwargs)

401 -

402 - class _Chat:

403 - def __init__(self, wrapper):

404 - self.completions = AsyncAIChatReplacement._Completions(wrapper)

405 -

406 - def __init__(self, wrapper, *args, **kwargs):

407 - self._wrapper = wrapper

408 - self.chat = AsyncAIChatReplacement._Chat(wrapper)

409 -

410 -

411 -from browser_use.llm import ChatOllama, ChatOpenRouter, ChatGoogle, ChatAnthropic, ChatGroq, ChatOpenAI

412 -

413 -class BrowserCompatibleChatWrapper(ChatOpenRouter):

363 +class BrowserCompatibleChatWrapper(LiteLLMChatWrapper):

364 """

365 A wrapper for browser agent that can filter/sanitize messages

366 before sending them to the LLM.

@@ -418,118 +368,31 @@ class BrowserCompatibleChatWrapper(ChatOpenRouter):

368

369 def __init__(self, *args, **kwargs):

370 turn_off_logging()

421 - # Create the underlying LiteLLM wrapper

422 - self._wrapper = LiteLLMChatWrapper(*args, **kwargs)

371 + super().__init__(*args, **kwargs)

372 # Browser-use may expect a 'model' attribute

424 - self.model = self._wrapper.model_name

425 - self.kwargs = self._wrapper.kwargs

426 -

427 - @property

428 - def model_name(self) -> str:

429 - return self._wrapper.model_name

373 + self.model = self.model_name

374

431 - @property

432 - def provider(self) -> str:

433 - return self._wrapper.provider

434 -

435 - def get_client(self, *args, **kwargs): # type: ignore

436 - return AsyncAIChatReplacement(self, *args, **kwargs)

437 -

438 - # -- Gemini helper -----------------------------------------

439 - def _gemini_clean_and_conform(self, text: str):

440 - obj = None

441 - try:

442 - # dirty_json parser is robust enough to handle markdown fences

443 - obj = dirty_json.parse(text)

444 - except Exception:

445 - return None # return None if parsing fails

446 -

447 - if not isinstance(obj, dict):

448 - return None

449 -

450 - # Conform actions to browser-use expectations

451 - if isinstance(obj.get("action"), list):

452 - normalized_actions = []

453 - for item in obj["action"]:

454 - if not isinstance(item, dict):

455 - continue # Skip non-dict items

456 -

457 - action_key, action_value = next(iter(item.items()), (None, None))

458 - if not action_key:

459 - continue

460 -

461 - # Create a mutable copy of the value

462 - v = (action_value or {}).copy()

463 -

464 - if action_key in ("scroll_down", "scroll_up", "scroll"):

465 - is_down = action_key != "scroll_up"

466 - v.setdefault("down", is_down)

467 - v.setdefault("num_pages", 1.0)

468 - normalized_actions.append({"scroll": v})

469 - elif action_key == "go_to_url":

470 - v.setdefault("new_tab", False)

471 - normalized_actions.append({action_key: v})

472 - elif action_key == "done":

473 - if "text" in v and "data" not in v:

474 - t = v.pop("text", "")

475 - v["data"] = {"title": "Task result", "response": t, "page_summary": t}

476 - v.setdefault("success", True)

477 - normalized_actions.append({action_key: v})

478 - else:

479 - normalized_actions.append(item)

480 - obj["action"] = normalized_actions

481 -

482 - return dirty_json.stringify(obj)

483 -

484 - async def _acall(

375 + def _call(

376 self,

377 messages: List[BaseMessage],

378 stop: Optional[List[str]] = None,

379 run_manager: Optional[CallbackManagerForLLMRun] = None,

380 **kwargs: Any,

490 - ):

491 - # Apply rate limiting if configured

492 - apply_rate_limiter_sync(self._wrapper.a0_model_conf, str(messages))

381 + ) -> str:

382 + turn_off_logging()

383 + result = super()._call(messages, stop, run_manager, **kwargs)

384 + return result

385

494 - # Call the model

495 - try:

496 - model = kwargs.pop("model", None)

497 - kwrgs = {**self._wrapper.kwargs, **kwargs}

498 -

499 - # hack from browser-use to fix json schema for gemini (additionalProperties, $defs, $ref)

500 - if "response_format" in kwrgs and "json_schema" in kwrgs["response_format"] and model.startswith("gemini/"):

501 - kwrgs["response_format"]["json_schema"] = ChatGoogle("")._fix_gemini_schema(self._wrapper.kwargs)

502 -

503 - resp = await acompletion(

504 - model=self._wrapper.model_name,

505 - messages=messages,

506 - stop=stop,

507 - **kwrgs,

508 - )

509 -

510 - # Gemini: strip triple backticks and conform schema

511 - try:

512 - msg = resp.choices[0].message # type: ignore

513 - if self.provider == "gemini" and isinstance(getattr(msg, "content", None), str):

514 - cleaned = self._gemini_clean_and_conform(msg.content) # type: ignore

515 - if cleaned:

516 - msg.content = cleaned

517 - except Exception:

518 - pass

519 -

520 - except Exception as e:

521 - raise e

522 -

523 - # another hack for browser-use post process invalid jsons

524 - try:

525 - if "response_format" in kwrgs and "json_schema" in kwrgs["response_format"] or "json_object" in kwrgs["response_format"]:

526 - if resp.choices[0].message.content is not None and not resp.choices[0].message.content.startswith("{"): # type: ignore

527 - js = dirty_json.parse(resp.choices[0].message.content) # type: ignore

528 - resp.choices[0].message.content = dirty_json.stringify(js) # type: ignore

529 - except Exception as e:

530 - pass

531 -

532 - return resp

386 + async def _astream(

387 + self,

388 + messages: List[BaseMessage],

389 + stop: Optional[List[str]] = None,

390 + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,

391 + **kwargs: Any,

392 + ) -> AsyncIterator[ChatGenerationChunk]:

393 + turn_off_logging()

394 + async for chunk in super()._astream(messages, stop, run_manager, **kwargs):

395 + yield chunk

396

397

398 class LiteLLMEmbeddingWrapper(Embeddings):

@@ -537,21 +400,15 @@ class LiteLLMEmbeddingWrapper(Embeddings):

400 kwargs: dict = {}

401 a0_model_conf: Optional[ModelConfig] = None

402

540 - def __init__(

541 - self,

542 - model: str,

543 - provider: str,

544 - model_config: Optional[ModelConfig] = None,

545 - **kwargs: Any,

546 - ):

403 + def __init__(self, model: str, provider: str, model_config: Optional[ModelConfig] = None, **kwargs: Any):

404 self.model_name = f"{provider}/{model}" if provider != "openai" else model

405 self.kwargs = kwargs

406 self.a0_model_conf = model_config

550 -

407 +

408 def embed_documents(self, texts: List[str]) -> List[List[float]]:

409 # Apply rate limiting if configured

410 apply_rate_limiter_sync(self.a0_model_conf, " ".join(texts))

554 -

411 +

412 resp = embedding(model=self.model_name, input=texts, **self.kwargs)

413 return [

414 item.get("embedding") if isinstance(item, dict) else item.embedding # type: ignore

@@ -561,7 +418,7 @@ class LiteLLMEmbeddingWrapper(Embeddings):

418 def embed_query(self, text: str) -> List[float]:

419 # Apply rate limiting if configured

420 apply_rate_limiter_sync(self.a0_model_conf, text)

564 -

421 +

422 resp = embedding(model=self.model_name, input=[text], **self.kwargs)

423 item = resp.data[0] # type: ignore

424 return item.get("embedding") if isinstance(item, dict) else item.embedding # type: ignore

@@ -570,13 +427,7 @@ class LiteLLMEmbeddingWrapper(Embeddings):

427 class LocalSentenceTransformerWrapper(Embeddings):

428 """Local wrapper for sentence-transformers models to avoid HuggingFace API calls"""

429

573 - def __init__(

574 - self,

575 - provider: str,

576 - model: str,

577 - model_config: Optional[ModelConfig] = None,

578 - **kwargs: Any,

579 - ):

430 + def __init__(self, provider: str, model: str, model_config: Optional[ModelConfig] = None, **kwargs: Any):

431 # Clean common user-input mistakes

432 model = model.strip().strip('"').strip("'")

433

@@ -598,18 +449,18 @@ class LocalSentenceTransformerWrapper(Embeddings):

449 self.model = SentenceTransformer(model, **st_kwargs)

450 self.model_name = model

451 self.a0_model_conf = model_config

601 -

452 +

453 def embed_documents(self, texts: List[str]) -> List[List[float]]:

454 # Apply rate limiting if configured

455 apply_rate_limiter_sync(self.a0_model_conf, " ".join(texts))

605 -

456 +

457 embeddings = self.model.encode(texts, convert_to_tensor=False) # type: ignore

458 return embeddings.tolist() if hasattr(embeddings, "tolist") else embeddings # type: ignore

459

460 def embed_query(self, text: str) -> List[float]:

461 # Apply rate limiting if configured

462 apply_rate_limiter_sync(self.a0_model_conf, text)

612 -

463 +

464 embedding = self.model.encode([text], convert_to_tensor=False) # type: ignore

465 result = (

466 embedding[0].tolist() if hasattr(embedding[0], "tolist") else embedding[0]

@@ -634,17 +485,10 @@ def _get_litellm_chat(

485 provider_name, model_name, kwargs = _adjust_call_args(

486 provider_name, model_name, kwargs

487 )

637 - return cls(

638 - provider=provider_name, model=model_name, model_config=model_config, **kwargs

639 - )

488 + return cls(provider=provider_name, model=model_name, model_config=model_config, **kwargs)

489

490

642 -def _get_litellm_embedding(

643 - model_name: str,

644 - provider_name: str,

645 - model_config: Optional[ModelConfig] = None,

646 - **kwargs: Any,

647 -):

491 +def _get_litellm_embedding(model_name: str, provider_name: str, model_config: Optional[ModelConfig] = None, **kwargs: Any):

492 # Check if this is a local sentence-transformers model

493 if provider_name == "huggingface" and model_name.startswith(

494 "sentence-transformers/"

@@ -654,10 +498,7 @@ def _get_litellm_embedding(

498 provider_name, model_name, kwargs

499 )

500 return LocalSentenceTransformerWrapper(

657 - provider=provider_name,

658 - model=model_name,

659 - model_config=model_config,

660 - **kwargs,

501 + provider=provider_name, model=model_name, model_config=model_config, **kwargs

502 )

503

504 # use api key from kwargs or env

@@ -670,9 +511,7 @@ def _get_litellm_embedding(

511 provider_name, model_name, kwargs = _adjust_call_args(

512 provider_name, model_name, kwargs

513 )

673 - return LiteLLMEmbeddingWrapper(

674 - model=model_name, provider=provider_name, model_config=model_config, **kwargs

675 - )

514 + return LiteLLMEmbeddingWrapper(model=model_name, provider=provider_name, model_config=model_config, **kwargs)

515

516

517 def _parse_chunk(chunk: Any) -> ChatChunk:

@@ -760,14 +599,10 @@ def _merge_provider_defaults(

599 return provider_name, kwargs

600

601

763 -def get_chat_model(

764 - provider: str, name: str, model_config: Optional[ModelConfig] = None, **kwargs: Any

765 -) -> LiteLLMChatWrapper:

602 +def get_chat_model(provider: str, name: str, model_config: Optional[ModelConfig] = None, **kwargs: Any) -> LiteLLMChatWrapper:

603 orig = provider.lower()

604 provider_name, kwargs = _merge_provider_defaults("chat", orig, kwargs)

768 - return _get_litellm_chat(

769 - LiteLLMChatWrapper, name, provider_name, model_config, **kwargs

770 - )

605 + return _get_litellm_chat(LiteLLMChatWrapper, name, provider_name, model_config, **kwargs)

606

607

608 def get_browser_model(

python/tools/browser_agent.py

+1 -2

@@ -148,7 +148,6 @@ class State:

148 ),

149 controller=controller,

150 enable_memory=False, # Disable memory to avoid state conflicts

151 - llm_timeout=3000, # TODO rem

151 sensitive_data=cast(dict[str, str | dict[str, str]] | None, secrets_dict or {}), # Pass secrets

152 )

153 except Exception as e:

@@ -388,7 +387,7 @@ class BrowserAgent(Tool):

387 def get_use_agent_log(use_agent: browser_use.Agent | None):

388 result = ["🚦 Starting task"]

389 if use_agent:

391 - action_results = use_agent.history.action_results() or []

390 + action_results = use_agent.state.history.action_results() or []

391 short_log = []

392 for item in action_results:

393 # final results

requirements.txt

+2 -2

@@ -1,6 +1,6 @@

1 a2wsgi==1.10.8

2 ansio==0.0.1

3 -browser-use==0.5.11

3 +browser-use==0.2.5

4 docker==7.1.0

5 duckduckgo-search==6.1.12

6 faiss-cpu==1.11.0

@@ -19,7 +19,7 @@ langchain-unstructured[all-docs]==0.1.6

19 openai-whisper==20240930

20 lxml_html_clean==0.3.1

21 markdown==3.7

22 -mcp==1.13.1

22 +mcp==1.12.4

23 newspaper3k==0.2.8

24 paramiko==3.5.0

25 playwright==1.52.0