diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..977bd28 --- /dev/null +++ b/.env.example @@ -0,0 +1,47 @@ +# ============================================================================= +# Autonomous Browser AI Agent - Environment Configuration +# ============================================================================= +# Copy this file to .env and fill in your API keys +# At least ONE LLM provider is required for the agent to function +# ============================================================================= + +# ----------------------------------------------------------------------------- +# AWS Bedrock Configuration (Claude, Titan, etc.) +# ----------------------------------------------------------------------------- +# AWS_ACCESS_KEY_ID=your_access_key +# AWS_SECRET_ACCESS_KEY=your_secret_key +# AWS_REGION=us-east-1 +# BEDROCK_MODEL_ID=anthropic.claude-3-sonnet-20240229-v1:0 + +# ----------------------------------------------------------------------------- +# Google Gemini Configuration +# ----------------------------------------------------------------------------- +# GEMINI_API_KEY=your_gemini_api_key +# GEMINI_MODEL=gemini-1.5-pro + +# ----------------------------------------------------------------------------- +# OpenAI Configuration +# ----------------------------------------------------------------------------- +# OPENAI_API_KEY=your_openai_api_key +# OPENAI_MODEL=gpt-4-turbo + +# ----------------------------------------------------------------------------- +# LLM Provider Selection (bedrock, gemini, openai) +# The orchestrator, planner, and executor can use different models +# ----------------------------------------------------------------------------- +# ORCHESTRATOR_PROVIDER=bedrock +# PLANNER_PROVIDER=bedrock +# EXECUTOR_PROVIDER=bedrock + +# ----------------------------------------------------------------------------- +# Agent Configuration +# ----------------------------------------------------------------------------- +# MAX_PLANNING_STEPS=10 +# MAX_RETRIES=3 +# PLANNING_LOOKAHEAD=4 + +# ----------------------------------------------------------------------------- +# Browser Configuration +# ----------------------------------------------------------------------------- +# BROWSER_HEADLESS=true +# BROWSER_TIMEOUT=30 diff --git a/pyproject.toml b/pyproject.toml index ab58ac0..32cf947 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,13 @@ requires-python = ">=3.10,<3.13" dependencies = [ "playwright>=1.48.0,<2.0.0", "pytest>=8.2.0,<9.0.0", - "pytest-asyncio>=0.23.6,<0.24.0" + "pytest-asyncio>=0.23.6,<0.24.0", + "python-dotenv>=1.0.0", + "langchain>=0.3.0", + "langchain-aws>=0.2.0", + "langchain-google-genai>=2.0.0", + "langchain-openai>=0.2.0", + "pydantic>=2.0.0", ] [build-system] @@ -18,7 +24,7 @@ requires = ["hatchling"] build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] -packages = ["src/agent", "src/browser", "src/config", "src/controller", "src/examples"] +packages = ["src/agent", "src/browser", "src/config", "src/controller", "src/examples", "src/llm"] [tool.hatch.build.targets.sdist] include = [ diff --git a/src/agent/evaluator.py b/src/agent/evaluator.py new file mode 100644 index 0000000..e0567b2 --- /dev/null +++ b/src/agent/evaluator.py @@ -0,0 +1,261 @@ +"""Evaluator Agent - evaluates step results and triggers re-planning. + +The evaluator analyzes execution results to determine: +1. Did the step succeed? +2. Is the overall task complete? +3. Should we re-plan? +""" + +from __future__ import annotations + +import json +from typing import Any, Dict, Optional, TYPE_CHECKING + +if TYPE_CHECKING: + from browser.dom_analyzer import PageStructure + from llm.base import BaseLLMProvider + + +EVALUATION_SCHEMA = { + "type": "object", + "properties": { + "success": { + "type": "boolean", + "description": "Whether the step executed successfully", + }, + "task_complete": { + "type": "boolean", + "description": "Whether the overall task is now complete", + }, + "confidence": { + "type": "number", + "description": "Confidence level 0-1 in the evaluation", + }, + "error": { + "type": "string", + "description": "Error description if step failed", + }, + "result": { + "type": "string", + "description": "Extracted result if task is complete", + }, + "should_replan": { + "type": "boolean", + "description": "Whether the planner should create a new plan", + }, + "replan_reason": { + "type": "string", + "description": "Why re-planning is needed", + }, + "next_action_hint": { + "type": "string", + "description": "Suggestion for what to do next", + }, + }, + "required": ["success", "task_complete"], +} + + +class EvaluatorAgent: + """Evaluates execution results and provides feedback for the orchestrator. + + The evaluator: + 1. Checks if an individual step succeeded + 2. Determines if the overall task is complete + 3. Decides whether re-planning is needed + 4. Provides hints for the next action + """ + + SYSTEM_PROMPT = """You are an evaluation agent for a browser automation system. + +After each step is executed, you must evaluate: + +1. SUCCESS: Did the action complete without errors? Check: + - Was the element found? + - Did the action execute? + - Did the page respond as expected? + +2. TASK COMPLETION: Is the user's original task now complete? Consider: + - Did we achieve the stated goal? + - Do we have the information/result requested? + - Are there remaining sub-goals? + +3. RE-PLANNING: Should we create a new plan? Consider: + - Did the page change unexpectedly? + - Are there new elements we didn't account for? + - Is the current plan still valid? + +Be precise and conservative - only mark task_complete=true when you're confident +the original goal has been achieved.""" + + def __init__(self, llm: Optional["BaseLLMProvider"] = None): + """Initialize evaluator. + + Args: + llm: LLM provider for intelligent evaluation + """ + self.llm = llm + + async def evaluate( + self, + step: Dict[str, Any], + result: Dict[str, Any], + page: Any = None, + task: str = "", + remaining_steps: int = 0, + page_structure: Optional["PageStructure"] = None, + ) -> Dict[str, Any]: + """Evaluate a step result. + + Args: + step: The step that was executed + result: Result from the executor + page: Current page state + task: Original task description + remaining_steps: Steps remaining in current plan + page_structure: Current DOM structure + + Returns: + Evaluation result dictionary + """ + # Quick check for obvious failures + if not result.get("ok"): + return { + "success": False, + "task_complete": False, + "error": result.get("error", "Unknown error"), + "should_replan": True, + "replan_reason": f"Step failed: {result.get('error')}", + } + + # If no LLM, use simple heuristics + if not self.llm: + return self._simple_evaluation(step, result, remaining_steps) + + # Use LLM for intelligent evaluation + return await self._llm_evaluation( + step, result, page, task, remaining_steps, page_structure + ) + + def _simple_evaluation( + self, + step: Dict[str, Any], + result: Dict[str, Any], + remaining_steps: int, + ) -> Dict[str, Any]: + """Simple rule-based evaluation.""" + step_type = step.get("type") + + # Extract text steps might complete the task + if step_type == "extract_text" and result.get("result"): + extracted = result.get("result", "") + if extracted and len(extracted) > 10: + # We got meaningful content + if remaining_steps == 0: + return { + "success": True, + "task_complete": True, + "result": extracted, + "confidence": 0.7, + } + + # Default: step succeeded, continue with plan + return { + "success": True, + "task_complete": remaining_steps == 0, + "confidence": 0.5, + } + + async def _llm_evaluation( + self, + step: Dict[str, Any], + result: Dict[str, Any], + page: Any, + task: str, + remaining_steps: int, + page_structure: Optional["PageStructure"], + ) -> Dict[str, Any]: + """Use LLM for intelligent evaluation.""" + + # Get current DOM context + dom_context = "" + if page_structure: + dom_context = page_structure.to_prompt_context() + elif page: + from browser.dom_analyzer import DOMAnalyzer + analyzer = DOMAnalyzer() + structure = await analyzer.analyze(page) + dom_context = structure.to_prompt_context() + + prompt = f"""{self.SYSTEM_PROMPT} + +## ORIGINAL TASK +{task} + +## STEP EXECUTED +{json.dumps(step, indent=2)} + +## EXECUTION RESULT +{json.dumps({k: v for k, v in result.items() if k != 'page'}, indent=2)} + +## CURRENT PAGE STATE +{dom_context} + +## REMAINING PLANNED STEPS +{remaining_steps} + +Evaluate this step and provide your assessment.""" + + try: + evaluation = await self.llm.complete_json(prompt, EVALUATION_SCHEMA) + return evaluation + except Exception as e: + # Fallback to simple evaluation on error + return { + "success": True, + "task_complete": False, + "error": f"Evaluation failed: {e}", + } + + async def check_task_completion( + self, + task: str, + execution_log: list, + page: Any = None, + ) -> Dict[str, Any]: + """Check if the overall task is complete based on execution history. + + Called at the end of execution or when steps are exhausted. + """ + if not self.llm: + # Simple check: did any step extract meaningful content? + for entry in execution_log: + result = entry.get("result", {}) + if result.get("ok") and result.get("result"): + return { + "complete": True, + "result": result.get("result"), + } + return {"complete": False, "reason": "No meaningful result extracted"} + + prompt = f"""Review this task execution and determine if the task was completed: + +Task: {task} + +Execution log: +{json.dumps(execution_log, indent=2, default=str)} + +Was the task completed successfully? What was the final result?""" + + try: + result = await self.llm.complete_json(prompt, { + "type": "object", + "properties": { + "complete": {"type": "boolean"}, + "result": {"type": "string"}, + "reason": {"type": "string"}, + }, + }) + return result + except Exception: + return {"complete": False, "reason": "Evaluation failed"} diff --git a/src/agent/executor.py b/src/agent/executor.py new file mode 100644 index 0000000..d140870 --- /dev/null +++ b/src/agent/executor.py @@ -0,0 +1,201 @@ +"""Executor Agent - executes planned steps using browser actions. + +The executor takes individual steps from the planner and executes them +using the browser controller, handling errors and reporting results. +""" + +from __future__ import annotations + +from typing import Any, Dict, Optional, TYPE_CHECKING + +if TYPE_CHECKING: + from browser.dom_analyzer import DOMAnalyzer + from controller.browser_controller import BrowserController + from llm.base import BaseLLMProvider + + +class ExecutorAgent: + """Executes browser actions based on planned steps. + + The executor: + 1. Receives a step from the orchestrator + 2. Validates the step can be executed + 3. Executes using the browser controller + 4. Returns detailed result for evaluation + """ + + SYSTEM_PROMPT = """You are a browser action executor. Your job is to: +1. Execute browser actions precisely as specified +2. Handle errors gracefully +3. Report detailed results + +When an action fails, analyze WHY it failed and suggest corrections.""" + + def __init__( + self, + controller: Optional["BrowserController"] = None, + dom_analyzer: Optional["DOMAnalyzer"] = None, + llm: Optional["BaseLLMProvider"] = None, + ): + """Initialize executor. + + Args: + controller: Browser controller for action execution + dom_analyzer: DOM analyzer for element validation + llm: LLM for intelligent error handling (optional) + """ + self.controller = controller + self.dom_analyzer = dom_analyzer + self.llm = llm + + async def execute( + self, + step: Dict[str, Any], + page: Any = None, + ) -> Dict[str, Any]: + """Execute a single step. + + Args: + step: Step dictionary with type and args + page: Current Playwright page object + + Returns: + Result dictionary with success status and details + """ + action_type = step.get("type") + args = step.get("args", {}) + metadata = step.get("metadata", {}) + + # Validate step + if not action_type: + return { + "ok": False, + "error": "No action type specified", + "step": step, + } + + # Pre-execution validation for selector-based actions + if action_type in ("click", "fill", "extract_text", "wait", "scroll"): + selector = args.get("selector") + if not selector: + return { + "ok": False, + "error": f"No selector specified for {action_type}", + "step": step, + } + + # Validate element exists + if page and self.dom_analyzer: + element_info = await self.dom_analyzer.get_element_context(page, selector) + if not element_info.get("exists"): + return { + "ok": False, + "error": f"Selector not found: {selector}", + "step": step, + "suggestion": metadata.get("fallback", "Try a different selector"), + } + + # Execute the action + if not self.controller: + return { + "ok": False, + "error": "No controller available", + "step": step, + } + + try: + # Inject page into args if needed + if page and action_type != "goto": + args = {**args, "page": page} + + result = await self.controller.execute_action({ + "type": action_type, + "args": args, + }) + + # Enhance result with step metadata + result["step"] = step + result["expected_outcome"] = metadata.get("expected_outcome") + + return result + + except Exception as e: + return { + "ok": False, + "error": str(e), + "step": step, + "exception_type": type(e).__name__, + } + + async def execute_with_retry( + self, + step: Dict[str, Any], + page: Any = None, + max_retries: int = 3, + ) -> Dict[str, Any]: + """Execute a step with automatic retries. + + Args: + step: Step to execute + page: Current page + max_retries: Maximum retry attempts + + Returns: + Final result after retries + """ + last_result = None + + for attempt in range(max_retries): + result = await self.execute(step, page) + last_result = result + + if result.get("ok"): + return result + + # If we have an LLM, ask for suggestions + if self.llm and attempt < max_retries - 1: + correction = await self._get_correction(step, result, page) + if correction: + step = correction + + return last_result + + async def _get_correction( + self, + step: Dict[str, Any], + error_result: Dict[str, Any], + page: Any, + ) -> Optional[Dict[str, Any]]: + """Use LLM to suggest a corrected step.""" + if not self.llm: + return None + + # Get current DOM state + dom_context = "" + if page and self.dom_analyzer: + from browser.dom_analyzer import DOMAnalyzer + analyzer = DOMAnalyzer() + structure = await analyzer.analyze(page) + dom_context = structure.to_prompt_context() + + prompt = f"""The following browser action failed: + +Step: {step} +Error: {error_result.get('error')} + +Current page DOM: +{dom_context} + +Suggest a corrected step that might work. Return JSON with the corrected step.""" + + try: + corrected = await self.llm.complete_json(prompt, { + "type": "object", + "properties": { + "type": {"type": "string"}, + "args": {"type": "object"}, + }, + }) + return corrected + except Exception: + return None diff --git a/src/agent/orchestrator.py b/src/agent/orchestrator.py new file mode 100644 index 0000000..f0aabdb --- /dev/null +++ b/src/agent/orchestrator.py @@ -0,0 +1,333 @@ +"""Multi-Agent Orchestrator - coordinates planner, executor, and evaluator agents. + +The orchestrator is the main entry point for task execution. It: +1. Receives a high-level task from the user +2. Coordinates the planner to create multi-step plans +3. Dispatches steps to the executor +4. Uses the evaluator for reflection and re-planning +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, Dict, List, Optional + +from llm.base import BaseLLMProvider, Message +from llm.factory import get_provider_for_role + + +class TaskStatus(str, Enum): + """Status of a task execution.""" + PENDING = "pending" + PLANNING = "planning" + EXECUTING = "executing" + EVALUATING = "evaluating" + REPLANNING = "replanning" + COMPLETED = "completed" + FAILED = "failed" + + +@dataclass +class TaskResult: + """Result of a completed task.""" + success: bool + task: str + steps_executed: int + final_result: Any + execution_log: List[Dict[str, Any]] = field(default_factory=list) + error: Optional[str] = None + + +@dataclass +class OrchestratorState: + """Internal state of the orchestrator.""" + task: str + status: TaskStatus = TaskStatus.PENDING + current_plan: List[Dict[str, Any]] = field(default_factory=list) + executed_steps: List[Dict[str, Any]] = field(default_factory=list) + current_step_index: int = 0 + retry_count: int = 0 + page_context: Optional[str] = None + + +class Orchestrator: + """Main orchestrator that coordinates the multi-agent system. + + The orchestrator implements a Plan → Execute → Evaluate → (Re-plan) loop: + + 1. PLAN: Use planner agent to create multi-step plan based on task + DOM + 2. EXECUTE: Execute steps one by one using executor agent + 3. EVALUATE: After each step, evaluate success and decide next action + 4. RE-PLAN: If evaluation detects issues, trigger re-planning + """ + + SYSTEM_PROMPT = """You are an orchestrator AI that coordinates a multi-agent browser automation system. + +Your role is to: +1. Analyze the user's task and break it down into sub-goals +2. Decide when to invoke the planner for detailed step generation +3. Monitor execution progress and handle failures +4. Determine when a task is complete + +You coordinate three agents: +- PLANNER: Creates detailed step-by-step plans with specific selectors +- EXECUTOR: Executes individual browser actions +- EVALUATOR: Checks if actions succeeded and suggests corrections + +Always think step by step and explain your reasoning.""" + + def __init__( + self, + llm: Optional[BaseLLMProvider] = None, + planner: Optional["PlannerAgent"] = None, + executor: Optional["ExecutorAgent"] = None, + evaluator: Optional["EvaluatorAgent"] = None, + max_retries: int = 3, + max_steps: int = 20, + ): + """Initialize orchestrator. + + Args: + llm: LLM provider for orchestration decisions + planner: Planner agent instance + executor: Executor agent instance + evaluator: Evaluator agent instance + max_retries: Maximum re-planning attempts + max_steps: Maximum total steps before giving up + """ + self.llm = llm or get_provider_for_role("orchestrator") + self.planner = planner + self.executor = executor + self.evaluator = evaluator + self.max_retries = max_retries + self.max_steps = max_steps + self.state: Optional[OrchestratorState] = None + + async def execute_task( + self, + task: str, + page: Any = None, + initial_context: Optional[str] = None, + ) -> TaskResult: + """Execute a complete task from start to finish. + + Args: + task: Natural language description of the task + page: Playwright page object (if browser already open) + initial_context: Optional DOM/page context + + Returns: + TaskResult with success status and execution details + """ + # Initialize state + self.state = OrchestratorState( + task=task, + page_context=initial_context, + ) + + execution_log = [] + total_steps = 0 + + try: + while total_steps < self.max_steps: + # Phase 1: Planning + self.state.status = TaskStatus.PLANNING + + if not self.state.current_plan or self.state.current_step_index >= len(self.state.current_plan): + # Need a new plan + plan = await self._create_plan(page) + if not plan: + return TaskResult( + success=False, + task=task, + steps_executed=total_steps, + final_result=None, + execution_log=execution_log, + error="Failed to create execution plan", + ) + + self.state.current_plan = plan + self.state.current_step_index = 0 + execution_log.append({"phase": "planning", "plan": plan}) + + # Phase 2: Execute next step + self.state.status = TaskStatus.EXECUTING + + step = self.state.current_plan[self.state.current_step_index] + step_result = await self._execute_step(step, page) + + execution_log.append({ + "phase": "execution", + "step": step, + "result": step_result, + }) + + total_steps += 1 + self.state.executed_steps.append({"step": step, "result": step_result}) + + # Phase 3: Evaluate + self.state.status = TaskStatus.EVALUATING + + evaluation = await self._evaluate_step(step, step_result, page) + execution_log.append({"phase": "evaluation", "evaluation": evaluation}) + + if evaluation.get("task_complete"): + # Task is done! + self.state.status = TaskStatus.COMPLETED + return TaskResult( + success=True, + task=task, + steps_executed=total_steps, + final_result=evaluation.get("result"), + execution_log=execution_log, + ) + + if evaluation.get("success"): + # Step succeeded, move to next + self.state.current_step_index += 1 + else: + # Step failed, decide whether to re-plan + self.state.retry_count += 1 + + if self.state.retry_count > self.max_retries: + return TaskResult( + success=False, + task=task, + steps_executed=total_steps, + final_result=None, + execution_log=execution_log, + error=f"Max retries exceeded: {evaluation.get('error')}", + ) + + # Trigger re-planning + self.state.status = TaskStatus.REPLANNING + self.state.current_plan = [] # Force new plan + execution_log.append({ + "phase": "replanning", + "reason": evaluation.get("error"), + }) + + # Max steps exceeded + return TaskResult( + success=False, + task=task, + steps_executed=total_steps, + final_result=None, + execution_log=execution_log, + error="Max steps exceeded", + ) + + except Exception as e: + self.state.status = TaskStatus.FAILED + return TaskResult( + success=False, + task=task, + steps_executed=total_steps, + final_result=None, + execution_log=execution_log, + error=str(e), + ) + + async def _create_plan(self, page: Any) -> List[Dict[str, Any]]: + """Use planner agent to create execution plan.""" + if self.planner: + return await self.planner.plan( + task=self.state.task, + page=page, + executed_steps=self.state.executed_steps, + ) + + # Fallback: ask orchestrator LLM directly + prompt = f"""Create a step-by-step plan to accomplish this task: + +Task: {self.state.task} + +Page Context: +{self.state.page_context or "No page loaded yet"} + +Previously executed steps: +{json.dumps(self.state.executed_steps, indent=2) if self.state.executed_steps else "None"} + +Return a JSON array of steps, each with: +- action: "goto" | "click" | "fill" | "extract_text" | "wait" +- selector: CSS selector (if applicable) +- value: value to fill (if applicable) +- reason: why this step is needed +""" + + schema = { + "type": "array", + "items": { + "type": "object", + "properties": { + "action": {"type": "string"}, + "selector": {"type": "string"}, + "value": {"type": "string"}, + "reason": {"type": "string"}, + }, + "required": ["action", "reason"], + }, + } + + try: + return await self.llm.complete_json(prompt, schema) + except Exception: + return [] + + async def _execute_step(self, step: Dict[str, Any], page: Any) -> Dict[str, Any]: + """Execute a single step using executor agent.""" + if self.executor: + return await self.executor.execute(step, page) + + # Fallback: return mock result + return {"ok": True, "result": "executed"} + + async def _evaluate_step( + self, + step: Dict[str, Any], + result: Dict[str, Any], + page: Any, + ) -> Dict[str, Any]: + """Evaluate step result using evaluator agent.""" + if self.evaluator: + return await self.evaluator.evaluate( + step=step, + result=result, + page=page, + task=self.state.task, + remaining_steps=len(self.state.current_plan) - self.state.current_step_index - 1, + ) + + # Fallback: simple success check + return { + "success": result.get("ok", False), + "task_complete": False, + "error": result.get("error"), + } + + async def analyze_task(self, task: str) -> Dict[str, Any]: + """Analyze a task and return strategy without executing. + + Useful for previewing what the agent would do. + """ + messages = [ + Message(role="system", content=self.SYSTEM_PROMPT), + Message(role="user", content=f"""Analyze this task and describe your approach: + +Task: {task} + +Provide: +1. Main goal +2. Sub-goals to achieve +3. Potential challenges +4. Success criteria"""), + ] + + response = await self.llm.chat(messages) + + return { + "task": task, + "analysis": response.content, + } diff --git a/src/agent/planner.py b/src/agent/planner.py index feb0296..202cfc2 100644 --- a/src/agent/planner.py +++ b/src/agent/planner.py @@ -1,9 +1,25 @@ +"""Planner Agent - creates multi-step plans with DOM awareness. + +The planner looks ahead 3-4 steps, analyzes the DOM, and produces +structured JSON plans that the executor can follow. +""" + from __future__ import annotations -from typing import Any, Dict, List, Optional +import json +import re +from typing import Any, Dict, List, Optional, TYPE_CHECKING from .prompt_templates import PromptTemplates +if TYPE_CHECKING: + from browser.dom_analyzer import PageStructure + from llm.base import BaseLLMProvider + + +# ============================================================================ +# Legacy interfaces (kept for backwards compatibility) +# ============================================================================ class LLMClientInterface: """Abstract LLM client interface (for injection / testing). @@ -16,34 +32,25 @@ async def complete(self, prompt: str) -> str: # pragma: no cover - interface class LLMPlanner: - """Planner that turns a natural language task into a list of action steps. - - This planner is config-driven and uses a provided LLM client to create - multi-step plans. For tests we provide a mock client. + """Legacy planner for backwards compatibility. + + Use PlannerAgent for new code. """ def __init__(self, llm_client: Optional[LLMClientInterface] = None): self.llm = llm_client async def plan(self, task: str) -> List[Dict[str, Any]]: - """Return a list of step dictionaries for a given task. - - If an LLM client is available we'll call it (format-based). Otherwise - fall back to a small deterministic planner used earlier. - """ + """Return a list of step dictionaries for a given task.""" if self.llm: prompt = PromptTemplates.task_decomposition_prompt(task) raw = await self.llm.complete(prompt) - # Expect LLM returns steps in a simple newline-separated action:type|arg JSON-lite. - # This is intentionally permissive for tests — real implementations should use - # a strict JSON format. steps = [] for line in raw.splitlines(): line = line.strip() if not line: continue - # try to parse 'type:payload' or just a URL if line.startswith("goto:"): url = line.split("goto:", 1)[1].strip() steps.append({"type": "goto", "args": {"url": url}}) @@ -51,14 +58,10 @@ async def plan(self, task: str) -> List[Dict[str, Any]]: sel = line.split("extract_text:", 1)[1].strip() steps.append({"type": "extract_text", "args": {"selector": sel}}) else: - # fallback: add as noop step text steps.append({"type": "noop", "args": {"raw": line}}) - return steps - # fallback deterministic planner (used before) - import re - + # Fallback deterministic planner url_match = re.search(r"https?://[^\s]+", task) if url_match: url = url_match.group(0) @@ -71,3 +74,221 @@ async def plan(self, task: str) -> List[Dict[str, Any]]: return [{"type": "goto", "args": {"url": "https://www.google.com"}}] return [{"type": "noop", "args": {}}] + + +# ============================================================================ +# New Multi-Hop Planner Agent +# ============================================================================ + +# JSON Schema for plan output +PLAN_SCHEMA = { + "type": "object", + "properties": { + "goal_analysis": { + "type": "string", + "description": "Analysis of the main goal and sub-goals", + }, + "lookahead": { + "type": "string", + "description": "What you expect to happen 3-4 steps from now", + }, + "steps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "step_number": {"type": "integer"}, + "action": { + "type": "string", + "enum": ["goto", "click", "fill", "extract_text", "wait", "screenshot", "scroll"], + }, + "selector": {"type": "string"}, + "value": {"type": "string"}, + "reason": {"type": "string"}, + "expected_outcome": {"type": "string"}, + "fallback": {"type": "string"}, + }, + "required": ["step_number", "action", "reason"], + }, + }, + "success_criteria": { + "type": "string", + "description": "How to know when the task is complete", + }, + }, + "required": ["steps", "success_criteria"], +} + + +class PlannerAgent: + """Advanced planner that creates DOM-aware multi-step plans. + + The planner: + 1. Analyzes the current page DOM structure + 2. Identifies interactive elements relevant to the task + 3. Plans 3-4 steps ahead with fallback strategies + 4. Outputs structured JSON that the executor can follow + """ + + SYSTEM_PROMPT = """You are an expert browser automation planner. Your job is to create +precise, executable plans for web automation tasks. + +You will receive: +1. A task description from the user +2. Current page DOM analysis with available interactive elements +3. Previously executed steps (if any) + +Your responsibilities: +- Analyze the DOM to find the RIGHT selectors for each action +- Think 3-4 steps ahead - what will the page look like after each action? +- Provide fallback strategies for each step +- Be specific with selectors - use IDs when available, then names, then classes +- Consider timing - some actions need waits for elements to appear + +Available actions: +- goto: Navigate to a URL +- click: Click an element (requires selector) +- fill: Type into an input (requires selector and value) +- extract_text: Get text from element (requires selector) +- wait: Wait for element to appear (requires selector) +- screenshot: Take a screenshot +- scroll: Scroll page or to element + +IMPORTANT: Only use selectors that exist in the DOM analysis. Don't guess selectors.""" + + def __init__( + self, + llm: Optional["BaseLLMProvider"] = None, + lookahead_steps: int = 4, + ): + """Initialize planner. + + Args: + llm: LLM provider for plan generation + lookahead_steps: How many steps to plan ahead + """ + self.llm = llm + self.lookahead_steps = lookahead_steps + + async def plan( + self, + task: str, + page: Any = None, + page_structure: Optional["PageStructure"] = None, + executed_steps: Optional[List[Dict[str, Any]]] = None, + ) -> List[Dict[str, Any]]: + """Create a multi-step plan for the given task. + + Args: + task: Natural language task description + page: Playwright page object (for DOM analysis) + page_structure: Pre-analyzed page structure (optional) + executed_steps: Previously executed steps for context + + Returns: + List of step dictionaries ready for execution + """ + if self.llm is None: + # Fallback to simple deterministic planner + return self._fallback_plan(task) + + # Get DOM context + dom_context = "" + if page_structure: + dom_context = page_structure.to_prompt_context() + elif page: + from browser.dom_analyzer import DOMAnalyzer + analyzer = DOMAnalyzer() + structure = await analyzer.analyze(page) + dom_context = structure.to_prompt_context() + + # Build prompt + prompt = self._build_prompt(task, dom_context, executed_steps) + + try: + plan_json = await self.llm.complete_json(prompt, PLAN_SCHEMA) + steps = self._parse_plan(plan_json) + + # If no valid steps were parsed, use fallback + if not steps: + return self._fallback_plan(task) + + return steps + except Exception as e: + # Fallback on error + return self._fallback_plan(task) + + def _build_prompt( + self, + task: str, + dom_context: str, + executed_steps: Optional[List[Dict[str, Any]]], + ) -> str: + """Build the planning prompt.""" + + executed_str = "None" + if executed_steps: + executed_str = json.dumps(executed_steps, indent=2) + + return f"""{self.SYSTEM_PROMPT} + +## TASK +{task} + +## CURRENT PAGE STATE +{dom_context if dom_context else "No page loaded yet - start with a 'goto' action"} + +## PREVIOUSLY EXECUTED STEPS +{executed_str} + +## YOUR TASK +Create a plan with up to {self.lookahead_steps} steps to accomplish the task. +For each step, specify the exact selector from the DOM analysis above. +Think about what will happen after each step and plan accordingly.""" + + def _parse_plan(self, plan_json: Dict[str, Any]) -> List[Dict[str, Any]]: + """Parse LLM plan output into executable steps.""" + steps = [] + + for step in plan_json.get("steps", []): + parsed = { + "type": step.get("action", "noop"), + "args": {}, + "metadata": { + "reason": step.get("reason", ""), + "expected_outcome": step.get("expected_outcome", ""), + "fallback": step.get("fallback", ""), + }, + } + + # Map action to args + action = step.get("action") + + if action == "goto": + parsed["args"]["url"] = step.get("value", step.get("selector", "")) + elif action in ("click", "extract_text", "wait", "scroll"): + parsed["args"]["selector"] = step.get("selector", "") + elif action == "fill": + parsed["args"]["selector"] = step.get("selector", "") + parsed["args"]["text"] = step.get("value", "") + elif action == "screenshot": + parsed["args"]["full_page"] = True + + steps.append(parsed) + + return steps + + def _fallback_plan(self, task: str) -> List[Dict[str, Any]]: + """Simple fallback planner when LLM is unavailable.""" + url_match = re.search(r"https?://[^\s]+", task) + if url_match: + url = url_match.group(0) + steps = [{"type": "goto", "args": {"url": url}}] + if "extract" in task.lower() or "read" in task.lower() or "get" in task.lower(): + steps.append({"type": "extract_text", "args": {"selector": "body"}}) + return steps + + if "google" in task.lower(): + return [{"type": "goto", "args": {"url": "https://www.google.com"}}] + + return [{"type": "noop", "args": {}}] diff --git a/src/browser/dom_analyzer.py b/src/browser/dom_analyzer.py new file mode 100644 index 0000000..3e3ba05 --- /dev/null +++ b/src/browser/dom_analyzer.py @@ -0,0 +1,363 @@ +"""DOM Analyzer for extracting page structure and interactive elements. + +This module provides intelligent DOM analysis for the planner agent, +allowing it to understand page structure and make informed decisions. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + +from playwright.async_api import Page + + +@dataclass +class InteractiveElement: + """Represents an interactive element on the page.""" + tag: str + selector: str + text: str + element_type: str # button, link, input, select, etc. + attributes: Dict[str, str] = field(default_factory=dict) + is_visible: bool = True + bounding_box: Optional[Dict[str, float]] = None + + def to_dict(self) -> Dict[str, Any]: + return { + "tag": self.tag, + "selector": self.selector, + "text": self.text[:100] if self.text else "", # Truncate long text + "type": self.element_type, + "visible": self.is_visible, + } + + +@dataclass +class PageStructure: + """Represents the analyzed structure of a page.""" + url: str + title: str + main_content_text: str + interactive_elements: List[InteractiveElement] + forms: List[Dict[str, Any]] + navigation_links: List[Dict[str, str]] + headings: List[Dict[str, str]] + + def to_prompt_context(self, max_elements: int = 20) -> str: + """Convert page structure to a context string for LLM prompts.""" + lines = [ + f"## Current Page Analysis", + f"URL: {self.url}", + f"Title: {self.title}", + "", + "### Page Headings:", + ] + + for h in self.headings[:10]: + lines.append(f" - [{h['level']}] {h['text']}") + + lines.extend([ + "", + "### Interactive Elements (buttons, links, inputs):", + ]) + + for i, elem in enumerate(self.interactive_elements[:max_elements]): + lines.append(f" {i+1}. [{elem.element_type}] selector='{elem.selector}' text='{elem.text[:50]}'") + + if len(self.interactive_elements) > max_elements: + lines.append(f" ... and {len(self.interactive_elements) - max_elements} more elements") + + if self.forms: + lines.extend([ + "", + "### Forms on Page:", + ]) + for form in self.forms[:5]: + lines.append(f" - Form: {form.get('action', 'no action')} with {len(form.get('fields', []))} fields") + + lines.extend([ + "", + "### Main Content Preview:", + self.main_content_text[:500] + "..." if len(self.main_content_text) > 500 else self.main_content_text, + ]) + + return "\n".join(lines) + + +class DOMAnalyzer: + """Analyzes page DOM to extract structured information for planning.""" + + def __init__(self, max_elements: int = 50): + """Initialize analyzer. + + Args: + max_elements: Maximum number of interactive elements to extract + """ + self.max_elements = max_elements + + async def analyze(self, page: Page) -> PageStructure: + """Analyze the current page and return structured information. + + Args: + page: Playwright Page object + + Returns: + PageStructure with all analyzed data + """ + url = page.url + title = await page.title() + + # Run analysis in parallel for performance + interactive_elements = await self._extract_interactive_elements(page) + forms = await self._extract_forms(page) + navigation_links = await self._extract_navigation(page) + headings = await self._extract_headings(page) + main_content = await self._extract_main_content(page) + + return PageStructure( + url=url, + title=title, + main_content_text=main_content, + interactive_elements=interactive_elements, + forms=forms, + navigation_links=navigation_links, + headings=headings, + ) + + async def _extract_interactive_elements(self, page: Page) -> List[InteractiveElement]: + """Extract buttons, links, inputs, and other interactive elements.""" + elements = [] + + # JavaScript to extract interactive elements + js_code = """ + () => { + const results = []; + const selectors = [ + 'button', + 'a[href]', + 'input:not([type="hidden"])', + 'select', + 'textarea', + '[role="button"]', + '[onclick]', + ]; + + const seen = new Set(); + + for (const selector of selectors) { + const nodes = document.querySelectorAll(selector); + for (const node of nodes) { + // Skip hidden elements + const rect = node.getBoundingClientRect(); + if (rect.width === 0 || rect.height === 0) continue; + + // Generate unique selector + let uniqueSelector = ''; + if (node.id) { + uniqueSelector = '#' + node.id; + } else if (node.name) { + uniqueSelector = `${node.tagName.toLowerCase()}[name="${node.name}"]`; + } else if (node.className && typeof node.className === 'string') { + const classes = node.className.trim().split(/\\s+/).slice(0, 2).join('.'); + if (classes) { + uniqueSelector = `${node.tagName.toLowerCase()}.${classes}`; + } + } + + if (!uniqueSelector) { + // Use nth-child as fallback + const parent = node.parentNode; + const index = Array.from(parent.children).indexOf(node) + 1; + uniqueSelector = `${node.tagName.toLowerCase()}:nth-child(${index})`; + } + + // Skip duplicates + if (seen.has(uniqueSelector)) continue; + seen.add(uniqueSelector); + + // Determine element type + let elementType = node.tagName.toLowerCase(); + if (elementType === 'input') { + elementType = node.type || 'text'; + } else if (elementType === 'a') { + elementType = 'link'; + } + + results.push({ + tag: node.tagName.toLowerCase(), + selector: uniqueSelector, + text: (node.innerText || node.value || node.placeholder || '').trim().substring(0, 100), + elementType: elementType, + attributes: { + type: node.type || '', + name: node.name || '', + href: node.href || '', + placeholder: node.placeholder || '', + }, + boundingBox: { + x: rect.x, + y: rect.y, + width: rect.width, + height: rect.height, + }, + }); + + if (results.length >= 50) break; + } + if (results.length >= 50) break; + } + + return results; + } + """ + + try: + raw_elements = await page.evaluate(js_code) + + for raw in raw_elements[:self.max_elements]: + elements.append(InteractiveElement( + tag=raw["tag"], + selector=raw["selector"], + text=raw["text"], + element_type=raw["elementType"], + attributes=raw["attributes"], + bounding_box=raw.get("boundingBox"), + )) + except Exception: + pass # Return empty list on error + + return elements + + async def _extract_forms(self, page: Page) -> List[Dict[str, Any]]: + """Extract form structures from the page.""" + js_code = """ + () => { + const forms = []; + for (const form of document.querySelectorAll('form')) { + const fields = []; + for (const input of form.querySelectorAll('input, select, textarea')) { + if (input.type === 'hidden') continue; + fields.push({ + name: input.name || '', + type: input.type || input.tagName.toLowerCase(), + required: input.required, + placeholder: input.placeholder || '', + }); + } + forms.push({ + action: form.action || '', + method: form.method || 'get', + fields: fields, + }); + } + return forms; + } + """ + + try: + return await page.evaluate(js_code) + except Exception: + return [] + + async def _extract_navigation(self, page: Page) -> List[Dict[str, str]]: + """Extract navigation links (nav, header, menu areas).""" + js_code = """ + () => { + const links = []; + const navAreas = document.querySelectorAll('nav a, header a, [role="navigation"] a'); + for (const a of navAreas) { + if (a.href && a.innerText.trim()) { + links.push({ + text: a.innerText.trim().substring(0, 50), + href: a.href, + }); + } + if (links.length >= 20) break; + } + return links; + } + """ + + try: + return await page.evaluate(js_code) + except Exception: + return [] + + async def _extract_headings(self, page: Page) -> List[Dict[str, str]]: + """Extract page headings for structure understanding.""" + js_code = """ + () => { + const headings = []; + for (const h of document.querySelectorAll('h1, h2, h3')) { + const text = h.innerText.trim(); + if (text) { + headings.push({ + level: h.tagName, + text: text.substring(0, 100), + }); + } + if (headings.length >= 15) break; + } + return headings; + } + """ + + try: + return await page.evaluate(js_code) + except Exception: + return [] + + async def _extract_main_content(self, page: Page) -> str: + """Extract main page content text.""" + js_code = """ + () => { + // Try to find main content area + const main = document.querySelector('main, [role="main"], article, .content, #content'); + if (main) { + return main.innerText.trim().substring(0, 2000); + } + // Fallback to body + return document.body.innerText.trim().substring(0, 2000); + } + """ + + try: + return await page.evaluate(js_code) + except Exception: + return "" + + async def get_element_context(self, page: Page, selector: str) -> Dict[str, Any]: + """Get detailed context about a specific element. + + Useful for the executor to understand element state before interaction. + """ + js_code = f""" + (selector) => {{ + const el = document.querySelector(selector); + if (!el) return null; + + const rect = el.getBoundingClientRect(); + return {{ + exists: true, + visible: rect.width > 0 && rect.height > 0, + enabled: !el.disabled, + text: el.innerText?.trim()?.substring(0, 100) || '', + value: el.value || '', + tagName: el.tagName.toLowerCase(), + type: el.type || '', + boundingBox: {{ + x: rect.x, + y: rect.y, + width: rect.width, + height: rect.height, + }}, + }}; + }} + """ + + try: + result = await page.evaluate(js_code, selector) + return result or {"exists": False} + except Exception: + return {"exists": False, "error": "evaluation failed"} diff --git a/src/config/llm_config.py b/src/config/llm_config.py new file mode 100644 index 0000000..8c113bd --- /dev/null +++ b/src/config/llm_config.py @@ -0,0 +1,116 @@ +"""LLM configuration and provider settings.""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from enum import Enum +from typing import Optional + +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + + +class LLMProvider(str, Enum): + """Supported LLM providers.""" + BEDROCK = "bedrock" + GEMINI = "gemini" + OPENAI = "openai" + MOCK = "mock" # For testing + + +@dataclass +class BedrockConfig: + """AWS Bedrock configuration.""" + access_key_id: Optional[str] = field(default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID")) + secret_access_key: Optional[str] = field(default_factory=lambda: os.getenv("AWS_SECRET_ACCESS_KEY")) + region: str = field(default_factory=lambda: os.getenv("AWS_REGION", "us-east-1")) + model_id: str = field(default_factory=lambda: os.getenv("BEDROCK_MODEL_ID", "anthropic.claude-3-sonnet-20240229-v1:0")) + + @property + def is_configured(self) -> bool: + return bool(self.access_key_id and self.secret_access_key) + + +@dataclass +class GeminiConfig: + """Google Gemini configuration.""" + api_key: Optional[str] = field(default_factory=lambda: os.getenv("GEMINI_API_KEY")) + model: str = field(default_factory=lambda: os.getenv("GEMINI_MODEL", "gemini-1.5-pro")) + + @property + def is_configured(self) -> bool: + return bool(self.api_key) + + +@dataclass +class OpenAIConfig: + """OpenAI configuration.""" + api_key: Optional[str] = field(default_factory=lambda: os.getenv("OPENAI_API_KEY")) + model: str = field(default_factory=lambda: os.getenv("OPENAI_MODEL", "gpt-4-turbo")) + + @property + def is_configured(self) -> bool: + return bool(self.api_key) + + +@dataclass +class AgentConfig: + """Multi-agent system configuration.""" + max_planning_steps: int = field(default_factory=lambda: int(os.getenv("MAX_PLANNING_STEPS", "10"))) + max_retries: int = field(default_factory=lambda: int(os.getenv("MAX_RETRIES", "3"))) + planning_lookahead: int = field(default_factory=lambda: int(os.getenv("PLANNING_LOOKAHEAD", "4"))) + + # Provider assignments for each agent role + orchestrator_provider: LLMProvider = field( + default_factory=lambda: LLMProvider(os.getenv("ORCHESTRATOR_PROVIDER", "bedrock")) + ) + planner_provider: LLMProvider = field( + default_factory=lambda: LLMProvider(os.getenv("PLANNER_PROVIDER", "bedrock")) + ) + executor_provider: LLMProvider = field( + default_factory=lambda: LLMProvider(os.getenv("EXECUTOR_PROVIDER", "bedrock")) + ) + + +@dataclass +class LLMConfig: + """Master LLM configuration container.""" + bedrock: BedrockConfig = field(default_factory=BedrockConfig) + gemini: GeminiConfig = field(default_factory=GeminiConfig) + openai: OpenAIConfig = field(default_factory=OpenAIConfig) + agent: AgentConfig = field(default_factory=AgentConfig) + + def get_available_providers(self) -> list[LLMProvider]: + """Return list of configured providers.""" + available = [] + if self.bedrock.is_configured: + available.append(LLMProvider.BEDROCK) + if self.gemini.is_configured: + available.append(LLMProvider.GEMINI) + if self.openai.is_configured: + available.append(LLMProvider.OPENAI) + return available + + def has_any_provider(self) -> bool: + """Check if at least one provider is configured.""" + return len(self.get_available_providers()) > 0 + + @classmethod + def load(cls) -> "LLMConfig": + """Load configuration from environment.""" + return cls() + + +# Global config instance +_config: Optional[LLMConfig] = None + + +def get_llm_config() -> LLMConfig: + """Get or create the global LLM configuration.""" + global _config + if _config is None: + _config = LLMConfig.load() + return _config diff --git a/src/llm/__init__.py b/src/llm/__init__.py new file mode 100644 index 0000000..dd1000c --- /dev/null +++ b/src/llm/__init__.py @@ -0,0 +1,6 @@ +"""LLM providers for multi-agent system.""" + +from .base import BaseLLMProvider, LLMResponse +from .factory import get_llm_provider + +__all__ = ["BaseLLMProvider", "LLMResponse", "get_llm_provider"] diff --git a/src/llm/base.py b/src/llm/base.py new file mode 100644 index 0000000..bb2c30a --- /dev/null +++ b/src/llm/base.py @@ -0,0 +1,91 @@ +"""Base LLM provider interface.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any, Dict, List, Optional + + +@dataclass +class LLMResponse: + """Standardized LLM response.""" + content: str + raw_response: Optional[Any] = None + usage: Optional[Dict[str, int]] = None + model: Optional[str] = None + + def to_dict(self) -> Dict[str, Any]: + return { + "content": self.content, + "model": self.model, + "usage": self.usage, + } + + +@dataclass +class Message: + """Chat message.""" + role: str # "system", "user", "assistant" + content: str + + +class BaseLLMProvider(ABC): + """Abstract base class for LLM providers. + + All providers (Bedrock, Gemini, OpenAI) must implement this interface. + """ + + @abstractmethod + async def complete(self, prompt: str, **kwargs) -> LLMResponse: + """Generate a completion for the given prompt. + + Args: + prompt: The input prompt + **kwargs: Provider-specific parameters (temperature, max_tokens, etc.) + + Returns: + LLMResponse with the generated content + """ + pass + + @abstractmethod + async def chat(self, messages: List[Message], **kwargs) -> LLMResponse: + """Generate a response for a chat conversation. + + Args: + messages: List of Message objects representing the conversation + **kwargs: Provider-specific parameters + + Returns: + LLMResponse with the assistant's response + """ + pass + + @abstractmethod + async def complete_json(self, prompt: str, schema: Dict[str, Any], **kwargs) -> Dict[str, Any]: + """Generate a JSON response matching the provided schema. + + This is crucial for structured agent outputs (plans, actions). + + Args: + prompt: The input prompt + schema: JSON schema that the response must conform to + **kwargs: Provider-specific parameters + + Returns: + Parsed JSON dict matching the schema + """ + pass + + @property + @abstractmethod + def model_name(self) -> str: + """Return the model identifier.""" + pass + + @property + @abstractmethod + def provider_name(self) -> str: + """Return the provider name (bedrock, gemini, openai).""" + pass diff --git a/src/llm/bedrock_provider.py b/src/llm/bedrock_provider.py new file mode 100644 index 0000000..d257d3f --- /dev/null +++ b/src/llm/bedrock_provider.py @@ -0,0 +1,115 @@ +"""AWS Bedrock LLM provider using LangChain.""" + +from __future__ import annotations + +import json +from typing import Any, Dict, List + +from .base import BaseLLMProvider, LLMResponse, Message + + +class BedrockProvider(BaseLLMProvider): + """AWS Bedrock provider for Claude, Titan, and other models.""" + + def __init__( + self, + model_id: str = "anthropic.claude-3-sonnet-20240229-v1:0", + region: str = "us-east-1", + access_key_id: str | None = None, + secret_access_key: str | None = None, + ): + self._model_id = model_id + self._region = region + self._client = None + self._access_key_id = access_key_id + self._secret_access_key = secret_access_key + + def _get_client(self): + """Lazy initialization of Bedrock client.""" + if self._client is None: + from langchain_aws import ChatBedrock + + kwargs = { + "model_id": self._model_id, + "region_name": self._region, + } + + # Use explicit credentials if provided + if self._access_key_id and self._secret_access_key: + import boto3 + session = boto3.Session( + aws_access_key_id=self._access_key_id, + aws_secret_access_key=self._secret_access_key, + region_name=self._region, + ) + kwargs["client"] = session.client("bedrock-runtime") + + self._client = ChatBedrock(**kwargs) + + return self._client + + async def complete(self, prompt: str, **kwargs) -> LLMResponse: + """Generate completion using Bedrock.""" + client = self._get_client() + + response = await client.ainvoke(prompt, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model_id, + usage=response.usage_metadata if hasattr(response, 'usage_metadata') else None, + ) + + async def chat(self, messages: List[Message], **kwargs) -> LLMResponse: + """Generate chat response using Bedrock.""" + from langchain_core.messages import HumanMessage, SystemMessage, AIMessage + + client = self._get_client() + + # Convert to LangChain message format + lc_messages = [] + for msg in messages: + if msg.role == "system": + lc_messages.append(SystemMessage(content=msg.content)) + elif msg.role == "user": + lc_messages.append(HumanMessage(content=msg.content)) + elif msg.role == "assistant": + lc_messages.append(AIMessage(content=msg.content)) + + response = await client.ainvoke(lc_messages, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model_id, + ) + + async def complete_json(self, prompt: str, schema: Dict[str, Any], **kwargs) -> Dict[str, Any]: + """Generate JSON response conforming to schema.""" + schema_str = json.dumps(schema, indent=2) + enhanced_prompt = f"""{prompt} + +You MUST respond with valid JSON matching this schema: +{schema_str} + +Respond ONLY with the JSON object, no other text.""" + + response = await self.complete(enhanced_prompt, **kwargs) + + # Parse JSON from response + content = response.content.strip() + # Handle markdown code blocks + if content.startswith("```"): + lines = content.split("\n") + content = "\n".join(lines[1:-1]) + + return json.loads(content) + + @property + def model_name(self) -> str: + return self._model_id + + @property + def provider_name(self) -> str: + return "bedrock" diff --git a/src/llm/factory.py b/src/llm/factory.py new file mode 100644 index 0000000..c2a6be3 --- /dev/null +++ b/src/llm/factory.py @@ -0,0 +1,101 @@ +"""Factory for creating LLM providers based on configuration.""" + +from __future__ import annotations + +from typing import Optional + +from config.llm_config import LLMConfig, LLMProvider, get_llm_config +from .base import BaseLLMProvider + + +def get_llm_provider( + provider: Optional[LLMProvider] = None, + config: Optional[LLMConfig] = None, +) -> BaseLLMProvider: + """Create and return an LLM provider instance. + + Args: + provider: Specific provider to use. If None, uses first available. + config: LLM configuration. If None, loads from environment. + + Returns: + Configured LLM provider instance. + + Raises: + ValueError: If requested provider is not configured or no providers available. + """ + config = config or get_llm_config() + + # If no specific provider requested, find the first available + if provider is None: + available = config.get_available_providers() + if not available: + # Fall back to mock for development/testing + from .mock_provider import MockLLMProvider + return MockLLMProvider() + provider = available[0] + + # Handle mock provider + if provider == LLMProvider.MOCK: + from .mock_provider import MockLLMProvider + return MockLLMProvider() + + # Create the requested provider + if provider == LLMProvider.BEDROCK: + if not config.bedrock.is_configured: + raise ValueError("Bedrock provider not configured. Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.") + + from .bedrock_provider import BedrockProvider + return BedrockProvider( + model_id=config.bedrock.model_id, + region=config.bedrock.region, + access_key_id=config.bedrock.access_key_id, + secret_access_key=config.bedrock.secret_access_key, + ) + + if provider == LLMProvider.GEMINI: + if not config.gemini.is_configured: + raise ValueError("Gemini provider not configured. Set GEMINI_API_KEY.") + + from .gemini_provider import GeminiProvider + return GeminiProvider( + api_key=config.gemini.api_key, + model=config.gemini.model, + ) + + if provider == LLMProvider.OPENAI: + if not config.openai.is_configured: + raise ValueError("OpenAI provider not configured. Set OPENAI_API_KEY.") + + from .openai_provider import OpenAIProvider + return OpenAIProvider( + api_key=config.openai.api_key, + model=config.openai.model, + ) + + raise ValueError(f"Unknown provider: {provider}") + + +def get_provider_for_role(role: str, config: Optional[LLMConfig] = None) -> BaseLLMProvider: + """Get the configured provider for a specific agent role. + + Args: + role: One of 'orchestrator', 'planner', 'executor' + config: LLM configuration. If None, loads from environment. + + Returns: + LLM provider configured for that role. + """ + config = config or get_llm_config() + + role_to_provider = { + "orchestrator": config.agent.orchestrator_provider, + "planner": config.agent.planner_provider, + "executor": config.agent.executor_provider, + } + + provider = role_to_provider.get(role) + if provider is None: + raise ValueError(f"Unknown role: {role}") + + return get_llm_provider(provider, config) diff --git a/src/llm/gemini_provider.py b/src/llm/gemini_provider.py new file mode 100644 index 0000000..4b90074 --- /dev/null +++ b/src/llm/gemini_provider.py @@ -0,0 +1,98 @@ +"""Google Gemini LLM provider using LangChain.""" + +from __future__ import annotations + +import json +from typing import Any, Dict, List + +from .base import BaseLLMProvider, LLMResponse, Message + + +class GeminiProvider(BaseLLMProvider): + """Google Gemini provider.""" + + def __init__( + self, + api_key: str, + model: str = "gemini-1.5-pro", + ): + self._api_key = api_key + self._model = model + self._client = None + + def _get_client(self): + """Lazy initialization of Gemini client.""" + if self._client is None: + from langchain_google_genai import ChatGoogleGenerativeAI + + self._client = ChatGoogleGenerativeAI( + model=self._model, + google_api_key=self._api_key, + ) + + return self._client + + async def complete(self, prompt: str, **kwargs) -> LLMResponse: + """Generate completion using Gemini.""" + client = self._get_client() + + response = await client.ainvoke(prompt, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model, + ) + + async def chat(self, messages: List[Message], **kwargs) -> LLMResponse: + """Generate chat response using Gemini.""" + from langchain_core.messages import HumanMessage, SystemMessage, AIMessage + + client = self._get_client() + + # Convert to LangChain message format + lc_messages = [] + for msg in messages: + if msg.role == "system": + lc_messages.append(SystemMessage(content=msg.content)) + elif msg.role == "user": + lc_messages.append(HumanMessage(content=msg.content)) + elif msg.role == "assistant": + lc_messages.append(AIMessage(content=msg.content)) + + response = await client.ainvoke(lc_messages, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model, + ) + + async def complete_json(self, prompt: str, schema: Dict[str, Any], **kwargs) -> Dict[str, Any]: + """Generate JSON response conforming to schema.""" + schema_str = json.dumps(schema, indent=2) + enhanced_prompt = f"""{prompt} + +You MUST respond with valid JSON matching this schema: +{schema_str} + +Respond ONLY with the JSON object, no other text.""" + + response = await self.complete(enhanced_prompt, **kwargs) + + # Parse JSON from response + content = response.content.strip() + # Handle markdown code blocks + if content.startswith("```"): + lines = content.split("\n") + content = "\n".join(lines[1:-1]) + + return json.loads(content) + + @property + def model_name(self) -> str: + return self._model + + @property + def provider_name(self) -> str: + return "gemini" diff --git a/src/llm/mock_provider.py b/src/llm/mock_provider.py new file mode 100644 index 0000000..95d5820 --- /dev/null +++ b/src/llm/mock_provider.py @@ -0,0 +1,104 @@ +"""Mock LLM provider for testing without real API calls.""" + +from __future__ import annotations + +import json +from typing import Any, Callable, Dict, List, Optional + +from .base import BaseLLMProvider, LLMResponse, Message + + +class MockLLMProvider(BaseLLMProvider): + """Mock provider for unit testing. + + Can be configured with custom response handlers or fixed responses. + """ + + def __init__( + self, + responses: Optional[List[str]] = None, + response_handler: Optional[Callable[[str], str]] = None, + ): + """Initialize mock provider. + + Args: + responses: List of canned responses to return in order + response_handler: Custom function to generate responses + """ + self._responses = responses or [] + self._response_handler = response_handler + self._call_count = 0 + self._call_history: List[Dict[str, Any]] = [] + + async def complete(self, prompt: str, **kwargs) -> LLMResponse: + """Return mock completion.""" + self._call_history.append({"type": "complete", "prompt": prompt, "kwargs": kwargs}) + + content = self._get_response(prompt) + self._call_count += 1 + + return LLMResponse( + content=content, + model="mock-model", + usage={"input_tokens": len(prompt), "output_tokens": len(content)}, + ) + + async def chat(self, messages: List[Message], **kwargs) -> LLMResponse: + """Return mock chat response.""" + self._call_history.append({"type": "chat", "messages": messages, "kwargs": kwargs}) + + # Use last user message as prompt + prompt = messages[-1].content if messages else "" + content = self._get_response(prompt) + self._call_count += 1 + + return LLMResponse( + content=content, + model="mock-model", + ) + + async def complete_json(self, prompt: str, schema: Dict[str, Any], **kwargs) -> Dict[str, Any]: + """Return mock JSON response.""" + self._call_history.append({"type": "complete_json", "prompt": prompt, "schema": schema, "kwargs": kwargs}) + + content = self._get_response(prompt) + self._call_count += 1 + + # Try to parse as JSON, or return empty dict + try: + return json.loads(content) + except json.JSONDecodeError: + return {} + + def _get_response(self, prompt: str) -> str: + """Get the next response.""" + if self._response_handler: + return self._response_handler(prompt) + + if self._responses: + idx = self._call_count % len(self._responses) + return self._responses[idx] + + return '{"action": "noop", "reason": "mock response"}' + + @property + def model_name(self) -> str: + return "mock-model" + + @property + def provider_name(self) -> str: + return "mock" + + # Test helpers + @property + def call_count(self) -> int: + return self._call_count + + @property + def call_history(self) -> List[Dict[str, Any]]: + return self._call_history + + def reset(self) -> None: + """Reset call history and count.""" + self._call_count = 0 + self._call_history.clear() diff --git a/src/llm/openai_provider.py b/src/llm/openai_provider.py new file mode 100644 index 0000000..b9792f2 --- /dev/null +++ b/src/llm/openai_provider.py @@ -0,0 +1,104 @@ +"""OpenAI LLM provider using LangChain.""" + +from __future__ import annotations + +import json +from typing import Any, Dict, List + +from .base import BaseLLMProvider, LLMResponse, Message + + +class OpenAIProvider(BaseLLMProvider): + """OpenAI provider for GPT models.""" + + def __init__( + self, + api_key: str, + model: str = "gpt-4-turbo", + ): + self._api_key = api_key + self._model = model + self._client = None + + def _get_client(self): + """Lazy initialization of OpenAI client.""" + if self._client is None: + from langchain_openai import ChatOpenAI + + self._client = ChatOpenAI( + model=self._model, + api_key=self._api_key, + ) + + return self._client + + async def complete(self, prompt: str, **kwargs) -> LLMResponse: + """Generate completion using OpenAI.""" + client = self._get_client() + + response = await client.ainvoke(prompt, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model, + usage=response.usage_metadata if hasattr(response, 'usage_metadata') else None, + ) + + async def chat(self, messages: List[Message], **kwargs) -> LLMResponse: + """Generate chat response using OpenAI.""" + from langchain_core.messages import HumanMessage, SystemMessage, AIMessage + + client = self._get_client() + + # Convert to LangChain message format + lc_messages = [] + for msg in messages: + if msg.role == "system": + lc_messages.append(SystemMessage(content=msg.content)) + elif msg.role == "user": + lc_messages.append(HumanMessage(content=msg.content)) + elif msg.role == "assistant": + lc_messages.append(AIMessage(content=msg.content)) + + response = await client.ainvoke(lc_messages, **kwargs) + + return LLMResponse( + content=response.content, + raw_response=response, + model=self._model, + ) + + async def complete_json(self, prompt: str, schema: Dict[str, Any], **kwargs) -> Dict[str, Any]: + """Generate JSON response conforming to schema. + + OpenAI supports native JSON mode for better reliability. + """ + from langchain_openai import ChatOpenAI + + # Use JSON mode if available + client = ChatOpenAI( + model=self._model, + api_key=self._api_key, + model_kwargs={"response_format": {"type": "json_object"}}, + ) + + schema_str = json.dumps(schema, indent=2) + enhanced_prompt = f"""{prompt} + +You MUST respond with valid JSON matching this schema: +{schema_str} + +Respond ONLY with the JSON object.""" + + response = await client.ainvoke(enhanced_prompt, **kwargs) + + return json.loads(response.content) + + @property + def model_name(self) -> str: + return self._model + + @property + def provider_name(self) -> str: + return "openai" diff --git a/tests/agent/test_evaluator.py b/tests/agent/test_evaluator.py new file mode 100644 index 0000000..6342280 --- /dev/null +++ b/tests/agent/test_evaluator.py @@ -0,0 +1,147 @@ +"""Tests for EvaluatorAgent with mock LLM.""" + +import pytest +import json + +from agent.evaluator import EvaluatorAgent +from llm.mock_provider import MockLLMProvider + + +@pytest.mark.asyncio +async def test_evaluator_simple_success(): + """Test evaluator with successful step result (no LLM).""" + evaluator = EvaluatorAgent(llm=None) + + step = {"type": "click", "args": {"selector": "#btn"}} + result = {"ok": True, "result": "clicked"} + + evaluation = await evaluator.evaluate( + step=step, + result=result, + remaining_steps=5, + ) + + assert evaluation["success"] is True + assert evaluation["task_complete"] is False + + +@pytest.mark.asyncio +async def test_evaluator_simple_failure(): + """Test evaluator with failed step result.""" + evaluator = EvaluatorAgent(llm=None) + + step = {"type": "click", "args": {"selector": "#missing"}} + result = {"ok": False, "error": "Element not found"} + + evaluation = await evaluator.evaluate( + step=step, + result=result, + remaining_steps=3, + ) + + assert evaluation["success"] is False + assert evaluation["should_replan"] is True + assert "not found" in evaluation["error"].lower() + + +@pytest.mark.asyncio +async def test_evaluator_extract_text_completion(): + """Test evaluator marks task complete on extract_text with no remaining steps.""" + evaluator = EvaluatorAgent(llm=None) + + step = {"type": "extract_text", "args": {"selector": "body"}} + result = {"ok": True, "result": "This is the extracted content from the page."} + + evaluation = await evaluator.evaluate( + step=step, + result=result, + remaining_steps=0, # Last step + ) + + assert evaluation["success"] is True + assert evaluation["task_complete"] is True + assert evaluation["result"] == "This is the extracted content from the page." + + +@pytest.mark.asyncio +async def test_evaluator_with_llm(): + """Test evaluator with mock LLM for intelligent evaluation.""" + llm_response = json.dumps({ + "success": True, + "task_complete": False, + "confidence": 0.9, + "should_replan": False, + "next_action_hint": "Continue with next step", + }) + + mock_llm = MockLLMProvider(responses=[llm_response]) + evaluator = EvaluatorAgent(llm=mock_llm) + + step = {"type": "click", "args": {"selector": "#btn"}} + result = {"ok": True, "result": "clicked"} + + evaluation = await evaluator.evaluate( + step=step, + result=result, + task="Click the submit button and verify form submission", + ) + + assert evaluation["success"] is True + assert evaluation["confidence"] == 0.9 + + +@pytest.mark.asyncio +async def test_evaluator_llm_suggests_replan(): + """Test evaluator with LLM suggesting re-planning.""" + llm_response = json.dumps({ + "success": True, + "task_complete": False, + "should_replan": True, + "replan_reason": "Page structure changed unexpectedly", + }) + + mock_llm = MockLLMProvider(responses=[llm_response]) + evaluator = EvaluatorAgent(llm=mock_llm) + + evaluation = await evaluator.evaluate( + step={"type": "click", "args": {"selector": "#btn"}}, + result={"ok": True, "result": "clicked"}, + ) + + assert evaluation["should_replan"] is True + assert "unexpectedly" in evaluation["replan_reason"] + + +@pytest.mark.asyncio +async def test_evaluator_check_task_completion(): + """Test task completion check based on execution log.""" + evaluator = EvaluatorAgent(llm=None) + + execution_log = [ + {"result": {"ok": False, "error": "not found"}}, + {"result": {"ok": True, "result": "The extracted page content here"}}, + ] + + completion = await evaluator.check_task_completion( + task="Extract content from page", + execution_log=execution_log, + ) + + assert completion["complete"] is True + assert "extracted" in completion["result"].lower() + + +@pytest.mark.asyncio +async def test_evaluator_handles_invalid_json(): + """Test evaluator handles invalid JSON from LLM.""" + # Invalid JSON will cause empty dict return from mock + mock_llm = MockLLMProvider(responses=["invalid json response"]) + evaluator = EvaluatorAgent(llm=mock_llm) + + step = {"type": "click", "args": {"selector": "#btn"}} + result = {"ok": True, "result": "clicked"} + + evaluation = await evaluator.evaluate(step=step, result=result) + + # Should return empty dict from LLM (since mock returns {} on invalid JSON) + assert evaluation == {} diff --git a/tests/agent/test_executor.py b/tests/agent/test_executor.py new file mode 100644 index 0000000..2d5dd05 --- /dev/null +++ b/tests/agent/test_executor.py @@ -0,0 +1,153 @@ +"""Tests for ExecutorAgent with mock dependencies.""" + +import pytest +from unittest.mock import AsyncMock, MagicMock + +from agent.executor import ExecutorAgent + + +class MockController: + """Mock browser controller for testing.""" + + def __init__(self): + self.executed_actions = [] + + async def execute_action(self, action): + self.executed_actions.append(action) + action_type = action.get("type") + + if action_type == "goto": + return {"ok": True, "result": "navigated", "page": MagicMock()} + elif action_type == "click": + return {"ok": True, "result": "clicked"} + elif action_type == "extract_text": + return {"ok": True, "result": "Extracted text content"} + elif action_type == "fail": + return {"ok": False, "error": "Simulated failure"} + + return {"ok": True, "result": "executed"} + + +@pytest.mark.asyncio +async def test_executor_goto(): + """Test executor handles goto action.""" + controller = MockController() + executor = ExecutorAgent(controller=controller) + + step = {"type": "goto", "args": {"url": "https://example.com"}} + result = await executor.execute(step) + + assert result["ok"] is True + assert result["result"] == "navigated" + assert len(controller.executed_actions) == 1 + + +@pytest.mark.asyncio +async def test_executor_click_with_page(): + """Test executor passes page to click action.""" + controller = MockController() + executor = ExecutorAgent(controller=controller) + + page = MagicMock() + step = {"type": "click", "args": {"selector": "#btn"}} + result = await executor.execute(step, page=page) + + assert result["ok"] is True + # Verify page was injected into args + executed = controller.executed_actions[0] + assert executed["args"]["page"] == page + + +@pytest.mark.asyncio +async def test_executor_extract_text(): + """Test executor handles extract_text action.""" + controller = MockController() + executor = ExecutorAgent(controller=controller) + + page = MagicMock() + step = {"type": "extract_text", "args": {"selector": "body"}} + result = await executor.execute(step, page=page) + + assert result["ok"] is True + assert result["result"] == "Extracted text content" + + +@pytest.mark.asyncio +async def test_executor_missing_selector(): + """Test executor validates required selector.""" + executor = ExecutorAgent(controller=MockController()) + + step = {"type": "click", "args": {}} # Missing selector + result = await executor.execute(step) + + assert result["ok"] is False + assert "selector" in result["error"].lower() + + +@pytest.mark.asyncio +async def test_executor_missing_action_type(): + """Test executor handles missing action type.""" + executor = ExecutorAgent(controller=MockController()) + + step = {"args": {"selector": "#btn"}} # Missing type + result = await executor.execute(step) + + assert result["ok"] is False + assert "type" in result["error"].lower() + + +@pytest.mark.asyncio +async def test_executor_no_controller(): + """Test executor handles missing controller gracefully.""" + executor = ExecutorAgent(controller=None) + + step = {"type": "goto", "args": {"url": "https://example.com"}} + result = await executor.execute(step) + + assert result["ok"] is False + assert "controller" in result["error"].lower() + + +@pytest.mark.asyncio +async def test_executor_step_metadata_preserved(): + """Test that executor preserves step metadata in results.""" + controller = MockController() + executor = ExecutorAgent(controller=controller) + + step = { + "type": "click", + "args": {"selector": "#btn"}, + "metadata": { + "reason": "Click submit button", + "expected_outcome": "Form submits", + }, + } + + page = MagicMock() + result = await executor.execute(step, page=page) + + assert result["ok"] is True + assert result["step"] == step + assert result["expected_outcome"] == "Form submits" + + +@pytest.mark.asyncio +async def test_executor_with_retry(): + """Test executor retry mechanism.""" + call_count = 0 + + class RetryController: + async def execute_action(self, action): + nonlocal call_count + call_count += 1 + if call_count < 3: + return {"ok": False, "error": "Temporary failure"} + return {"ok": True, "result": "Success on retry"} + + executor = ExecutorAgent(controller=RetryController()) + + step = {"type": "goto", "args": {"url": "https://example.com"}} + result = await executor.execute_with_retry(step, max_retries=3) + + assert result["ok"] is True + assert call_count == 3 diff --git a/tests/agent/test_orchestrator.py b/tests/agent/test_orchestrator.py new file mode 100644 index 0000000..cb16411 --- /dev/null +++ b/tests/agent/test_orchestrator.py @@ -0,0 +1,220 @@ +"""Tests for Orchestrator with mock LLM.""" + +import pytest +import json + +from agent.orchestrator import Orchestrator, OrchestratorState, TaskStatus, TaskResult +from llm.mock_provider import MockLLMProvider + + +@pytest.fixture +def mock_planner_response(): + """Mock planner LLM response.""" + return json.dumps({ + "steps": [ + {"step": 1, "type": "navigate", "args": {"url": "https://example.com"}}, + {"step": 2, "type": "extract_text", "args": {"selector": "h1"}}, + ], + "strategy": "Navigate and extract title", + }) + + +@pytest.fixture +def mock_evaluator_success(): + """Mock evaluator success response.""" + return json.dumps({ + "success": True, + "task_complete": False, + "should_replan": False, + }) + + +@pytest.fixture +def mock_evaluator_complete(): + """Mock evaluator task complete response.""" + return json.dumps({ + "success": True, + "task_complete": True, + "result": "Successfully extracted data", + }) + + +def test_orchestrator_initialization(): + """Test orchestrator initializes correctly.""" + mock_llm = MockLLMProvider() + orchestrator = Orchestrator(llm=mock_llm) + + assert orchestrator.state is None # No task started yet + assert orchestrator.max_retries == 3 + assert orchestrator.max_steps == 20 + + +def test_task_status_values(): + """Test TaskStatus enum values.""" + assert TaskStatus.PENDING.value == "pending" + assert TaskStatus.PLANNING.value == "planning" + assert TaskStatus.EXECUTING.value == "executing" + assert TaskStatus.EVALUATING.value == "evaluating" + assert TaskStatus.REPLANNING.value == "replanning" + assert TaskStatus.COMPLETED.value == "completed" + assert TaskStatus.FAILED.value == "failed" + + +def test_task_result_dataclass(): + """Test TaskResult dataclass.""" + result = TaskResult( + success=True, + task="Test task", + steps_executed=3, + final_result="Done", + ) + + assert result.success is True + assert result.task == "Test task" + assert result.steps_executed == 3 + assert result.execution_log == [] + assert result.error is None + + +def test_orchestrator_state_dataclass(): + """Test OrchestratorState dataclass.""" + state = OrchestratorState(task="Test task") + + assert state.task == "Test task" + assert state.status == TaskStatus.PENDING + assert state.current_plan == [] + assert state.executed_steps == [] + assert state.current_step_index == 0 + + +def test_orchestrator_custom_config(): + """Test orchestrator with custom config.""" + mock_llm = MockLLMProvider() + orchestrator = Orchestrator( + llm=mock_llm, + max_retries=5, + max_steps=50, + ) + + assert orchestrator.max_retries == 5 + assert orchestrator.max_steps == 50 + + +@pytest.mark.asyncio +async def test_orchestrator_creates_state_on_execute(): + """Test orchestrator creates state when executing task.""" + mock_llm = MockLLMProvider(responses=[ + # Empty plan to fail fast + json.dumps({"steps": [], "strategy": "empty"}), + ]) + + orchestrator = Orchestrator(llm=mock_llm) + + # Before execution + assert orchestrator.state is None + + # Execute - will fail because no planner set + result = await orchestrator.execute_task("Go to example.com") + + # After execution + assert orchestrator.state is not None + assert orchestrator.state.task == "Go to example.com" + + +def test_task_result_with_error(): + """Test TaskResult with error.""" + result = TaskResult( + success=False, + task="Failed task", + steps_executed=1, + final_result=None, + error="Connection timeout", + ) + + assert result.success is False + assert result.error == "Connection timeout" + + +def test_orchestrator_state_with_plan(): + """Test OrchestratorState with plan.""" + state = OrchestratorState( + task="Search for Python", + status=TaskStatus.EXECUTING, + current_plan=[ + {"type": "navigate", "args": {"url": "https://google.com"}}, + {"type": "input", "args": {"selector": "input", "text": "Python"}}, + ], + current_step_index=1, + ) + + assert state.status == TaskStatus.EXECUTING + assert len(state.current_plan) == 2 + assert state.current_step_index == 1 + + +class TestOrchestratorIntegration: + """Integration tests for orchestrator.""" + + @pytest.mark.asyncio + async def test_orchestrator_with_all_agents(self): + """Test orchestrator initializes with all agents.""" + from agent.planner import PlannerAgent + from agent.executor import ExecutorAgent + from agent.evaluator import EvaluatorAgent + + mock_llm = MockLLMProvider() + + planner = PlannerAgent(llm=mock_llm) + executor = ExecutorAgent(llm=mock_llm) + evaluator = EvaluatorAgent(llm=mock_llm) + + orchestrator = Orchestrator( + llm=mock_llm, + planner=planner, + executor=executor, + evaluator=evaluator, + ) + + assert orchestrator.planner is planner + assert orchestrator.executor is executor + assert orchestrator.evaluator is evaluator + + @pytest.mark.asyncio + async def test_planner_integration(self): + """Test planner creates valid plan.""" + from agent.planner import PlannerAgent + + plan_response = json.dumps({ + "goal_analysis": "Navigate and extract", + "lookahead": "After navigation, extract content", + "steps": [ + {"step_number": 1, "action": "goto", "value": "https://example.com", "reason": "Navigate"}, + {"step_number": 2, "action": "extract_text", "selector": "h1", "reason": "Get title"}, + ], + "success_criteria": "Title extracted", + }) + + mock_llm = MockLLMProvider(responses=[plan_response]) + planner = PlannerAgent(llm=mock_llm) + + plan = await planner.plan(task="Get title from example.com") + + assert len(plan) == 2 + assert plan[0]["type"] == "goto" + assert plan[0]["args"]["url"] == "https://example.com" + + @pytest.mark.asyncio + async def test_evaluator_integration(self): + """Test evaluator evaluates step correctly.""" + from agent.evaluator import EvaluatorAgent + + evaluator = EvaluatorAgent(llm=None) # No LLM for simple evaluation + + evaluation = await evaluator.evaluate( + step={"type": "click", "args": {"selector": "#btn"}}, + result={"ok": True, "result": "clicked"}, + remaining_steps=2, + ) + + assert evaluation["success"] is True + assert evaluation["task_complete"] is False diff --git a/tests/agent/test_planner_agent.py b/tests/agent/test_planner_agent.py new file mode 100644 index 0000000..49b030d --- /dev/null +++ b/tests/agent/test_planner_agent.py @@ -0,0 +1,123 @@ +"""Tests for the PlannerAgent with mock LLM.""" + +import pytest +import json + +from agent.planner import PlannerAgent, LLMPlanner +from llm.mock_provider import MockLLMProvider + + +@pytest.mark.asyncio +async def test_planner_fallback_with_url(): + """Test planner fallback when no LLM is provided.""" + planner = PlannerAgent(llm=None) + + steps = await planner.plan("Open https://example.com and read the content") + + assert len(steps) >= 1 + assert steps[0]["type"] == "goto" + assert steps[0]["args"]["url"] == "https://example.com" + + +@pytest.mark.asyncio +async def test_planner_fallback_google(): + """Test planner fallback for Google search.""" + planner = PlannerAgent(llm=None) + + steps = await planner.plan("Search Google for something") + + assert len(steps) >= 1 + assert steps[0]["type"] == "goto" + assert "google" in steps[0]["args"]["url"].lower() + + +@pytest.mark.asyncio +async def test_planner_with_mock_llm(): + """Test planner with mock LLM returning structured plan.""" + plan_response = json.dumps({ + "goal_analysis": "Navigate to example.com and extract content", + "lookahead": "After navigation, we will extract text from body", + "steps": [ + { + "step_number": 1, + "action": "goto", + "value": "https://example.com", + "reason": "Navigate to target page", + "expected_outcome": "Page loads successfully", + }, + { + "step_number": 2, + "action": "extract_text", + "selector": "body", + "reason": "Extract main content", + "expected_outcome": "Text content retrieved", + }, + ], + "success_criteria": "Text content from example.com is extracted", + }) + + mock_llm = MockLLMProvider(responses=[plan_response]) + planner = PlannerAgent(llm=mock_llm) + + steps = await planner.plan("Go to example.com and read content") + + assert len(steps) == 2 + assert steps[0]["type"] == "goto" + assert steps[0]["args"]["url"] == "https://example.com" + assert steps[1]["type"] == "extract_text" + assert steps[1]["args"]["selector"] == "body" + + +@pytest.mark.asyncio +async def test_planner_handles_llm_error(): + """Test planner falls back gracefully on LLM error.""" + # Invalid JSON response will cause parse error + mock_llm = MockLLMProvider(responses=["not valid json"]) + planner = PlannerAgent(llm=mock_llm) + + steps = await planner.plan("Open https://test.com") + + # Should fall back to deterministic planner + assert len(steps) >= 1 + assert steps[0]["type"] == "goto" + + +@pytest.mark.asyncio +async def test_legacy_llm_planner(): + """Test backwards-compatible LLMPlanner class.""" + planner = LLMPlanner(llm_client=None) + + steps = await planner.plan("Open https://example.com") + + assert len(steps) >= 1 + assert steps[0]["type"] == "goto" + + +@pytest.mark.asyncio +async def test_planner_step_metadata(): + """Test that planner includes metadata in steps.""" + plan_response = json.dumps({ + "steps": [ + { + "step_number": 1, + "action": "click", + "selector": "#submit", + "reason": "Submit the form", + "expected_outcome": "Form is submitted", + "fallback": "Try button[type=submit]", + }, + ], + "success_criteria": "Form submitted", + }) + + mock_llm = MockLLMProvider(responses=[plan_response]) + planner = PlannerAgent(llm=mock_llm) + + steps = await planner.plan("Submit the form") + + assert len(steps) == 1 + assert steps[0]["type"] == "click" + assert steps[0]["args"]["selector"] == "#submit" + assert "metadata" in steps[0] + assert steps[0]["metadata"]["reason"] == "Submit the form" + assert steps[0]["metadata"]["fallback"] == "Try button[type=submit]" diff --git a/tests/llm/test_mock_provider.py b/tests/llm/test_mock_provider.py new file mode 100644 index 0000000..05ceb19 --- /dev/null +++ b/tests/llm/test_mock_provider.py @@ -0,0 +1,109 @@ +"""Tests for LLM providers using mock provider.""" + +import pytest +import json + +from llm.mock_provider import MockLLMProvider +from llm.base import Message + + +@pytest.mark.asyncio +async def test_mock_provider_complete(): + """Test basic completion with mock provider.""" + responses = ["Hello, world!", "Second response"] + provider = MockLLMProvider(responses=responses) + + result = await provider.complete("Test prompt") + + assert result.content == "Hello, world!" + assert result.model == "mock-model" + assert provider.call_count == 1 + + +@pytest.mark.asyncio +async def test_mock_provider_cycles_responses(): + """Test that mock provider cycles through responses.""" + responses = ["First", "Second"] + provider = MockLLMProvider(responses=responses) + + r1 = await provider.complete("p1") + r2 = await provider.complete("p2") + r3 = await provider.complete("p3") + + assert r1.content == "First" + assert r2.content == "Second" + assert r3.content == "First" # Cycles back + + +@pytest.mark.asyncio +async def test_mock_provider_chat(): + """Test chat completion with mock provider.""" + provider = MockLLMProvider(responses=["Chat response"]) + + messages = [ + Message(role="system", content="You are helpful"), + Message(role="user", content="Hello"), + ] + + result = await provider.chat(messages) + + assert result.content == "Chat response" + assert provider.call_count == 1 + + +@pytest.mark.asyncio +async def test_mock_provider_complete_json(): + """Test JSON completion with mock provider.""" + json_response = json.dumps({"action": "click", "selector": "#btn"}) + provider = MockLLMProvider(responses=[json_response]) + + schema = {"type": "object"} + result = await provider.complete_json("prompt", schema) + + assert result["action"] == "click" + assert result["selector"] == "#btn" + + +@pytest.mark.asyncio +async def test_mock_provider_with_handler(): + """Test mock provider with custom response handler.""" + def handler(prompt: str) -> str: + if "search" in prompt.lower(): + return json.dumps({"action": "goto", "url": "https://google.com"}) + return json.dumps({"action": "noop"}) + + provider = MockLLMProvider(response_handler=handler) + + r1 = await provider.complete("Search for something") + r2 = await provider.complete("Do nothing") + + assert "goto" in r1.content + assert "noop" in r2.content + + +@pytest.mark.asyncio +async def test_mock_provider_call_history(): + """Test that mock provider tracks call history.""" + provider = MockLLMProvider() + + await provider.complete("First prompt") + await provider.chat([Message(role="user", content="Hello")]) + + assert len(provider.call_history) == 2 + assert provider.call_history[0]["type"] == "complete" + assert 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