L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Développement · SKILL.md

core-web-agent

core-web-agent

Build, run, debug, and cost-check LaVague WebAgent workflows with WorldModel, ActionEngine, PythonEngine, logging, and safe dry-run scaffolds.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: core-web-agent description: "Build, run, debug, and cost-check LaVague WebAgent workflows with WorldModel, ActionEngine, PythonEngine, logging, and safe dry-run scaffolds." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LaVague core WebAgent workflows

Use this sub-skill when the task is to create, adapt, debug, or explain a LaVague WebAgent run loop: WorldModel, ActionEngine, PythonEngine, navigation controls, logging, token counting, and returned ActionResult handling.

Route here for

  • Building a minimal agent with WorldModel, ActionEngine, a browser driver, and WebAgent.
  • Choosing between agent.run(...), agent.run_step(...), agent.demo(...), or a dry-run template.
  • Wiring custom LLM/embedding/multimodal objects after a context has been chosen.
  • Interpreting ActionResult.success, ActionResult.output, generated code, logs, token/cost estimates, and debug node displays.
  • Troubleshooting core-agent errors after browser and provider dependencies are already selected.

Route elsewhere

  • Selenium/Playwright binary, profile, iframe, tab, scroll, screenshot, and browser construction issues: use ../browser-drivers/SKILL.md.
  • OpenAI/Azure/Anthropic/Gemini/Fireworks contexts, cache contexts, Cohere, or custom retriever pipelines: use ../contexts-and-retrievers/SKILL.md.
  • Gradio demos, Chrome extension server, AgentServer, DriverServer, or lavague-serve: use ../server-extension-gradio/SKILL.md.
  • lavague-qa or lavague-test CLI workflows: use ../qa-and-test-runner/SKILL.md.

Start points

  1. Read references/workflows.md for minimal, context-backed, step-by-step, and dry-run WebAgent recipes.
  2. Read references/api-reference.md for verified constructor signatures, method names, and return objects.
  3. Read references/logging-and-debugging.md when the user asks about token costs, logs, node inspection, SQLite logging, or step-by-step debugging.
  4. Read references/troubleshooting.md for missing API keys, telemetry warnings, NLTK/proxy warnings, pkg_resources failures, stale result/debug state, and core API misuse.
  5. Run the safe helper from the la-vague skill root before spending browser or model-provider time:
python sub-skills/core-web-agent/scripts/lavague_minimal_agent.py --dry-run

The helper validates imports and prints a runnable template by default. It does not start a browser, contact a model provider, browse the web, or write logs unless --run-live is explicitly supplied.

Minimal live shape

from lavague.core import WorldModel, ActionEngine
from lavague.core.agents import WebAgent
from lavague.drivers.selenium import SeleniumDriver

# Requires browser support and the default model-provider credentials.
driver = SeleniumDriver(headless=True)
action_engine = ActionEngine(driver)
world_model = WorldModel()
agent = WebAgent(world_model, action_engine, n_steps=5)
agent.get("https://example.com")
result = agent.run("Summarize the visible page")
print(result.success, result.output)

Before running a live objective, confirm the matching browser route and provider-context route. The default LaVague stack uses OpenAI-backed models and embeddings, so OPENAI_API_KEY is normally required unless you pass a custom context/model stack.

Core decisions

  • Use WorldModel.from_context(context) and ActionEngine.from_context(context, driver) when a provider context owns the LLM, multimodal LLM, and embedding choices.
  • Pass llm=, embedding=, mm_llm=, retriever=, or custom engines only when mixing components deliberately.
  • Use agent.run_step(objective) for manual loops or debugging one instruction at a time.
  • Use agent.run(objective, display=True) only when notebook/display output is useful and safe.
  • Use agent.run(..., log_to_db=True) only when a local SQLite log file is acceptable.
  • Use agent.demo(...) only after routing to the Gradio/server sub-skill because it imports lavague-gradio lazily.

Validation checklist

  • Python version is supported by the installed package set.
  • lavague.core, the selected driver package, and selected context package import.
  • Required model-provider environment variables are present but not printed.
  • Browser binaries/profile/session requirements are satisfied.
  • LAVAGUE_TELEMETRY=NONE is set if telemetry must be disabled.
  • The objective contains no personal data that should not be sent to model providers or telemetry.
  • Live work is explicitly permitted; otherwise stay in dry-run/template/probe mode.
Skills enregistrées