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Umbrella: OpenManus research adoption — browser perception loop, budgets, external MCP #11536

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Description

@mrveiss

Context

/research https://github.com/FoundationAgents/OpenManus (2026-07-10) compared OpenManus (FoundationAgents, ~57k stars, MetaGPT team) against AutoBot. Most of OpenManus is weaker than what we have (sandboxing, planning, LLM resilience, fetch/crawl pipeline), but the comparison surfaced 8 concrete adoptables/gaps, each verified against our code with file:line evidence.

The unifying theme: OpenManus runs a closed perception-action loop for browsing (indexed clickable elements + screenshot injected into every LLM step), while AutoBot's LLM browses blind — guessing CSS selectors against pages it can't see, with screenshots reduced to the text "Screenshot captured successfully" (autobot-backend/chat_workflow/tool_handler.py:2042-2046).

Tasks

Suggested order

#11539 first (security-adjacent session bleed, and #11544 tabs depend on its per-session contexts), then #11537 + #11538 together (they form the perception loop), then the rest independently.

Source references

  • OpenManus perception loop: app/agent/browser.py:47-79, app/tool/browser_use_tool.py:479-537
  • OpenManus token budget: app/llm.py:240-262 (cumulative pre-request check, non-retryable TokenLimitExceeded)
  • OpenManus MCP client: app/agent/manus.py:67-129 (SSE/stdio, hot-merge per server ID)

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