OpenManus
An open, general-purpose agent framework from FoundationAgents — MIT, 58.4k★ and 10.1k forks off 535 commits (GitHub API, re-checked 2026-09-27; 57.6k★ and 526 commits at ingest in July; last push 2026-08-22), which is the wiki's steepest star-to-commit ratio and the first thing to explain about it. Its README opens: "No fortress, purely open ground. OpenManus is Coming."
Built as a jailbreak of a closed product
Manus shipped as a proprietary general agent gated behind an invite code. Core authors Xinbin Liang and Jinyu Xiang, with contributors from the MetaGPT community, had a working prototype in three hours and put the pitch in one line: "OpenManus can achieve any idea without an Invite Code."
The stars are for that, not for ~530 commits of engineering. What the repo demonstrates is how thin the moat around a general-purpose agent product turned out to be — the scarce thing was access, not architecture. Compare the corpus's other open-vs-closed instances (openclaw, hermes-agent): those replaced closed harnesses; this one replaced a closed waitlist.
What it actually is
A Python 3.12 framework (conda or uv install, config.toml for keys) that takes an idea from the
terminal and executes it. Three modes ship: the general OpenManus Agent, a DataAnalysis agent
for visualization work, and an MCP tool version. Capabilities named:
browser automation via Playwright, data analysis, computer vision, and further tools through MCP.
Default model is GPT-4o with base_url/key overrides for any other provider, so it sits on the
provider-agnostic side of the agentic-coding-harness pattern.
The README is thin exactly where this spoke cares most: the agent loop is not documented. Multi-agent support and tool integration are asserted; how the agent decides, retries, or verifies isn't described. 301 open issues and 192 open PRs against 526 commits was its own signal about maintenance load in July; by 2026-09-27 it was 249 issues and 208 PRs against 535 commits. Issues are being closed; pull requests are piling up faster than they merge.
The sibling that argues with this wiki
OpenManus-RL, a collaboration with UIUC researchers, tunes LLM agents with reinforcement learning (GRPO). That is a different theory of where agent competence comes from than the one this spoke has been accumulating evidence for: don't scaffold the model, train it. The corpus has no other source on RL-tuned agents, and this one is a pointer rather than a study — see synthesis, where it's filed as a standing tension rather than a settled contradiction.
Related
foundation-agents · agentic-tooling · agentic-coding-harness · model-context-protocol · openclaw · hermes-agent