Google Antigravity vs Cursor 2.0: Which IDE should you choose in 2025?

In late 2025 the AI-assisted development landscape took another big step: Google launched Antigravity, an “agent-first” development platform built around Gemini 3 Pro, and Cursor shipped Cursor 2.0 with its Composer model and a new multi-agent interface. Both promise to change how software teams build software with AI — but they take different design decisions, tradeoffs, and target slightly different workflows.

What is Google Antigravity and what are its core features?

Google positions Antigravity as a full development platform rather than merely an assistant: an IDE + “Manager” surface where autonomous agents can be spawned, observed and orchestrated across editors, terminals and embedded browsers. The design goal is to let agents plan, execute, verify, and iterate on multi-step tasks while producing human-friendly artefacts that prove what they did and why. Antigravity ships in a public preview across Windows, macOS and Linux and includes model choice (Gemini 3 Pro by default, plus optional Sonnet/third-party models).

Key features (at a glance)

• Agent-first Manager surface — a mission-control UI for spawning, orchestrating, and observing multiple agents across workspaces (concurrent, long-running jobs).
• Editor view + agent side-panel — a familiar IDE experience with tight agent integration for synchronous workflows.
• Artifacts (proof of work) — agents generate structured deliverables (task plans, implementation plans, screenshots, browser walkthroughs) so humans can quickly validate results instead of parsing long raw logs.
• Browser automation & DOM capture — agents can control an embedded browser, capture recordings, and interact with page DOM for verification and end-to-end testing.
• Model choice & quotas — Gemini 3 Pro is the flagship model, with options for other models; Google provides “generous” rate limits in the public preview.

Architecture and developer ergonomics

Antigravity is intended as an opinionated platform: agents are first-class citizens, able to access the editor, terminal, and browser in a controlled fashion. The platform exposes autonomy controls — terminal execution policies (Off / Auto / Turbo) and review policies (Always proceed / Agent decides / Request review) — so teams can tune how much agency they grant agents before human sign-off. The UI emphasizes artifacts and commentable feedback, mirroring a doc-style review flow rather than raw tool logs

What is Cursor 2.0 and what are its core features?

Cursor began as an AI-first code editor built around the idea of “vibe coding” — keeping engineers in flow with an editor that understands whole codebases. Cursor 2.0 (released in late October 2025) is an evolution: a new agent interface + Composer, Cursor’s first native coding model designed specifically for agentic interactions. Key claims include significantly lower latency, multi-agent execution, and integrated browser testing.

Core capabilities

• Composer model: Cursor developed Composer as a frontier coding model optimized for low latency and the “short, iterative turns” typical of interactive coding. Cursor claims Composer is roughly 4× faster than similarly capable models on their workloads; it’s trained with tool access such as semantic code search and edit primitives (their published materials stress RL-style training on engineering tasks).
• Multi-agent interface: Cursor 2.0 introduces a sidebar and plans capability where you can run up to eight agents in parallel against isolated worktrees or remote machines to avoid merge conflicts. The UI is built around lightweight parallelism so agents can work on separate tasks simultaneously.
• Native browser tool: Cursor added an embedded browser that allows agents to inspect the DOM, run end-to-end frontend tests, and iterate until the produced output satisfies interactive checks — similar in spirit to Antigravity’s browser integrations but implemented inside Cursor’s desktop/VS Code environment.

How do the two platforms compare on agent orchestration and scale?

Which platform handles multi-agent workflows better?

• Antigravity: Designed from the ground up as “agent-first.” It provides mission control for potentially many agents, the ability to grant agents access to tool surfaces (editor, terminal, browser), and artifact generation for traceability. That makes it strong for large, cross-functional agent orchestration and complex automation pipelines.
• Cursor 2.0: Also supports multi-agent workflows but with a stronger emphasis on safety through isolated worktrees and tight Git integration. Cursor’s parallelism (e.g., running multiple agents across isolated code copies) is engineered to avoid file conflicts and make multi-agent experimentation safe and fast.

Verdict: If your primary requirement is mission-level agent orchestration across many tool surfaces with rich artifact tracing, Antigravity leans towards that vision. If instead you want fast iterative multi-agent experimentation constrained to developer workflows and Git safety, Cursor’s approach is more conservative and practical.

Antigravity vs Cursor 2.0 — Feature Comparison

How do they compare on model and compute choices?

What models do they use and can you plug in your own?

• Antigravity is tightly coupled to Gemini 3 Pro by design (Google’s flagship), with first-class support but also the ability to leverage other models. That gives Google the advantage when you want deep Gemini optimizations (latency, tool access, specialized capabilities).
• Cursor 2.0 ships its own Composer model—optimized for coding and agentic tasks—and emphasizes fast inference and practical throughput for developer tasks. Cursor also remains model-agnostic in many integrations, enabling teams to choose the model that best fits cost and accuracy requirements.

Verdict: Expect Antigravity to shine when Gemini-specific features matter (tooling synergy, LLM-native interfaces). Cursor’s Composer aims for cost-effective speed and a smaller latency footprint tuned for coding tasks.

How do they compare on developer experience and integrations?

What is the feel inside the editor and the external integrations?

• Antigravity: Editor resembles a familiar IDE but with agent sidebars and artifact creation. It aims for deep integration across editor, terminal, and browser, letting agents operate across the full development stack. This can dramatically reduce context switching when agents are trusted to run tests, patch files, and demonstrate behavior via recorded browser sessions.
• Cursor 2.0: Feels like an AI-powered IDE built specifically for teams that want to keep normal dev tools and Git flows first. The multi-agent editor uses isolated worktrees and integrates AI code review, making agent results easier to incorporate via standard PR flows. Cursor emphasizes safe collaboration between humans and agents.

Which integrates better with existing CI/CD and enterprise tooling?

Both platforms explicitly design to be integrated:

• Cursor emphasizes Git provider integrations and editor-level code review features that slot directly into developer pipelines.
• Antigravity’s artifact system and broader tool access make it conceptually powerful for automating end-to-end flows (e.g., automated E2E testing, browser interactions), but that also requires careful governance at enterprise scale.

Verdict: For teams that want low friction integration into existing Git/CI flows, Cursor 2.0 is more immediately plug-and-play. Antigravity offers more transformative automation potential, but with higher governance and integration overhead.

Practical examples: using Antigravity and Cursor (illustrative code)

Below are illustrative examples showing how teams might interact with each platform. These examples are pseudocode / conceptual snippets meant to demonstrate typical workflows; consult the official docs when implementing production automation. (Referenced docs and codelabs are linked in the sources.)

Example 1 — Antigravity mission definition (illustrative JSON)

This example shows how a developer might define a mission that instructs an Antigravity agent to add a new API endpoint, run tests, and produce artifacts.

{
  "mission_name": "add_user_endpoint_v1",
  "description": "Create POST /api/users endpoint, unit tests, and run CI.",
  "agents": [
    {
      "name": "PlanAgent",
      "role": "create a step-by-step plan",
      "prompt": "Create tasks to add a users API: router, handler, tests, docs."
    },
    {
      "name": "CoderAgent",
      "role": "implement code",
      "permissions": ["edit_files", "run_tests", "open_browser"],
      "model": "gemini-3-pro"
    },
    {
      "name": "VerifierAgent",
      "role": "run tests and verify results",
      "permissions": ["run_tests"]
    }
  ],
  "artifact_policy": {
    "capture_screenshots": true,
    "record_terminal": true,
    "log_level": "verbose"
  }
}

Notes: Antigravity’s artifact generation is an explicit feature designed to make agent actions inspectable and documentable.

Example 2 — Cursor Composer parallel agents (illustrative Python)

Cursor 2.0 emphasizes isolated worktrees so parallel agents don’t conflict. The following pseudocode demonstrates launching two agents to implement a feature and a test in parallel, then merging results via git.

# Pseudocode - illustrative only
from cursor_sdk import CursorClient

client = CursorClient(api_key="CURSOR_API_KEY", model="composer-v1")

# create isolated worktrees for each agent
agent_a = client.spawn_agent(name="feature_impl", worktree="worktree-feature")
agent_b = client.spawn_agent(name="tests_impl", worktree="worktree-tests")

# send tasks
agent_a.run("Add POST /api/users handler and update router. Create basic validation.")
agent_b.run("Create unit and integration tests for POST /api/users.")

# wait for agents to finish and fetch patches
patch_a = agent_a.get_patch()
patch_b = agent_b.get_patch()

# apply patches to local branches, run tests locally, open PRs
apply_patch_to_branch("feature/users", patch_a)
apply_patch_to_branch("feature/users-tests", patch_b)

# run CI locally
run_command("pytest -q")

# create PRs for human review
create_pr("feature/users", base="main", title="feat: add users endpoint")
create_pr("feature/users-tests", base="main", title="test: add users tests")

Notes: Cursor’s isolated worktrees and Git integration are core to its design — this reduces merge conflicts and keeps changes auditable in standard PR workflows.

Conclusion

Antigravity and Cursor 2.0 represent two sensible answers to the same problem: how do we integrate powerful LLM agents into everyday software development? Antigravity goes for a broader, mission-control vision that treats agents like autonomous teammates across editors, terminals, and browsers. Cursor 2.0 opts for a measured, developer-centric approach that keeps Git and code review at the center while enabling fast multi-agent experimentation.

Both are significant advances. For teams, the decision will come down to whether you want transformational automation (and can absorb the governance overhead) or incremental, tightly integrated productivity gains. Either way, the era of agentic development is here — and it will reward teams that treat security, observability, and human-in-the-loop verification as first-class concerns.

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