What Makes LevelCode Unique

LevelCode is an open-source AI coding agent that coordinates specialized sub-agents instead of using one model for everything.

The result: better code quality and up to 3x faster performance than Claude Code, built on a deep agent framework continuously refined by our in-house evals

LevelCode is dramatically faster—often completing tasks in 1/3 the time.

In real-world tests:

• Claude Code: 19m 37s for a feature
• LevelCode: 6m 45s for the same feature

That's 100+ seconds faster on average per prompt. We achieve this through parallel agents, prompt caching, and smarter file discovery.

See our detailed comparison with Claude Code.

Claude Code can spend 5+ minutes grep-ing and reading file excerpts one at a time.

LevelCode's approach:

1. Parse your entire codebase: We analyze all source files and extract function names, class names, and type names
2. Build a code tree: This creates a compact tree of all directories, files, and symbols in your project
3. Grok 4.1 Fast scans the tree: We feed this code tree to Grok 4.1 Fast, which identifies up to 12 relevant files in seconds
4. Gemini Flash summarizes: Those 12 files are read and summarized by Gemini Flash
5. Main agent reads multiple files at once: With the summaries, the main agent knows exactly what to read

This entire process takes just a few seconds and efficiently conveys a lot of information to the agent. No more watching your agent slowly explore your codebase.

In MAX mode, LevelCode doesn't just try once—it tries three times in parallel with different strategies and picks the best result.

How it works:

1. The orchestrator spawns multiple editor agents, each with a different strategy
2. All implementations run in parallel, reusing the prompt cache
3. A selector agent chooses the best implementation
4. The selector can incorporate good ideas from other attempts

This is remarkably efficient because all parallel agents share the cached conversation history—you only pay once for reading files.

Every prompt gets reviewed before LevelCode finishes.

• A reviewer agent spawns automatically
• It runs in parallel with typechecks and tests
• Catches bugs, dead code, and quality issues
• Fixes are applied before you see the result

In MAX mode, multiple reviewers analyze your code from different angles—all reusing the prompt cache.

Other tools show you "% context used" and make you worry about it.

LevelCode handles context automatically:

• Smart compaction: After the prompt cache expires (5 min idle), we automatically summarize the conversation—much more efficient for long sessions
• Non-lossy summaries: 10-20 roundtrips preserved with full details
• Deterministic strategy: User messages, assistant messages, tool calls—all kept
• Immediate re-reading: LevelCode quickly re-reads any relevant files it needs after compaction

You never think about context. It just works.

Our entire agent framework is open source. The same code that powers LevelCode powers your custom agents.

Key innovations:

• Agents as the composable unit: Not individual LLM calls, but complete agents with tools and prompts
• Optional inherited context: Subagents can optionally inherit conversation history (Claude Code's subagents always start with blank context)
• Arbitrary nesting: Agents can spawn agents that spawn agents—unlimited depth (Claude Code only supports 1 level of subagents)
• Programmatic control: Mix LLM calls with TypeScript code using generator functions
• Orchestrator pattern: One agent with no tools except spawning other agents—perfect context management for free

// Simplified example of the orchestrator pattern
const orchestrator = {
  tools: [spawnAgent],
  spawnableAgents: [filePicker, editor, reviewer, thinker, researcher]
}

Spawned agents contribute only their final output, keeping the orchestrator's context clean and focused.

We built LevelCode Benchmark—our custom eval suite that tests agent configurations across 175+ real implementation tasks from open source repos.

LevelCode Benchmark takes a fundamentally different approach from benchmarks like SWE Bench. Instead of passing predefined tests, our evals challenge coding agents to reimplement real git commits through multi-turn conversations. An AI judge scores implementations on completion, efficiency, code quality, and overall correctness—comparing against the ground truth commit.

• Data-driven optimization: We measure quality, speed, and cost across many agent combinations
• Ship what wins: Only the highest-scoring, fastest, most cost-effective configurations go live
• Most complex agent system: After testing countless subagent combinations, we ship the most robust multi-agent architecture of any major coding agent
• Continuous improvement: We believe going deeper on agent research will unlock significant further advantages that no one else will find

Our research isn't theoretical—it's deployed in production, constantly refined by real-world testing.

LevelCode optionally displays ads above the input box. Each impression earns you credits you can spend on more coding agent usage.

• Earn while you code: Ad impressions convert directly to credits
• Completely optional: Turn ads off at any time in settings
• Use credits for more prompts: Earned credits work just like purchased credits

LevelCode's CLI is built on OpenTUI—a React-based terminal framework.

• No flicker, ever
• Hover and click support
• Sleek, polished experience

After every response, LevelCode suggests three follow-up prompts you can click to execute.

• LevelCode often has ideas you didn't think of
• One click to continue building
• A step toward LevelCode as a collaborative partner

When you ask LevelCode to do something, it just does it. No permission prompts. No "Are you sure?" dialogs.

You can step away and come back to finished work.

npm install -g @levelcode/cli

Then cd to your project and run levelcode. Experience the difference in seconds.