Agentic Coding LLM
Code understanding, generation, and execution — powered by LongCat-2.0's 1M context and MOPD architecture.
What is agentic coding?
Agentic coding goes beyond single-turn code completion. An agentic coding model must understand entire codebases, plan multi-step changes, call tools, execute commands, debug failures, and iterate autonomously — the way a senior engineer works across a full project lifecycle.
This requires long-context understanding, reliable tool use, and stable execution in real terminal and IDE environments — not just benchmark-level snippet generation.
Why LongCat-2.0 for agentic coding
- 1M native context (LSA): See an entire repository — not just the current file
- Token-level dynamic compute: Zero-computation experts allocate resources where complexity demands it
- MOPD expert fusion: Agent, reasoning, and interaction experts routed per task
- SWE-bench Pro 59.5: Leading deep software engineering benchmark
- Terminal-Bench 70.8: Stable real terminal command execution and error recovery
- Open-source: 1.6T MoE model trained and released by Meituan LongCat
Common agentic coding workflows
- Codebase migration: Refactor legacy code to new APIs with full feature parity
- Full-stack app generation: From idea to runnable product in one session
- AI SQL agents: Natural language to data insights with tool orchestration
- Debug and fix: Autonomous error diagnosis and patch generation in CI/CD pipelines
- Multi-file refactoring: Cross-module changes with million-token context consistency