multi-agent-coding-system
Reached #13 on Stanford's Terminal Bench leaderboard. Orchestrator, explorer & coder agents working together with intelligent context sharing.
The multi-agent-coding-system is an orchestrated AI coding framework that achieved #13 on Stanford's TerminalBench leaderboard by coordinating specialized sub-agents to solve complex software tasks. An orchestrator agent delegates work to explorer agents (for read-only analysis) and coder agents (for implementation), while maintaining an intelligent context store that shares knowledge artifacts across all agents. The system prevents redundant work through persistent memory and builds compound intelligence where each action meaningfully builds on previous discoveries. Developers can run it with Claude Sonnet-4 or Qwen-3-Coder and deploy it for autonomous codebase exploration, bug fixes, and feature implementation.
Key Features
Use Cases
- 01Autonomous codebase exploration and system understanding for unfamiliar projects
- 02Complex bug fixes requiring investigation across multiple files and system components
- 03Multi-step feature implementation with automatic verification and testing
- 04Infrastructure recovery tasks like Terraform state restoration from backups
- 05Reinforcement learning research on multi-agent coordination (includes RL training framework)
- 06Benchmark evaluation on Stanford TerminalBench or similar agentic coding assessments
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multi-agent-coding-system — FAQ
What is the multi-agent-coding-system?+
It's an orchestrated AI coding framework where an orchestrator agent coordinates specialized explorer and coder sub-agents to autonomously complete complex software tasks. The system reached #13 on Stanford's TerminalBench leaderboard, outperforming Claude Code.
How do I install and run this AI agent system?+
Clone the repository from GitHub, install dependencies with 'uv sync', then run evaluations using './run_terminal_bench_eval.sh'. For quick testing, refer to the examples in the /tests directory.
Which AI models does this system support?+
The system supports Claude Sonnet-4 and Qwen-3-Coder out of the box, with the ability to use different models for the orchestrator and sub-agents. It uses LiteLLM and OpenRouter for flexible model switching.
Do I need API keys to run this multi-agent system?+
Yes, you'll need API keys for your chosen LLM provider (Anthropic for Claude or access to Qwen-3-Coder via OpenRouter). The system uses these models to power all three agent types.
Is the multi-agent-coding-system free and open source?+
Yes, the orchestration code, agent system messages, and complete framework are fully open sourced on GitHub. However, you'll incur LLM API costs when running the agents (evaluations cost ~$218-$264 for the full TerminalBench).
What makes the context sharing system unique?+
The orchestrator explicitly defines what knowledge artifacts sub-agents must return, then reuses these artifacts across future tasks. This creates compound intelligence where discoveries persist and build on each other, eliminating redundant exploration work.
How do I install multi-agent-coding-system?+
Open the source repository on GitHub and follow its README. multi-agent-coding-system is a agent — MCP Agents Market links you directly to the official repo.
Is multi-agent-coding-system free?+
multi-agent-coding-system is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.