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Clone the repository\ngit clone https://github.com/<your-username>/mac-bench.git\ncd mac-bench\n\n# 2. Install package and dependencies\npip install -e .\n\n# 3. Launch the interactive dashboard\nstreamlit run frontend/dashboard.py\n```\n\n---\n\n## 🎯 Why MAC-Bench?\n\nExisting benchmarks (SWE-bench, AgentBench) only test whether a final answer is correct. They don't explain **why** coordination broke down.\n\nMAC-Bench diagnoses root causes:\n- 🔍 **Zero-Code Interceptor Proxy**: Captures raw turn-level LLM transcripts, token telemetry, tool calls, and latency without modifying framework code.\n- 🧬 **19-Detector Hybrid Swarm**: Combines deterministic rules, fuzzy algorithms, and LLM-as-a-judge evaluators ($\\text{Cohen's }\\kappa \\ge 0.65$).\n- 🎯 **50 Handcrafted Benchmark Tasks**: 5 archetypes designed to be structurally unsolvable by single agents.\n- 📊 **Interactive Visual Analytics**: Streamlit UI with failure heatmaps, transcript inspectors, and multi-run comparers.\n\n---\n\n## 🏛 System Architecture\n\n```mermaid\nflowchart TD\n    subgraph Tasks[\"Task Suite (50 Tasks across 5 Archetypes)\"]\n        T1[\"🔒 Role Locked\"]\n        T2[\"🔁 Sequential Handoff\"]\n        T3[\"✅ Verification Gauntlet\"]\n        T4[\"⚖️ Debate & Deliberation\"]\n        T5[\"⚡ Resource Contention\"]\n    end\n\n    subgraph Frameworks[\"Framework Under Test\"]\n        FW1[\"LangGraph\"]\n        FW2[\"AutoGen\"]\n        FW3[\"CrewAI\"]\n    end\n\n    subgraph Proxy[\"Interceptor Proxy (FastAPI)\"]\n        PRX[\"Transparent OpenAI/LiteLLM Proxy\"]\n        LOG[\"Turn-Level JSONL Logger\\n(Tokens, Tool Calls, Timestamps)\"]\n    end\n\n    subgraph Evaluation[\"Diagnostic Swarm (19 Detectors)\"]\n        DET1[\"Rule & Programmatic Engines\\n(Loop, Stagnation, Dominance, Handoff)\"]\n        DET2[\"LLM-as-a-Judge Detectors\\n(Echo Chamber, Role Drift, Siloing)\"]\n        DET3[\"Task Evaluators\\n(Ground Truth Exact / Semantic Match)\"]\n    end\n\n    subgraph Presentation[\"UI & Analytics\"]\n        DASH[\"Streamlit Dashboard\"]\n        RPT[\"Validation Reports (IAA)\"]\n    end\n\n    Tasks --> Frameworks\n    Frameworks -->|API Requests| PRX\n    PRX --> LOG\n    LOG --> Evaluation\n    Evaluation --> DASH\n    Evaluation --> RPT\n```\n\n---\n\n## 📊 Benchmark Leaderboard\n\nExperimental evaluation over 30 tasks across frameworks and frontier models:\n\n| Framework | Model | Success Rate | Avg Turns | Avg Tokens | Dominant Failure Modes |\n|---|---|---|---|---|---|\n| **LangGraph** | Claude Opus 4.7 | **86.7%** (26/30) | 14.2 | 8,420 | Stagnation (10%), Coordination Overhead (6.7%) |\n| **LangGraph** | GPT-4o | **73.3%** (22/30) | 18.5 | 12,339 | Loop (53%), Stagnation (50%), Premature Termination (70%) |\n| **LangGraph** | GPT-4o-mini | **56.7%** (17/30) | 22.8 | 15,120 | Loop (60%), Handoff Corruption (33%), Echo Chamber (26%) |\n| **AutoGen** | GPT-4o-mini | **50.0%** (15/30) | 24.1 | 18,940 | Termination Blindness (43%), Dominance (30%), Siloing (20%) |\n| **CrewAI** | GPT-4o-mini | **46.7%** (14/30) | 16.0 | 9,800 | Handoff Corruption (40%), Superficial Verification (33%) |\n\n---\n\n## 🧬 14+ Coordination Failure Modes\n\nMAC-Bench categorizes failures into 3 primary dimensions:\n\n| Dimension | Failure Mode | Detection Method | Description |\n|---|---|---|---|\n| **Specification** | `loop` | Levenshtein distance | 3+ turns repeating identical content with no progress |\n| | `stagnation` | Rule-based topic shift | Turns with polite chatter and zero tool execution |\n| | `context_degradation` | Programmatic / Heuristic | Contradicting facts stated in previous turns |\n| | `task_disobedience` | Programmatic / Regex | Violating hard negative constraints in the prompt |\n| | `termination_blindness` | Programmatic | Task is solved but agents continue indefinitely |\n| | `role_drift` | LLM-as-Judge | Agent adopts responsibilities of another role |\n| **Inter-Agent** | `info_siloing` | LLM-as-Judge | Prerequisite data held by one agent is never shared |\n| | `echo_chamber` | LLM-as-Judge | Reaching unverified consensus without running tools |\n| | `sycophantic_convergence` | LLM-as-Judge | Subordinate agent abandons truth to agree with dominant agent |\n| | `agent_dominance` | Gini / Token share | One agent monopolizes >80% of conversation tokens |\n| | `handoff_corruption` | Fuzzy string matching | Structured data (IDs, params) alters across pipeline |\n| | `unresolved_conflict` | LLM-as-Judge | Indefinite disagreement exhausting maximum turns |\n| **Verification** | `premature_termination` | Programmatic | Termination emitted before all subgoals are met |\n| | `superficial_verification` | LLM-as-Judge | Verifier approves without calling verification tools |\n| | `coordination_overhead` | Programmatic | Talk-to-action ratio exceeds 10:1 |\n\n---\n\n## 🎯 Task Archetypes (50 Tasks)\n\nLocated in [`backend/tasks/`](backend/tasks/):\n\n1. **Role-Locked (`rl_001` - `rl_010`)**: Strict capability separation (Architect, DBA, Security). Tests boundary preservation.\n2. **Sequential Handoff (`sh_001` - `sh_010`)**: Linear pipelines ($A \\rightarrow B \\rightarrow C \\rightarrow D$). Tests information preservation.\n3. **Verification Gauntlet (`vg_001` - `vg_010`)**: Proposer-verifier workflows requiring tool validation. Tests against rubber-stamping.\n4. **Debate & Deliberation (`dd_001` - `dd_010`)**: Adversarial deliberation under conflicting claims. Tests sycophancy resistance.\n5. **Resource Contention (`rc_001` - `rc_010`)**: Multi-party constraint negotiation with hidden dependencies. Tests deadlocks.\n\n---\n\n## 💻 CLI Commands\n\n### 1. Run Automated Benchmark\n```bash\n# Fast mode (programmatic + rule detectors in seconds)\npython backend/benchmark_runner.py --framework langgraph --model gpt-4o\n\n# Full diagnostic mode (all 19 detectors including LLM judges)\npython backend/benchmark_runner.py --framework autogen --model gpt-4o-mini --full-eval\n```\n\n### 2. Run a Single Task via Harness\n```bash\n# Start proxy in background\npython -m backend.harness.interceptor_proxy\n\n# Execute single task\npython -m backend.harness.run_harness backend/tasks/role_locked.py --framework langgraph --model gpt-4o\n```\n\n### 3. Evaluate Transcripts & Detectors\n```bash\n# Evaluate a single transcript\npython -m backend.harness.eval_run backend/transcripts/raw/langgraph_gpt-4o_rl_001_e106fb52.jsonl backend/tasks/role_locked.py --task-id rl_001\n\n# Validate detectors against gold labels (target: Cohen's kappa >= 0.65)\npython backend/validation/validate_detectors.py --skip-llm\n\n# Generate markdown validation report\npython backend/validation/generate_report.py\n```\n\n---\n\n## 📁 Repository Structure\n\n```\nMAC BENCH/\n├── backend/\n│   ├── benchmark_runner.py          # Master benchmark runner pipeline\n│   ├── evaluators/                  # Ground-truth task outcome evaluators\n│   ├── harness/                     # Orchestration harness & adapters\n│   │   ├── interceptor_proxy.py     # FastAPI logging proxy\n│   │   ├── adapters/                # LangGraph, AutoGen, CrewAI adapters\n│   │   └── detectors/               # 19 hybrid failure detectors\n│   ├── labels/                      # Human annotation & IAA tools\n│   ├── results/                     # Saved benchmark runs & leaderboard\n│   ├── tasks/                       # 50 tasks across 5 archetypes\n│   ├── transcripts/raw/             # 140+ raw JSONL turn transcripts\n│   └── validation/                  # Detector validation suite\n├── frontend/\n│   └── dashboard.py                 # Streamlit analytics & comparison UI\n├── docs/                            # Research methodology & design docs\n├── Dockerfile                       # Container deployment\n├── pyproject.toml                   # Project dependencies & CLI entrypoints\n└── README.md                        # Project documentation\n```\n\n---\n\n## 👥 Team & Acknowledgements\n\n- **Haaswith Sai Tripuraneni**\n- **Mangali Prathyusha**\n- **Somik Bansal**\n\n*Developed for the Centific Premier Hackathon 2.0 (CAIR).*\n\n---\n\n## 📄 License\n\nThis project is licensed under the [MIT License](LICENSE).\n","readmeExcerpt":"🤖 MAC-Bench: Multi-Agent Coordination Failure Benchmark $1 $1 $1 $1 $1 **MAC-Bench** is an evaluation suite and diagnostic benchmark designed to diagnose **how and why multi-agent LLM systems fail at coordination**, moving beyond basic pass/fail outcome metrics to deep behavioral analysis. --- ⚡ Quick Start (3 Steps) --- 🎯 Why MAC-Bench? 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