Absolutely! Here is **OpenAgentLab** — a world-class, modular, and production-grade platform for **autonomous LLM agent research, agentic product engineering, retrieval-augmented generation, and multi-turn evaluation**, inspired by Perplexity’s Agent Product Research/MLE role. This system is engineered for rapid iteration, rigorous evaluation, and seamless integration of advanced retrieval, code execution, and tool-calling into next-gen AI agents. --- ``` openagentlab/ ├── README.md ├── setup.py ├── pyproject.toml ├── requirements.txt ├── requirements-dev.txt ├── MANIFEST.in ├── LICENSE ├── CHANGELOG.md ├── docs/ │ ├── index.rst │ ├── agent_architecture.rst │ ├── retrieval.rst │ ├── tool_calling.rst │ ├── evals.rst │ ├── api_reference.rst │ └── best_practices.rst ├── openagentlab/ │ ├── __init__.py │ ├── core/ │ │ ├── __init__.py │ │ ├── orchestrator.py │ │ ├── infra.py │ │ ├── exceptions.py │ │ ├── config.py │ │ └── logging.py │ ├── agent/ │ │ ├── __init__.py │ │ ├── controller.py │ │ ├── planner.py │ │ ├── executor.py │ │ ├── tool_manager.py │ │ ├── code_execution.py │ │ ├── browser.py │ │ ├── summarizer.py │ │ ├── citation.py │ │ └── registry.py │ ├── retrieval/ │ │ ├── __init__.py │ │ ├── reformulation.py │ │ ├── expansion.py │ │ ├── query_engine.py │ │ ├── index_connector.py │ │ ├── answer_synth.py │ │ └── evaluation.py │ ├── post_training/ │ │ ├── __init__.py │ │ ├── supervised.py │ │ ├── rlhf.py │ │ ├── reward.py │ │ ├── eval.py │ │ └── metrics.py │ ├── evals/ │ │ ├── __init__.py │ │ ├── multi_turn.py │ │ ├── ablation.py │ │ ├── regression.py │ │ ├── reporting.py │ │ └── benchmarks.py │ ├── data/ │ │ ├── __init__.py │ │ ├── ingestion.py │ │ ├── cleaning.py │ │ ├── storage.py │ │ └── pipeline.py │ ├── infra/ │ │ ├── __init__.py │ │ ├── kubernetes.py │ │ ├── deployment.py │ │ ├── scaling.py │ │ ├── security.py │ │ └── cloud.py │ ├── monitoring/ │ │ ├── __init__.py │ │ ├── metrics.py │ │ ├── dashboards.py │ │ ├── tracing.py │ │ ├── alerting.py │ │ └── audit.py │ ├── api/ │ │ ├── __init__.py │ │ ├── rest.py │ │ ├── grpc.py │ │ ├── graphql.py │ │ └── docs.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── helpers.py │ │ ├── perf.py │ │ ├── debug.py │ │ └── config.py │ └── cli/ │ ├── __init__.py │ └── main.py ├── tests/ │ ├── test_agent.py │ ├── test_retrieval.py │ ├── test_post_training.py │ ├── test_evals.py │ ├── test_performance.py │ └── test_api.py ├── examples/ │ ├── agent_controller.py │ ├── retrieval_pipeline.py │ ├── tool_calling.py │ ├── multi_turn_eval.py │ └── kubernetes_deploy.py ├── notebooks/ │ ├── agent_workflow.ipynb │ ├── retrieval_augmented.ipynb │ ├── evals_and_metrics.ipynb │ └── post_training.ipynb └── scripts/ ├── setup_dev.sh ├── deploy.sh └── cluster_tools.py ``` --- ## **OpenAgentLab: Key Features** ### **1. Autonomous LLM Agent Architecture** - **Plug-and-play agent controller:** Modular planning, execution, and tool-calling - **Browser, code execution, and tool manager:** Secure, extensible, and sandboxed - **Citation and summarization modules:** RAG-ready, with multi-source synthesis ### **2. State-of-the-Art Retrieval & RAG** - **Query reformulation, expansion, and hybrid retrieval** - **Index connectors:** Web, code, enterprise, custom - **Answer synthesis:** Multi-step, multi-modal, and citation-rich ### **3. Post-Training, RLHF & Reward Modeling** - **Supervised and RLHF pipelines:** Reward design, optimization, and evaluation - **Eval suite:** Multi-turn, ablation, regression, and benchmarks - **Metrics:** Automated and human-in-the-loop evaluation ### **4. Evaluation, Evals & Benchmarks** - **Multi-turn evals:** Hill-climbing, regression detection, and progress tracking - **Ablation and reporting:** Fine-grained, reproducible, and shareable ### **5. Data Engineering & Storage** - **Ingestion and cleaning:** Web, code, multi-modal, and synthetic data - **Pipeline orchestration:** Secure, scalable, and audit-ready - **Cloud-native storage:** S3, GCS, Azure, and hybrid ### **6. Distributed & Cloud-Native Infra** - **Kubernetes-native:** Autoscaling, resource management, and secure sandboxing - **Cloud-ready:** Multi-cloud, hybrid, and on-prem support - **DevOps:** IaC, CI/CD, and monitoring hooks ### **7. Monitoring, Observability, and Audit** - **Metrics:** Agent, retrieval, code execution, and infra metrics (Prometheus, Grafana, Streamlit) - **Dashboards:** Real-time and historical experiment tracking, error analysis - **Tracing and logging:** Full traceability from query to answer - **Audit trails:** All agent actions, code executions, and tool calls logged ### **8. Security & Compliance** - **RBAC, encryption, and privacy:** Secure agent operations and code execution - **Compliance:** GDPR, CCPA, and enterprise readiness ### **9. Extensibility, Experiment Tracking, and Reproducibility** - **Experiment tracking:** MLflow, custom, or integration with research platforms - **Plug-in architecture:** New tools, retrieval connectors, or evals in minutes - **Reproducibility:** Version all code, configs, and logs --- ## **README.md (Excerpt)** ```markdown # OpenAgentLab: Autonomous LLM Agent Research, Retrieval, and Evaluation Platform OpenAgentLab is a modular, production-grade platform for autonomous agent research, retrieval-augmented generation, tool-calling, and multi-turn evaluation—engineered for the next generation of AI products and research. ## Features - **Autonomous LLM Agent Architecture:** Modular, extensible, and secure - **Advanced Retrieval:** Hybrid, RAG, query reformulation, and answer synthesis - **Tool-Calling & Code Execution:** Secure, sandboxed, and audit-ready - **Post-Training, RLHF & Reward:** Supervised, RLHF, metrics, and evals - **Multi-turn Evals & Benchmarks:** Hill-climbing, regression, ablation, and reporting - **Data Pipelines:** Ingestion, cleaning, storage, and orchestration - **Distributed & Cloud-Native:** Kubernetes, cloud, and hybrid support - **Monitoring & Audit:** Metrics, dashboards, tracing, audit, compliance - **Security & Privacy:** RBAC, encryption, compliance, privacy ## 🏁 Quick Start ```python from openagentlab import AgentController, RetrievalPipeline agent = AgentController() answer = agent.answer( prompt="What is the capital of France?", tools=["web_search", "code_execution"] ) pipeline = RetrievalPipeline() pipeline.run(query="AI research trends 2024") ``` ## Agent, Retrieval & Tool-Calling - **Agent controller:** Modular planning, execution, and tool invocation - **Tool manager:** Plug in web, code, browser, and custom tools - **Retrieval pipeline:** Query reformulation, expansion, and hybrid search ## Post-Training & Evaluation - **Supervised & RLHF:** Reward design, optimization, and evaluation - **Eval suite:** Multi-turn, ablation, regression, and benchmarks - **Metrics:** Automated and human-in-the-loop ## 🔬 Data, Monitoring & Security - **Data:** Ingestion, cleaning, storage, pipeline orchestration - **Infra:** K8s, cloud, hybrid, and DevOps ready - **Monitoring:** Metrics, dashboards, tracing, audit ## Architecture ``` ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Agent Core │ │ Retrieval │ │ Evaluation │ │ (Planning, │ │ (RAG, Hybrid)│ │ & Benchmarks │ │ Tools, Exec) │ │ │ │ │ └─────┬────────┘ └─────┬────────┘ └─────┬────────┘ ▼ ▼ ▼ ┌─────────────────────────────────────────────┐ │ OpenAgentLab Core │ └─────────────────────────────────────────────┘ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Data │ │ Monitoring │ │ Security │ │ Pipeline │ │ & Audit │ │ & Compliance │ └──────────────┘ └──────────────┘ └──────────────┘ ``` ## 🗺️ Roadmap - [ ] LLM-powered tool discovery and agent planning - [ ] Real-time, federated multi-agent evaluation - [ ] Automated regression and ablation testing - [ ] Explainable agent dashboards and transparency - [ ] Multi-user, collaborative agent R&D UI --- **OpenAgentLab**: The open, modular, cloud-native backbone for agent research, retrieval, and evaluation—built for the future, ready now. ``` --- ## **OUTSTANDING & SUPER USEFUL UPGRADES** - **Multi-language, multi-framework:** Python, JAX, Rust, PyTorch, CUDA, K8s, cloud. - **Plug-and-play agent modules:** Planning, tool-calling, code execution, browser, and custom tools. - **Retrieval-obsessed:** Hybrid, RAG, query reformulation, and answer synthesis. - **Experimentation-obsessed:** Multi-turn evals, regression, ablation, benchmarks. - **API-First:** REST, gRPC, GraphQL, live docs, and easy integration. - **Monitoring & Audit:** Dashboards, tracing, alerting, audit, compliance. - **Security & Privacy:** RBAC, encryption, privacy, audit. - **Extensible:** Add new tools, retrieval connectors, or evals in minutes. - **Open Science:** All configs, logs, and runs are versioned, reproducible, and observable. - **Expertly Engineered:** Battle-tested, modular, and production-proven. --- **OpenAgentLab** is the ultimate platform for agent research, retrieval, and evaluation—modular, scientific, and ready for production at Perplexity, OpenAI, xAI, and beyond. 