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EvaluationExperimental
Agentic_Evals
by abhineer
Notebook-driven benchmarks and test patterns for evaluating production agents
Jupyter Notebook
Updated Aug 27, 2026
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Overview
Provides practical evaluation techniques, notebooks, and benchmarks for production-style AI agents focused on RAG, tool use, planning, memory, safety, and cost. Uses runnable Jupyter examples to show evaluation evaluation techniques patterns, metric definitions, and failure-mode probes that you can adapt to your stack. Includes concrete test cases for tool-calling, retrieval-augmented workflows, and safety scenarios to reproduce common agent failures benchmarks.
Key Benefits
As agents take on more autonomous roles, teams need repeatable ways to surface reliability and safety regressions. This repository codifies evaluation patterns and hands-on benchmarks so you can measure agent behavior, tool integration, and memory over time. Treating evaluation as code makes it easier to build an agent track record and compare A2A performance across releases. For ongoing quality, monitor regressions.
Ideal For
Developers and ML engineers who need reproducible, notebook-based evaluations for RAG, tool use, planning, memory, and safety.
Real-World Examples
- Validate retrieval-augmented (RAG) pipelines for answer fidelity and hallucination rates
- Test and benchmark agent tool-calling behavior and error handling under edge cases
- Exercise planning and memory modules to detect regressions in multi-step workflows
Works With
langgraphopenaillm
Topics
agent-evaluationagentic-aiai-agentsbenchmarkevaluationgenailanggraphllmllm-evaluationopenai+2 more
Similar Tools
agent-playgroundautogen
Keywords
agent-evaluationA2A evaluationagent track recordagent reliability