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Open-source framework for continuously improving AI agents.

Agentune helps teams analyze, improve, and evaluate customer-facing AI agents through measurable, data-driven iterations — not guesswork.

Instead of tweaking prompts and hoping for the best, Agentune connects real conversations, context data, and simulations into a repeatable optimization loop that drives actual KPI improvements such as conversion, CSAT, and retention.

Most agents are launched and left to stagnate — tuned by intuition, not evidence.

Agentune enables continuous agent improvement by combining analytics, optimization, and simulation in a single open framework:

• Analyze – uncover what drives your agent’s KPIs up or down
• Improve – generate actionable recommendations to lift performance
• Simulate – safely test and benchmark improvements before deployment

The result: agents that don’t just respond — they learn what works.

Agentune Simulate is a separately installable library that enables you to create customer simulations to test and benchmark your agent's behavior before production.

Together with agentune, it forms the Analyze → Improve → Simulate loop — a disciplined framework for building smarter, higher-performing AI agents.

A future version of agentune-simulate will merge it into the main agentune package.

Agentune is built for teams who want to move beyond trial-and-error:

• AI platform / infra teams managing production-grade agents across multiple domains or use cases
• ML / data teams accountable for KPI impact, not just model accuracy
• Product / ops teams who need to measure and harden conversational behavior before it reaches users

Common scenarios:

• Diagnose why conversion or CSAT is dropping
• Quantify which behaviors, intents, or flows impact KPIs
• Test new prompt or policy versions safely
• Continuously improve deployed agents over time

Turn real conversations into insights that measurably improve your AI agents.

Agentune Analyze & Improve helps teams discover what drives an agent’s KPIs up or down — and generate concrete recommendations to enhance performance.
It transforms messy operational data into interpretable, data-driven actions that actually move business metrics.

Most AI agents are optimized by intuition: a few sample chats, some prompt edits, and best guesses.

Agentune replaces guesswork with evidence.
Using structured and unstructured data from real conversations, it:

• Identifies patterns that correlate with KPI outcomes
• Surfaces interpretable insights (not opaque scores)
• Recommends targeted changes to prompts, policies, and logic

No more trial-and-error tuning — just measurable improvement grounded in data.

For example: suppose you built a sales agent and now have a dataset of conversations with labeled outcomes as win, undecided, or lost. Using Agentune Analyze & Improve, you can discover insights showing which patterns or intents correlate with those outcomes and receive concrete recommendations to refine the agent’s playbook — for instance, improving how it handles discounts, competitor mentions, or shipping questions.

Agentune Analyze & Improve follows a transparent, two-step process:

• Ingests conversations, outcomes, and optional context data (e.g., product, policy, CRM).
• Generates semantic and structural features that capture patterns in language, behavior, or flow.
• Selects statistically significant features correlated with KPI changes — these become your drivers of performance.

Example insights:

• "Mentions of competitors early in chat increase conversion probability."
• "Discount discussion combined with shipping-time questions lowers CSAT."

• Maps the discovered drivers into actionable recommendations — changes to prompts, tool usage, escalation logic, or playbooks.
• Outputs a ranked list of improvement opportunities, each linked to its supporting data.

These recommendations can then be validated using Agentune Simulate before deployment.

1. Getting Started - 01_getting_started.ipynb for an introductory walkthrough of library fundamentals
2. End-to-End Script Example - e2e_script_example.md - a runnable example executing the entire analysis workflow
3. Advanced Examples - advanced_examples.md for customizing components, using LLM requests caching, and advanced workflows

We've tested Agentune Analyse with the combination of OpenAI o3 and gpt-4o-mini. In our tests, the cost per conversation was approximately 5-10 cents per conversation.

pip install agentune

Requirements

• Python ≥ 3.12
• Note for Mac users: If you encounter errors related to lightgbm, you may need to install OpenMP first: brew install libomp. See the LightGBM macOS installation guide for details.

• 🧩 Feature Generation – semantic, structural, and behavioral signals derived from real interactions
• 📈 Feature Selection – statistical and semantic correlation with target KPIs
• 💡 Actionable Insights – interpretable drivers with examples and metrics
• 🧠 Context Awareness (upcoming) – integrates CRM, product, and policy metadata for deeper understanding

Current focus: advancing Analyze & Improve with structured, context-aware optimization.

Planned milestones:

• Context-aware feature generation and insight discovery
• Integration of context features into the recommendation layer for targeted improvement actions
• Expanded evaluation and visualization tooling for Analyze & Improve results
• Visualization tools for insight exploration
• Seamless flow into agentune-simulate for validating improvements

Longer-term:

• Multi KPI analytics: understand how improving one KPI impacts other KPIs and account for that in the suggested improvement recommendations.
• Optional multi-agent analytics and cross-agent benchmarking

We welcome contributions from engineers who care about robust, measurable agents.

• Open issues for bugs, integrations, or feature proposals
• Early adopters: reach us at agentune-dev@sparkbeyond.com
• 💬 Join our community on Discord to connect with maintainers, share ideas, and get support