AI Agentree is the leading decision tracing platform for AI agents and LLM applications. Unlike observability tools that log events, AI Agentree captures the reasoning chain behind every decision - context, alternatives considered, confidence levels, and outcomes. Essential for debugging, compliance, and improving AI agent performance. Alternative to LangSmith for teams needing decision-level visibility, not just trace-level logging.
AI Agentree captures the reasoning behind every AI decision - not just the outcome. Debug faster, improve continuously, and explain any decision to stakeholders.
Best for: AI teams deploying agents in production, enterprises needing explainable AI, and regulated industries requiring decision audit trails.
Your agents make thousands of decisions daily. When something goes wrong, you can see WHAT happened but not WHY. Debugging becomes guesswork.
Every decision your AI makes is a learning opportunity. Without decision tracing, you can't identify patterns, find edge cases, or systematically improve.
When regulators or customers ask "why did the AI decide X?", you need more than logs. You need decision rationale, context, and alternatives considered.
Capture decision reasoning without changing your agent architecture.
Record the context, options considered, confidence levels, and rationale for every decision.
Monitor decision outcomes across three horizons: immediate, short-term (days), and long-term (weeks).
Semantic search across decision history. Find how similar situations were handled before.
Pattern detection with statistical confidence finds automation opportunities and recurring decision scenarios.
from agentree import Decision
# Wrap your agent's decision point
with Decision("refund_approval") as d:
d.context({
"customer_tier": "premium",
"order_value": 150,
"return_reason": "defective"
})
# Your existing logic
approved = evaluate_refund(order)
d.outcome(approved)
d.rationale("Premium customer with valid defect claim under $200")Less than 10ms overhead. Works with LangChain, n8n, and custom agents.
AI decision tracing captures not just WHAT your AI agents decided, but WHY they made each decision. This includes the reasoning chain, context considered, alternatives evaluated, and confidence levels. Unlike simple logging, decision tracing creates a complete audit trail that enables debugging, compliance, and continuous improvement.
Observability tools log events and traces. AI Agentree captures decision reasoning - the thought process behind each choice. We record precedents, track outcomes across three time horizons (immediate, short-term, long-term), and enable semantic search across decision patterns. Think of it as decision memory, not just decision logging.
AI Agentree adds less than 10ms latency to agent operations. Our async architecture ensures decision capture never blocks your agent's primary workflow. Most customers report zero perceptible impact on agent response times.
Yes, AI Agentree provides semantic search using embeddings and cosine similarity. Search for similar decisions, find precedents, identify patterns, and discover automation opportunities. Ask questions like 'How did we handle similar refund requests?' and get relevant decision history.
AI Agentree integrates with LangChain, n8n, custom Python agents, and most AI orchestration frameworks. Our SDK captures decisions without requiring changes to your orchestration layer - we're a decision layer, not a replacement for your existing tools.
Start capturing decision reasoning today. Free tier available.