Evaluate Your Agent's
Every Move

Get full observability of your agent. Evals for all its steps. Completely automatic. Two lines of code to integrate.


Open source · MITFree, no credit card neededChoose your own infra

See Everything Your Agent Does

Every node, tool call, token count, and timing — captured automatically and visualised in one place.

Trace timeline showing every LLM call and tool invocation in sequence

Every step, every token, every millisecond

AgentSnoop records the full trajectory of each agent run: LLM calls, tool invocations with inputs and outputs, per-step token usage, and precise timing. Everything lands in a single MongoDB document — easy to query, easy to diff.

Dashboard showing evaluation results for a custom eval that checks if the agent's final answer is correct

Evaluate whether your agent is actually improving

AgentSnoop scores the agent's performance on dozens of dimension. Accuracy, latency, tool usage patterns — everything matters to you in building an accurate, safe agent.

Dashboard showing issues found by an eval that checks for harmful content in the agent's final answer

Find agent issues, and know how to fix them.

AgentSnoop helps you identify and resolve issues in your agent's behavior before they impact users. It also tells you what wwent wrong and how to fix it ASAP.

agent_trace.py
1# pip install agent-snoop[mongo,langgraph]
2 
3import agent_snoop
4from agent_snoop.integrations.langgraph import AgentSnoopCallbackHandler
5from langchain_core.messages import HumanMessage
6 
7# Reads MONGODB_URI or AGENTSNOOP_API_KEY from env
8tracer = agent_snoop.init(agent_name="my-agent", framework="langgraph")
9 
10query = "What is the warranty on my MacBook Pro M5?"
11 
12handler = AgentSnoopCallbackHandler(
13 handle=tracer.trace(input=query, tags=["prod"])
14)
15 
16result = await graph.ainvoke(
17 {"messages": [HumanMessage(content=query)]},
18 config={"callbacks": [handler]},
19)
20 
21handler.on_chain_end_final(result) # ✓ trace saved
Python 3.9+UTF-8

Up and Running in 3 Minutes

Install & Configure Storage

Run pip install agent-snoop[mongo,langgraph], then export your MongoDB URI or liten.tech API key. No new infra required.

Wrap Your Agent

Add AgentSnoopCallbackHandler to your existing graph.ainvoke config. No rewrites, no wrappers around your business logic.

Inspect on liten.tech

Open liten.tech/traces to explore timelines, step breakdowns, token counts, and tool call outputs — for every run you've ever made.

Dashboard showing aggregated token usage and latency across many agent runs

Your data stays where you put it

Bring your own MongoDB — local, Atlas, or any hosted provider. AgentSnoop writes traces directly to your cluster. Connect liten.tech to visualise them without moving a byte. Or skip the infra entirely and use an API key to store traces on liten.tech.

Side-by-side comparison of two agent runs in the liten.tech dashboard

Debug regressions before they reach production

Compare runs side by side. Filter by tag, agent name, or time range. Spot the exact step where latency spiked or a tool returned garbage — without adding a single log statement to your codebase.

Stop guessing what your agent actually did.

AgentSnoop gives you the full picture — every step, every tool, every token — stored exactly where you want it.


Open source · MITYour data, your infra