feedback to fix, fast
one workspace for teams to find issues, align on failures, and ship fixes together.
get agents to production faster and keep them reliable after launch
your agent worked in the demo
then reality showed up
- the issue is there, but buried in traces
- someone knows the output is wrong, but the feedback is vague
- engineering gets pulled in, but root cause takes too long
- a fix ships, but nobody fully trusts it yet
that’s not a model problem,
it’s a collaboration problem
the people who can spot the issue and the people who can fix it are not working in different tools,
and they don’t have the same context
You are the Support Operations Agent for a SaaS product. Your job is to analyze the customer's email, determine what operation they are requesting, and select the right tool. Rules: - First understand what the customer is actually asking for. - Use the tool descriptions carefully. They explain when each tool should be used. …
Customer email: Hi support team, We need to give our external design agency access to one dashboard so they can review work in progress. They should not count as a paid seat. …
{"email":"agency@partner.co","role":"viewer"}
{"status":"success","member_id":"mem_4821","billable_seat_created":true}
LLMmodelPaid seat createdSlow LLM Response1.7s$0.0058{ "operation_selected": "grant_workspace_access", "tool_used": "add_member", "result": "Created viewer member access for agency@partner.co", "confirmation_for_email_agent": "The user was added to the workspace as a viewer. This action created a billable member seat." }
add_member, which is why the customer got billed for a paid seat.
add_member instead of invite_guest?
add_member and invite_guest both look like valid ways to give someone access. Nothing in the descriptions tells the model that external, non-billable access should go through invite_guest.One place to go from
issue to feedback to fix
Developers and domain experts can look at the same run, understand what happened, and collaborate on the next step.
most teams don't have
a visibility problem
they have a handoff problem
the issue gets spotted in one place, discussed in another, and fixed in a third. neatlogs closes that loop
Built for AI agents
Purpose-built tracing for LangGraph, CrewAI, LangChain, and any agentic workflow your team ships.
Shared context, always
Domain experts and developers see the same thread — no re-explaining what broke or why it matters.
Surface the issue
Detection triggers instantly. Alerts hit Slack and email before your team notices something's wrong.
monitor for recurrence
after the fix ships, neatlogs watches for the same pattern so you know if it comes back
built for the people
who spot the problem
and the people who fix it
different depth for domain experts and devs. shared context all the way through
Agent runs
Your agent fires in production. Neatlogs auto-captures the trace — no custom instrumentation, no waking the engineer.
- OpenAI, Anthropic, local models
- LangGraph, CrewAI, LangChain
- Zero setup instrumentation
Trace captured
Product and QA see the full run: inputs, tool calls, outputs. They flag the weird behavior without having to ask an engineer what just happened.
- Every span annotated in plain english
- Inputs, outputs, tool calls
- Comment on the exact failure
Fix shipped
Context flows into Cursor or your PR. From flagged issue to shipped fix in the same afternoon — no re-explaining, no handoff lag.
- Context streams into Cursor
- PR-ready suggestions
- Shipped from one thread
Fits into the way your team works
neatlogs plugs into your existing stack — from agent frameworks and notifications to tickets and coding agents
start free, scale when ready
every team gets full access to start. upgrade when your ai agents are in production
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- 50k events / month
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- 10 users
- 10 projects
- 1 team
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Everything in Free, plus:
- Unlimited traces
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- Unlimited projects
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- Dedicated Support Engineer
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start debugging together
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common
questions
you've got questions about how neatlogs drops into your workflow. here are the answers
still need help?
we're happy to jump on a call and dive into your stack