Move beyond the black box.
NOMOO is mission control for AI agents. Instrument any agent with a single line, trace every decision as it happens, and let automated optimization tune your prompts and parameters, so the systems you ship get measurably more reliable at scale.
Backed by a startup acceleratortools.search_kb
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Relevant passages retrieved on the first attempt. Grounding for the final response looks solid.
One SDK, any stack. Instrument agents built with the frameworks you already use, with no rewrites and no lock-in.
An agent that fails quietly is worse than one that crashes
A crash gives you a stack trace. An agent gives you a plausible answer, produced by a path nobody saw, built from a dozen decisions that were never recorded. By the time someone notices, the run is gone and the only evidence left is an unhappy user.
The silent retry loop
An agent calls the same tool four times with the same arguments because nothing told it the first answer was good enough. Latency triples, token spend quadruples, and the response still looks fine.
The ungrounded answer
Retrieval returns nothing useful, so the model fills the gap from memory. The sentence reads well and cites nothing. Without the captured tool result beside it, no reviewer can tell the difference.
Plan drift
A prompt edit shipped on Tuesday makes the planner decompose tasks one step further than before. Every run now costs more and wanders wider, and no single trace looks broken enough to investigate.
The regression nobody owns
Quality slid four percent across a model upgrade. There is no baseline, no scored history, and no way to prove which change caused it, so the team argues about prompts instead of fixing the cause.
Debugging by print statement
Someone adds logging to the one function they suspect, redeploys, and waits for the bug to happen again. The flat log loses the parent and child relationships that made the decision legible in the first place.
Prompt engineering as a full-time job
Improvement depends on one engineer's intuition, tested by hand, a few variants at a time. The results live in a spreadsheet, and the reasoning behind the current prompt leaves when they do.
What all six have in common
None of them are model problems. They are visibility problems. Every one becomes tractable the moment you can read the full execution record of a run, compare it against the runs before it, and act on the difference.
Put NOMOO to work across the entire agent lifecycle
Autonomous agents make decisions and call tools along paths that are hard to see and harder to debug. NOMOO turns that opacity into a clear, navigable record, then uses the record to make your agents better. Instrument, trace, monitor, evaluate, detect, and optimize, all from one place.
One line to full visibility
Add the framework-agnostic SDK and call a single function. Automatic instrumentation wraps your agent and starts capturing every execution, with no manual spans and minimal configuration.
# pip install nomoo import nomoo from my_agents import support_agent nomoo.init(project="support-triage") # one line to connect # automatic instrumentation, no manual spans agent = nomoo.instrument(support_agent) result = agent.run("Where is my refund?") # every nested call, argument and result is now traced
Every call, argument, and result
NOMOO records nested function calls, the arguments passed in, and the values returned, preserving the hierarchical relationships between each step of a run.
Follow the path to the outcome
Interactive, hierarchical visualizations lay out parent and child relationships alongside execution flow, so you can see exactly how an agent reached a given result.
Watch active sessions live
Real-time session monitoring streams live updates as your agents run in production, so you catch behavior the moment it happens instead of reconstructing it afterwards.
Automated quality analysis
An automated reviewer critiques agent behavior, assigns severity-based feedback, and attaches actionable suggestions to the exact step that needs attention.
Redundant tool call. lookup_order was invoked twice with identical arguments within one run. Suggestion: memoize tool results per run or add a cache guard.
Plan looks well scoped. Sub-tasks map cleanly to available tools and no missing capabilities were detected.
Grounded answer. The response cites retrieved passages and no unsupported claims were found.
Catch regressions automatically
NOMOO tracks performance over time and flags regressions automatically when an agent's behavior degrades, before your users feel it.
Tune prompts and parameters automatically
NOMOO continuously analyzes performance patterns and automatically tunes prompts and parameters to lift response quality, reducing the manual prompt engineering your team carries.
Build and refine networks of agents
From a single agent to a coordinated network, NOMOO gives you an end-to-end loop to build, test, and refine, with full visibility at every layer.
Start in one line
Install the SDK, wrap your agent, and open the dashboard. Automatic instrumentation handles the rest, capturing nested calls, arguments, results, and timing into a single trace.
# pip install nomoo import nomoo from my_agents import support_agent nomoo.init(project="support-triage") # 1. connect agent = nomoo.instrument(support_agent) # 2. instrument result = agent.run("Where is my refund?") # 3. observe # traces stream to your NOMOO dashboard in real time
// npm install @nomoo/sdk import { init, instrument } from "@nomoo/sdk"; import { supportAgent } from "./agents"; init({ project: "support-triage" }); // 1. connect const agent = instrument(supportAgent); // 2. instrument const result = await agent.run("Where is my refund?"); // 3. observe // framework-agnostic, the same on any stack
Install
Add the SDK from your package manager. There is no agent infrastructure to stand up and nothing to host.
Instrument
Call one function. Automatic instrumentation wraps the agent and discovers its nested calls as they run.
Observe
Open the dashboard and watch traces, sessions, and quality scores arrive while the run is still in flight.
Complete visibility into every execution
The NOMOO SDK captures detailed information about every run: nested function calls, the arguments passed, the results returned, and the hierarchical relationships between each step. Opaque, autonomous behavior becomes a record you can read, search, and hand to a colleague.
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One-line integration
Connect the SDK and instrument your agent with a single call. The tooling joins your existing infrastructure in minutes, not sprints.
Automatic instrumentation
No manual spans to write. NOMOO captures nested calls, arguments, results, and the relationships between steps on its own.
Framework-agnostic
Whatever your agents are built on, NOMOO instruments them the same way, so you get one consistent record across the whole stack.
Watch your agents think, live
Real-time session monitoring streams updates from active agent sessions while an automated quality-analysis system critiques what it sees, assigning severity-based feedback and offering specific suggestions tied to the exact step that triggered them.
shipping.quote() across 3 carriersAutomated review: the same carrier was quoted twice with identical inputs. Suggestion: deduplicate quote calls within a session to cut latency and token spend.
Live sessions
Active agent sessions update in real time as they run, with each step appearing the moment it executes rather than after the run closes.
Severity-based feedback
Automated critique grades behavior by severity, from informational to error, so the issues that matter most rise to the top of the queue.
Actionable suggestions
Every critique carries a concrete next step, pinned to the exact span that triggered it, rather than a score with no explanation.
Agents that get better over time
NOMOO tracks performance across runs and detects regressions the moment behavior degrades. Then it goes further, analyzing performance patterns continuously and tuning prompts and parameters to improve response quality, so your team spends less time hand-tuning and more time shipping.
Performance trends
Quality and latency tracked run over run, so improvement and drift are both visible without building a reporting pipeline.
Regression detection
Behavior drops are flagged automatically and attributed to the release that introduced them.
Prompt auto-tuning
NOMOO proposes prompt edits drawn from the patterns in your own runs.
Parameter optimization
Sampling parameters tuned toward the settings that score best on your traffic.
Quality scoring
Every run scored by automated critique, so trends become measurable instead of anecdotal.
Pattern analysis
Recurring failure modes surfaced across thousands of runs, not one trace at a time.
Cost attribution
Tokens and latency resolved to the step that spent them, not to the run as a whole.
Evaluation suites
Promote reviewed runs into a suite your pipeline can gate a release on.
Drop into your existing AI workflow
NOMOO is designed to integrate into the way you already build. One-line integration, automatic instrumentation, and a framework-agnostic SDK mean the tooling slots into your infrastructure quickly, whatever your agents are built on.
One-line integration
Connect and instrument with a single call, the fastest path from black box to full trace.
Automatic instrumentation
Calls, arguments, results, and step relationships captured for you, with no manual spans.
Framework-agnostic
No lock-in. The same SDK instruments agents on any framework or runtime you choose.
Minimal configuration
Sensible defaults out of the box. Add detail only where you want it and ship the rest as is.
Three kinds of agent, three kinds of blindness
The shape of the problem changes with the job. A retrieval agent fails differently from one that holds a budget. These are the patterns NOMOO was built around.
Answering from a knowledge base
The agent retrieves, reasons, and replies in one breath. When retrieval comes back thin, the model papers over the gap and the answer still reads as confident.
- What stays hidden
- Which passages were actually retrieved, what score they carried, and whether the final sentence used any of them.
- What NOMOO shows
- The retrieval span with its arguments and hits beside the composed answer, plus a grounding check on the response itself.
Long multi-step investigation
Dozens of steps, branching plans, and tools that call other tools. A run that takes four minutes is almost impossible to review by reading logs.
- What stays hidden
- Where the time and the tokens went, which branch produced the useful material, and which steps were pure overhead.
- What NOMOO shows
- The full hierarchy with timing at every depth, so the expensive subtree is obvious before anyone opens a profiler.
Agents that take actions
This agent does not just answer, it applies a shipping method, issues a credit, updates a record. Every call has a consequence outside your system.
- What stays hidden
- Whether a write was attempted twice, what arguments were sent, and what the downstream service returned at the time.
- What NOMOO shows
- An exact, replayable record of each side-effecting call with its inputs, outputs, and status, captured as it happened.
Frequently asked
Do I have to rewrite my agent to use NOMOO?
No. You install the SDK, call init() once with a project name, and pass your existing agent through instrument(). Automatic instrumentation discovers the nested calls as they happen, so your agent code and control flow stay exactly as they are.
Which frameworks are supported?
The SDK is framework-agnostic rather than framework-specific. Agents built on LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, Haystack, or your own in-house orchestration are instrumented through the same entry point and produce the same trace structure.
That matters more than a long compatibility list: when you swap frameworks or run two side by side, the history you have already collected stays comparable.
What exactly gets captured in a trace?
Nested function calls, the arguments passed into each one, the values returned, timing and status per step, and the parent and child relationships that place every step in the run. That hierarchy is what makes a trace readable rather than a flat stream of log lines.
How is this different from a general application tracing tool?
Generic tracing tells you a function took 486 milliseconds. It does not tell you that the retrieval behind it returned nothing useful, or that the answer composed afterwards cited none of it.
NOMOO models the parts of a run that are specific to agents: planning steps, tool invocations, model calls, retrieval, and the quality of what came out. It also reads those traces back, scores them, and proposes changes.
Does automated optimization change my prompts without asking?
Proposals arrive as reviewable changes. You see the suggested prompt edit as a diff next to the runs and patterns that motivated it, along with the parameter values it recommends, and you decide what to adopt.
What happens to the data NOMOO collects?
Traces belong to your project. You control what is captured through redaction rules, how long it is kept through retention settings, and where it runs: in our cloud or inside your own network. Spans can also be exported in OpenTelemetry format to the systems you already operate.
What does instrumentation cost at runtime?
Capture is designed to stay out of the critical path. Spans are buffered and shipped asynchronously, so the dominant cost in an instrumented run remains what it was before: the model and tool calls themselves.
Can I start with one agent?
That is the usual starting point. Teams instrument the one agent that is hardest to debug, get the trace history for it, then widen out to the rest of the network once the record is paying for itself.
Schedule a demonstration
Bring your hardest-to-debug agent. We will instrument it live and walk through the trace together, from the first planning step to the answer your users see.