Neuron AI is a PHP framework for building AI applications in which agents connect language models, tools, data loaders, vector databases, memory, and user interfaces. PHP developers use it to create and manage applications with agent workflows, multi-agent coordination, streaming, monitoring, and human involvement.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add neuron-core/neuron-ai --skill neuron-monitoringgit clone --depth 1 https://github.com/neuron-core/neuron-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/neuron-core/neuron-ai/neuron-monitoring)<a href="https://agentmods.dev/skills/neuron-core/neuron-ai/neuron-monitoring"><img src="https://agentmods.dev/badge/skills/neuron-core/neuron-ai/neuron-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/neuron-core/neuron-ai/neuron-monitoring"><img src="https://agentmods.dev/badge/skills/neuron-core/neuron-ai/neuron-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.02865 |
| Opus 5.5 | $0.00032 | $0.01146 |
| Sonnet 5.5 | $0.00016 | $0.00573 |
| Haiku 4.5 | $0.00008 | $0.00286 |
Grade A, and why
neuron-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- neuron-monitoring — 92% identical, 202 lines differ
How it starts
The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuron AI Monitoring
This skill helps you debug and monitor Neuron AI applications using the framework's event-driven observability system.
The progression is always the same: components emit events → you subscribe
listeners. A LogListener writing to stdout and a custom tracing listener
are the same mechanism, differing only in where the events go.
Event System Observability
Neuron uses a PSR-14 event dispatcher owned by each Workflow/Agent instance for monitoring all framework events. There is no global state: listeners are registered on the instance and observe every run of it.
Event Emission
All components emit event objects automatically. Every event class extends
NeuronAI\Observability\ObservabilityEvent and lives in the Observability
namespace of the module that emits it:
// NeuronAI\Workflow\Observability — the lifecycle, emitted by the workflow engine:
// WorkflowStart, WorkflowEnd, WorkflowNodeStart, WorkflowNodeEnd,
// MiddlewareStart, MiddlewareEnd, BranchStart, BranchEnd, ChannelError,
// WorkflowInterrupted (run paused for external input), WorkflowError (failure)
//
// NeuronAI\Agent\Observability — emitted by agent nodes:
// InferenceStart, InferenceStop, ToolCalling, ToolCalled,
// MessageSaving, MessageSaved, SchemaGeneration, SchemaGenerated,
// Extracting, Extracted, Deserializing, Deserialized, Validating, Validated
//
// NeuronAI\RAG\Observability — emitted by RAG nodes:
// Retrieving, Retrieved, PreProcessing, PreProcessed, PostProcessing, PostProcessed
Each event carries its own data plus the emission context stamped by the
framework: ->source (the emitting component), ->execution (the run identity)
and ->branchId (the parallel branch, or null). ->name() returns the string
name ('inference-start') and ->toArray() the event's own data.
Every node and middleware start is followed by its end, whatever the outcome:
WorkflowNodeEnd and MiddlewareEnd carry ->outcome, a NodeOutcome
(Completed, Suspended or Failed), so a span can be closed and marked as
soon as its node ends.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago Changed · -123 lines · -8 tokens per session 47e2105b4450
- 15d ago First seen · 505 lines · 88 tokens per session scan A 94b4545f313d
neuron-monitoring is a skill published in the GitHub repository neuron-core/neuron-ai (2,140 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 2,865 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-01.
Other skills, from other repositories
neuron-monitoring
Monitor Neuron AI agents and workflows with events observability, logging, and performance analysis. Use this skill whenever the user mentions debugging, monitoring, observability, performance analysis, tracing, connecting to the Neuron Cloud platform, or needs to understand why an agent is behaving a certain way.…
agent-performance-optimizer
Agent skill for performance-optimizer - invoke with $agent-performance-optimizer.
agent-refinement
Agent skill for refinement - invoke with $agent-refinement.
code-search
Search a codebase efficiently with ripgrep regular expressions, file globs, and git history search. Use to locate symbols, usages, and definitions instead of reading whole files.
agent-identity
Verify an agent's live address and billing account before sharing a chat link, diagnosing a deploy mismatch, or deciding why two hosts share an identity.
plano-observability-debugging
Improve Plano tracing and debugging workflows. Use for sampling strategy, span attributes, and trace query-based root-cause analysis.