Helios: Open-Source AI Agent Observability with Tracing and Cost Monitoring

Helios is a brand-new open-source observability platform purpose-built for AI agents that combines execution tracing, cost analytics, and security monitoring — including PII detection and prompt injection detection — in a single self-hosted stack. Launched on July 22, 2026 under the MIT license, Helios addresses the critical infrastructure gap behind the statistic that 88% of AI agents fail to reach production, offering teams a unified dashboard to understand exactly what their agents are doing, how much they cost, and whether they are safe.

What Is Helios?

Helios is an open-source observability platform designed specifically for AI agent workloads. Unlike general-purpose LLM tracing tools that focus narrowly on prompt-response logging, Helios provides three integrated capabilities in one platform: agent execution tracing with full span-level visibility, real-time cost analytics across models and providers, and security monitoring that detects PII leaks and prompt injection attacks. The project is MIT-licensed, self-hostable via a single Docker Compose command, and comes with a Python SDK that requires fewer than 10 lines of code to integrate.

The project was created on July 22, 2026 and is in its earliest stage — currently at 1 GitHub star. This makes it a project to watch closely as it evolves from foundation to production-ready, especially for teams that want to influence its roadmap from day one.

Why Agent Observability Matters in 2026

The AI agent market is exploding. According to Digital Applied’s Agentic AI Statistics 2026, the agentic AI market is projected to grow from $7.6 billion to $236 billion by 2034, representing a compound annual growth rate of over 40%. Yet the same research reveals a sobering reality: 88% of AI agents fail to reach production. For the 79% of enterprises that have adopted AI agents in some form, only 11% are running them in production.

This massive failure rate is not random. The primary reason agents stall is a lack of observability — teams simply cannot see what their agents are doing when things go wrong. Traditional application monitoring tools are designed for deterministic, request-response systems. AI agents are non-deterministic, multi-step, tool-calling systems that branch unpredictably. When an agent hallucinates, calls the wrong tool, enters an infinite loop, or leaks sensitive data, standard APM tools provide no useful signal.

The agents that do reach production deliver an average 171% ROI (192% in the US), according to the same report. This means the incentive to get observability right is enormous. The teams that can debug, optimize, and secure their agents effectively will capture disproportionate value from the AI agent revolution.

Helios Architecture — Tracing, Cost, and Security in One Stack

Helios is built on an asynchronous architecture designed for production agent workloads. The system has two layers that work together:

Fast synchronous ingestion layer. When an agent makes a call, the Helios SDK sends trace data to the ingestion endpoint synchronously with minimal overhead. This ensures the agent’s own latency is not impacted by observability processing.

Async analysis pipeline. Once ingested, traces flow into an asynchronous pipeline that handles three parallel workloads: cost calculation (computing token usage and pricing across models), security scanning (running PII detection and prompt injection classifiers), and risk scoring (combining multiple detector signals into a per-run risk score).

This architecture means the SDK call is fast — the heavy analysis happens in the background. Teams get real-time visibility into agent execution without sacrificing agent performance.

The entire stack runs on a single Docker Compose command, making self-hosting straightforward. All data stays on your infrastructure, eliminating the vendor lock-in and data sovereignty concerns that come with cloud-only observability platforms.

Key Features Deep Dive

Agent Execution Tracing

Helios provides full span-level tracing for agent runs. Each agent execution is captured as a trace containing multiple spans — one for each LLM call, tool invocation, or sub-agent delegation. The trace view shows the complete execution tree, including timing, input/output payloads, and error states.

This is critical for debugging agent failures. When an agent produces a wrong answer, the trace reveals exactly which step went wrong: did the LLM hallucinate? Did a tool return unexpected data? Did the agent enter a loop? Without tracing, debugging an agent is essentially guessing.

Helios supports both automatic instrumentation (wrapping popular frameworks) and manual instrumentation via decorators for custom agent logic. The SDK is designed for drop-in integration with existing agents.

Cost Analytics Dashboard

Cost monitoring is a first-class feature in Helios, not an afterthought. The cost analytics dashboard breaks down spending by model, by provider, by agent, and by time period. Teams can see exactly how much each agent run costs, which models are driving the most expense, and how costs trend over time.

With organizations running dozens of agents across multiple models (GPT-4o, Claude Sonnet, Gemini, open-source models), cost visibility is essential for budgeting and optimization. Helios surfaces the data needed to make informed decisions about model selection, caching strategies, and agent design.

The async cost calculation pipeline computes costs based on actual token usage against provider pricing, so teams get accurate, real-time cost data without manual estimation.

Security Monitoring (PII, Prompt Injection, Risk Scoring)

This is Helios’s strongest differentiator. Most agent observability tools focus exclusively on tracing and cost. Helios adds a built-in security monitoring layer that scans every agent run for:

PII detection. The security pipeline automatically scans LLM inputs and outputs for personally identifiable information — email addresses, phone numbers, credit card numbers, social security numbers, and other sensitive data patterns. If an agent accidentally leaks PII in its output or receives PII in a prompt, Helios flags it immediately.

Prompt injection detection. Helios analyzes prompts for known injection patterns, including jailbreak attempts, prompt leaking, and indirect injection via tool outputs. This is increasingly important as agents gain access to external data sources and tools that could be vectors for injection attacks.

Risk scoring engine. The most innovative feature is Helios’s built-in risk scoring engine, which combines signals from multiple detectors into a single per-run risk score. A run that triggers PII detection AND shows prompt injection patterns gets a higher risk score than one with only a single signal. This composite score lets teams triage security incidents by severity rather than drowning in individual alerts.

Helios vs. The Competition

The open-source LLM observability space has several established players. Here is how Helios compares:

Langfuse — The LLM Trace Store Leader

Langfuse is the dominant open-source LLM observability platform with 31,857 GitHub stars. It offers a comprehensive platform including tracing, evaluations, prompt management, a playground, and datasets. Langfuse is excellent for general LLM application observability and has a mature feature set built over years of development.

However, Langfuse is primarily an LLM tracing platform, not an agent observability platform. Teams using Langfuse for agent workloads often need to build their own evaluation and security layers on top. Langfuse does not include built-in PII detection, prompt injection scanning, or risk scoring. For teams that need those capabilities, Helios offers them out of the box.

Arize Phoenix — The OTel-Compatible Veteran

Arize Phoenix (10,733 stars) is a leading AI observability platform with strong OpenTelemetry compatibility. It is an excellent choice for teams already invested in the OpenTelemetry ecosystem. The open-source Phoenix project provides core tracing, and Arize AX adds hosted dashboards, longer retention, and evaluation workflows.

Phoenix’s strength is its OTel-native approach, which makes it easy to integrate into existing observability stacks. Its weakness for agent workloads is the same as Langfuse’s — no built-in security monitoring. Teams need to add separate security tooling. Helios’s all-in-one approach (tracing + cost + security) means fewer integrations to manage.

OpenLLMetry — The OTel Standard Approach

OpenLLMetry by Traceloop (7,328 stars) takes a pure OpenTelemetry approach to LLM observability. It provides standard OTel instrumentation for GenAI applications, making it lightweight to integrate into existing OTel infrastructure. For teams that already run the OTel collector stack, OpenLLMetry is a natural fit.

The trade-off is that OpenLLMetry is intentionally minimal — it provides instrumentation and trace export, but the visualization, cost analytics, and security features depend on whatever backend you connect it to. Helios provides a complete, self-contained experience.

Helicone — The Proxy-Based Alternative

Helicone (5,995 stars, YC W23) takes a unique approach: it operates as a proxy layer between your application and LLM providers. This proxy-based architecture means integration can be as simple as changing the API endpoint URL. Helicone also includes a caching layer that can improve response times and reduce costs.

Helicone’s proxy approach is elegant for simple use cases but becomes more complex for agent workloads that involve multiple tool calls, sub-agent delegation, and non-LLM operations. Helios’s SDK-based approach gives finer-grained control over what gets traced in complex agent execution flows.

AgentOps — The Agent-Specific SDK

AgentOps (5,729 stars) is the closest competitor to Helios in terms of focus. It provides a Python SDK specifically for AI agent monitoring, with LLM cost tracking and benchmarking. It integrates with CrewAI, Agno, OpenAI Agents SDK, LangChain, AutoGen, AG2, and CamelAI.

AgentOps and Helios share the agent-specific focus, but Helios differentiates with its built-in security monitoring layer (PII detection, prompt injection detection, risk scoring). AgentOps has a more mature integration ecosystem today, but Helios’s security features address a growing pain point that AgentOps does not cover.

Where Helios Stands Out

Helios’s key differentiators are clear:

1. Security monitoring built in. No other open-source agent observability tool includes PII detection, prompt injection detection, and risk scoring as first-class features. This is Helios’s strongest moat.

2. All-in-one platform. Tracing, cost analytics, and security in a single self-hosted stack. Teams do not need to stitch together multiple tools.

3. Async architecture. Fast ingestion with background analysis means observability does not slow down agents.

4. Self-hosted with full data ownership. No vendor lock-in, no data leaving your infrastructure.

5. Early stage = influence. At 1 star and 4 days old, early adopters can shape the project’s direction.

Getting Started with Helios

Getting started with Helios is straightforward. The project is available on GitHub and the self-hosted backend runs on a single Docker Compose command:

git clone https://github.com/helios-observability/helios
cd helios
docker compose up

The Python SDK can be installed via pip and integrated in fewer than 10 lines of code:

from helios import Helios

helios = Helios(api_key="your-api-key")

@helios.trace_agent()
def my_agent(query: str):
    # Your agent logic here
    response = llm_call(query)
    return response

The SDK supports automatic instrumentation for popular agent frameworks and manual decorators for custom logic. Once integrated, traces appear in the Helios dashboard in real time, with cost calculations and security scans running in the background.

For teams already running agents in production, the SDK is designed for gradual adoption — you can instrument a single agent first, verify the integration, then roll out across your entire agent fleet.

Roadmap and Future Direction

Helios is a very early-stage project (created July 22, 2026, 1 GitHub star, MIT licensed). The current release provides the core architecture: trace ingestion, cost calculation, security scanning, and the dashboard UI. Based on the project’s stated direction, the following capabilities are likely on the roadmap:

Framework integrations. Deeper integrations with popular agent frameworks like CrewAI, LangChain, AutoGen, and OpenAI Agents SDK for automatic instrumentation without manual decorators.

Alerting and notifications. Configurable alerts for cost thresholds, security incidents, and agent failure rates, delivered via Slack, email, or webhook.

Extended security detectors. Additional security scanning capabilities including toxicity detection, bias detection, and content policy enforcement.

Multi-team support. Organization-level dashboards with team-based access control for enterprises running many agents across multiple teams.

Export and API. Programmatic access to trace data via API for custom analytics, compliance reporting, and integration with existing observability stacks.

OpenTelemetry bridge. An OTel exporter that allows Helios traces to be forwarded to existing OTel-compatible backends, bridging the gap for teams with established observability infrastructure.

The project’s early stage means the roadmap is fluid and community input will shape priorities. For teams that want a say in how an agent observability platform evolves, now is the time to get involved.

Verdict — Who Should Use Helios?

Helios is not yet a production-ready platform — it is a 4-day-old project with 1 GitHub star. But it addresses a real and growing need that no other open-source tool fully covers.

Use Helios if: You are building AI agents and want an all-in-one observability platform that includes security monitoring from day one. You prefer self-hosted solutions with full data ownership. You want to influence an open-source project’s direction from its earliest stage.

Wait if: You need a battle-tested, production-proven platform today. Langfuse or Arize Phoenix are more mature choices for general LLM observability. You need deep integrations with specific agent frameworks that Helios does not yet support.

Watch if: You are evaluating observability options for a future agent deployment. Helios’s combination of tracing, cost analytics, and security monitoring is unique. If the project executes on its roadmap, it could become the leading open-source agent observability platform.

The 88% agent failure rate is not inevitable. The teams that invest in observability infrastructure — tracing to debug failures, cost analytics to optimize spending, and security monitoring to prevent data leaks — will be the ones that capture the 171% ROI that production agents deliver. Helios offers a promising foundation for that infrastructure, built specifically for the unique challenges of AI agent workloads.

Frequently Asked Questions

What is Helios and how does it differ from Langfuse? Helios is an open-source AI agent observability platform that combines execution tracing, cost analytics, and security monitoring (PII detection, prompt injection detection, risk scoring) in a single stack. Langfuse focuses primarily on LLM tracing and evaluations but does not include built-in security monitoring. Helios is purpose-built for agent workloads rather than general LLM applications.

Is Helios free to use? Yes, Helios is completely free and open source under the MIT license. You can self-host it on your own infrastructure with no licensing fees, usage limits, or vendor lock-in. There is no paid tier or cloud offering at this stage.

How do I integrate Helios with my existing AI agents? Helios provides a Python SDK that requires fewer than 10 lines of code to integrate. You install the SDK via pip, initialize it with your API key, and add decorators to your agent functions. The SDK supports both automatic instrumentation for popular frameworks and manual instrumentation for custom agent logic.

Does Helios support OpenTelemetry? Not natively at this stage. Helios uses its own SDK-based instrumentation rather than OpenTelemetry. However, an OTel bridge is a likely future addition to the roadmap, which would allow Helios traces to be forwarded to existing OTel-compatible backends.

Can Helios detect prompt injection attacks in real time? Helios’s security scanning runs in the async analysis pipeline, which means detection happens shortly after ingestion rather than blocking the agent call in real time. The risk scoring engine combines PII detection, prompt injection signals, and other detector outputs into a per-run risk score for triage. For real-time blocking, teams would need to combine Helios with a guardrail tool that runs inline before the LLM call.