Observe AI agent and LLM API network traffic without code changes
From $4/mo (free plan available)Visit WebsiteFreemiumVisit Website
Tracked since2026
The Bottom Line
- Price
- From $4/mo (free plan available). Compare plans
Key facts
- Passively monitors AI agent and LLM API performance from network traffic.
- Reconstructs multi-call agent interactions into complete narratives.
- Provides performance metrics and exports data for fine-tuning LLMs.
Pros
- Zero intrusion: No SDK changes, proxies, or modifications to observed workloads required.
- Comprehensive observability: Reconstructs full agent narratives from raw network data.
- Valuable for fine-tuning: Exports real agent traffic into usable fine-tuning datasets.
- Flexible deployment: Can analyze `.pcap` files or live network interfaces.
- Detailed metrics: Provides granular performance data for AI agent interactions.
Cons
- Requires traffic decryption: Needs to be installed where traffic is already plaintext or use eBPF for encrypted traffic.
- Lacks cross-cluster client tracing: Focuses on passive evidence chain rather than distributed tracing.
- Linux-specific features: Experimental eBPF source is Linux-only.
What is Heron?
Heron is a passive observability tool designed for AI agents and Large Language Model (LLM) APIs. It functions like a "Wireshark for AI Agents," capturing and analyzing network packet data to reconstruct agent turns, tool calls, and LLM interactions without requiring any SDK changes, proxies, or modifications to the observed workloads. This allows for non-intrusive performance monitoring, providing insights into metrics like Time To First Token (TTFT), latency, throughput, and error rates.
The tool is ideal for developers, MLOps engineers, and teams managing AI agent deployments who need deep visibility into the performance and behavior of their LLM-powered applications. It can analyze both live traffic and `.pcap` files, offering a unique approach to understanding complex agent workflows by stitching together multi-call interactions into coherent narratives. Heron also supports exporting agent turn data into fine-tuning formats, making it valuable for improving LLM models based on real-world usage patterns.
Key Features
- Passive network packet capture and analysis
- Agent turn reconstruction (stitches multi-call interactions)
- Service topology visualization for inference fleets
- Export SFT (Supervised Fine-Tuning) trajectory data (OpenAI-style messages JSONL)
- Live performance metrics (TTFT, latency, throughput, error rate)
- Support for `.pcap` file replay and live interface capture
- Experimental eBPF source for on-host TLS-encrypted traffic capture
- Wire-API detection for various LLM providers (e.g., Claude, OpenAI Codex, vLLM, SGLang, Ollama)
Pricing Plans
Free TrialPricing checked Oct 4, 2026
| Plan | Price | Details |
|---|---|---|
| Free | $0 USD per month |
+3 moreShow less
|
| Team | $4 USD per user/month |
+9 moreShow less
|
| Enterprise | Starting at $21 USD per user/month |
+7 moreShow less
|
Is Heron worth the price?
Good value
This pricing structure is generous, especially with the robust Free tier offering unlimited repositories and decent CI/CD minutes.
The Team tier at $4 per user/month provides excellent value for growing teams needing advanced collaboration features. Enterprise, starting at $21 per user/month, caters well to large organizations with strict compliance and management needs.
Hidden Costs & Gotchas
Package storage exceeding tier limits
How Heron's pricing compares
At $4/user/mo, Heron is the most affordable of its 4 comparable direct competitors.
Entry paid plan, monthly, per user. Pricing checked Oct 4, 2026.
Reviews
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Heron FAQ
How does Heron provide observability for AI agents without code changes?
Heron functions as a passive observability tool by capturing and analyzing network packet data. It reconstructs agent turns, tool calls, and LLM interactions directly from network traffic, eliminating the need for SDK changes, proxies, or modifications to the observed workloads.
Which teams benefit most from using Heron?
Heron is ideal for developers, MLOps engineers, and teams managing AI agent deployments. It provides deep visibility into the performance and behavior of LLM-powered applications, which is crucial for optimizing and troubleshooting these systems.
How does Heron compare to LangSmith for AI agent monitoring?
Heron differentiates itself from tools like LangSmith by offering zero-intrusion observability, capturing network traffic without requiring any code changes or SDK integrations. It reconstructs full agent narratives from raw network data, whereas other tools often rely on explicit instrumentation.
What kind of data can Heron export for model improvement?
Heron can export real agent turn data into fine-tuning formats. This capability is valuable for improving LLM models by training them on actual usage patterns observed in production environments.
What are the primary limitations when deploying Heron?
A primary limitation of Heron is its requirement for traffic decryption; it needs to be installed where traffic is already plaintext or utilize eBPF for encrypted traffic. Additionally, its experimental eBPF source is currently Linux-only.
How is Heron priced?
Heron is available on a free tier, allowing users to get started without initial cost. Paid plans are offered for those requiring more extensive usage and additional features.
Can Heron analyze past AI agent interactions?
Yes, Heron offers flexible deployment options that allow it to analyze both live network traffic and pre-recorded .pcap files. This enables users to review and understand past AI agent interactions and performance.
Source: github.com