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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Zero intrusion: No SDK changes, proxies, or modifications to observed workloads required.
Biggest con
Requires traffic decryption: Needs to be installed where traffic is already plaintext or use eBPF for encrypted traffic.
TL;DR - Heron
- 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.
Pricing: Free plan available
Best for: Growing teams
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.
Pros & Cons
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.
Key Features
Passive network packet capture and analysisAgent turn reconstruction (stitches multi-call interactions)Service topology visualization for inference fleetsExport 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 captureExperimental eBPF source for on-host TLS-encrypted traffic captureWire-API detection for various LLM providers (e.g., Claude, OpenAI Codex, vLLM, SGLang, Ollama)
Pricing Plans
Free TrialPricing checked Jun 25, 2026
Free
$0 USD per month
- Unlimited public/private repositories
- Dependabot security and version updates
- 2,000 CI/CD minutes/month (Free for public repositories)
- 500MB of Packages storage (Free for public repositories)
- Issues & Projects
- Community support
Team
$4 USD per user/month
- Everything included in Free
- Access to GitHub Codespaces
- Repository rules
- Multiple reviewers in pull requests
- Draft pull requests
- Code owners
- Required reviewers
- Pages and Wikis
Enterprise
Starting at $21 USD per user/month
- Everything included in Team
- Data residency
- Enterprise Managed Users
- User provisioning through SCIM
- Enterprise Account to centrally manage multiple organizations
- Environment protection rules
- Repository rules
- Audit Log API
How Heron's pricing compares
At $4/mo, Heron is the most affordable of its 4 direct competitors.
Entry paid plan, monthly. Pricing checked Jun 25, 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