AI tool comparison
AgentTap vs Skrun
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
AgentTap
Capture every LLM call from any agent — no instrumentation needed
50%
Panel ship
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Community
Paid
Entry
AgentTap is an open-source observability tool that intercepts AI agent traffic at the network level using a split VPN and local MITM proxy. Instead of requiring you to add tracing SDKs to every agent, AgentTap sits in front of your network and captures all calls to OpenAI, Anthropic, Cohere, and other LLM providers automatically — with zero per-app configuration. The tool streams captured traces in real time, reconstructing the full prompt-response pairs, tool calls, and token counts from raw network traffic. You can observe agents running in any language, any framework, or any black-box binary — even commercial tools you don't control the source of. It's the network packet analyzer equivalent for AI agents. Built in TypeScript with a Rust-based VPN core, AgentTap is currently at 3 stars and very early — but the architectural approach is genuinely novel. Existing tools like LangSmith, Helicone, and Braintrust all require explicit SDK integration. AgentTap's bet is that the right observability layer is the network, not the application.
Developer Tools
Skrun
Deploy any agent skill as a production REST API in one command
50%
Panel ship
—
Community
Paid
Entry
Skrun is an open-source tool that wraps agentic skills — the discrete, reusable capabilities you build for AI agents (web search, data extraction, file transformation, API calls) — into deployable REST APIs with a single command. The idea is that skills you build for one agent context shouldn't be locked to that agent's runtime. With Skrun, you define a skill once with a standard function signature, and get a hosted endpoint with automatic request validation, retry logic, rate limiting, and an OpenAPI spec generated automatically. The project addresses a real architectural tension in the current AI tools ecosystem: agent skills are written in a dozen different formats (LangChain tools, MCP tools, function call JSON, OpenAI tool specs) and are essentially stranded assets — they only work within their specific orchestration framework. Skrun normalizes this by wrapping any skill definition format and exposing it as a framework-agnostic HTTP endpoint that any agent or pipeline can call. This appeared on Hacker News with a small but thoughtful discussion focused on the "skills as microservices" architectural pattern. Critics noted that adding HTTP round-trips to every tool call introduces latency; proponents argued that the composability and reusability benefits outweigh the cost. The early version focuses on stateless skills; stateful/conversational skill deployment is on the roadmap.
Reviewer scorecard
“Treating agent observability as a network problem is a genuinely smart idea. Being able to observe any LLM calls — including from tools you didn't write — is a superpower for debugging multi-agent systems. Zero instrumentation overhead is huge.”
“The framework portability angle is the real value prop — I have dozens of custom tools built for Claude that I can't reuse in other contexts without rebuilding them. If Skrun actually normalizes this cleanly across tool formats, that's a genuine pain solver.”
“Running a MITM proxy through all your LLM traffic is a serious security commitment — you're decrypting TLS in-process. In corporate environments this will fail security reviews immediately. Also, 3 stars and created two days ago. Give it six months.”
“Wrapping every agent skill in an HTTP call is a latency antipattern — a skill that takes 50ms locally becomes 120ms+ through a hosted endpoint with cold starts. For skills called hundreds of times per agent run, this adds up fast. I'd want colocation support before using this in production.”
“As agents become black boxes running across systems we don't control, network-level observability becomes the only viable audit layer. AgentTap is pioneering the right approach — what Wireshark did for networks, this could do for AI infrastructure.”
“Skills-as-services is the right architectural direction as agent ecosystems mature. The future is marketplaces of composable agent capabilities that any orchestrator can call — Skrun is early infrastructure for that world.”
“This is squarely a backend DevOps tool and the setup complexity (VPN + proxy + certs) puts it out of reach for most creative practitioners. Cool concept but the audience is very narrow.”
“Too deep in infrastructure for my workflow, but the auto-generated OpenAPI spec is a nice touch for anyone who needs to share custom skills with a team without writing documentation manually.”
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