When you build a product on top of an LLM, the model doesn't just give you an answer — it often calls tools, hands off to sub-agents, and takes several steps before it produces anything a user sees. When that chain of steps goes wrong, a normal error log doesn't tell you which step actually broke, what the model saw at that point, or whether your last prompt change made things better or worse. That's what LLM observability tools are for: they record every step so you can actually debug it instead of guessing. LangSmith, built by the LangChain team, is the best-known name here, but it's most at home if you're already using LangChain, and teams on a different stack — or who want to self-host their traces instead of sending them to a third party — often look elsewhere. Here are 6 real alternatives, pulled from actual product listings, not marketing copy.
The short version: if you want a one-line proxy with a genuinely generous free tier, Helicone is the easiest to bolt on. If your product is built around multi-step AI agents rather than one-off LLM calls, Laminar traces every tool call and sub-agent, open source. For a single dashboard covering evaluation, human feedback and production monitoring, Parea AI covers the most ground. If you need to rewind a failed run to the exact step that broke and replay it, Retrace is built specifically for that. And if you want to pay nothing and keep every trace on your own machine, AgentOS or OpenSmith are both free and open source.
Why teams look past LangSmith
LangSmith's tracing is genuinely tight if your stack is LangChain or LangGraph — that's what it was built for. The friction shows up for everyone else: teams on a custom stack, on a different framework, or who simply want their LLM traces (which can include real user data) to stay on infrastructure they control instead of a third-party SaaS. There's also a growing set of teams building multi-agent systems specifically, where generic LLM-call logging matters less than seeing which sub-agent called which tool, in what order, and where the chain actually broke.
1. Helicone — one-line proxy, gateway and observability combined
Helicone is an open-source AI gateway and LLM observability tool: one API for 100+ models, plus cost and latency tracking and prompt management, added with a single-line proxy integration instead of a full SDK rewrite. The free tier covers 10,000 requests/month, paid plans are usage-based, and the full stack can be self-hosted for free under Apache 2.0.
Good fit if: you want the fastest possible integration and a model-routing gateway in the same tool. Watch out for: routing requests through a proxy adds a dependency in your request path, and the self-hosted stack is five separate services to run.
2. Parea AI — evaluation, human feedback and monitoring in one place
Parea AI is built for teams shipping products on top of LLMs who need to know whether a prompt change actually made answers better or worse, then watch what the model does once it's live — evaluation, human feedback and production observability in a single tool, with Python and TypeScript SDKs that stay framework- and provider-agnostic. The free tier is unusually generous for testing (2 members, 3,000 logs/month, 10 deployed prompts); Team is $150/month for 100,000 logs/month.
Good fit if: you want evaluation and production monitoring together instead of stitching two tools. Watch out for: $150/month is a real jump once you're past the free tier, and self-hosting is Enterprise-only, on-quote.
3. Laminar — built specifically for multi-step AI agents
Laminar shows you exactly what your AI agent did, step by step, when it fails — every tool call, every sub-agent, every LLM response — including parallel branches, instead of leaving you to piece it together from a wall of logs. It also syncs browser recordings for agents that do browser automation. It's Apache 2.0 open source with real GitHub traction (3,100+ stars) and supports full self-hosting. Free tier: 1GB, 7-day retention. Starter is $30/month (3GB, 30-day retention, unlimited seats); Pro is $150/month (10GB, 6-month retention).
Good fit if: you're debugging genuinely multi-step agent behavior, not single LLM calls. Watch out for: the agent-specific framing is overkill if your use case is simple one-shot prompts, and the company is younger and smaller than LangSmith or Langfuse.
4. Retrace — rewind a failed run and replay it
Retrace lets you rewind a failed agent run to the exact step that broke, change one thing, and replay it — turning every failure into a regression test you can rerun in CI/CD, with a clear verdict (improved / regressed / unchanged) after a fix. It encrypts traces with TLS and AES-256-GCM and auto-redacts PII. Free tier: 1,000 traces/month, 7-day retention. Starter is $29/month (10,000 traces); Pro $99/month (50,000 traces); Teams $399/month (500,000 traces, 10 users).
Good fit if: you want to prove a fix actually worked instead of hoping it did. Watch out for: the Starter tier's 10,000 traces get tight fast for a team in production, and its integration ecosystem is smaller than the established players.
5. AgentOS — free, open source, built for supervising multiple agents
AgentOS is a free, open-source dashboard for running several AI agents like a small company — giving each one a role, a task list, and a way for a human to check in on what they're actually doing, with centralized supervision (transcripts, token usage, human approvals) and Telegram/Discord/Slack integrations. It's MIT-licensed and fully self-hosted on your own machine.
Good fit if: you're running multiple agents together and need a human-in-the-loop approval layer, for free. Watch out for: it requires Node.js 24+ and the OpenClaw framework specifically, is local-first rather than multi-tenant, and is still a young project (81 GitHub stars).
6. OpenSmith — free, self-hosted, local-first by design
OpenSmith lets you see exactly what your AI application did step by step, without sending any of that data to a third-party cloud at all. It's completely free and open source under MIT, with multi-database support and both a web UI and CLI.
Good fit if: keeping LLM trace data off any third-party cloud is a hard requirement, not a nice-to-have. Watch out for: it's Python-only, the community is still maturing, and export options are more limited than a mature SaaS.
Side-by-side
| Tool | Deployment | Free tier | Best for |
|---|---|---|---|
| Helicone | Hosted or self-hosted | 10k requests/mo | Fastest one-line integration + gateway |
| Parea AI | Hosted | 3k logs/mo | Eval + human feedback + monitoring in one |
| Laminar | Hosted or self-hosted | 1GB, 7-day retention | Multi-step AI agent tracing |
| Retrace | Hosted | 1,000 traces/mo | Replay/debug failed agent runs |
| AgentOS | Self-hosted (free) | Free, self-hosted | Supervising multiple agents with approvals |
| OpenSmith | Self-hosted (free) | Free, self-hosted | Keeping traces fully off third-party clouds |
None of these fully replace LangSmith's native LangChain/LangGraph integration if that's the framework your whole stack is built on. But if what's actually bothering you is the third-party data question, the framework lock-in, or the lack of agent-specific tracing, several of these solve that directly and for less money. Want to keep everything on your own infrastructure for free: try OpenSmith or AgentOS. Want the fastest hosted setup: try Helicone. Building genuinely multi-step agents: try Laminar or Retrace.