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ifivo vs LangSmith
This is the one people compare us to most often — and they solve different problems. LangSmith is observability and evaluation: traces, prompts, evals, regression. ifivo is a runtime control plane that decides whether an agent action runs at all. Most mature agent stacks end up with both.
- Pre-execution policy engine — allow / require_approval / block
- Slack + web approval queue, org-wide kill switch, per-agent quarantine
- Framework-agnostic gateway; works with any SDK or raw HTTP
- Immutable action ledger, policy versioning, SSO-grade audit log
- Deep LLM trace capture: prompts, completions, tokens, latency
- Eval harness — LLM-as-judge, regression datasets, prompt A/B
- Native LangChain & LangGraph integration; LangGraph Cloud for deployment
- Developer free tier (5K traces / 14 days); Plus $39/seat + overage
Side-by-side capabilities
How the two compare on the things teams tell us matter most when they evaluate an agent control plane.
| Capability | ifivo Agent control plane | LangSmith LLM observability & evals |
|---|---|---|
Stops a bad agent action before it executes Pre-execution policy evaluation; allow / require_approval / block. | ||
Human-in-the-loop approval queue Slack, email, in-app — with the full action diff. | ||
Org-wide kill switch | ||
Deterministic policy rules (vendor, action, amount, metadata) | ||
LLM trace capture with prompt / completion / tokens LangSmith's core strength; ifivo captures action records, not prompts. | ||
Eval harness (LLM-as-judge, regression datasets) | ||
Deep LangChain / LangGraph integration | ||
Framework-agnostic (OpenAI SDK, Anthropic SDK, raw HTTP) | ||
Immutable audit log for auditors and finance Action records, policy version, approver identity — not trace data. | ||
MCP endpoint for ChatGPT / Claude / Gemini operators | ||
Transparent starter pricing LangSmith Developer free (5K traces/14d); Plus $39/seat + overage; Enterprise custom. |
Partial means the capability is possible but not turnkey. Our read based on public docs and onboarding conversations; corrections welcome at hello@ifivo.com.
When LangSmith is the right call
Pick LangSmith if your primary question is "is the model doing a good job?" — prompt regressions, eval scores, trace debugging, cost attribution across LLM calls. That is their home turf and they are excellent at it.
If you are building on LangChain / LangGraph, the depth of integration and the eval harness pay for themselves during model upgrades.
LangGraph Cloud also solves a legitimate deployment / hosting problem for long-running agent graphs that ifivo does not attempt to address.
When ifivo is the right call
Pick ifivo when the question is "should this action happen, and who approved it?" — refunds, outbound messages, large API spend, destructive ops, payments. The kind of thing that ends up in an incident report or an auditor's sample.
You get the approval queue, kill switch, and the audit log that finance and compliance actually ask for — without needing prompt-level tracing of every LLM call.
Framework-agnostic matters here: ifivo does not assume LangChain; it works just as well with the OpenAI SDK, Anthropic SDK, raw HTTP, or an MCP client.
Using them together, or migrating
Running both is common and correct. Put LangSmith at the LLM-call layer (traces, evals, prompt drift) and ifivo at the tool-call edge (policy, approvals, ledger). The integration is one HTTP call from your LangGraph node to the ifivo Gateway — see the integrate page for the pattern.
If you want LangSmith trace IDs attached to ifivo action records for cross-lookup, pass them through metadata on the Gateway call and they show up on the decision ledger.
Frequently asked
Isn't LangSmith the 'LangChain control plane'?
LangChain calls LangGraph Cloud / LangGraph Platform a 'control plane' — but in their usage, that means deployment management: hosting, scaling, versioning your graphs. It is not an action-time policy engine, approval queue, or kill switch. Different meaning of the word 'control'. ifivo runs before an agent's action executes and decides whether it's allowed.
Do I need both?
Many teams do. LangSmith gives you traces, evals, and prompt regression over time — extremely useful during development and model upgrades. ifivo sits at the tool-call edge in production: policy, approvals, audit, kill switch. Observability watches; control plane decides. They are stacked, not substitutes.
I'm all-in on LangChain / LangGraph — how does ifivo fit?
Call ifivo from your LangGraph node that's about to make the tool call. POST the intended action to the ifivo Gateway; on 'allow' you execute the real call, on 'require_approval' you pause the graph and resume when the approval webhook fires. There's a working pattern in our /integrate docs, and the payload is just JSON — no LangChain-specific SDK required.
Does ifivo do evaluation?
No. We don't run LLM-as-judge harnesses, regression datasets, or prompt A/B tests. If that's your primary need, use LangSmith. We focus on production governance of actions — the thing that shows up in a SOC 2 audit.
What about LangSmith's alerts and thresholds?
LangSmith can alert on latency, cost, or eval score regressions — reactive. ifivo blocks at the action — preventive. If the bug is 'my agent refunded $12K to one customer in a loop,' LangSmith shows the trace after the fact; ifivo was the thing that let the first one through policy and held the second one for approval.
Try ifivo in under an hour.
Connect one agent in shadow mode. Watch a week of traffic. Turn policies on when you trust the picture. No migration, no rebuild.
Sources: LangSmith pricing & plan details from PE Collective's LangSmith pricing breakdown and LangChain's own product pages. Comparison reflects public information as of April 22, 2026; we update this page when either product changes meaningfully.