Intutic vs LangSmith
Last reviewed: 2026-10-10
Intutic decides agent tool calls before they run, across 43 supported harnesses: native hook gates in 18 of them and in-process SDK gates in 17 allow or block the call, hook gates, SDK gates and the MCP governance proxy can hold it for human approval, and the other eight are governed through the proxies, a bridge or the harness they orchestrate. Around the gates, a policy proxy redacts sensitive data in model traffic, an MCP governance proxy governs MCP tools, an egress firewall and sandboxed execution stop the agent routing around governance, and execution traces are sealed into a signed audit trail you can verify. LangSmith, from LangChain, is a platform for tracing, evaluating and improving LLM applications. Its LLM Gateway, in public beta since 2026-07-30 on the Plus and Enterprise plans, sits inline on model traffic to enforce hard spend caps, rate limits and model-access policies, fall back between models, and redact PII and secrets, with setup guides for Claude Code, Codex and Gemini CLI. LangSmith Sandboxes, generally available since 2026-05-13, run code in microVMs, inject credentials through an auth proxy and restrict network access to allowed domains; they contain the code you send to them, and do not gate the tool calls of a coding agent running on a developer's machine. Its Fleet agents can add a hardening skill that asks for approval before sensitive actions. LangSmith governs and records the model call and contains code run in its sandboxes; Intutic governs the action wherever the agent runs.
Comparison
| Intutic | LangSmith | |
|---|---|---|
| Primary job | Runtime enforcement and audit for AI agents | Tracing, evaluation and prompt engineering for LLM applications |
| Where it enforces | Pre-execution gates inside the agent (native hook gates in 18 of the 43 harnesses, in-process SDK gates in 17 agent frameworks), plus request and response proxy, MCP governance proxy, egress firewall and sandbox | LLM Gateway (public beta), inline on model traffic; Sandboxes, for code sent to them |
| Coding agents | 43 supported harnesses, including Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf and Cline | Gateway guides for Claude Code, Codex and Gemini CLI |
| Decisions | Allow, warn, require approval (the call is refused and held for review; with a control plane, once a reviewer approves it the identical retry passes if the workspace has opted in, and with none it stays refused), block, redact, re-ask, shadow | Block on a spend cap (HTTP 402), a rate limit or a model-access policy; fall back to another model; redact PII and secrets |
| Sensitive data | Secrets and credentials matched by pattern and validated PII detectors for payment cards, IBANs and US Social Security numbers (email and phone opt-in), redacted or blocked in requests before they leave, in responses before the agent sees them, and in MCP tool calls; patterns you define (such as PHI) in the proxy's configuration apply to model traffic | Gateway redaction: a PII policy built on Presidio (names, places, nationality, religion, political affiliation, ages; a separate Enterprise entitlement) and a Secrets policy (US phone numbers, US Social Security numbers, API keys and tokens). System prompts, tool-call arguments and response content are not scanned. Masking of trace inputs and outputs |
| Cost control | Daily spend caps enforced before a request leaves; a loop run that exceeds its budget is stopped | Hard spend caps and rate limits in the gateway |
| Containment | Opt-in default-deny egress firewall on the machine the agent runs on; sandboxed runs in a container or Firecracker microVM | Sandboxes: microVMs with credentials injected by an auth proxy and domain allow and deny lists, for code an agent sends there. A local coding agent's own tool calls are not gated |
| Evaluation | Shadow mode measures how often a rule would act before it enforces; judge verdicts in the uncertain band go to a human review queue | Datasets and evaluators for LLM application quality |
| Source | Open core (MIT) | Closed platform |
Where LangSmith is stronger
- Evaluation and debugging. Detailed traces of nested chains and agents, datasets and evaluators built for improving application quality.
- LangChain and LangGraph depth. First-party tracing for the LangChain ecosystem.
- Model-based PII detection. The gateway's PII policy uses Presidio to find names, places, nationality, religion, political affiliation and ages, which Intutic's pattern detectors do not cover. Restoring redacted values in the response is an Enterprise option on request.
- Hosted sandboxes. Managed microVMs with credential injection and domain allow and deny lists, for agents that send code to run there.
When to choose Intutic
- Your agents run commands, edit files and call MCP tools, and you need each call decided before it runs.
- You want risky calls from the coding agents and frameworks you already run held for human approval.
- You need egress control and sandboxing where the agent itself runs, including a developer's machine, so it cannot route around governance; LangSmith Sandboxes contain only the code sent to them.
When to choose LangSmith
- You are building and improving LLM applications and need tracing and evaluation.
- Spend caps, rate limits and redaction on model traffic cover your runtime needs.
- You want hosted sandboxes for code your agents generate.
- You build on LangChain or LangGraph and want first-party tooling.
Use them together
LangSmith can trace and evaluate your agents while Intutic gates their tool calls. LangGraph agents take Intutic's in-process gate from intutic-clawde (see LangGraph), so the same graph can be traced in LangSmith and governed by Intutic.