JamJet vs LangSmith
for AI agents in production
LangSmith is the observability and evaluation platform from LangChain Inc. It traces every LLM call, every chain, and every tool invocation; it bundles a powerful eval framework with datasets, annotation queues, and a prompt playground. It is the most established product in its category and the right tool when you need to see what your agent is doing.
JamJet sits one layer down. It is not a tracing tool; it is the safety layer that decides what the agent is allowed to do. Policy enforcement, durable execution, human approval nodes, cost caps, agent memory, and signed audit evidence in one runtime — across LangChain, LangChain4j, Spring AI, OpenAI Agents SDK, MCP, and your custom code. This page is an honest comparison.
Last updated 2026-05-15
The quick version
Choose LangSmith when
- You need traces, evals, and a prompt playground — the LangChain/LangGraph integration is unmatched
- You're running iterative prompt engineering with human-rated datasets
- You want offline regression suites and A/B comparison across model versions
- Observability is the primary need; you don't need runtime enforcement today
- You're already invested in the LangChain ecosystem and want the path of least resistance
Choose JamJet when
- You need to block unsafe tool calls, not just record them after the fact
- You need durable execution — runs that survive crashes and replay the event log
- You need first-class human-in-the-loop nodes with audit-grade evidence
- You need signed audit exports for EU AI Act / financial / healthcare compliance
- Your stack spans more than LangChain (Spring AI, LangChain4j, OpenAI SDK, MCP, vanilla Python)
The core difference: observe vs enforce
Both tools sit between your agent and the rest of the world. The difference is what they do with that position.
| Posture | What it does at runtime | What it gives you after the fact |
|---|---|---|
| LangSmith | Records every LLM call, chain step, and tool invocation as a trace. Computes costs, latencies, token counts. Lets users annotate good/bad outputs. | A trace tree you can inspect, datasets you can evaluate against, a playground to tweak prompts. Excellent root-cause analysis. |
| JamJet | Evaluates policy before each tool call runs. Routes high-risk actions to approval. Halts the run when a cost cap is exceeded. Persists every decision as a durable event. | A signed evidence package suitable for auditors. Replay cassettes that re-execute the run deterministically. The same trace tree, plus the policy decisions that shaped it. |
LangSmith shows you what happened. JamJet shapes what happens. They are complementary primitives, not competitors — most teams that adopt JamJet keep LangSmith for evals.
Feature comparison
| Capability | LangSmith | JamJet |
|---|---|---|
| Tracing | Deep tree view, LangChain-native, custom span attributes, search and filter UI. | OpenTelemetry-compatible spans; trace lineage across agents; narrower span UI today. |
| Evaluations | Datasets, evaluators, regression suites, LLM-as-judge, annotation queues, A/B test framework. Mature. | Eval harness with replay cassettes; statistical compare on the CLI. Smaller surface; no datasets UI yet. |
| Prompt playground | Full prompt-iteration UI with versioning, model-side-by-side, dataset replay. | Not in scope. |
| Policy enforcement (block unsafe calls) | Not in scope. Annotations don't block calls. | 4-level hierarchy (global → tenant → workflow → node). Glob tool blocking, model allowlists, delegation scoping. |
| Durable execution | Not in scope (LangSmith is a tracing endpoint). | Event-sourced + checkpoint snapshots. Rust core + Java native runtime (8.9x faster than REST sidecar). |
| Human-in-the-loop | Annotation queues for offline review. | First-class pause/resume/approval nodes, durable across restarts. Slack + email routing. |
| Cost governance | Cost reporting per trace and project. | Per-workflow and per-agent token/dollar budgets enforced at runtime — execution halts or branches when exceeded. |
| Audit exports for compliance | Trace export (JSON / CSV) — useful for analysis, not signed. | Ed25519-signed evidence packages. PDF / OTLP / Splunk / Datadog renderers. Per-entry retention. |
| Agent memory | Not in scope. | Engram — temporal knowledge graph, fact extraction, conflict detection, 11 MCP tools. |
| MCP support | Traces MCP calls if instrumented. | Client + server, full spec, 11 Engram tools, MCP Registry listed. |
| Framework integrations | LangChain / LangGraph (deepest), LangChain4j, OpenAI SDK, generic OTel. | LangChain4j, Spring AI, OpenAI Agents SDK (via patcher), CrewAI (wrap), LangGraph (wrap), MCP, vanilla Python. |
| Language SDKs | Python, TypeScript. | Python, Java; TypeScript shipped (alpha); Go on the roadmap. |
| Deployment | SaaS (langsmith.com). Self-host on Enterprise plan. | OSS self-host today (Docker, JVM, Kubernetes roadmap). JamJet Cloud beta available. |
| License | Proprietary SaaS (LangChain Inc). | Apache 2.0. |
| Pricing | Free tier (5K traces/mo), Plus $39/seat, Enterprise custom. Per-seat. | Free tier (5K traces/mo), Starter $29, Team $99, Business $499, Enterprise custom. Per-workspace, usage-based. |
Where LangSmith goes deep
LangSmith is one of the strongest products in the AI-observability category. If your problem is seeing, evaluating, and iterating on agent behavior, it is the obvious choice.
- Best-in-class trace UI — designed by the team that built LangChain
- Eval framework with datasets, custom evaluators, LLM-as-judge, regression suites
- Prompt playground with model side-by-side and version history
- Annotation queues for systematic human review of agent outputs
- Tight integration with LangChain, LangGraph, and the broader LC ecosystem
- Mature product, well-funded company, large community
Where JamJet goes further
JamJet's thesis is that observability alone isn't enough for AI agents that take consequential actions in production. There are five concerns a tracing platform can't address — not because LangSmith got them wrong, but because they live in the runtime, not in the trace endpoint.
Active enforcement, not passive observation
An annotation queue catches problems on review. A policy engine catches them before the call executes. JamJet evaluates a 4-level policy hierarchy (global → tenant → workflow → node) at the call site and blocks unsafe tool invocations, restricts model allowlists, and narrows delegated-token scope before harm happens. LangSmith records the call; JamJet shapes it.
Durable execution that survives crashes
LangSmith is a tracing endpoint — when your worker dies, your run dies. JamJet's event-sourced runtime replays the log and resumes from the failed step. Rust core or embedded Java runtime (8.9x faster than a REST sidecar in our benchmarks). Crash recovery is the execution model, not an add-on.
Human-in-the-loop as a workflow primitive
LangSmith's annotation queues are for offline review. JamJet has approval nodes — the workflow pauses, routes the decision to Slack or email with SLA tracking, survives restarts, and resumes when the human decides. Every decision lands in the audit trail with the approver's identity.
Signed audit evidence for compliance
EU AI Act, financial, and healthcare auditors don't want a CSV. They want an evidence package. JamJet ships Ed25519-signed exports in PDF, OTLP, Splunk, and Datadog formats with per-entry retention windows. Turning a LangSmith trace export into a compliance artifact is on you.
Agent memory, governed by the same runtime
A tracing platform has no opinion about what the agent remembers between sessions. Engram is JamJet's memory primitive — temporal knowledge graph, fact extraction, conflict detection, hybrid retrieval, consolidation — and it's governed by the same policy engine as the rest of the agent.
Can you use both?
Yes, and it's the most common combination we see. The clean composition: keep LangSmith for tracing, evaluation, and prompt iteration; add JamJet (in wrap mode) for the runtime safety layer — durability, policy, HITL, signed audit, cost caps, memory.
JamJet emits OpenTelemetry-compatible spans, so LangSmith picks up traces from JamJet-wrapped agents without extra instrumentation. You see the calls in LangSmith; you control them with JamJet.
The capability tradeoff
LangSmith gives you
The deepest trace and evaluation experience for agent development, the best LangChain integration, datasets, annotation queues, and a prompt playground. Observability is the primitive; runtime enforcement, durable execution, signed audit, HITL nodes, and cost caps are on you.
JamJet gives you
Policy enforcement, durable execution, memory (Engram), replay, signed audit evidence, HITL nodes, and cost caps in one fabric — across LangChain, LangChain4j, Spring AI, OpenAI Agents SDK, and MCP. Tracing today is OTel-compatible but the trace UI is narrower than LangSmith's; if all you need is observability, LangSmith covers more.
Ready to try JamJet?
Start with a 60-second quickstart. Keep LangSmith for tracing. Add JamJet for control.