Compare

How AgentInspect relates to console.log, hosted observability platforms, and OpenTelemetry.

AgentInspect is a local-first trace workbench for TypeScript AI agents: capture runs as local JSONL, inspect and diff them from the CLI, gate them in CI, and redact before sharing. It is designed for the inner loop, not as a replacement for hosted observability, dataset evaluation, or production monitoring platforms. Those tools complement AgentInspect; this page maps where each fits.

Docs site: https://agentinspect.vercel.app/docs/compare/

Complementary handoff (v6.12)

Position AgentInspect as the inner-loop local evidence layer:

text

local evidence and inner-loop debugging → AgentInspect
production observability → LangSmith / Langfuse / MLflow / Phoenix / APM
prompt/output and red-team eval → Promptfoo / Evalite / other eval tools
generic OTel trace access via MCP → OTel MCP servers

Handoff story:

text

debug locally with AgentInspect
export OpenInference/OTLP when production tooling is needed

Portfolio presentation: POSITIONING-AND-PORTFOLIO.md.

Category comparison

Dimension	AgentInspect	Hosted dashboards (LangSmith, Langfuse)	Eval platforms (Braintrust)	Production APM / OTel pipelines
Where traces live	Local JSONL on your disk	Vendor or self-hosted backend	Vendor backend	Your collector + backend
Account / setup	None; `npm install` + CLI	Account or deployment	Account	SDK + collector + backend
Primary surface	CLI (`view`, `report`, `diff`, `check`, suites/gates), local viewer, optional Studio Beta	Web dashboards	Web dashboards, eval UI	Dashboards, alerting
Evals	Deterministic local heuristics and CI gates (`check`, `eval`, `circuit`, `guardrails`)	Platform evals	Datasets, scoring, regressions at scale	Not the focus
Retention / fleet view	Not the goal	Strong	Strong	Strong
Data sharing	Explicit `redact` + `scan` / `verify-safe` before you share a file	Team access controls	Team access controls	Org pipelines
Network behavior	No default upload; explicit customer-owned ingest only	Uploads traces by design	Uploads by design	Ships telemetry by design
Team review	Optional customer-owned Studio Beta (not maintainer-hosted)	Strong	Strong	Strong

If you need hosted retention, fleet dashboards, dataset management, or org-wide pipelines, use one of those platforms alongside AgentInspect. Boundaries are listed in LIMITATIONS.md; concrete inner-loop workflows in USE-CASES.md.

AgentInspect vs console.log

console.log is flat: logs are a stream of lines without run grouping or step boundaries.
AgentInspect adds structure: runs, nested steps, step types (tool/LLM), durations, status summaries, and local trace files you can inspect later.
console.log still matters: use it for quick values or ad-hoc debugging inside a step.
Existing structured logs work too: the log ingest path parses JSON logs into trees without instrumentation (LOGGING-PLAYBOOK.md).

AgentInspect vs LangSmith

LangSmith is a hosted/platform workflow for tracing, evaluation, and observability in the LangChain ecosystem.

AgentInspect is local-first CLI debugging:

Use AgentInspect to debug locally before or alongside LangSmith when iterating on agent logic; @agent-inspect/langchain captures LangChain callbacks locally.
AgentInspect does not provide hosted dashboards, dataset/eval management workflows, or production tracing pipelines.

AgentInspect vs Langfuse

Langfuse provides broader LLM observability (tracing, prompt management, datasets/evals, dashboards).

AgentInspect focuses on local execution trees and CLI workflows:

Complementary: use AgentInspect for quick local run understanding and PR/debug artifacts; use Langfuse for dashboards and longer-lived observability workflows.
Not a replacement.

AgentInspect vs Braintrust

Braintrust is strong for evals, regressions, datasets, and production AI quality workflows.

AgentInspect is lighter and local-first:

Use AgentInspect to understand a single run locally and run deterministic trace checks (check, @agent-inspect/eval heuristics, @agent-inspect/guardrails, @agent-inspect/circuit) before sharing artifacts.
Use Braintrust when you want repeatable evals, comparisons at scale, and production quality workflows.

AgentInspect vs Phoenix / OpenInference

Phoenix and the OpenInference ecosystem are standards-oriented and useful for trace visualization and interoperability.

AgentInspect can export OpenInference-compatible JSON and OTLP JSON as local files:

Exports are compatibility-oriented and experimental; validate them against your target backend or collector.
AgentInspect does not claim that every backend will accept these exports without configuration.

AgentInspect vs OpenTelemetry setup

OpenTelemetry is powerful, but setup can be heavier (SDK configuration, exporters, collectors, backend).

AgentInspect avoids SDK/collector setup for local debugging:

Use AgentInspect when you want quick, local execution trees with no collector required; no OTel SDK is added to the root package.
Use OpenTelemetry when you need organization-wide production telemetry pipelines.
Local OTLP JSON export can help bridge later, but AgentInspect is not an OpenTelemetry SDK replacement.

Quick decision table

Need	AgentInspect fit
Local agent debugging	Strong fit
No-account CLI tracing	Strong fit
Framework-native capture (AI SDK, OpenAI Agents, LangChain, MCP)	Good fit (optional adapter packages)
Deterministic local eval heuristics and CI gates	Good fit
Loop / retry / timeout analysis (`circuit`)	Good fit
Share-safe local redaction copy	Good fit
Existing structured logs to trees	Good fit
VS Code trace review (in-repo extension)	Good fit; Marketplace listing separate
Production dashboards	Not the goal
Hosted eval datasets	Not the goal
Prompt management	Not the goal
Standards-aligned local export	Partial (compatibility-oriented)
Full observability platform	Use a dedicated platform

v3.5 positioning (local inner loop)

AgentInspect v3.5 is the adoption release. The npm map is one core package (agent-inspect: APIs + CLI) plus optional packages that stay out of the root dependency graph: framework adapters (ai-sdk, openai-agents, langchain, mcp, adapter-sdk), CI and quality gates (vitest, jest, eval, guardrails, circuit, harness), and inspection surfaces (viewer, tui, mcp-server, redact). See the package map.

Use it when:

You want traces on disk before or alongside hosted tools
You need CI gates (check, eval, circuit) without a vendor account
You want metadata-only defaults and explicit redact before sharing

Keep using LangSmith, Langfuse, Braintrust, Phoenix, or OTel when you need hosted retention, fleet dashboards, dataset management, or org-wide production pipelines. AgentInspect is complementary.

Before posting any exported trace to an issue, PR, or chat, follow SAFE-TRACE-SHARING.md and SECURITY.md: redaction is a key-based safeguard, and exports deserve a human review.

Full adoption path: ADOPTION.md · Boundaries: LIMITATIONS.md · Scenarios: USE-CASES.md