I built agent-inspect to debug TypeScript AI agent trajectories

Developer Raju Dandigam has released agent-inspect, an open-source local evidence debugger and trajectory-test toolkit for TypeScript AI agents. The tool converts a single local trace into a readable execution tree, a deterministic regression gate, and a shareable Evidence v2 bundle, addressing the challenge of debugging complex agent flows where flat logs and output-only tests fail to reveal wrong paths. AgentInspect supports manual instrumentation and adapters for AI SDK, OpenAI Agents JS, LangChain, and LangGraph, with no account or default upload required.

Your AI agent failed. Again. The final answer is wrong, but the logs look fine: tool call started model call started tool call completed model call completed fallback used error: timeout Which tool caused the timeout? Did the model answer before retrieval finished? Was the fallback expected? Did the agent call the same tool twice? This is where console.log stops feeling like debugging and starts feeling like archaeology. I kept hitting this problem while building TypeScript AI agents. Once the flow moved beyond a single model call, the debugging loop became a system: plan → retrieve → rank → generate → validate → maybe retry → maybe hand off Flat logs lost the structure. Output only tests could miss a bad path that happened to produce a plausible answer. Model graded evals helped with semantic quality, but they were a poor fit for every deterministic CI rule. And raw traces were too risky to paste casually into issues or pull requests. So I built agent-inspect https://github.com/rajudandigam/agent-inspect . AgentInspect is a local evidence debugger and trajectory-test toolkit for TypeScript AI agents. It turns one local trace into three things: a readable execution tree, a deterministic regression gate, and a derived Evidence v2 bundle that you can review before sharing. No account. No collector. No default upload. Metadata only by default. one local JSONL trace ├─ Debug → view · report · explain ├─ Prevent → check · contract · CI └─ Share → redact · bundle · verify A support agent can return a plausible answer after doing almost everything wrong. The healthy path might be: plan-request └─ retrieve policy └─ rank-results └─ generate answer └─ policyShown: passed The regression might be: generate answer <- answered before retrieval retrieve policy retrieve policy <- duplicate call search docs <- wrong tool, failed policyShown: failed An output only test may pass. A flat log may contain every event. Neither makes the wrong path obvious. The final answer is only one fact about the run. Tool choice, ordering, repetition, completion, duration, token usage, and observed outcomes are facts too. Together, those facts form the agent's trajectory . That trajectory should be inspectable. It should also be testable. You can start with manual instrumentation: js import { inspectRun, observeOutcome, step } from "agent-inspect"; const answer = await inspectRun "support-agent", async = { const policy = await step "retrieve policy", = retrievePolicy , { type: "tool", metadata: { toolName: "retrieve policy" }, }, ; const result = await step "generate answer", = draftAnswer policy , { type: "llm", metadata: { model: "your-model" }, }, ; await observeOutcome "policyShown", { expectation: "The answer cites a retrieved policy", status: "passed", method: "custom", } ; return result; }, { traceDir: ".agent-inspect" }, ; The wrapper records those boundaries as local JSONL while preserving the application's return value and errors. Raw prompts and model outputs are not required for the core workflow. If your application already emits structured logs or uses AI SDK, OpenAI Agents JS, LangChain, or LangGraph you can use an adapter or reader instead of wrapping every step manually. The shortest path uses a generated synthetic demo: npm install agent-inspect npx agent-inspect init --yes node examples/agent-inspect-demo.mjs npx agent-inspect list --dir .agent-inspect init writes a small config and demo into your project. The demo does not call a model or upload a trace. Copy the run ID printed by list , then use the same local artifact for the three jobs below. npx agent-inspect view <run-id --dir .agent-inspect --summary npx agent-inspect report <run-id --dir .agent-inspect npx agent-inspect explain <run-id --dir .agent-inspect The tree restores the structure that flat logs lose: nested steps, tool and model calls, durations, safe metadata, errors, and observed outcomes. explain summarizes local trace facts deterministically; its default path makes no provider call. The useful question changes from: Did the run fail? to: Where did the passing and failing trajectories first diverge? That distinction matters when the visible answer looks fine but the agent skipped a required retrieval, safety, or validation step. Some agent quality questions are subjective. Helpfulness, tone, and open ended answer quality can benefit from model graded evaluation. But many regressions are structural: retrieve policy called? search docs tool appear?Those checks do not need another model. They can be deterministic: npx agent-inspect check <run-id --dir .agent-inspect \ --preset trajectory \ --required-tool retrieve policy \ --forbidden-tool search docs \ --fail-on-observation failed The preset and explicit shorthand rules are additive. A healthy run exits 0 ; a trajectory-rule failure exits 1 . For the committed regression fixture, the result is concrete: Check status: fail Summary: 2 failed, 0 warning s , 0 error s - outcome.status: Observed outcome count 1 matched failed . - tool.usage: Forbidden tool search docs appeared. Same trace. Same rules. Same verdict. No model judge and no provider call in the check path. That makes it suitable for a normal CI step. If your test fixture writes a trace to a stable path: - name: Run deterministic agent fixture run: node run-agent-fixture.mjs - name: Check agent trajectory run: | npx agent-inspect check .agent-inspect/ci-run.jsonl \ --preset trajectory \ --required-tool retrieve policy \ --fail-on-observation failed \ --evidence-on fail --evidence-on fail writes local Evidence for triage when the check fails. It does not upload the artifact. When CLI flags outgrow one command, the Beta TraceContract API expresses the same expectations in TypeScript: js import { openTraceFile } from "agent-inspect/readers"; import { defineTraceContract, evaluateTraceContractRead, } from "agent-inspect/checks"; const read = await openTraceFile "./.agent-inspect/demo-regression.jsonl" ; const contract = defineTraceContract { run: { requireCompleted: true }, tools: { required: "retrieve policy" , forbidden: "search docs" , }, observations: { failOn: "failed" }, } ; const result = evaluateTraceContractRead read, contract ; if result.status == "pass" process.exitCode = 1; The principle is simple: use deterministic trace facts for structural CI rules, and reserve model grading for semantic quality. A failing trace is often the best debugging artifact. It can also contain prompts, tool arguments, retrieved documents, customer identifiers, error messages, or secrets. The collaboration strategy should not be “paste the raw trace into Slack.” AgentInspect keeps the source trace read-only and creates a derived bundle: npx agent-inspect verify-safe <run-id --dir .agent-inspect npx agent-inspect bundle <run-id --dir .agent-inspect \ --profile share \ --out ./evidence npx agent-inspect bundle verify ./evidence The bundle can include: evidence.html self-contained offline review surface evidence.json versioned manifest and SHA-256 file hashes trace.jsonl redacted derived trace check-results.json deterministic findings redaction-report.json detector summary without secret values summary.md human-readable overview bundle verify checks the manifest, listed files, hashes, assessment, and provenance offline. This is an integrity check. It is not a signature or a compliance certificate. The wording matters: the artifact is share-checked , not “certified safe.” verify-safe and redaction are best-effort controls. Review the generated HTML and safety results before attaching a bundle to a pull request, incident, or public issue. The local evidence model is not tied to one agent framework. | Your stack | Capture path | |---|---| | Custom TypeScript functions or classes | inspectRun , step , observe , or createInspector | | Vercel AI SDK | @agent-inspect/ai-sdk | | OpenAI Agents JS | @agent-inspect/openai-agents | | LangChain or LangGraph | @agent-inspect/langchain | | Existing structured logs | agent-inspect logs readers | | OpenInference or OTLP JSON | local standards readers | | Vitest or Jest | reporters plus experimental trace matchers | The root package is enough for custom capture, the CLI, deterministic checks, and Evidence. Optional packages add only the integration you need. The Preview MCP path exposes configured local evidence through bounded, read-only tools: npx agent-inspect mcp configure --client cursor The command is a dry run by default, so you can review the generated configuration before enabling it. A connected coding assistant can then investigate the same TraceFacts used by the CLI: What failed first? Which required tool was missing? What changed between the passing and failing runs? This is not replay, an auto fix engine, or a hidden upload path. It is optional read-only access to explicitly configured local evidence. AgentInspect owns the laptop to pull request evidence loop: capture locally → understand the path → fail CI on structural drift → derive reviewable evidence It complements hosted observability and evaluation platforms. Use hosted tools when you need production dashboards, long term retention, fleet wide alerting, team wide trace search, hosted datasets, or prompt management. Use AgentInspect when you need to inspect one TypeScript agent run immediately, enforce deterministic trajectory expectations in CI, compare a passing and failing local run, or hand off a redacted, hash-verifiable artifact. The boundary is intentional. AgentInspect is not: The current release is 6.17.2 , requires Node.js 20 or newer, uses persisted schema 1.0 , and is MIT licensed. Legacy v0.1 and v0.2 traces remain readable. npm install agent-inspect npx agent-inspect init --yes node examples/agent-inspect-demo.mjs npx agent-inspect list --dir .agent-inspect Then inspect, check, and derive Evidence from the run: npx agent-inspect view <run-id --dir .agent-inspect --summary npx agent-inspect check <run-id --dir .agent-inspect --preset trajectory npx agent-inspect bundle <run-id --dir .agent-inspect \ --profile share \ --out ./evidence npx agent-inspect bundle verify ./evidence One local trace should be able to tell you what the agent did, prove that the regression stays fixed, and give a teammate evidence they can review without making upload the price of admission. AgentInspect is open source and MIT licensed. If this workflow is useful to you: Most of all, leave a comment below . How do you debug agent runs today? Which trajectory rule would you put in CI first? And if AgentInspect does not fit your workflow, tell me why that feedback is just as useful.