Lookspan

Local-first observability dashboard for AI agents. MCP-native. See every span your agents emit.

npx lookspan          # → http://127.0.0.1:3100

▶ Watch the full 75-second presentation

Agent (MCP · LangGraph · CrewAI · OpenTelemetry · HTTP)  →  POST /api/ingest  →  SQLite  →  real-time dashboard

🇪🇸 ¿Prefieres español? Lee el README en español.

⭐ If Lookspan saves you a debugging session, give it a star — it's the #1 way to help it grow. Got a use case, a bug, or a framework you'd like supported? Open an issue or say hi in Discussions.

The problem

When an AI agent misbehaves — fails, stalls, or quietly burns more tokens than
expected — there's no native way to see what happened step by step. Existing
observability tools are cloud-first: they want accounts, API keys, and shipping
your production data to someone else's servers.

Lookspan takes the opposite approach: everything runs on your machine, data
never leaves it, and infra cost is zero. Instrument your agent with an adapter
(or just POST JSON) and open the dashboard in your browser.

Quick start

npx lookspan              # → http://127.0.0.1:3100, no install, no cloud

Send your first span from any language:

curl -X POST http://127.0.0.1:3100/api/ingest \
  -H "Content-Type: application/json" \
  -d '{"spans":[{"traceId":"t1","spanId":"s1","parentSpanId":null,"type":"llm_call","name":"agent.run","startedAt":"2026-06-02T10:00:00Z","endedAt":"2026-06-02T10:00:01Z","status":"ok","framework":"custom","model":"gpt-4o","provider":"openai","usage":{"inputTokens":1000,"outputTokens":500,"costUsd":0}}]}'

Open http://127.0.0.1:3100 and watch the trace appear — with its cost computed server-side.

Features

• HTTP span ingest — POST /api/ingest accepts JSON batches of spans. Works with any agent that can make an HTTP request.
• MCP-native — the @lookspan/mcp TypeScript SDK wraps any McpClient and emits a span per MCP tool call, without changing your agent code.
• Python SDKs — lookspan (generic client) plus adapters for LangGraph/LangChain (lookspan-langgraph) and CrewAI (lookspan-crewai).
• OpenTelemetry — an OTLP/HTTP receiver at POST /v1/traces; point any OTel exporter at it with no Lookspan SDK. gen_ai.* attributes map to provider/model/tokens.
• Real-time streaming — SSE endpoint GET /api/stream pushes span.ingested, trace.updated and alert.triggered to the dashboard, no polling.
• React dashboard — recent traces with a health strip + per-row latency/cost mini-bars; trace detail with a timeline (waterfall) or tree view and a conversation transcript of the prompt/response; replay diffs and A/B run comparison; costs & overview (error rate, latency p50/p95/p99, cost per day); alerts history.
• Cost tracking — aggregates input/output/cached/reasoning tokens and computes cost_usd per span and per trace from a model pricing table, overridable with --pricing.
• Export & audit — download the trace set as CSV (spreadsheet-friendly, UTF-8 BOM, formula-injection safe), JSON (metadata-only by default; ?raw=1 to include attributes), or a self-contained printable HTML audit report (format=html) with provenance, summary cards and hand-drawn SVG charts. GET /api/export/traces?format=csv|json|html; honours the active framework/status/session filters. Every response carries provenance/integrity (X-Lookspan-Export-Sha256, -Count, -Truncated).
• Alerts — get notified when a trace fails or exceeds a cost/token/duration threshold (toast + desktop notification + CLI + persisted history).
• Evaluation scores — attach metrics to a trace (POST /api/traces/:id/scores) from an LLM judge, an assertion, or by hand.
• Replay & LLM-as-judge — re-run a trace's captured prompt against the same or a different model and diff cost/latency/output, or have a judge model score the response 0–1. Needs a provider key (env, in-memory only).
• Datasets & experiments — collect prompts into a test set (seed from a trace or add by hand), run the whole set against a model in batch and score each output with the judge — aggregate cost/latency/score per run.
• Local SQLite (default), optional Postgres — versioned migrations. SQLite file at ~/.lookspan/lookspan.db by default; pass a postgres://… URL to --db / LOOKSPAN_DB to use the Postgres driver instead (same schema, same features — see docs/CONFIGURATION.md → Postgres). Optional retention with --retention.
• Security — binds to 127.0.0.1 by default; optional --token auth; server-side redaction of credential-looking attributes before storage.
• One-line CLI — npx lookspan starts the server and the dashboard with no global install.

Integrating your agents

OpenAI SDK (drop-in)

Wrap your client in one line — every model call is traced (no OTel, no proxy):

npm install @lookspan/openai

import OpenAI from 'openai';
import { observeOpenAI } from '@lookspan/openai';

const openai = observeOpenAI(new OpenAI());
await openai.chat.completions.create({ model: 'gpt-4o', messages });

Anthropic SDK (drop-in)

npm install @lookspan/anthropic

import Anthropic from '@anthropic-ai/sdk';
import { observeAnthropic } from '@lookspan/anthropic';

const anthropic = observeAnthropic(new Anthropic());
await anthropic.messages.create({ model: 'claude-sonnet-4-6', max_tokens: 1024, messages });

TypeScript / MCP

npm install @lookspan/mcp

import { wrapMcpClient, HttpSpanExporter } from '@lookspan/mcp';

const exporter = new HttpSpanExporter({ endpoint: 'http://127.0.0.1:3100/api/ingest' });
const { client } = wrapMcpClient(mcpClient, { exporter, agentId: 'my-agent' });

// Use it exactly as before — every callTool emits a tool_call span.
await client.callTool({ name: 'read_file', arguments: { path: '/tmp/foo.txt' } });
await exporter.flush();

Python (generic, LangGraph, CrewAI)

pip install lookspan            # + lookspan-langgraph / lookspan-crewai

from lookspan import LookspanClient
from lookspan_langgraph import LookspanCallbackHandler

client = LookspanClient(endpoint="http://127.0.0.1:3100/api/ingest")
handler = LookspanCallbackHandler(client=client, agent_id="my-agent")

result = graph.invoke({"messages": []}, config={"callbacks": [handler]})
client.flush()

OpenTelemetry (no SDK)

Point any OTel exporter at the standard OTLP endpoint:

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://127.0.0.1:3100/v1/traces
# protobuf (the OTel default) and JSON are both accepted

More runnable examples in examples/.

Evaluate & replay

The drop-in SDKs capture each call's prompt and reply (captureContent, on by
default; secrets are scrubbed server-side). With that, Lookspan can close the
loop from observe to improve — open a trace and use the Replay & judge
panel, or call the API directly:

# Provider keys live in memory only — never written to the DB or logged.
LOOKSPAN_OPENAI_API_KEY=sk-... npx lookspan
#   ...or LOOKSPAN_ANTHROPIC_API_KEY / --openai-key / --anthropic-key

# Replay the captured prompt against another model and diff cost/latency/output
curl -X POST localhost:3100/api/traces/<id>/replay -H 'content-type: application/json' \
  -d '{"model":"gpt-4o-mini"}'   # omit "model" to re-run the original

# Score the response 0–1 with an LLM judge (stored as an "llm-judge" score)
curl -X POST localhost:3100/api/traces/<id>/judge -H 'content-type: application/json' \
  -d '{"metric":"correctness"}'

To keep prompts/outputs out of Lookspan entirely, pass { captureContent: false }
to observeOpenAI / observeAnthropic — replay & judge then stay disabled.

Datasets & experiments

Scale evaluation from one trace to a whole test set. Build a dataset (seed
items from real traces or add them by hand), then run it against a model —
each item is replayed and, optionally, scored by the judge, with aggregate
cost/latency/score per run. Manage it all under Datasets in the dashboard, or:

# Create a dataset and add the captured prompt of a trace as an item
DS=$(curl -s -X POST localhost:3100/api/datasets -d '{"name":"regressions"}' -H 'content-type: application/json' | jq -r .dataset.id)
curl -X POST localhost:3100/api/datasets/$DS/items/from-trace -H 'content-type: application/json' -d '{"traceId":"<id>"}'

# Run the whole set against a model, judging each output
curl -X POST localhost:3100/api/datasets/$DS/run -H 'content-type: application/json' \
  -d '{"model":"gpt-4o-mini","judge":true,"metric":"correctness"}'

HTTP API

CLI options

npx lookspan [options]
  -p, --port <port>        Port to listen on            (default: 3100)
      --host <host>        Host to bind to              (default: 127.0.0.1)
      --db <path|url>      SQLite path or postgres:// URL (default: ~/.lookspan/lookspan.db)
      --retention <dur>    Prune traces older than e.g. 7d, 24h, 30m
      --token <token>      Require Authorization: Bearer <token> on the API
      --pricing <file>     Custom model pricing table (JSON)
      --alert-error                Alert when a trace fails
      --alert-cost <usd>           Alert when a trace costs more than <usd>
      --alert-tokens <n>           Alert when a trace exceeds <n> tokens
      --alert-duration <ms>        Alert when a trace takes longer than <ms>
      --open               Open the dashboard in your browser
  -h, --help               Show help
  -v, --version            Show version

Every flag has a LOOKSPAN_* environment-variable equivalent (LOOKSPAN_PORT, LOOKSPAN_TOKEN, LOOKSPAN_PRICING, LOOKSPAN_ALERT_*, …). Replay & LLM-as-judge read LOOKSPAN_OPENAI_API_KEY / LOOKSPAN_ANTHROPIC_API_KEY (or --openai-key / --anthropic-key); these stay in memory and are never persisted.

See docs/CONFIGURATION.md for the complete flag + environment-variable reference, defaults, and examples.

How it compares

Lookspan isn't trying to be a full platform. It bets on being the zero-setup
observability layer for the TypeScript/MCP agent stack, with the best
first-five-minutes experience. See the ROADMAP.

Security

Lookspan binds to 127.0.0.1 (loopback) and requires no auth by default — right
for local use. If you expose it (--host 0.0.0.0), protect it with a token:

LOOKSPAN_TOKEN=my-token npx lookspan --host 0.0.0.0
# /api/* and /v1/* then require Authorization: Bearer my-token (/api/health is exempt).

The collector also redacts values of credential-looking keys
(authorization, api_key, token, secret, password, cookie…) from
input/attributes before persisting, so telemetry never drags secrets into
the database.

Development

This is an npm-workspaces monorepo. packages/ holds internal libraries, apps/
the dashboard, python/ the standalone Python SDKs.

git clone https://github.com/JoniMartin27/lookspan.git
cd lookspan
npm install
npm run dev        # API on :3100, dashboard with hot-reload on :5173
npm run ci         # typecheck + lint + test + build

Contributions welcome — see .github/CONTRIBUTING.md.
Release process in docs/PUBLISHING.md. Security policy: SECURITY.md.

License

MIT — Copyright (c) 2026 Jonathan Martin. See LICENSE.

Lookspan is part of Fervon, the studio behind a portfolio of open-source developer tools (Trace, InferBench, ClaudeScope, Launchpad and more). The Fervon brand identity is being rolled out to the landing — see the feat/fervon-theme branch.