io.github.spanlens/mcp-server

Official

by spanlens · TypeScript

Query Spanlens LLM observability from Cursor, Claude Desktop, or Continue via MCP.

Query LLM observability data from Spanlens within Cursor, Claude Desktop, or Continue.

Topics

ai-agentsanthropicclickhousegeminillmllmopsmonitoringnextjsobservabilityollamaopenaipythonself-hostedtracingtypescriptcost-trackingllm-monitoringllm-observabilityopen-source

README.md22 KBView on GitHub

Spanlens

Open-source LLM observability and monitoring. Record every OpenAI / Anthropic / Gemini / Mistral / OpenRouter / Azure OpenAI / Ollama call with one line of code. Plugs into Vercel AI SDK, LangChain, and LlamaIndex too. Query the same data directly from Cursor, Claude Desktop, or Continue via the bundled MCP server. Get cost, latency, tokens, traces, anomalies, PII scan, and model-swap suggestions out of the box. Self-hostable. MIT.

⭐ If Spanlens is useful to you, please star the repo. It takes a second and helps other developers find it.

Hosted: spanlens.io · npm: @spanlens/sdk · PyPI: spanlens · CLI: @spanlens/cli · MCP: @spanlens/mcp-server · Status: status.spanlens.io · Changelog: spanlens.io/changelog

Spanlens request log: filter every LLM call, then drill into the full prompt, response, cost, latency, and tokens

Live demo (no signup): spanlens.io/demo/requests

One key swap. Every LLM call, observed.

Spanlens dashboard showing anomaly alerts, spend forecast and traffic chart

Why Spanlens?

Helicone was acquired and its roadmap is uncertain.
Langfuse is powerful but complex to set up and expensive to scale.
Spanlens ships the 20% of features that cover 80% of real production needs. You get request log, cost tracking, agent tracing, anomaly detection, PII scanning, and prompt versioning with a clean UI, a two-minute setup, and pricing that doesn't punish growth.

	Spanlens	Langfuse Pro	Helicone
Open source	✅ MIT	✅ MIT	✅ MIT
Self-hostable	✅ Docker one-liner	✅	✅
Free tier	50K req/mo	50K events/mo	10K req/mo
Team plan (1M req/mo)	$149/mo	$271/mo	~$200/mo
Agent tracing	✅	✅	⚠️ limited
LLM-as-judge evals	✅	✅	❌
PII + injection scan	✅	❌	❌
Model recommendations	✅	❌	❌
Prompt A/B experiments	✅	✅	❌

Spanlens Team $149/mo vs Langfuse Pro $271/mo at 1M requests per month

Predictable bills, no quota cliff. Free hits a hard 429 at 50K requests so a runaway loop in dev can't cost you money. Paid plans use a soft limit with authorized overage (Pro: +$8 / 100K, Team: +$5 / 100K) up to a hard cap you control, so a traffic spike charges you fairly instead of dropping requests.

Seats: Free 1 · Pro 3 · Team 10 · Enterprise unlimited. Unlimited projects on every paid tier.

⚡ Quick start in 30 seconds

TypeScript / JavaScript (Next.js)

bash

npx @spanlens/cli init

The wizard:

Installs @spanlens/sdk with your package manager (npm / pnpm / yarn / bun)
Writes SPANLENS_API_KEY to .env.local
Rewrites every new OpenAI({ apiKey, baseURL }) into createOpenAI()

Paste your Spanlens API key once, confirm two prompts, done. Your LLM calls are now flowing through the Spanlens proxy and visible in www.spanlens.io/requests.

Manual TypeScript setup

import { createOpenAI } from '@spanlens/sdk/openai'
const openai = createOpenAI()  // reads SPANLENS_API_KEY, uses Spanlens proxy baseURL

Python

bash

pip install "spanlens[openai]"

python

from spanlens.integrations.openai import create_openai

client = create_openai()  # reads SPANLENS_API_KEY from env
res = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
)

For agent tracing in Python (multi-step, async, tool calls) see the Python SDK README.

Framework integrations

Already using an orchestration framework? Plug Spanlens in as a callback. No code rewrites.

Vercel AI SDK (Next.js / edge friendly)

import { SpanlensClient } from '@spanlens/sdk'
import { createSpanlensTracker } from '@spanlens/sdk/vercel-ai'

const tracker = createSpanlensTracker({
  client: new SpanlensClient({ apiKey: process.env.SPANLENS_API_KEY! }),
  modelName: 'gpt-4o',
})

await generateText({
  model: openai('gpt-4o'),
  messages,
  onStepFinish: tracker.onStepFinish,
  onFinish: tracker.onFinish,
})

LangChain JS / LangGraph

import { createSpanlensCallbackHandler } from '@spanlens/sdk/langchain'

const handler = createSpanlensCallbackHandler({ client })
await chain.invoke({ input }, { callbacks: [handler] })   // LangChain
await graph.invoke({ input }, { callbacks: [handler] })   // LangGraph

LlamaIndex TS

import { Settings } from 'llamaindex'
import { registerSpanlensCallbacks } from '@spanlens/sdk/llamaindex'

const unregister = registerSpanlensCallbacks(Settings, { client })
// ... run queries ... unregister() on shutdown

Python: LangChain — from spanlens.integrations.langchain import SpanlensCallbackHandler. Same BaseCallbackHandler contract, works with chains, LCEL, and LangGraph.

More integrations: AWS Bedrock, CrewAI, Flowise, Instructor, LlamaIndex, OpenAI Assistants, MCP server. Full setup walkthroughs at spanlens.io/docs/integrations.

Ollama (local LLMs) — Ollama runs on your machine, so it does not go through the hosted proxy. Get a ready client with createOllama() and wrap each call with observeOllama() so the span is logged and tagged as Ollama.

import { SpanlensClient } from '@spanlens/sdk'
import { createOllama, observeOllama } from '@spanlens/sdk/ollama'

const spanlens = new SpanlensClient({ apiKey: process.env.SPANLENS_API_KEY! })
const ollama = createOllama() // points at http://localhost:11434/v1

const trace = spanlens.startTrace({ name: 'chat' })
const res = await observeOllama(trace, 'chat', (headers) =>
  ollama.chat.completions.create(
    { model: 'llama3.1', messages: [{ role: 'user', content: 'Hello' }] },
    { headers },
  ),
)
await trace.end({ status: 'completed' })

What you see

Spanlens request log showing every LLM call with latency, cost, tokens and status

Every request logged with model, provider, latency, tokens, cost, and full prompt + response body. Filter, search, export. Streaming responses reconstructed automatically.

What you get

Feature	Description
Request log	Every LLM call logged with model, tokens, cost, latency, and full request/response body (streaming reconstructed too)
Agent tracing	Multi-step workflows as Gantt waterfall span trees with Critical Path highlighted (the longest dependency chain across a fan-out, not just the slowest single span), plus a node-and-edge graph topology view for LangChain / LangGraph callback traces
Cost tracking	Per-request cost breakdown with daily rollups and budget alerts. Prompt-cache tokens (`cache_read` / `cache_creation` on Anthropic, `prompt_tokens_details.cached_tokens` on OpenAI) are parsed separately and billed at the discounted rate so you can see actual cache savings, not just sticker price
Per-end-user analytics	Tag calls with `x-spanlens-user` (SDK: `withUser()` / `with_user()`) and the /users page shows per-user cost, tokens, errors, models, last seen
Anomaly detection	3σ deviations in latency, cost, or error rate vs. your 7-day baseline, with root-cause hints (token delta, HTTP status breakdown)
Alerts	Threshold rules on budget, error rate, and p95 latency. Delivered via Email (Resend), Slack, or Discord webhooks. Evaluated on a 15-minute cron with at-least-once delivery
PII + prompt-injection scan	Regex-based detection on request and response bodies; optional per-project blocking (422) for injections; instant alert emails to workspace owner
Savings (model recommendations)	The `/savings` dashboard surfaces calls that match a cheaper model's profile ("Your gpt-4o calls look like classification. Try gpt-4o-mini") with estimated monthly savings
Prompt versioning + A/B	Register prompt templates, run traffic-split experiments, compare versions side by side on latency / cost / error rate — reported with Welch's t-test on latency and cost plus a z-test on error rate, so you get statistical significance rather than just averages
Prompts Playground	Execute any prompt version with variable injection directly in the dashboard to see real cost and response before shipping
Datasets	Reusable (input, expected_output) test sets you can rerun against any prompt version or model. Upload CSV / JSONL files directly from the dashboard or POST programmatically. Powers offline evals and regression checks
Evals & Experiments	Build LLM-as-judge evaluators (judge with OpenAI, Anthropic, or Gemini — pick the cheapest/best for the criterion) with rubric anchors and confidence intervals on pass rates. Supports pairwise A vs B mode for head-to-head prompt comparison, agent trajectory mode for scoring whole traces (not just final text), and judge-result caching keyed by `(evaluator, response)` to skip duplicate LLM calls on re-runs. Human annotation is queued for sampling, with Pearson r (numeric) or Cohen's κ (categorical) measuring judge-human agreement
OpenAPI 3.0 spec + Swagger UI	Machine-readable spec at `GET /api/v1/openapi.json` and interactive explorer at `GET /api/v1/docs`. A drift test enforces that every router stays documented
Saved filters	Pin frequently used request-log queries (model, status, cost range, tags) and share them across the workspace
Outbound webhooks	Subscribe to `request.created` / `trace.completed` / `alert.triggered` events. Payloads are HMAC-signed via `X-Spanlens-Signature: sha256=…` so receivers can verify origin
OpenTelemetry / OTLP ingest	`POST /v1/traces` accepts OTLP/HTTP JSON exports using the `gen_ai.*` semantic conventions, so you can drop in any OTel SDK without writing Spanlens-specific code
Provider-key security	Weekly digest emails for stale (unused 90d+) provider keys + daily GitGuardian leak scan against your active keys, with per-key scan history
Privacy controls	Per-request `x-spanlens-log-body: full \| meta \| none` header lets customers shrink what Spanlens stores (drop bodies, drop end-user IDs) without dropping the request itself
Data export	CSV or JSON download for requests, traces, anomalies, and flagged security events (`GET /api/v1/exports/{requests,traces,anomalies,security}?format=csv`). Streamed server-side for 100K+ row pulls so big exports don't OOM

Team & workspaces

Spanlens is multi-user out of the box. Invite teammates, hand out roles, and spin up a separate workspace per client.

Roles are admin (members + billing), editor (data + settings), and viewer (read-only). The last admin is protected against demotion / removal.
Email invitations have a 7-day expiry with sha256-hashed tokens. Sent via Resend when RESEND_API_KEY is set; falls back to console-logging the accept URL for local dev.
The pending-invitation banner surfaces unaccepted invites at the top of the dashboard, even if the recipient never opened the email. Accept joins and auto-switches the active workspace; Decline removes the row.
Multi-workspace lets you switch between workspaces from the sidebar (sb-ws cookie + hard reload so middleware re-resolves scope). Useful for consultants juggling multiple clients or one team running prod / staging as separate workspaces.
Two-step onboarding sends new signups to /onboarding: name your workspace, answer two optional survey questions, done. Invitees get a short-circuited variant where Accept skips workspace creation entirely.
The audit log (Settings → Audit log) records every membership / role / invitation event with actor + timestamp.

Monorepo structure

code

Spanlens/
├── apps/
│   ├── web/             — Next.js 14 dashboard (www.spanlens.io)
│   └── server/          — Hono LLM proxy + REST API (server.spanlens.io)
├── packages/
│   ├── sdk/             — @spanlens/sdk:  TypeScript / JavaScript SDK
│   ├── sdk-python/      — spanlens (PyPI): Python SDK
│   ├── cli/             — @spanlens/cli:  npx wizard for 1-command setup
│   └── mcp-server/      — @spanlens/mcp-server: MCP server for Cursor / Claude Desktop / Continue
├── clickhouse/
│   ├── migrations/      — ClickHouse schema for the `requests` log table
│   └── apply.ts         — `pnpm ch:migrate` runner (idempotent)
└── supabase/
    ├── migrations/      — Postgres schema (orgs, projects, keys, prompts, … — RLS-gated)
    └── seeds/           — model_prices.sql etc.

Storage split

Spanlens uses two databases, each for what it's good at.

Supabase (Postgres) handles transactional, relational, RLS-gated data: organizations, projects, members, API + provider keys, prompts, datasets, alerts, billing, audit log.

ClickHouse handles the high-volume append-only requests table (every LLM call). All reads go through apps/server/src/lib/requests-query.ts, which auto-injects the organization_id filter and the per-plan retention window (free=14d / pro=90d / team=365d). If ClickHouse is briefly unreachable, the proxy falls back to a Supabase queue (requests_fallback) that a cron replays every 5 minutes with no log loss.

Projects, unified keys, and headers

A workspace can hold multiple projects (e.g. dev / staging / prod, or one per app). Each project gets its own quota slice, provider keys, and prompt namespace.
Unified API keys give you one sl_live_* key per project that is provider-agnostic. Spanlens infers the provider from the request path (/proxy/openai/*, /proxy/anthropic/*, /proxy/gemini/*, /proxy/mistral/*, /proxy/openrouter/*, /proxy/azure/*), so you only need one Spanlens key even if you call multiple model vendors.
X-Spanlens-* headers (set automatically by the SDK helpers withUser(), withSession(), withPromptVersion(), withLogBody()): tag a request with end-user / session IDs, link it to a prompt-version experiment, or limit how much body Spanlens stores. Full list in /docs/proxy.
Streaming safety ensures proxy responses are gracefully closed at 290s with a truncated=true flag in the log, so long streams never silently disappear.

Local development

Prerequisites: Node 20+, pnpm 10.33.0+, Docker (for local Supabase), Vercel CLI optional.

bash

# 1. Clone + install
git clone https://github.com/spanlens/Spanlens.git
cd Spanlens
pnpm install

# 2. Start local Supabase + ClickHouse (both require Docker)
supabase start
supabase db push        # apply Postgres migrations
supabase gen types --lang typescript --local > supabase/types.ts

docker compose up -d clickhouse   # start ClickHouse only (web/server run from pnpm dev)
pnpm ch:migrate                   # apply ClickHouse migrations

# 3. Env vars (see apps/server/.env.example)
cp apps/server/.env.example apps/server/.env

# 4. Run everything (web on :3000, server on :3001)
pnpm dev

Running tests + lint

bash

pnpm typecheck          # TS across all packages
pnpm lint               # ESLint
pnpm test               # Vitest — server + sdk + cli suites
pnpm build              # production build smoke test

See CLAUDE.md for architecture rules and Known Gotchas (streaming, RLS, Paddle billing, Vercel Edge runtime, npm publish).

Self-hosting

The easiest way to self-host is with the included docker-compose.yml. It runs the dashboard (web), the proxy/API server, and a local ClickHouse instance together using pre-built images from GHCR.

Run Spanlens in your own VPC with one Docker command

1. Apply the Supabase schema (one-time)

Open your Supabase project → SQL Editor → New query, paste the contents of supabase/init.sql, and click Run. That's it. No CLI needed.

Alternative (psql):

bash

psql "postgresql://postgres:<password>@db.<ref>.supabase.co:5432/postgres" \
  -f supabase/init.sql

2. Create a `.env` file

bash

# Supabase (cloud or self-hosted)
NEXT_PUBLIC_SUPABASE_URL=https://xxxx.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=eyJ...
SUPABASE_URL=https://xxxx.supabase.co
SUPABASE_ANON_KEY=eyJ...
SUPABASE_SERVICE_ROLE_KEY=eyJ...

# Encryption key (generate with: openssl rand -base64 32)
ENCRYPTION_KEY=<32-byte base64>

# Random secret for cron endpoint
CRON_SECRET=<random string>

# ClickHouse (request logs, required)
CLICKHOUSE_URL=http://clickhouse:8123        # the in-network service from docker-compose
CLICKHOUSE_USER=spanlens
CLICKHOUSE_PASSWORD=<choose a strong password>
CLICKHOUSE_DB=spanlens

# Optional (for invite emails)
# WEB_URL=https://your-domain.com
# RESEND_API_KEY=re_...
# RESEND_FROM=Spanlens <no-reply@your-domain.com>

# Optional (Paddle billing, only if you sell paid plans on your instance)
# PADDLE_API_KEY=...
# PADDLE_NOTIFICATION_SECRET=...
# PADDLE_ENVIRONMENT=sandbox  # or production

3. Start

bash

docker compose up -d
pnpm ch:migrate                # one-time: apply ClickHouse schema (requests table)

Dashboard: http://localhost:3000
API / proxy: http://localhost:3001
ClickHouse HTTP: http://localhost:8123
Health: GET /health (liveness) and GET /health/deep (ClickHouse + fallback queue depth)

The web container passes NEXT_PUBLIC_* vars as build arguments (Next.js bakes them into the client bundle), so they must be present before docker compose build.

Server-only (no dashboard)

If you only need the proxy/API and run the dashboard separately:

bash

docker pull ghcr.io/spanlens/spanlens-server:latest
docker run -p 3001:3001 \
  -e SUPABASE_URL=... \
  -e SUPABASE_ANON_KEY=... \
  -e SUPABASE_SERVICE_ROLE_KEY=... \
  -e ENCRYPTION_KEY=... \
  ghcr.io/spanlens/spanlens-server:latest

Point your SDK at your self-hosted URL

const openai = createOpenAI({
  baseURL: 'https://your-spanlens.example.com/proxy/openai/v1',
})

Your Spanlens instance talks to your Supabase + ClickHouse. We never see your data.

Background jobs (Vercel Cron / your scheduler)

The hosted instance ships with the following cron tasks (see apps/server/vercel.json). On self-host, point any scheduler at the same paths with the CRON_SECRET bearer:

Path	Schedule	Purpose
`/cron/evaluate-alerts`	every 15m	Evaluate threshold + anomaly alerts, fire notifications
`/cron/snapshot-anomalies`	daily 01:00	Materialize daily anomaly baselines
`/cron/replay-fallback`	every 5m	Replay `requests_fallback` queue into ClickHouse
`/cron/stale-key-reminders`	weekly Mon 09:00	Email digest of idle provider keys
`/cron/leak-detect-keys`	daily 04:00	GitGuardian scan of active provider keys
`/cron/recommend-savings-alerts`	daily 09:00	Email model-swap savings opportunities
`/cron/check-past-due-downgrades`	daily 10:00	D-3 / D-1 warnings + auto-downgrade past-due subs
`/cron/execute-pending-deletions`	every 6h	Hard-delete accounts past their 30-day soft-delete grace
`/cron/run-background-migrations`	every 5m	Drain background-migration queue (data backfills)
`/cron/events-reconciliation`	daily 02:00	Reconcile OTLP events dual-write between Supabase and ClickHouse
`/cron/detect-missing-model-prices`	hourly	Catch new model IDs in the request log that have no row in `model_prices`
`/cron/self-monitor`	hourly :31	Spanlens dogfoods itself — heartbeat eval into the internal workspace
`/cron/detect-orphan-spans`	hourly :17	Flag spans whose parent trace never arrived
`/cron/prune-judge-cache`	daily 03:00	TTL-evict stale `(evaluator, response)` judge cache entries
`/cron/keep-warm`	every 5m	Lightweight ping that keeps the Vercel function warm (skip on always-on platforms like Fly.io / Railway)

Contributing

PRs and issues welcome. See CONTRIBUTING.md for the project layout, local-dev setup, coding conventions, and what we look for in a PR. Commit messages follow Conventional Commits and the PR template walks through the safety checklist.

Security issues: please email support@spanlens.io instead of opening a public issue. See SECURITY.md.

License

MIT. Use, fork, self-host, or build on top freely. The hosted service at spanlens.io is the recommended way to run Spanlens, but you can always pull the Docker image and run it yourself (see docs/self-host).

Install

Configuration

Environment variables

SPANLENS_API_KEYrequiredsecret

Spanlens API key. Must be a scope=public key (sl_live_pub_*) issued from the Public Keys card at https://spanlens.io/projects — the server refuses to start with a full-access sl_live_* key.

SPANLENS_BASE_URLdefault https://server.spanlens.io

Override the API endpoint for self-hosted Spanlens. Default: https://server.spanlens.io

claude_desktop_config.json

{
  "mcpServers": {
    "mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@spanlens/mcp-server@0.1.4"
      ],
      "env": {
        "SPANLENS_API_KEY": "<YOUR_SPANLENS_API_KEY>",
        "SPANLENS_BASE_URL": "https://server.spanlens.io"
      }
    }
  }
}

Topics

Spanlens

Why Spanlens?

⚡ Quick start in 30 seconds

TypeScript / JavaScript (Next.js)

Manual TypeScript setup

Python

Framework integrations

What you see

What you get

Team & workspaces

Monorepo structure

Storage split

Projects, unified keys, and headers

Local development

Running tests + lint

Self-hosting

1. Apply the Supabase schema (one-time)

2. Create a .env file

3. Start

Server-only (no dashboard)

Point your SDK at your self-hosted URL

Background jobs (Vercel Cron / your scheduler)

Contributing

License

Install

Configuration

2. Create a `.env` file