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Foxhound

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MCP server for querying Foxhound traces from Claude Code, Cursor, and other MCP clients

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Описание

MCP server for querying Foxhound traces from Claude Code, Cursor, and other MCP clients

README

Scale targets — 1B spans/day per customer · ingest p99 < 500 ms · trace query p95 < 2 s · dashboard query p95 < 300 ms. Architecture tour10-minute walk from SDK to dashboard. Scale readinesslive gap matrix · RFC index.

Source-available, self-hostable observability for AI agent fleets. Multi-tenant. OTel-compatible. Evaluators, Run Diff, Session Replay, cost budgets, SLA monitoring, behavior regression, CI quality gate.


FOXHOUND banner

Compliance-grade observability for AI agent fleets.

Trace every decision. Evaluate every response. Budget every dollar.

CI All Rights Reserved npm sdk npm mcp PyPI

Docs · Live Sandbox · Quickstart · Tooling · Self-Hosting · Development


Licensing notice This repository is publicly visible for reference and evaluation only. No permission is granted to use, copy, modify, distribute, sublicense, or sell this software without prior written permission from Caleb Love. Licensing inquiries: [email protected]

See LICENSE for full terms.

What is Foxhound?

Foxhound is a source-available observability platform purpose-built for AI agent systems. Generic APM and logging tools do not model agent behavior well: tool calls, LLM invocations, branching workflows, replay, evaluation, and regression detection all get flattened into the wrong abstractions.

Foxhound gives you the missing layer:

  • trace every agent run as a structured span tree
  • compare executions to see exactly where behavior diverged
  • evaluate responses with datasets, experiments, and LLM-as-judge workflows
  • enforce budgets and SLAs before costs and latency drift silently
  • audit and isolate tenant data for security-sensitive deployments

Not a generic logging product. AI agent observability.

Sandbox

The sandbox is a self-contained demo workspace that tells a realistic seven-day story: a returns resolution agent regresses after a prompt rollout, gets detected through traces and regressions, is investigated via run diff and session replay, and recovers through a dataset-backed experiment.

Run locally with no external dependencies:

pnpm install
pnpm dev:web:demo
# Open http://localhost:3001/sandbox

What you will see:

  • Fleet Overview: platform posture, operator focus items, and trend charts (Recharts)
  • Traces: 568 seeded traces with tree-structured span timelines, cost badges on LLM calls, and parent/child indentation
  • Run Diff: side-by-side comparison showing exactly where two agent runs diverged
  • Session Replay: step-through playback with play/pause, speed control, seek bar, and live state diff
  • Regressions: behavior drift detection by agent version with linked investigation paths
  • Experiments: dataset-backed evaluation with promotion-ready candidates
  • Budgets and SLAs: per-agent cost budgets and latency/reliability targets
  • Prompts: versioned prompt registry with label-based promotion and diff view
  • Notifications: alert routing to Slack, webhook, GitHub, Linear, PagerDuty
  • Settings: theme switcher, SDK ingestion simulator, CI quality gate demo
  • Guided tour: first-visit walkthrough with keyboard navigation (auto-shows once, re-trigger from the Tour button)
  • Command palette: Cmd+K for quick navigation across all surfaces
  • Mobile responsive: hamburger menu with slide-over drawer on small screens

Core capabilities

Area What you get
Tracing Structured traces and spans for every run · trace explorer · metadata and event capture
Replay & Diff Session replay · run diff · trace timeline inspection
Evaluation LLM-as-judge evaluators · dataset curation from production traces · experiment comparison
Agent intelligence Cost budgets · SLA monitoring · regression detection by agent version
Prompt management Prompt templates · label-based promotion such as stagingproduction
Operations API keys · notifications · audit logging · multi-tenant isolation
Developer tooling TypeScript SDK · Python SDK · CLI · MCP server · GitHub quality gate

Quickstart

1. Clone and install

git clone https://github.com/caleb-love/foxhound.git
cd foxhound
pnpm install

2. Start local infrastructure

docker compose -f docker-compose.dev.yml up -d
cp apps/api/.env.example apps/api/.env
pnpm --filter @foxhound/db db:migrate

3. Run the app

pnpm dev       # API
pnpm dev:web   # dashboard in another terminal

Default local endpoints:

  • API: http://localhost:3000
  • Web: http://localhost:3001

4. Send your first trace

Python

pip install foxhound-ai
from foxhound import FoxhoundClient

fox = FoxhoundClient(
    api_key="fh-...",
    endpoint="http://localhost:3000",
)

async with fox.trace(agent_id="support-agent") as tracer:
    span = tracer.start_span(name="tool:search", kind="tool_call")
    span.set_attribute("query", "refund policy")
    span.end()

TypeScript

npm install @foxhound-ai/sdk
import { FoxhoundClient } from "@foxhound-ai/sdk";

const fox = new FoxhoundClient({
  apiKey: process.env.FOXHOUND_API_KEY!,
  endpoint: "http://localhost:3000",
});

const trace = fox.trace({ agentId: "support-agent" });
const span = trace.startSpan({ name: "tool:search", kind: "tool_call" });
span.setAttribute("query", "refund policy");
span.end();
await trace.flush();

Tooling

Artifact Install Purpose
Python SDK pip install foxhound-ai Instrument Python agent systems
TypeScript SDK npm install @foxhound-ai/sdk Instrument Node.js / TypeScript runtimes
CLI npm install -g @foxhound-ai/cli Inspect traces and operate Foxhound from the terminal
MCP Server npm install -g @foxhound-ai/mcp-server Query Foxhound from Claude Code, Cursor, and other MCP clients
GitHub Action caleb-love/foxhound-quality-gate Block PRs that fail eval or quality thresholds

Architecture

SDKs / OTLP  ->  API (Fastify)  ->  PostgreSQL
                  |
                  ->  Worker (BullMQ) -> Redis
                  ->  Web dashboard (Next.js)

Current monorepo layout:

apps/api/               Fastify REST API
apps/web/               Next.js dashboard
apps/worker/            BullMQ workers
packages/sdk/           TypeScript SDK
packages/sdk-py/        Python SDK
packages/cli/           CLI
packages/mcp-server/    MCP server
packages/api-client/    Typed API client
packages/db/            Drizzle schema + queries
packages/types/         Shared types
packages/billing/       Billing + entitlements
packages/notifications/ Notification delivery

Self-hosting

Foxhound is designed to run on your own infrastructure.

Minimum stack:

  • PostgreSQL 16+
  • Redis
  • Node.js 20+
  • pnpm 9+

Primary local/dev commands:

pnpm build
pnpm test
pnpm lint
pnpm typecheck

For API configuration, see:

  • apps/api/.env.example

Security

Foxhound is built for security-sensitive, multi-tenant environments.

Current repo expectations include:

  • API keys hashed at rest
  • tenant-scoped data access via org_id
  • JWT auth for user-facing operations
  • audit logging for sensitive actions
  • rate limiting and security headers on the API/web surfaces

If you discover a vulnerability, use GitHub security advisories or follow SECURITY.md if present.

Development

pnpm install
pnpm build
pnpm test
pnpm lint
pnpm typecheck
pnpm format

Useful dev commands:

pnpm dev       # API only
pnpm dev:web   # web only
pnpm dev:all   # API + web
pnpm --filter web verify   # canonical web verification lane

For the web preview surface, /sandbox is canonical and /demo is compatibility-only. See apps/web/README.md and docs/reference/sandbox-compatibility-retirement-checklist.md.

Contributing

Contributions are welcome.

  • open issues for bugs and feature requests
  • keep multi-tenant safety and security review standards high
  • prefer small, verifiable changes over broad speculative refactors

See CONTRIBUTING.md for contribution conventions.

License

All rights reserved. This repository is public for reference and evaluation only. No permission is granted to use, copy, modify, distribute, sublicense, or sell this software without prior written permission. See LICENSE and contact [email protected] for licensing inquiries.

from github.com/caleb-love/foxhound

Установить Foxhound в Claude Desktop, Claude Code, Cursor

Рекомендуется · одна команда, все IDE
unyly install foxhound

Ставит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.

Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh

Или настроить вручную

Выполни в терминале:

claude mcp add foxhound -- npx -y @foxhound-ai/mcp-server

FAQ

Foxhound MCP бесплатный?

Да, Foxhound MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Foxhound?

Нет, Foxhound работает без API-ключей и переменных окружения.

Foxhound — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Foxhound в Claude Desktop, Claude Code или Cursor?

Открой Foxhound на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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