Discover, share, and monitor AI coding agents with full observability built in.

If you find Observal useful, please consider giving it a star. It helps others discover the project and keeps development going.

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Observal is a self-hosted AI agent registry with built-in observability. Think Docker Hub, but for AI coding agents.

Browse agents created by others, publish your own, and pull complete agent configurations, all defined in a portable YAML format that templates out to Claude Code, Codex CLI, Gemini CLI, and more. Every agent bundles its MCP servers, skills, hooks, prompts, and sandboxes into a single installable package. One command to install, zero manual config.

Every interaction generates traces, spans, and sessions that flow into a telemetry pipeline, giving you full observability, traceability, and real-time metrics for your agents in production. The built-in eval engine (WIP) scores agent sessions so you can measure performance and make your agents better over time.

Supported tools: Claude Code, Codex CLI, Gemini CLI, and Kiro CLI are fully supported. Cursor and VS Code have MCP/rules file support.

See the Changelog for recent updates.

Quick Start

Everything works out of the box with defaults. No configuration needed for local development.

git clone https://github.com/BlazeUp-AI/Observal.git
cd Observal
cp .env.example .env

cd docker && docker compose up --build -d && cd ..
uv tool install --editable .
observal auth login            # auto-creates admin on fresh server

That's it. The .env.example ships with working defaults for every setting. The server starts 7 services (API, web UI, PostgreSQL, ClickHouse, Redis, background worker, OpenTelemetry collector) and creates all database tables on first boot.

Already have MCP servers in your IDE? Instrument them in one command:

observal scan                  # auto-detect, register, and instrument everything
observal pull <agent> --ide cursor  # install a complete agent

This detects MCP servers from your IDE config files, registers them with Observal, and wraps them with observal-shim for telemetry without breaking your existing setup. A timestamped backup is created automatically.

The Problem

AI coding agents today are hard to share and impossible to measure. Components (MCP servers, skills, hooks, prompts) are scattered across repos with no standard way to package them together. There's no visibility into what's actually working, and no way to compare one version of an agent against another on real workflows.

Observal solves this by giving you a registry to package and distribute complete agents, and a telemetry pipeline to measure them.

How It Works

Agents in the registry are defined in YAML. Each agent bundles its components (MCP servers, skills, hooks, prompts, sandboxes) into a single configuration. When you run observal pull <agent>, it installs everything and generates the right config files for your tool.

A transparent shim (observal-shim for stdio, observal-proxy for HTTP) sits between your tool and the MCP server. It never modifies traffic, it only observes. Every request/response pair becomes a span, spans group into traces, and traces form sessions. All of this streams into ClickHouse for analysis.

Tool  <-->  observal-shim  <-->  MCP Server / Sandbox
                |
                v (fire-and-forget)
          Observal API  -->  ClickHouse (traces, spans, scores)
                |
                v
          Eval Engine (SLM-as-a-Judge / Deductive Penalty Scoring)  -->  Scorecards

The eval engine runs on collected traces after the fact. It scores agent sessions across five dimensions: Goal Completion, Tool Call Efficiency, Tool Call Failures, Factual Grounding, and Thought Process. Scorecards let you compare agent versions, identify bottlenecks, and track improvements over time.

The Registry

Observal manages 6 component types that agents bundle together:

Component	Description
Agents	Complete configurations that bundle all the components below
MCP Servers	Model Context Protocol servers that expose tools to agents
Skills	Portable instruction packages that agents load on demand
Hooks	Lifecycle callbacks that fire during agent sessions
Prompts	Managed templates with variable substitution
Sandboxes	Docker execution environments for code running and testing

Anyone can publish components to the registry. Admin review controls visibility in the public listing, but your own items are usable immediately without approval. Browse the web UI or CLI to discover agents and components shared by others.

CLI Reference

The CLI is organized into command groups. Run observal --help or observal <group> --help for full details.

Primary Workflows

observal pull <agent> --ide <ide>    # install a complete agent with all dependencies
observal scan [--ide <ide>]          # detect and instrument existing IDE configs
observal use <git-url|path>          # swap IDE configs to a git-hosted profile
observal profile                     # show active profile and backup info

Authentication - observal auth

observal auth login            # auto-creates admin on fresh server, or login with key
observal auth logout           # clear saved credentials
observal auth whoami           # show current user
observal auth status           # check server connectivity and health
observal auth reset-password   # reset a forgotten password (uses server-logged code)

Forgot your password? If you've lost access to an account (e.g. an admin account created before passwords were set up), use the reset flow:

observal auth reset-password --email admin@localhost

This requests a 6-character reset code that gets logged to the server console. Check the server logs (make logs or docker logs <container>) for a line like:

WARNING - PASSWORD RESET CODE for admin@localhost: A7X9B2 (expires in 15 minutes)

Enter the code and your new password to regain access. The same flow is available from the web UI via the "Forgot password?" link on the login page.

For CI/scripts, use environment variables:

export OBSERVAL_SERVER_URL=http://localhost:8000
export OBSERVAL_API_KEY=<your-key>

Component Registry - observal registry <type>

All 5 component types (mcp, skill, hook, prompt, sandbox) support the same core commands:

observal registry <type> submit [<git-url> | --from-file <path>]
observal registry <type> list [--search <term>]
observal registry <type> show <id-or-name>
observal registry <type> install <id-or-name> --ide <ide>
observal registry <type> delete <id-or-name>

Prompts also have observal registry prompt render <id> --var key=value.

Agent Authoring - observal agent

# Browse and manage
observal agent create              # interactive agent creation
observal agent list [--search <term>]
observal agent show <id>
observal agent install <id> --ide <ide>
observal agent delete <id>

# Local YAML workflow
observal agent init                # scaffold observal-agent.yaml
observal agent add <type> <id>     # add component (mcp, skill, hook, prompt, sandbox)
observal agent build               # validate against server (dry-run)
observal agent publish             # submit to registry

Observability - observal ops

observal ops overview              # dashboard stats
observal ops metrics <id> [--type mcp|agent] [--watch]
observal ops top [--type mcp|agent]
observal ops traces [--type <type>] [--mcp <id>] [--agent <id>]
observal ops spans <trace-id>
observal ops rate <id> --stars 5 [--type mcp|agent] [--comment "..."]
observal ops feedback <id> [--type mcp|agent]
observal ops telemetry status
observal ops telemetry test

Admin - observal admin

# Invite team members
observal admin invite              # generate invite code (e.g. OBS-A7X9B2)
observal admin invites             # list all invite codes

# Settings and users
observal admin settings
observal admin set <key> <value>
observal admin users

# Review workflow
observal admin review list
observal admin review show <id>
observal admin review approve <id>
observal admin review reject <id> --reason "..."

# Evaluation engine
observal admin eval run <agent-id> [--trace <trace-id>]
observal admin eval scorecards <agent-id> [--version "1.0.0"]
observal admin eval show <scorecard-id>
observal admin eval compare <agent-id> --a "1.0.0" --b "2.0.0"
observal admin eval aggregate <agent-id> [--window 50]

# Penalty and weight tuning
observal admin penalties
observal admin penalty-set <name> [--amount 10] [--active]
observal admin weights
observal admin weight-set <dimension> <weight>

Configuration - observal config

observal config show               # show current config
observal config set <key> <value>  # set a config value
observal config path               # show config file path
observal config alias <name> <id>  # create @alias for an ID
observal config aliases            # list all aliases

Self-Management & Diagnostics

observal self upgrade              # upgrade CLI to latest version
observal self downgrade            # downgrade to previous version
observal doctor [--ide <ide>] [--fix]  # diagnose IDE settings compatibility

Tech Stack

Component	Technology
Frontend	Next.js 16, React 19, Tailwind CSS 4, shadcn/ui, Recharts
Backend API	Python, FastAPI, Uvicorn
Database	PostgreSQL 16 (primary), ClickHouse (telemetry)
ORM	SQLAlchemy (async) + AsyncPG
CLI	Python, Typer, Rich
Eval Engine	AWS Bedrock / OpenAI-compatible LLMs
Background Jobs	arq + Redis
Real-time	GraphQL subscriptions (Strawberry + WebSocket)
Dependency Management	uv
Telemetry Pipeline	OpenTelemetry Collector
Deployment	Docker Compose (7 services)

Setup & Configuration

See SETUP.md for local development setup, eval engine configuration, and troubleshooting.

API Endpoints

Auth

Method	Endpoint	Description
POST	/api/v1/auth/bootstrap	Auto-create admin on fresh server
POST	/api/v1/auth/login	Login with API key or email+password
POST	/api/v1/auth/exchange	Exchange one-time OAuth code for credentials
GET	/api/v1/auth/whoami	Current user info
POST	/api/v1/auth/request-reset	Request password reset (code logged to server console)
POST	/api/v1/auth/reset-password	Reset password with code + new password
POST	/api/v1/auth/invite	Create invite code (admin)
POST	/api/v1/auth/redeem	Redeem invite code → get API key
GET	/api/v1/auth/invites	List invite codes (admin)

Registry (per type: mcps, agents, skills, hooks, prompts, sandboxes)

All {id} parameters accept either a UUID or a name.

Method	Endpoint	Description
POST	/api/v1/{type}	Submit / create
GET	/api/v1/{type}	List approved items
GET	/api/v1/{type}/{id}	Get details
POST	/api/v1/{type}/{id}/install	Get IDE config snippet
DELETE	/api/v1/{type}/{id}	Delete
GET	/api/v1/{type}/{id}/metrics	Metrics
POST	/api/v1/agents/{id}/pull	Pull agent (installs all components)

Scan

Method	Endpoint	Description
POST	/api/v1/scan	Bulk register items from IDE config scan

Review

Method	Endpoint	Description
GET	/api/v1/review	List pending submissions
GET	/api/v1/review/{id}	Submission details
POST	/api/v1/review/{id}/approve	Approve
POST	/api/v1/review/{id}/reject	Reject

Telemetry

Method	Endpoint	Description
POST	/api/v1/telemetry/ingest	Batch ingest traces, spans, scores
POST	/api/v1/telemetry/events	Legacy event ingestion
GET	/api/v1/telemetry/status	Data flow status

Evaluation

Method	Endpoint	Description
POST	/api/v1/eval/agents/{id}	Run evaluation
GET	/api/v1/eval/agents/{id}/scorecards	List scorecards
GET	/api/v1/eval/scorecards/{id}	Scorecard details
GET	/api/v1/eval/agents/{id}/compare	Compare versions
GET	/api/v1/eval/agents/{id}/aggregate	Aggregate scoring stats

Feedback

Method	Endpoint	Description
POST	/api/v1/feedback	Submit rating
GET	/api/v1/feedback/{type}/{id}	Get feedback
GET	/api/v1/feedback/summary/{id}	Rating summary

Admin

Method	Endpoint	Description
GET	/api/v1/admin/settings	List settings
PUT	/api/v1/admin/settings/{key}	Set a value
GET	/api/v1/admin/users	List users
POST	/api/v1/admin/users	Create user
PUT	/api/v1/admin/users/{id}/role	Change role
PUT	/api/v1/admin/users/{id}/password	Reset user password (admin)
GET	/api/v1/admin/penalties	List penalty catalog
PUT	/api/v1/admin/penalties/{id}	Modify penalty
GET	/api/v1/admin/weights	Get dimension weights
PUT	/api/v1/admin/weights	Set dimension weights

GraphQL

Endpoint	Description
/api/v1/graphql	Traces, spans, scores, metrics (query + subscription)

Health

Method	Endpoint	Description
GET	/health	Health check

Environment Variables

All settings have sensible defaults that work for local development. Just cp .env.example .env and you are good to go. Override what you need for production.

Variable	Default	Description
DATABASE_URL	postgresql+asyncpg://postgres:postgres@localhost:5432/observal	PostgreSQL connection string
CLICKHOUSE_URL	clickhouse://localhost:8123/observal	ClickHouse connection string
REDIS_URL	redis://localhost:6379	Redis connection string
SECRET_KEY	change-me-to-a-random-string	Session signing key. Generate a real one for production: python3 -c "import secrets; print(secrets.token_urlsafe(32))"
POSTGRES_USER	postgres	PostgreSQL container user
POSTGRES_PASSWORD	postgres	PostgreSQL container password
FRONTEND_URL	http://localhost:3000	Frontend URL (used for OAuth redirects and CORS)
CORS_ALLOWED_ORIGINS	http://localhost:3000	Comma-separated allowed CORS origins
OAUTH_CLIENT_ID	disabled	OAuth/OIDC client ID (SSO is disabled when unset)
OAUTH_CLIENT_SECRET	disabled	OAuth/OIDC client secret
OAUTH_SERVER_METADATA_URL	disabled	OIDC discovery URL
EVAL_MODEL_URL		OpenAI-compatible endpoint for the eval engine
EVAL_MODEL_API_KEY		API key for the eval model
EVAL_MODEL_NAME		Model name (e.g. us.anthropic.claude-3-5-haiku-20241022-v1:0)
EVAL_MODEL_PROVIDER		bedrock, openai, or empty for auto-detect
AWS_REGION	us-east-1	AWS region for Bedrock

Running Tests

make test      # quick (526 tests)
make test-v    # verbose

All tests mock external services. No Docker needed.

Community

Have a question, idea, or want to share what you've built? Head to GitHub Discussions. Please use Discussions for questions instead of opening issues. Issues are reserved for bug reports and feature requests.

Security

To report a vulnerability, please use GitHub Private Vulnerability Reporting or email contact@blazeup.app. Do not open a public issue. See SECURITY.md for full details.

Contributing

See CONTRIBUTING.md for the full guide. The short version:

1. Fork and clone
2. make hooks to install pre-commit hooks
3. Create a feature branch
4. Make changes, run make lint and make test
5. Open a PR

See AGENTS.md for internal codebase context useful when working with AI coding agents.

Star History

If you find Observal useful, please star the repo. It helps others discover the project and motivates continued development.

License

Apache License 2.0. See LICENSE.