Observal

Observal is the control plane and system of record for internal AI components

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What is Observal and what does it solve?

Observal is the control plane and system of record for internal AI components. Every tech-forward organization today creates internal Skills, Agents, MCP servers and other AI components to boost productivity. Though the creation of these components has been prolific, the adoption and usage of such components is sparse. Developer/AI users today end up creating their own version of AI components without reusing existing packages.

The cause is largely due to two problems:

1. Lack of a discoverability layer

Organizations store their AI components and agents in siloed github repositories with little to no documentation. Users are not able to locate similar components and this results in multiple developers creating the same/similar components again.

2. Missing Feedback Loop

Any software where usage patterns are not understood and the principle of user-centric development is violated tends to fade out. Such is the problem with development of MCPs, Skills and Agents. Developers publish and maintain these components with little visibility into how they're actually used. Additionally, AI failures don't trigger static error codes—they hallucinate or provide subtly incorrect answers. This leaves users clueless about what went wrong compounding the feedback problem.

Observal solves this by providing a centralized discovery layer for AI components alongside useful insights into AI usage patterns. It turns silent failures into actionable feedback, ensuring internal AI tools are continuously optimized for the people using them.

Observal supports Claude Code, Cursor, Kiro, Pi, Copilot, Codex, OpenCode, and other tools.

Why teams use Observal

• Package workflows into reusable agents: Bundle Skills, MCP servers, hooks, prompts, sandboxes, and policy into one versioned unit.
• Run a governed registry: Review submissions, approve internal agents, inspect version diffs, and give developers one trusted place to install from.
• Render across multiple Coding IDE/CLI: Generate the correct config for each supported harness instead of maintaining separate setup instructions for every harness.
• Learn what works: Use real adoption and session data to find which agents, tools, prompts, and workflows are helping teams.
• Replay sessions when needed: Use traces as evidence for debugging, review, audits, and deeper analysis.

Supported harnesses

One command to install any agent into any supported harness. The config files are generated per-harness automatically.

Quick Start

Observal has two parts: a server (API + web UI + databases) you self-host, and a CLI you install on each developer machine.

1. Deploy the server

One-line install (requires Docker Engine ≥ 24.0 with Compose v2):

curl -fsSL https://raw.githubusercontent.com/Observal/Observal/main/install-server.sh | bash

This downloads a Docker Compose package, runs guided setup (domain, secrets, ports), pulls container images from GHCR, and starts the full stack (API, web UI, PostgreSQL, ClickHouse, Redis, worker, load balancer, Prometheus, Grafana).

Deployment docs are linked directly from this README:

• Setup guide: fastest path from zero to a working stack
• Self-hosting overview: deployment models and operator docs
• Production deployment: hardened production topology
• Databases: Postgres, ClickHouse, migrations, retention
• Upgrades: safe upgrade and rollback flow
• Backup and restore: backup plan before upgrades

From source (for contributors):

git clone https://github.com/Observal/Observal.git && cd Observal
cp .env.example .env
make up

2. Install the CLI

Standalone binary (no Python required):

curl -fsSL https://raw.githubusercontent.com/Observal/Observal/main/install.sh | bash

Python (3.11+):

uv tool install observal-cli
# or: pipx install observal-cli

3. Connect your harness

observal auth login
observal doctor --patch

This authenticates with your server, detects your harness, installs telemetry hooks, starts capturing sessions automatically, and prepares it for agent installs and registry commands.

Once logged in, run /observal inside your harness and it takes the wheel. Pull agents, submit components, browse the registry, run diagnostics:

/observal pull security-auditor
/observal scan
/observal doctor

Or just tell your agent what you want and it figures out the right commands.

How Observal works

Agents are portable context packages

An agent bundles 5 component types into a single installable package: MCP servers, skills, hooks, prompts, and sandboxes. You define the agent once, publish it to the registry, and Observal generates the right config files for whichever supported harness or harness the user runs.

observal pull security-auditor --harness pi

The registry is the distribution layer

Browse published agents, see which harnesses they support, check download counts and ratings, and install with one command. Admins review submissions before they go live. Version diffs show exactly what changed between releases, so teams can safely evolve shared context.

Insights show what is helping

Observal turns real usage into reports about which agents, prompts, tools, and workflows are working or getting in the way. Use those insights to improve shared context instead of guessing from anecdotes.

Session traces provide the evidence

When you need to debug, audit, or understand a result, Observal can replay the full coding session: user prompts, thinking blocks, assistant responses, and tool calls with their inputs and outputs. The traces support registry and insight workflows rather than defining the product.

Agent Registry

Browse, search, and install agents with harness compatibility badges:

Build agents visually with live config preview for every harness:

Components library: MCPs, Skills, Hooks, Prompts, Sandboxes:

Agent Insights

AI-powered insight reports analyze usage patterns across all sessions, what's working, what's hindering, and quick wins. Powered by LiteLLM, works with any provider (Anthropic, OpenAI, Bedrock, Gemini, Azure, Ollama).

See Insights LLM Setup for configuration.

Session Replay

Full session overview with token counts, models, tools, and turn-by-turn timeline:

**Every turn captured: u