by obsei
Open source, privacy-first Voice of Customer analytics. A self-hosted alternative to Enterpret and Unwrap that strips PII from feedback before any LLM sees it.
# Add to your Claude Code skills
git clone https://github.com/obsei/obseiGuides for using mcp servers skills like obsei.
Last scanned: 10/18/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-10-18T08:33:50.691Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how obsei compares with popular alternatives.
obsei is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by obsei. Open source, privacy-first Voice of Customer analytics. A self-hosted alternative to Enterpret and Unwrap that strips PII from feedback before any LLM sees it. It has 1,439 GitHub stars.
Yes. obsei passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/obsei/obsei" and add it to your Claude Code skills directory (see the Installation section above).
obsei is primarily written in Python. It is open-source under obsei on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh obsei against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Teams want to understand what customers say in reviews, tickets, calls and surveys, but often can't send that text to another SaaS or a public LLM. obsei runs entirely inside your own infrastructure:
obsei[embeddings], a local multilingual model groups the same issue across 50+ languages;
without it, the offline embedder groups by wording, so each language forms its own themes.obsei provides controls that support compliance with laws such as the GDPR, India's DPDP Act, Brazil's LGPD and California's CCPA. It makes no compliance claims; you remain the data controller.
[!NOTE] 1.0 is a new codebase, not compatible with 0.0.x. The legacy 0.0.x toolkit lives on the
legacy/0.0.xbranch (pip install "obsei<1"); see Migrate from 0.0.x.
mkdir voc && cd voc
uvx --from "obsei[recommended]>=1.0" obsei init # obsei.yaml, secrets, sample data, local model
obsei try # table of redacted, labelled feedback; nothing stored
obsei try https://apps.apple.com/us/app/whatsapp-messenger/id310633997 # or GitHub, HN, a feed, a CSV
obsei add appstore # add sources and sinks without editing YAML (obsei integrations)
obsei add slack # asks for the webhook, keeps it in .obsei/secrets.env
obsei check # every source and sink: secrets, egress, credentials
obsei run # fetch, redact, label, deliver, store
obsei serve # scheduled pipelines, webhooks, MCP and Studio
obsei doctor # environment, secrets, encryption, egress mode, plugins
obsei[recommended] is the full local setup (the classifier and multilingual themes); obsei alone
is enough for redaction, sources and sinks. To keep the obsei command, install it once:
uv tool install "obsei[recommended]" (or pip install). obsei init writes a random database key and pseudonym salt to
.obsei/secrets.env (owner-only, git-ignored), and every command loads it; on servers and in CI,
set OBSEI_DB_KEY and OBSEI_PSEUDONYM_SALT instead, or mount the file. With obsei[local], the
classify enricher labels sentiment, intent and language on the CPU with no model server. A
decision model on
llama.cpp or any OpenAI-compatible endpoint also works: Ollama, vLLM, Azure OpenAI, OpenAI, Mistral,
or a LiteLLM proxy for Bedrock and Vertex (benchmark).
Public endpoints are refused until you choose OBSEI_EGRESS_MODE=private (with OBSEI_EGRESS_ALLOW)
or hybrid.
| Built in | |
|---|---|
| Sources | CSV, JSON Lines, declarative REST, webhooks, App Store (any country), App Store Connect, Google Play (official API), GitHub issues, Hacker News, Bluesky, YouTube, RSS/Atom, SQL databases and warehouses, file drops, IMAP mailboxes, any MCP server, Zendesk, Freshdesk, Intercom, Gong; Reddit as an unpublished community plugin (install from Git) |
| Enrichers | Classification with an in-process zero-shot model (default), a decision model (Julia-1, Clef) or an LLM: sentiment, intent, language and custom choice, score and yes/no fields with confidence; low-confidence answers go to a stronger model or human review; filter drops or tags spam and off-topic records |
| Sinks | Signed webhooks (Standard Webhooks, CloudEvents) with retries, Slack, Telegram, Discord, email and Teams via Apprise (obsei[notify]), GitHub issues, Jira, Linear, Parquet, SQL databases and warehouses; ordered routes on labels, confidence and scores (routing) |
| Analysis | Stable themes (offline, or multilingual with obsei[embeddings]) with near-duplicate detection, k-anonymous themes, obsei ask with cited answers and an optional grounding judge, read-only Studio with a knowledge-graph explorer and privacy, decisions, trends and routes panels (demo), Slack /obsei command, alerts on theme spikes, new themes, digests, the review queue and pipeline health |
| Privacy | Redaction of emails, phones, cards and IBANs, plus national IDs for 16 countries in the Americas, Europe, Asia-Pacific and Africa, checksum-validated where the format has one, in any script; optional name redaction (obsei[names]); salted author pseudonyms; encrypted DuckDB; obsei forget, obsei export and obsei audit |
| Server | obsei serve: scheduled pipelines, signed webhook intake, MCP over HTTP, Studio, role-based access and SSO-proxy support |
Agents: obsei mcp serves MCP tools (read-only, plus proposals you approve); the Claude Code plugin is
/plugin marketplace add obsei/obsei. See the docs and
integrations.
Examples: ready-to-run configs for app reviews in many countries, helpdesks, support-ticket triage with a decision model and routes, social listening, REST APIs (Trustpilot, HubSpot, ServiceNow, Mastodon), warehouses, mailboxes and air-gapped enterprise setups are in the examples gallery.
Docker (run in the project directory; images are tagged by release):
docker run --rm -v "$PWD:/data" -w /data --user "$(id -u):$(id -g)" \
ghcr.io/obsei/obsei:1.1.1 run # reads .obsei/secrets.env, or pass -e OBSEI_DB_KEY ...
The MCP server and voice-of-customer skill install straight from this repository (obsei isn't
in any tool's official plugin directory yet):
| Tool | Install |
|---|---|
| Claude Code | /plugin install obsei --marketplace obsei/obsei |
| Codex | codex plugin marketplace add obsei/obsei, then codex plugin add obsei@obsei |
| GitHub Copilot CLI | copilot plugin install obsei/obsei:integrations/claude/obsei |
| Gemini CLI | gemini extensions install https://github.com/obsei/obsei |
| Any MCP client | uvx --from "obsei>=1.0" obsei mcp |
VS Code, Devin Desktop, Zed, JetBrains, Kiro, Goose, Cline and ChatGPT: see AI tools.
Phases 0.1 to 0.5 (private core, MCP and Claude plugin, enterprise bring-your-own, themes and Studio, decision models and routing) shipped in 1.0. See ROADMAP.md for what is next.
Questions and ideas go to Discussions. See CONTRIBUTING.md for setup, conventions and the DCO sign-off.