obsei

Voice of Customer without violating privacy.
Open-source, self-hosted, AI-native feedback analytics. Bring your own sources, models and agents.

Docs · Live demo · Website · Blog · Discussions

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:

• Private by design. PII is redacted (and optionally names) and authors are pseudonymised at
  ingest. Encrypted database, air-gapped by default, no telemetry, erasure and an audit log built in.
• Bring your own. Your sources, your keys and your model: Ollama, vLLM, Azure OpenAI, OpenAI,
  Bedrock and Vertex through LiteLLM, or a small local decision model such as Julia-1 for fast labels
  with probabilities.
• AI-native. A read-only MCP server and a Claude plugin, so agents answer questions about
  customers with cited, privacy-filtered evidence.
• Every language. Feedback is classified and quoted in its own language. With
  obsei[embeddings], a local multilingual model groups the same issue across 50+ languages; the
  default 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, and is in pre-release: install it with the >=1.0.0rc1 specifier.
A plain pip install obsei still gives 0.0.15, whose code lives on the legacy/0.0.x branch.

Quickstart

uv tool install "obsei[mcp]>=1.0.0rc1"   # or: pip install "obsei[mcp]>=1.0.0rc1"
mkdir voc && cd voc
obsei init                             # obsei.yaml plus a 10-language sample dataset
export OBSEI_DB_KEY="$(openssl rand -hex 24)" OBSEI_PSEUDONYM_SALT="$(openssl rand -hex 24)"
obsei try                              # preview redacted records; nothing stored or sent
obsei run                              # fetch, redact, enrich, deliver, store
obsei serve                            # scheduled pipelines, webhooks, MCP and Studio
obsei doctor                           # environment, encryption, egress mode, plugins

Uncomment the classify enricher in obsei.yaml to label sentiment, intent and language with a
local model (Ollama by default). Any OpenAI-compatible endpoint works: vLLM, llama.cpp, Azure
OpenAI, OpenAI, Mistral, or a LiteLLM proxy for Bedrock and Vertex; a decision
model on llama.cpp labels on a CPU. Public endpoints are refused
until you choose OBSEI_EGRESS_MODE=private (with OBSEI_EGRESS_ALLOW) or hybrid.

Agents: obsei mcp serves read-only MCP tools; 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)" \
  -e OBSEI_DB_KEY -e OBSEI_PSEUDONYM_SALT ghcr.io/obsei/obsei:1.0.0-rc.1 run

Roadmap

Phases 0.1 to 0.5 (private core, MCP and Claude plugin, enterprise bring-your-own, themes and
Studio, decision models and routing) are in the first release candidate, 1.0.0rc1. Next:
live-connector validation, further release candidates as needed, then the 1.0.0 launch. See ROADMAP.md.

Contributing

Questions and ideas go to Discussions. See
CONTRIBUTING.md for setup, conventions and the DCO sign-off.

License

Apache-2.0