sipcapture/homer — HOMER - 100% Open-Source SIP, VoIP, RTC Packet Capture & Monitoring

Description and use

**Brief Summary (2‑3 sentences)**
HOMER (sipcapture/homer) is a fully open‑source platform for capturing, storing, and visualising SIP, VoIP and other real‑time communications packets. Written in Go and backed by a vibrant community (≈ 2 k stars, 250 forks), it provides a ready‑made data pipeline that can be enriched with AI/ML models for analytics, RAG or autonomous agent workflows. Its active development and broad adoption make it a solid foundation for quickly prototyping AI‑enhanced monitoring solutions without building a packet‑capture stack from scratch.

**Value**

• **Instant data source** – HOMER already ingests and indexes every SIP/RTC packet, delivering clean, time‑series telemetry that AI models can consume directly.
• **Accelerated AI integration** – Because the platform exposes APIs and stores raw payloads, you can plug in speech‑analytics, anomaly‑detection, or RAG pipelines with minimal glue code, turning raw call data into actionable insights.
• **Cost‑effective observability** – Being 100 % open source eliminates licensing fees while providing the same visibility as commercial call‑monitoring suites, enabling budget‑constrained teams to experiment with AI‑driven diagnostics.

**Practical Adoption Path**

1. **Proof‑of‑Concept** – Clone the repo, follow the README to spin up the Docker‑compose stack, and verify packet capture on a test SIP trunk.
2. **Data Export** – Use HOMER’s REST/GraphQL endpoints or its built‑in Kafka connector to stream captured payloads to your ML pipeline.
3. **Model Hook‑up** – Deploy a lightweight inference service (e.g., a FastAPI wrapper around a Whisper or anomaly‑detection model) that consumes the stream, enriches records, and writes results back to HOMER’s database for visualisation.
4. **Iterate & Scale** – Once the AI‑enhanced workflow is validated, expand to production‑grade deployment with HA PostgreSQL/ClickHouse back‑ends, TLS‑secured collectors, and Kubernetes orchestration.

**Production Readiness**

• **Code health** – Recent commits (as of 2026‑05‑14), > 1 900 stars, and a strong fork network indicate an active maintainer base.
• **Observability ecosystem** – Built‑in dashboards, Grafana integration, and export adapters cover most monitoring needs out of the box.
• **Scalability** – Supports clustered collectors and sharded storage, suitable for high‑volume carrier environments.
• **Risks** – The license (Apache‑2.0) is permissive, but a final security audit and verification of maintainer responsiveness are advisable before full production rollout.

Overall, HOMER is a mature OSS candidate that can serve as the data backbone for AI‑augmented SIP/VoIP observability, with a clear, low‑friction path from sandbox to production.

Problem

sipcapture/homer helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

HOMER - 100% Open-Source SIP, VoIP, RTC Packet Capture & Monitoring

Source