Mandala v0.3: Open-Source Async Runtime to Unify Logistics Telemetry as OpenTelemetry Spans for Agent Reasoning
Mandala (GitHub) is a new open-source (Apache 2.0) runtime that bridges logistics data silos into a single event stream for agent-based reasoning. Currently at v0.3, it’s primarily Python (83%) with a Rust (12%) event projection layer. The project has 4 stars and CI passing.
Architecture
Mandala ingests data from Samsara truck telemetry, Descartes customs filings, Vizion rail status, and FMCSA carrier safety data via webhooks. It ships events as OpenTelemetry spans to any observability backend (Jaeger, Tempo, Honeycomb, Datadog) and exposes a state store via Redis Streams with 14-day TTL. A set of MCP tools allows LLM agents to query live state.
MCP Tools for Agents
The runtime provides read-only MCP tools covering the full logistics surface, including:
get_shipmentget_truckcheck_customs_statusget_fleet_near_borderget_trucks_at_poe_without_filingget_cold_chain_breachesget_trailer_handoff_chain
These tools are designed for agents to reason over state without mutating vendor systems. Every shipment is traced as a distributed trace, enabling the agent to debug the full lifecycle of any package.
Data Flow
Data flows from truck sensors, shipment/customs systems, and rail status into Mandala, which enriches and pushes alerts to an MCP tool layer consumable by Claude or any LLM. State is stored in Redis Streams for low-latency queries. Telemetry is also shipped via OTLP to observability backends and materialized into dbt models for analytics. No phone-home.
Roadmap
The project is actively seeking agent developers to compose Mandala into longer workflow chains (routing, weather, dispatch, brokerage, Slack notifications), and Rust contributors for the event projection layer.
📖 Read the full source: r/openclaw
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