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astromesh
by monaccode
YAML-driven multi-model agent runtime with orchestration, RAG, and observability
Python
Updated Oct 6, 2026
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Overview
Implements a multi-model agent runtime where agents are declared in YAML and exposed as REST/WebSocket APIs. Orchestrates agent behavior with ReAct, Plan&Execute, fan-out, pipelines, supervisors and swarm patterns while connecting to multiple LLM providers and RAG/memory tools. Includes guardrails, MCP utilities and OpenTelemetry observability for tracing agent interactions and failures.
Key Benefits
As agents delegate work across models and services, visibility into interactions and failure modes becomes essential for trust. Astromesh gives teams a single runtime to standardize agent behavior, capture interaction traces, and surface signals you can use for reputation or A2A evaluation. For practitioners building agent fleets, it moves agent observability and governance from ad-hoc scripts to a repeatable platform.
Best For
Teams building prototype or early-stage multi-agent systems that need standardized orchestration, RAG integration, and traceable agent interactions.
Real-World Examples
- Define and deploy agent workflows quickly using YAML-driven configurations
- Trace and debug agent delegation and failure modes with OpenTelemetry traces
- Prototype multi-model pipelines that combine LLMs, RAG, and memory across orchestration patterns
Works With
openaianthropichuggingfacellamaindexopentelemetryvector-database
Topics
agent-frameworkagent-orchestrationagent-runtimeagentic-aiai-agentsai-infrastructureai-platformdeveloper-toolsdeveloper-tools-ai-agentgenerative-ai+10 more
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Keywords
multi-agent orchestrationmulti-agent trustagent-to-agent evaluationobservability