dynamiq and astromesh
Astromesh is an orchestration runtime for multi-model AI agents, providing pre-built patterns and deployment options, while Dynamiq is a more general framework for defining and managing the conversational flows and state of agentic AI applications, suggesting they could be complementary where Astromesh provides the execution environment and patterns, and Dynamiq handles the higher-level orchestration logic and state management.
About dynamiq
dynamiq-ai/dynamiq
Dynamiq is an orchestration framework for agentic AI and LLM applications
Orchestrates complex agentic workflows through a node-based graph system with built-in support for parallel and sequential execution, tool integration (E2B sandbox, custom tools), and async processing. Integrates with multiple LLM providers (OpenAI, etc.) via pluggable connection objects and includes ReAct agent patterns with configurable loops and role-based reasoning. Built around composable nodes—LLMs, agents, tools—connected via dependency declarations and input transformers for multi-step RAG and agent pipelines.
About astromesh
monaccode/astromesh
Multi-model AI agent runtime. Define agents in YAML, connect 6 LLM providers, orchestrate with ReAct/Plan&Execute/Fan-Out/Pipeline/Supervisor/Swarm patterns, and deploy as REST/WebSocket API with RAG, memory, MCP tools, guardrails, and OpenTelemetry observability.
Astromesh provides a modular toolchain across three complementary packages—the core runtime (Python 3.12+), an Agent Development Kit (ADK) for custom tool/memory plugins, and Orbit for cloud-native deployment—enabling standardized agentic workflows without rebuilding orchestration infrastructure. The architecture uses declarative YAML configuration for agent definitions and reasoning patterns, with built-in support for multi-layer memory (conversational, semantic, episodic) across Redis/PostgreSQL/vector stores, plus 18 native tools augmented by MCP servers for code execution and shell access. Deployment spans seven modes including containerized Kubernetes setups, with Python/Node/CLI interfaces and native WhatsApp integration for direct bot deployment.
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