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ToolExperimentalA2AMCP
agentx-python
by AgentX-ai
Python SDK for composing and coordinating A2A multi-agent workforces
Python
Updated Aug 24, 2026
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Overview
Provides a Python SDK for building and coordinating multi-agent AI workforces. Exposes Agent-to-Agent Protocol (A2A) primitives for agent messaging, task delegation, and lifecycle management so teams can compose specialist agents into cooperative workflows. Includes message routing, simple policy hooks, and integrations with common LLM providers for quick prototyping. With Model Context Protocol (MCP) primitives, it supports structured context sharing across agents.
Key Benefits
As agents delegate work to other agents, tracking who did what and why becomes essential for trust and evaluation. AgentX gives teams a shared runtime and message model so agent interactions are observable and auditable, enabling early-stage agent-to-agent evaluation and basic reputation signals. Until more mature trust infrastructures exist, having a consistent SDK to structure agent dialogues and record interactions reduces blind spots that break multi-agent reliability.
Ideal For
Teams building prototype multi-agent systems who need a lightweight A2A/MCP SDK to prototype delegation, logging, and interaction patterns.
Real-World Examples
- Prototype agent delegation patterns and message flows in a Python runtime
- Log and inspect agent interactions to generate initial trust/reputation signals
- Coordinate specialist agents for multi-step tasks while recording provenance for evaluation
Works With
openaihuggingfacelangchain
Topics
a2aa2a-protocolagentllmmcp
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Keywords
multi-agenta2aagent-delegationagent-to-agent evaluation