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ToolExperimentalA2A
agentweave
by sauravsingla
Discovery, selection, and reputation-aware orchestration for heterogeneous AI agents
Python
Updated Sep 28, 2026
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Overview
Orchestrates discovery, validation, selection and routing of heterogeneous AI agents across cloud, marketplace, enterprise and edge environments. Uses capability discovery pattern and knowledge matching plus Evaluation-Driven Development pattern to pick and compose agents, with Agent-to-Agent Protocol (A2A) interoperability for agent-to-agent calls.
Key Benefits
As agents become autonomous and delegate work, knowing which agents reliably deliver outcomes is essential. AgentWeave brings reputation and capability signals into the selection loop, letting systems prefer agents with proven track records and update trust after each interaction. This matters because continuous, outcome-driven evaluation reduces brittle failures in multi-agent workflows and surfaces agent failure modes over time. See the Dynamic Task Routing Pattern for how these signals guide flow control in real-time environments.
Ideal For
Teams building multi-agent systems that need capability matching, A2A interoperability, and outcome-driven reputation for agent selection. See the Agent Registry Pattern to formalize discovery and registration of agents in the ecosystem.
Real-World Examples
- When you need to discover and match agents by capability and knowledge across marketplaces and clouds
- When you want continuous reputation updates and outcome-driven selection to reduce agent failures
- When you must route tasks across heterogeneous agents with A2A interoperability and audit logs
Works With
openai
Topics
a2a-protocolagent-discoveryagent-evaluationagent-interoperabilityagent-orchestrationagent-routingagent-selectionagentic-aiai-agentsbenchmarking+8 more
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Keywords
multi-agent trustagent-to-agent evaluationagent track recordagent reputation