BeeAI Framework
Provide a multi-language (Python + TypeScript) framework for production-ready multi-agent systems with a RequirementAgent that enforces declared rules across LLMs, multi-agent workflows, event-driven observability, agent state serialisation, MCP/A2A protocol support, and a unified backend over multiple LLM providers.
Description
BeeAI (formerly the IBM Bee Agent Framework) is the Apache-2.0 multi-agent framework now governed under the Linux Foundation AI & Data program. Tagline: 'Build production-ready multi-agent systems in Python or TypeScript.' Its headline abstraction is the RequirementAgent — a rule-driven agent that produces 'predictable, controlled behavior across different LLMs by setting rules the agent must follow'. The framework ships unified Backend connectors, built-in tools (web search, weather, code execution) plus custom tool support, RAG with vector stores, conversation Memory, multi-agent Workflows, event-based observability, agent serialisation for resumption, and protocol support for MCP and A2A.
Solution
Rule-driven tool-calling loop. A RequirementAgent runs an LLM-backed loop in which each step is checked against declared requirements (rules) before tools are called; the unified Backend abstracts the underlying provider so the same rules apply across LLMs. Multi-agent topologies are built on top via Workflows that orchestrate several agents. Event emitters fire on every step for logging, tracing, and error handling. Agent state is serialised via Save/load for cross-session persistence; MCP and A2A protocols expose and consume external agents and tools.
Primary use cases
- rule-bound agents that behave consistently across LLM providers
- multi-agent workflows with complex execution flows
- agents that persist state across sessions (serialisation)
- MCP- and A2A-integrated agents
- event-driven observability of agent behaviour
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