Open-source framework for building production-ready AI agents with equal Python and TypeScript support, constraint-based governance, multi-agent orchestration, and native MCP/A2A protocol integration under Linux Foundation governance.
IBM's enterprise framework for building reliable AI agents that follow rules and work together to solve complex problems.
BeeAI Framework is a free, open-source AI agent development framework (Apache 2.0, Linux Foundation) for building production-ready single- and multi-agent systems in Python or TypeScript, with constraint-based governance, native MCP/A2A protocol support, and provider-agnostic LLM backends.
While frameworks like LangChain prioritize Python with limited JavaScript support, and CrewAI remains Python-exclusive, BeeAI Framework treats both Python and TypeScript as first-class citizens. This dual-language approach eliminates the common friction of forcing TypeScript teams to adopt Python tooling or vice versa.
The framework's unique "Requirement Agent" system sets it apart from competitors by enforcing deterministic behavioral constraints while preserving natural language reasoning capabilities. This approach solves the critical production challenge where agents behave unpredictably across different LLM providers.
BeeAI Framework provides enterprise-grade building blocks for intelligent agent development:
Agent Types:Originally developed by IBM Research, BeeAI Framework was donated to the Linux Foundation AI & Data program, establishing vendor-neutral governance. The project has accumulated over 5,200 GitHub stars and maintains an active contributor community across both Python and TypeScript SDKs. Its adoption spans enterprise teams in financial services, healthcare, and technology sectors seeking governed, auditable agent behavior.
Install via pip (pip install beeai-framework) or npm (npm install beeai-framework), configure your preferred LLM backend, and define your first agent in under 20 lines of code. The framework's building-block API gives you fine-grained control while the RequirementAgent pattern provides guardrails for production deployment.
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BeeAI Framework delivers the industry's most comprehensive dual-language agent development platform, combining Python and TypeScript feature parity with enterprise-grade governance, sophisticated constraint enforcement, and native protocol support for building production-ready AI systems.
Both SDKs ship with the same agent classes, workflow primitives, tool interfaces, and backend adapters, allowing teams to standardize on a single framework across data science and application engineering stacks without porting agent logic between languages.
Instead of expressing rules in prompts, developers attach declarative requirements — allowed tools, ordering, conditional steps, output constraints — to an agent. The framework enforces these at runtime, producing more predictable behavior and easier auditing than prompt-only approaches.
Workflows compose multiple specialist agents with shared memory, conditional routing, and explicit state transitions, enabling patterns like planner/executor, debate, and supervisor architectures without writing custom orchestration glue.
First-class implementations of the Model Context Protocol and Agent-to-Agent protocol let BeeAI agents call external MCP tool servers and be invoked by — or invoke — agents in other A2A-compatible frameworks, avoiding bespoke integration code.
A unified backend abstraction supports IBM watsonx, OpenAI, Anthropic, Google Gemini, Groq, Cohere, Mistral, DeepSeek, Ollama, and custom providers. Switching models is typically a single configuration change, which simplifies cost/quality experimentation and on-prem deployments.
Built-in serialization for pause/resume of agent state, OpenTelemetry-based tracing and metrics, event emitters for instrumentation, retry/timeout controls, and a sandboxed code interpreter for safely executing model-generated code in long-running services.
Free
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