BeeAI Framework Tutorial: Get Started in 5 Minutes [2026]

What it does:

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.

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What it does:

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.

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What it does:

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.

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What it does:

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.

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What it does:

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.

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What it does:

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.

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