BESSER Agentic Framework (BAF) Generator

The BAF generator produces a BAF Agent based on a given B-UML agent model. Let’s generate the agent for Greetings Agent defined in Agent model. You should create a BAFGenerator object, provide the agent model, and use the generate method as follows:

from besser.generators.agents.baf_generator import BAFGenerator

generator: BAFGenerator = BAFGenerator(model=agent)
generator.generate()

Optional constructor parameters:

  • config_path: Path to a YAML configuration file for the agent.

  • config: Configuration dictionary (alternative to config_path).

  • openai_api_key: OpenAI API key for LLM-powered agent features.

The corresponding agent.py file and its config file titled config.yaml will be generated in the <<current_directory>>/output folder.

Check out the BAF documentation for more details on how to use the generated agent: BESSER Agentic Framework Documentation.

Generation Modes

The BAF generator supports three generation modes via the generation_mode parameter:

from besser.generators.agents.baf_generator import BAFGenerator, GenerationMode

# Default: full pipeline (personalization + templated code)
generator = BAFGenerator(model=agent, generation_mode=GenerationMode.FULL)

# Skip personalization, render templates immediately
generator = BAFGenerator(model=agent, generation_mode=GenerationMode.CODE_ONLY)

# Run personalization JSON/model export only (no code templates)
generator = BAFGenerator(model=agent, generation_mode=GenerationMode.PERSONALIZED_ONLY)

  • FULL (default): Runs personalization (if configured) followed by templated code generation.

  • CODE_ONLY: Skips personalization helpers and renders templates immediately. Use this when you do not need personalization assets.

  • PERSONALIZED_ONLY: Runs only the personalization JSON/model export. Use this to produce personalization artifacts without generating the agent code.

Personalization

The BAF generator can adapt the generated agent to an end-user’s profile — language, style, readability, modality, platform, LLM, and more. The personalization flow is opt-in: with no config passed the generator behaves identically to the classic pipeline.

See Agent Personalization for the structured configuration schema, the two recommendation backends (rule-based and LLM-based), the variant mechanisms (languages, variations, configuration variants, personalization mapping), and the OPENAI_API_KEY lookup order.

RAG Support

If the agent model includes RAG elements (see Agent model), the generator produces the vector store setup (Chroma), text splitter configuration, and session.run_rag() calls. A data folder is created for each RAG element where you should place your PDF documents before running the agent. The folder name is derived from the RAG element name (e.g. "Knowledge Base" becomes knowledge_base/).

Reasoning States and Multi-LLM

The generator emits the multi-LLM and reasoning constructs described in Agent model:

  • Every LLM registered via agent.new_llm() is generated as an agent.new_llm(...) call, and agent.set_default_llm(...) is emitted when the chosen default differs from the first-registered (auto) default.

  • ReasoningState states are generated through the new_reasoning_state factory, carrying their llm reference, max_steps, enable_task_planning, stream_steps, system_prompt and fallback_message.

  • Agent-level tools, skills and workspaces are emitted as agent.new_tool(), agent.new_skill() and agent.new_workspace() calls.

Consumers (LLMReply, DBReply, RAG, reasoning states) reference their LLM by llm_name; when omitted, the agent’s default LLM is used.

Missing BAF Features

Currently, some features available in BAF are stil missing in the B-UML agent model and the BAF Generator. Most notably:

  • Platform Configuration

  • Entities

  • Processors