Bee Agent Framework

Open-source framework for building, deploying, and serving powerful agentic workflows at scale.

The Bee framework makes it easy to build agentic worfklows with leading proprietary and open-source models. We’re working on bringing model-agnostic support to any LLM to help developers avoid model provider lock-in and embrace the latest open-source LLMs.

Key Features

• 🤖 AI agents: Use our powerful Bee agent or build your own.
• 🛠️ Tools: Use our built-in tools or create your own in Javascript/Python.
• 👩‍💻 Code interpreter: Run code safely in a sandbox container.
• 💾 Memory: Multiple strategies to optimize token spend.
• ⏸️ Serialization Handle complex agentic workflows and easily pause/resume them without losing state.
• 🔍 Traceability: Get full visibility of your agent’s inner workings, log all running events, and use our MLflow integration (coming soon) to debug performance.
• 🎛️ Production-level control with caching and error handling.
• 🚧 (Coming soon) Evaluation: Run evaluation jobs with your own data source (custom csv or Airtable).
• 🚧 (Coming soon) Model-agnostic support: Change model providers in 1 line of code without breaking your agent’s functionality.
• 🚧 (Coming soon) Chat UI: Serve your agent to users in a delightful GUI with built-in transparency, explainability, and user controls.
• ... more on our Roadmap

Get started with Bee

Installation

npm install bee-agent-framework

or

yarn add bee-agent-framework

Example

import { BeeAgent } from "bee-agent-framework/agents/bee/agent";
import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
import { TokenMemory } from "bee-agent-framework/memory/tokenMemory";
import { DuckDuckGoSearchTool } from "bee-agent-framework/tools/search/duckDuckGoSearch";
import { OpenMeteoTool } from "bee-agent-framework/tools/weather/openMeteo";

const llm = new OllamaChatLLM(); // default is llama3.1 (7b), it is recommended to use 70b model
const agent = new BeeAgent({
  llm, // for more explore 'bee-agent-framework/adapters'
  memory: new TokenMemory({ llm }), // for more explore 'bee-agent-framework/memory'
  tools: [new DuckDuckGoSearchTool(), new OpenMeteoTool()], // for more explore 'bee-agent-framework/tools'
});

const response = await agent
  .run({ prompt: "What's the current weather in Las Vegas?" })
  .observe((emitter) => {
    emitter.on("update", async ({ data, update, meta }) => {
      console.log(`Agent (${update.key}) 🤖 : `, update.value);
    });
  });

console.log(`Agent 🤖 : `, response.result.text);

➡️ See a more advanced example.

➡️ All examples can be found in the examples directory.

Local Installation (Python Interpreter + Interactive CLI)

Note: yarn should be installed via Corepack (tutorial)

Note: To make any asset available to a local code interpreter place them the following directory: ./examples/tmp/local

Note: Docker distribution with support for compose is required, the following are supported:

• Rancher - recommended
• Docker
• Podman - requires compose and rootful machine (if your current machine is rootless, please create a new one)

1. Clone the repository git clone git@github.com:i-am-bee/bee-agent-framework.
2. Install dependencies yarn install.
3. Create .env (from .env.template) and fill in missing values (if any).
4. Start the code interpreter yarn run infra:start-code-interpreter.
5. Start the agent yarn run start:bee (it runs ./examples/agents/bee.ts file).

🛠️ Tools

Name	Description
PythonTool	Run arbitrary Python code in the remote environment.
WikipediaTool	Search for data on Wikipedia.
DuckDuckGoTool	Search for data on DuckDuckGo.
LLMTool	Uses an LLM to process input data.
DynamicTool	Construct to create dynamic tools.
ArXivTool	Retrieves research articles published on arXiv.
WebCrawlerTool	Retrieves content of an arbitrary website.
CustomTool	Runs your own Python function in the remote environment.
OpenMeteoTool	Retrieves current, previous, or upcoming weather for a given destination.
➕ Request

🔌️ Adapters (LLM - Inference providers)

Name	Description
Ollama	LLM + ChatLLM support (example)
LangChain	Use any LLM that LangChain supports (example)
WatsonX	LLM + ChatLLM support (example)
BAM (IBM Internal)	LLM + ChatLLM support (example)
➕ Request

📦 Modules

The source directory (src) provides numerous modules that one can use.

Name	Description
agents	Base classes defining the common interface for agent.
llms	Base classes defining the common interface for text inference (standard or chat).
template	Prompt Templating system based on Mustache with various improvements_.
memory	Various types of memories to use with agent.
tools	Tools that an agent can use.
cache	Preset of different caching approaches that can be used together with tools.
errors	Base framework error classes used by each module.
adapters	Concrete implementations of given modules for different environments.
logger	Core component for logging all actions within the framework.
serializer	Core component for the ability to serialize/deserialize modules into the serialized format.
version	Constants representing the framework (e.g., latest version)
internals	Modules used by other modules within the framework.

To see more in-depth explanation see docs.

Tutorials

🚧 Coming soon 🚧

Roadmap

• Evaluation with MLFlow integration
• JSON encoder/decoder for model-agnostic support
• Chat Client (GUI)
• Structured outputs
• Improvements to base Bee agent
• Guardrails
• 🚧 TBD 🚧

Contribution guidelines

The Bee Agent Framework is an open-source project and we ❤️ contributions.

Feature contributions

You can get started with any ticket market as “good first issue”.

Have an idea for a new feature? We recommend you first talk to a maintainer prior to spending a lot of time making a pull request that may not align with the project roadmap.

Bugs

We are using GitHub Issues to manage our public bugs. We keep a close eye on this, so before filing a new issue, please check to make sure it hasn't already been logged.

Code of conduct

This project and everyone participating in it are governed by the Code of Conduct. By participating, you are expected to uphold this code. Please read the full text so that you can read which actions may or may not be tolerated.

Legal notice

All content in these repositories including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward.