https://github.com/i-am-bee/beeai-framework
Build production-ready AI agents in both Python and Typescript
https://github.com/i-am-bee/beeai-framework
agents ai ai-agent framework llm multiagent python typescript
Last synced: 3 days ago
JSON representation
Build production-ready AI agents in both Python and Typescript
- Host: GitHub
- URL: https://github.com/i-am-bee/beeai-framework
- Owner: i-am-bee
- License: apache-2.0
- Created: 2024-08-23T14:03:06.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-04-12T07:12:20.000Z (4 days ago)
- Last Synced: 2025-04-13T00:36:56.590Z (3 days ago)
- Topics: agents, ai, ai-agent, framework, llm, multiagent, python, typescript
- Language: TypeScript
- Homepage: https://i-am-bee.github.io/beeai-framework/
- Size: 6.06 MB
- Stars: 2,260
- Watchers: 35
- Forks: 266
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
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- awesome-rainmana - i-am-bee/beeai-framework - Build production-ready AI agents in both Python and Typescript (TypeScript)
README
BeeAI Framework
**Build production-ready multi-agent systems in Python or TypeScript.**
[](https://github.com/i-am-bee/beeai-framework/tree/main/python)
[](https://github.com/i-am-bee/beeai-framework/tree/main/typescript)
[](https://github.com/i-am-bee/beeai-framework?tab=Apache-2.0-1-ov-file#readme)
[](https://bsky.app/profile/beeaiagents.bsky.social)
[](https://discord.com/invite/NradeA6ZNF)
[](https://lfaidata.foundation/projects/)
## Latest updates
| Date | Language | Update Description
|------------|---------------|-------------------------------------------------------------------------------------|
| 2025/02/19 | Python | Launched Python library alpha. See [getting started guide](#installation). |
| 2025/02/07 | TypeScript | Introduced [Backend](/typescript/docs/backend.md) module to simplify working with AI services (chat, embedding). See [migration guide](/typescript/docs/migration_guide.md). |
| 2025/01/28 | TypeScript | Added support for DeepSeek R1, check out the [Competitive Analysis Workflow example](/typescript/examples/workflows/competitive-analysis). |
| 2025/01/09 | TypeScript | Introduced [Workflows](/typescript/docs/workflows.md), a way of building multi-agent systems. Added support for [Model Context Protocol](/typescript/docs/tools.md#using-the-mcptool-class). |
| 2024/12/09 | TypeScript | Added support for LLaMa 3.3. See [multi-agent workflow example using watsonx](/typescript/examples/workflows/multiAgents.ts) or explore [other available providers](/typescript/docs/backend.md#providers-implementations). |
| 2024/11/21 | TypeScript | Added an experimental [Streamlit agent](/typescript/examples/agents/experimental/streamlit.ts). |
For a full changelog, see our [releases page](https://github.com/i-am-bee/beeai-framework/releases).
---
## Why BeeAI?
**🏆 Build for your use case.** Implement simple to complex multi-agent patterns using [Workflows](/python/docs/workflows.md), start with a [ReActAgent](/python/examples/agents/react.py), or easily [build your own agent architecture](/python/docs/agents.md#creating-your-own-agent). There is no one-size-fits-all agent architecture, you need full flexibility in orchestrating agents and defining their roles and behaviors.
**🔌 Seamlessly integrate with your models and tools.** Get started with any model from [Ollama](/python/examples/backend/providers/ollama.py), [Groq](/python/examples/backend/providers/groq.py), [OpenAI](/python/examples/backend/providers/openai_example.py), [watsonx.ai](/python/examples/backend/providers/watsonx.py), and [more](/python/docs/backend.md#supported-providers). Leverage tools from [LangChain](/python/examples/tools/langchain.py), connect to any server using the [Model Context Protocol](/python/docs/tools.md#mcp-tool), or build your own [custom tools](/python/docs/tools.md#creating-custom-tools). BeeAI is designed to integrate with the systems and capabilities you need.
**🚀 Scale with production-grade controls.** Optimize token usage through configurable [memory strategies](/python/docs/memory.md), persist and restore agent state via [(de)serialization](/python/docs/serialization.md), generate structured outputs, and execute generated code in a sandboxed environment (coming soon). When things go wrong, the [emitter](/python/docs/emitter.md) system tracks the full agent workflow, generating detailed [events](/python/docs/events.md) for monitoring and analysis. [Telemetry](/python/docs/instrumentation.md) and [logging](/python/docs/logger.md) capabilities capture key diagnostic data. When issues arise, BeeAI handles [errors](/python/docs/errors.md) gracefully with clear, well-defined exceptions.
> [!TIP]
> Get started quickly with the [beeai-framework-py-starter](https://github.com/i-am-bee/beeai-framework-py-starter) template.
---
## Installation
To install the Python library:
```shell
pip install beeai-framework
```
To install the TypeScript library:
```shell
npm install beeai-framework
```
For more guidance and starter examples in your desired language, head to the docs pages for [Python](/python/README.md) and [TypeScript](/typescript/README.md).
---
## Quick example
This example demonstrates how to build a multi-agent workflow using BeeAI framework in Python.
```py
import asyncio
from beeai_framework.backend.chat import ChatModel
from beeai_framework.tools.search.wikipedia import WikipediaTool
from beeai_framework.tools.weather.openmeteo import OpenMeteoTool
from beeai_framework.workflows.agent import AgentWorkflow, AgentWorkflowInput
async def main() -> None:
llm = ChatModel.from_name("ollama:granite3.1-dense:8b")
workflow = AgentWorkflow(name="Smart assistant")
workflow.add_agent(
name="Researcher",
role="A diligent researcher.",
instructions="You look up and provide information about a specific topic.",
tools=[WikipediaTool()],
llm=llm,
)
workflow.add_agent(
name="WeatherForecaster",
role="A weather reporter.",
instructions="You provide detailed weather reports.",
tools=[OpenMeteoTool()],
llm=llm,
)
workflow.add_agent(
name="DataSynthesizer",
role="A meticulous and creative data synthesizer",
instructions="You can combine disparate information into a final coherent summary.",
llm=llm,
)
location = "Saint-Tropez"
response = await workflow.run(
inputs=[
AgentWorkflowInput(
prompt=f"Provide a short history of {location}.",
),
AgentWorkflowInput(
prompt=f"Provide a comprehensive weather summary for {location} today.",
expected_output="Essential weather details such as chance of rain, temperature and wind. Only report information that is available.",
),
AgentWorkflowInput(
prompt=f"Summarize the historical and weather data for {location}.",
expected_output=f"A paragraph that describes the history of {location}, followed by the current weather conditions.",
),
]
).on(
"success",
lambda data, event: print(
f"\n-> Step '{data.step}' has been completed with the following outcome.\n\n{data.state.final_answer}"
),
)
print("==== Final Answer ====")
print(response.result.final_answer)
if __name__ == "__main__":
asyncio.run(main())
```
_Source: [python/examples/workflows/multi_agents_simple.py](/python/examples/workflows/multi_agents.py)_
TypeScript version of this example can be found [here](/typescript/examples/workflows/multiAgents.ts).
### Running the example
> [!Note]
>
> To run this example, be sure that you have installed [ollama](https://ollama.com) with the [granite3.1-dense:8b](https://ollama.com/library/granite3.1-dense) model downloaded.
To run projects, use:
```shell
python [project_name].py
```
Explore more in our examples for [Python](/python/examples/README.md) and [TypeScript](/typescript/examples/README.md).
---
## Roadmap
- Python parity with TypeScript
- Standalone docs site
- Integration with watsonx.ai for deployment
- More multi-agent reference architecture implementations using workflows
- More OTTB agent implementations
- Native tool calling with supported LLM providers
To stay up-to-date on our [public roadmap](https://github.com/orgs/i-am-bee/projects/1/views/2).
---
## Contribution guidelines
BeeAI framework is open-source and we ❤️ contributions.
To help build BeeAI, take a look at our:
- [Python contribution guidelines](/python/CONTRIBUTING.md)
- [TypeScript contribution guidelines](/typescript/CONTRIBUTING.md)
## Bugs
We use GitHub Issues to manage bugs. 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](./CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code. Please read the [full text](./CODE_OF_CONDUCT.md) so that you know 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.
## Maintainers
For information about maintainers, see [MAINTAINERS.md](https://github.com/i-am-bee/beeai-framework/blob/main/MAINTAINERS.md).
## Contributors
Special thanks to our contributors for helping us improve BeeAI framework.
---
Developed by contributors to the BeeAI project, this initiative is part of the [Linux Foundation AI & Data program](https://lfaidata.foundation/projects/). Its development follows open, collaborative, and community-driven practices.