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Open-source framework for building, deploying, and serving powerful agentic workflows at scale.The Hive Agent Framework makes it easy to build scalable agent-based workflows with your model of choice. The framework is Hiven designed to perform robustly with IBM Granite and Llama 3.x models, and we're actively working on optimizing its performance with other popular LLMs. Our goal is to empower developers to adopt the latest open-source and proprietary models with minimal changes to their current agent implementation.
🤖 AI agents: Use our powerful Hive agent refined for Llama 3.1 and Granite 3.0, 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.
🔍 Instrumentation: Use Instrumentation based on Emitter to have full visibility of your agent’s inner workings.
🎛️ Production-level control with caching and error handling.
🔁 API: Integrate your agents using an OpenAI-compatible Assistants API and Python SDK.
🖥️ Chat UI: Serve your agent to users in a delightful UI with built-in transparency, explainability, and user controls.
or
➡️ See a more advanced example.
➡️ you can run this example after local installation, using the command yarn start examples/agents/simple.ts
Clone the repository git clone git@github.com:i-am-Hive/Hive-agent-framework
.
Install dependencies yarn install --immutable && yarn prepare
.
Create .env
(from .env.template
) and fill in missing values (if any).
Start the agent yarn run start:Hive
(it runs /examples/agents/Hive.ts
file).
➡️ All examples can be found in the examples directory.
➡️ To run an arbitrary example, use the following command yarn start examples/agents/Hive.ts
(just pass the appropriate path to the desired example).
The source directory (src
) provides numerous modules that one can use.
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
Error classes and helpers to catch errors fast.
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)
emitter
Bringing visibility to the system by emitting events.
internals
Modules used by other modules within the framework.
To see more in-depth explanation see overview.
Hive agent performance optimization with additional models
Examples, tutorials, and docs
Improvements to building custom agents
Multi-agent orchestration