Rawr Agent is an open-source Python library for building autonomous AI agents using LangChain. It enables defining multi-step task pipelines, integrating custom tools, configuring memory stores, and orchestrating LLM calls. With both YAML and Python APIs, developers can customize prompts, logging, caching, and error handling. Rawr Agent provides modular components for chaining tasks, managing state, and extending functionality with custom toolkits, streamlining the creation of intelligent agents.
Rawr Agent is an open-source Python library for building autonomous AI agents using LangChain. It enables defining multi-step task pipelines, integrating custom tools, configuring memory stores, and orchestrating LLM calls. With both YAML and Python APIs, developers can customize prompts, logging, caching, and error handling. Rawr Agent provides modular components for chaining tasks, managing state, and extending functionality with custom toolkits, streamlining the creation of intelligent agents.
Rawr Agent is a modular, open-source Python framework that empowers developers to build autonomous AI agents by orchestrating complex workflows of LLM interactions. Leveraging LangChain under the hood, Rawr Agent lets you define task sequences either through YAML configurations or Python code, specifying tool integrations such as web APIs, database queries, and custom scripts. It includes memory components for storing conversational history and vector embeddings, caching mechanisms to optimize repeated calls, and robust logging and error handling to monitor agent behavior. Rawr Agent’s extensible architecture allows adding custom tools and adapters, making it suitable for tasks like automated research, data analysis, report generation, and interactive chatbots. With its simple API, teams can rapidly prototype and deploy intelligent agents for diverse applications.
Who will use Rawr Agent?
AI Developers
Software Engineers
Data Scientists
AI Researchers
Product Managers
Automation Engineers
How to use the Rawr Agent?
step1: pip install rawr-agent
step2: Create a YAML or Python configuration to define your task pipeline
step3: Import and instantiate RawrAgent, then load your configuration
step4: Register required tools, memory stores, and prompt templates
step5: Call agent.run() to execute the workflow
step6: Monitor outputs, logs, and metrics via console or configured logger