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Basic Information

Lagent is a lightweight open source framework for developers to build and orchestrate large language model based agents. It provides a PyTorch inspired design where agents and components behave like layers and message passing is the primary abstraction. The repository focuses on enabling construction of single agents and multi agent workflows, with built in primitives for message objects, memory management, aggregators, output formatting and tool invocation. It includes both synchronous and asynchronous variants of components to support debugging and large scale inference. The README illustrates usage patterns, examples and recommended practices such as implementing forward rather than overriding call and explicitly passing session identifiers for isolated memory and tool execution. The project is intended for researchers and engineers who want to assemble LLMs, custom aggregators and tool executors into reproducible agent pipelines.

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Features
The project exposes modular components that are easy to combine and extend. Core features include AgentMessage as the communication data structure, per agent memory and state management, DefaultAggregator plus customizable aggregators that convert memory to LLM inputs, and flexible output formatting with parsers such as ToolParser for structured tool calls. ActionExecutor and provided tools like IPythonInterpreter and WebBrowser enable consistent tool invocation and result handling. Hooks and processors such as InternLMActionProcessor let users adapt messages before and after actions. Dual interfaces provide synchronous and asynchronous variants for LLMs, agents and executors. Examples demonstrate multi agent patterns including coding assistants, asynchronous bloggers, and workflows integrating retrieval, data collection and plotting. The package emphasizes session isolation, tool validation, and configurable LLM wrappers.
Use Cases
Lagent helps teams and developers prototype, debug and deploy LLM based agent systems without building orchestration from scratch. Its memory abstraction keeps conversation state per session and its aggregators simplify prompt construction and few shot inclusion. Tool parsing and action executors make it straightforward to convert model outputs into tool calls and to incorporate execution results back into agent dialogues. Dual sync and async implementations let users run simple experiments locally and scale to concurrent inference workloads. The modular hooks and parsers foster reuse and customization for domain specific workflows such as code writing, critique and revision loops, information retrieval pipelines and chart generation. Overall it reduces boilerplate, enforces consistent data shapes across components, and enables iterative multi agent development.

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