Langroid

What does this agent do, and when should you use it?

Langroid is a Python framework for LLM applications built around the Agent and Task abstractions. An Agent can hold conversation state and optionally use an LLM, vector store, and tools or functions, while a Task iterates responders and delegates recursively to hierarchical subtasks. It supports OpenAI models and documented local or remote model paths through proxy libraries and compatible model servers, alongside structured output, async methods, logging, and message lineage. Specialized agents cover document RAG, SQL, Neo4j, and tabular data, making it a fit for teams that want to express multi-agent workflows directly in Python.

Developers create a ChatAgent or another Agent, configure its model through ChatAgentConfig, and call llm_response() for stateful model interaction. Wrapping an agent in Task enables Task.run() to iterate among LLM, Agent, and User responders; add_sub_task() adds subordinate tasks to a multi-agent loop. A developer defines a Pydantic-backed ToolMessage, enables it with enable_message(), and implements an agent handler that accepts the tool message and returns a result; Langroid supports both OpenAI function calling and its native tool mechanism. DocChatAgent reads local paths or URLs, shards and embeds documents, stores them in a vector database, and performs retrieval-augmented question answering; its MCP tool adapter converts MCP Server tools into ToolMessage instances.

1. A Python team building a teacher-and-student workflow can create ChatAgents, wrap them in Tasks, and connect the student tasks with add_sub_task().
2. A legal-operations application extracting nested lease terms can combine a retrieval-backed DocChatAgent with a ToolMessage-based extraction agent that emits structured information.
3. A research or support team with PDFs, URLs, or local text can use DocChatAgentConfig.doc_paths to create a cited, vector-retrieval question-answering workflow.
4. An analyst querying a CSV, URL, or Pandas DataFrame can use TableChatAgent, whose LLM generates Pandas code that the agent executes through its tool/function mechanism.
5. A developer who needs an LLM agent to use existing MCP Server tools can use the documented MCP adapter to expose those tools as ToolMessage instances.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Langroid requires Python 3.11+. In an activated virtual environment, install the core package with:

pip install langroid

For HuggingFace sentence-transformers embeddings:

pip install "langroid[hf-embeddings]"

Install langroid[doc-chat] for document parsers and langroid[db] for database chat. The simplest OpenAI setup requires OPENAI_API_KEY: copy .env-template to .env and set it, or export the variable in the shell.

How do you use this agent?

A minimal task invocation is:

import langroid as lr
agent = lr.ChatAgent()
task = lr.Task(agent, name="Bot")
task.run("Hello")

For an explicit OpenAI model configuration:

import langroid.language_models as lm
mdl = lm.OpenAIGPT(lm.OpenAIGPTConfig(chat_model=lm.OpenAIChatModel.GPT4o))
response = mdl.chat("What is the capital of Ontario?", max_tokens=10)

For a local or OpenAI-compatible endpoint, set chat_model to a documented value such as "ollama/mistral" or "local/localhost:8000".

How does this agent compare with similar options?

FAQ