Langroid is an open-source Python framework for building multi-agent LLM applications that treats agents as first-class citizens capable of collaborating through hierarchical task delegation and structured message passing.¹⁾.com/langroid/langroid|github.com/langroid/langroid]])) Created with a focus on developer ergonomics, Langroid provides a clean, code-driven abstraction where each agent wraps an LLM along with optional tools and vector stores.

• GitHub: github.com/langroid/langroid
• License: MIT
• Language: Python

Langroid's core design centers on two key abstractions:

Agents: First-class objects encapsulating LLM conversation state, memory (via vector stores), and tools. Each agent maintains its own context and can be specialized for particular tasks.

Tasks: Wrappers around agents that define specific instructions and enable hierarchical, recursive delegation. Tasks can spawn subtasks and delegate work to other agent-task pairs, forming collaborative workflows.

Key architectural features:

• Hierarchical delegation: Agents can recursively delegate subtasks to other agents, forming tree-structured workflows
• Pydantic-based tool calling: Structured outputs with automatic validation and error handling
• Vector store integration: Built-in support for Qdrant, Chroma, and LanceDB for RAG capabilities
• Knowledge graph support: Integration with Neo4j and ArangoDB for graph-based reasoning
• LLM flexibility: Compatible with any LLM (OpenAI, Anthropic, local models, open-source via API)
• Redis caching: Prompt/response caching for performance optimization
• Message provenance: Detailed logging with source tracking for debugging and observability

import langroid as lr
from langroid.agent.tools.orchestration import DoneTool
 
# Configure the LLM (works with [[openai|OpenAI]], [[anthropic|Anthropic]], local models)
llm_config = lr.language_models.OpenAIGPTConfig(
    chat_model="[[openai|openai]]/gpt-4o",
)
 
# Create a research agent with specific instructions
researcher_config = lr.ChatAgentConfig(
    name="Researcher",
    llm=llm_config,
    system_message="You research topics and provide detailed factual summaries.",
)
researcher = lr.ChatAgent(researcher_config)
 
# Create a writer agent that refines research into polished text
writer_config = lr.ChatAgentConfig(
    name="Writer",
    llm=llm_config,
    system_message="You take research notes and write clear, engaging prose.",
)
writer = lr.ChatAgent(writer_config)
 
# Wrap agents in Tasks for orchestration
researcher_task = lr.Task(researcher, interactive=False)
writer_task = lr.Task(writer, interactive=False)
 
# The writer delegates research subtasks to the researcher
writer_task.add_sub_task(researcher_task)
 
# Run the pipeline: writer receives a goal and delegates as needed
result = writer_task.run(
    "Write a brief overview of how HNSW graphs work for vector search."
)
print(result)

Langroid excels in scenarios requiring fine-grained multi-agent collaboration:

• Advanced RAG: Knowledge bases with cited answers from documents
• Business intelligence: Automated data queries, SQL integration, and workflow automation
• Complex problem-solving: Strategic planning, diagnostics, multi-step reasoning
• AI-driven development: Code generation, analysis, and security auditing
• Debate platforms: Multi-agent setups with dynamic agent parameterization for structured discussions

Choose Langroid when you need full control over agent hierarchies, task delegation logic, and integrations without a GUI layer. Choose CrewAI for production orchestration with monitoring, or AutoGen for conversational multi-agent patterns.

• XAgent: Autonomous LLM Agent for Complex Tasks
• MetaGPT: Multi-Agent Framework with SOP-Based Collaboration
• LangGraph
• AgentVerse: Facilitating Multi-Agent Collaboration
• Managed Agents vs Agent SDK

• CrewAI
• AutoGen
• Multi-Agent Systems
• Retrieval-Augmented Generation