Back to Ecosystem Pulse
ToolProduction Ready
langroid
by langroid
Python framework for building and orchestrating multi-agent LLM workflows
Python
Updated Feb 11, 2026
Share:
What It Does
Provides a Python framework to build multi-agent LLM applications and agent workflows. Uses agent abstractions, task delegation patterns, and pluggable connectors (LLMs, retrievers) to compose collaboratives of specialists. Distinctive features include conversational agent choreography, function-calling support, and retrieval-augmented generation integrations for grounded responses.
Key Benefits
As agent systems grow, coordinating specialists and tracking their behavior becomes essential for trust and reliability. Langroid makes it easier to prototype multi-agent orchestration and delegation patterns so teams can observe how agents interact and where failures arise. That visibility is a practical first step toward agent-to-agent evaluation and building reputational signals across runs.
When to Use
Teams prototyping or shipping multi-agent applications that need structured agent delegation, RAG integration, and conversational orchestration.
How It's Used
- Compose specialist agents (researcher, writer, verifier) to collaborate on complex tasks
- Prototype agent delegation strategies and workflow routing with RAG-backed evidence
- Run pre-production experiments to observe agent failure modes and interaction patterns
Works With
openaillamahuggingfacelocal-llmretrieval-augmented-generation
Topics
agentsaichatgptfunction-callinggptgpt-4gpt4information-retrievallanguage-modelllama+8 more
Similar Tools
autogencrewailangchain
Keywords
multi-agent orchestrationagent delegationretrieval-augmented-generationmulti-agent trust