Langroid

Langroid implements a message-passing architecture where agents communicate through a central message bus, automatically routing tasks between specialized agents based on message content and agent capabilities. Each agent declares its tools and responsibilities, and the framework uses LLM-guided routing to determine which agent should handle incoming messages, enabling multi-turn conversations that span multiple specialized agents without explicit orchestration code.

Langroid provides a decorator-based system for binding Python functions as tools that agents can invoke, automatically generating JSON schemas from function signatures and managing tool execution within the agent's action loop. Tools are declared at the agent level, and the framework handles schema generation, LLM function-calling protocol adaptation (OpenAI, Anthropic, etc.), and result injection back into the agent's context.

Langroid supports running multiple agents or conversations concurrently using Python's asyncio, allowing efficient batch processing of requests without blocking. The framework manages async context, handles concurrent tool calls, and aggregates results from parallel agent executions. Developers can process hundreds of conversations simultaneously with minimal resource overhead.

Langroid can configure agents to generate structured outputs (JSON, dataclasses) that conform to predefined schemas, using LLM function-calling or prompt engineering to enforce structure. The framework validates outputs against schemas and provides error messages when outputs don't match, enabling reliable extraction of structured data from LLM responses.

Langroid provides utilities to ingest documents (PDFs, text files, web pages) and automatically chunk them into manageable pieces for agent processing. The framework handles different document formats, applies configurable chunking strategies (sliding window, semantic boundaries), and prepares chunks for embedding and storage in vector databases.

Langroid can serialize agent state (conversation history, memory, configuration) to disk or external storage, enabling agents to resume from saved checkpoints. The framework handles serialization of complex objects (tool definitions, LLM configs) and provides utilities to load agents from saved states, supporting long-running or interrupted agent processes.

Langroid maintains conversation history within each agent, automatically managing context windows by summarizing or truncating older messages when approaching token limits. The framework tracks message metadata (sender, timestamp, tool calls) and provides configurable strategies for deciding which messages to keep, drop, or summarize when the conversation exceeds the LLM's context window.

Langroid provides a memory system that can store agent interactions in vector databases (e.g., Qdrant, Weaviate), enabling agents to retrieve relevant past conversations or documents using semantic search. Agents can query their memory store to find contextually relevant information before responding, and the framework handles embedding generation, vector storage operations, and result ranking automatically.

Langroid abstracts LLM provider differences through a unified interface, supporting OpenAI, Anthropic, Ollama, and other providers with automatic protocol translation. The framework handles differences in function-calling APIs, token counting, and response formats, allowing developers to switch providers or use multiple providers simultaneously without changing agent code.

Langroid allows agents to spawn child agents dynamically to handle sub-tasks, with automatic context passing and result aggregation. When an agent encounters a complex task, it can create specialized sub-agents, delegate work to them, and collect their results back into the parent agent's reasoning loop. The framework manages the lifecycle of sub-agents and ensures proper cleanup.

Langroid supports streaming LLM responses at the token level, allowing agents to process and act on partial responses before the full completion is available. The framework provides hooks to intercept tokens as they arrive, enabling real-time response formatting, early termination, or dynamic tool invocation based on partial outputs.

Langroid implements a turn-based conversation model where agents take turns responding to messages, with configurable rules for who speaks next. The framework manages dialogue state, prevents infinite loops, and ensures that conversations progress toward resolution. Agents can explicitly pass control to other agents or request input from users.

Langroid provides both YAML configuration files and Python DSL for declaratively defining agents, their tools, memory backends, and LLM settings. Configurations can be loaded from files, composed, and overridden at runtime, enabling environment-specific setup (dev vs. production) without code changes. The framework validates configurations and provides helpful error messages for misconfigurations.

Langroid provides debugging utilities to inspect agent messages, tool calls, and reasoning steps, with optional logging to files or external services. Developers can trace the flow of messages through agents, see what tools were called and why, and replay conversations for debugging. The framework supports different logging levels and can capture full message history for post-mortem analysis.