{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langroid--langroid","slug":"langroid--langroid","name":"langroid","type":"agent","url":"https://langroid.github.io/langroid/","page_url":"https://unfragile.ai/langroid--langroid","categories":["ai-agents"],"tags":["agents","ai","chatgpt","function-calling","gpt","gpt-4","gpt4","information-retrieval","language-model","llama","llm","llm-agent","llm-framework","local-llm","multi-agent-systems","openai-api","rag","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langroid--langroid__cap_0","uri":"capability://planning.reasoning.multi.agent.task.orchestration.with.hierarchical.delegation","name":"multi-agent task orchestration with hierarchical delegation","description":"Langroid implements a two-level Agent-Task abstraction where Tasks wrap Agents and manage message routing, delegation, and hierarchical task spawning. Tasks provide three core responder methods (llm_response, agent_response, user_response) that coordinate LLM interactions, tool execution, and user communication. Agents communicate through structured ChatDocument messages, enabling loose coupling and composable workflows where subtasks can be spawned with specialized agents to handle complex multi-step problems.","intents":["Build multi-agent systems where specialized agents collaborate on complex tasks","Delegate work from parent agents to child agents with different capabilities","Manage conversation state and message routing across multiple agents","Create hierarchical workflows where agents can spawn subtasks dynamically"],"best_for":["Teams building complex LLM applications requiring agent specialization","Developers migrating from single-agent to multi-agent architectures","Applications requiring hierarchical task decomposition and delegation"],"limitations":["Message-passing overhead increases latency with agent count; no built-in optimization for large agent networks","Requires explicit task definition and agent configuration; no auto-discovery of agent capabilities","State management across agents relies on ChatDocument history; no built-in distributed state persistence"],"requires":["Python 3.9+","LLM API key (OpenAI, Anthropic, or local Ollama instance)","Task and Agent class definitions for each specialized role"],"input_types":["structured ChatDocument messages","user text queries","tool/function call results"],"output_types":["ChatDocument responses","tool invocation requests","delegation messages to child agents"],"categories":["planning-reasoning","multi-agent-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_1","uri":"capability://tool.use.integration.schema.based.function.calling.with.composable.toolmessage.subclasses","name":"schema-based function calling with composable toolmessage subclasses","description":"Langroid provides a ToolMessage abstraction where each tool is defined as a dataclass subclass with automatic schema generation for LLM function calling. Tools are registered with agents and automatically converted to OpenAI/Anthropic function schemas. The framework handles parsing LLM tool-call responses, validating against schemas, and routing calls to handler methods. Supports multi-provider function calling (OpenAI, Anthropic, Ollama) with unified interface.","intents":["Define tools as Python dataclasses with automatic schema generation","Enable agents to call external APIs and functions with type-safe arguments","Support function calling across multiple LLM providers with consistent API","Validate tool arguments against schemas before execution"],"best_for":["Developers building agents that need to interact with external systems","Teams requiring type-safe function calling with schema validation","Multi-provider LLM deployments needing unified tool interface"],"limitations":["Tool schemas must be manually defined as dataclasses; no automatic introspection of arbitrary Python functions","Schema generation limited to JSON-serializable types; complex nested structures require custom serialization","No built-in retry logic for failed tool calls; requires explicit error handling in agent logic"],"requires":["Python 3.9+","LLM provider supporting function calling (OpenAI, Anthropic, or Ollama with function-calling support)","Tool definitions as ToolMessage subclasses"],"input_types":["ToolMessage dataclass definitions","LLM function-call responses (JSON)"],"output_types":["JSON function schemas","parsed tool arguments","tool execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_10","uri":"capability://text.generation.language.openai.assistants.integration.with.native.api.support","name":"openai assistants integration with native api support","description":"Langroid provides OpenAIAssistant agent type that wraps OpenAI's Assistants API, enabling agents to leverage OpenAI's managed assistant infrastructure including built-in code interpreter, retrieval, and function calling. The framework handles API communication, thread management, and response parsing while maintaining compatibility with Langroid's multi-agent architecture.","intents":["Use OpenAI Assistants API within Langroid's multi-agent framework","Leverage OpenAI's built-in code interpreter and retrieval capabilities","Manage assistant threads and conversation state through OpenAI API","Combine OpenAI Assistants with other Langroid agents"],"best_for":["Teams already using OpenAI Assistants API","Applications requiring OpenAI's code interpreter or retrieval","Organizations preferring managed assistant infrastructure"],"limitations":["OpenAI Assistants API has higher latency than direct API calls; not suitable for real-time applications","Limited customization of assistant behavior compared to custom agents","Thread management is handled by OpenAI; no local control over conversation state"],"requires":["Python 3.9+","OpenAI API key","Assistant ID from OpenAI platform"],"input_types":["user messages","file uploads for retrieval"],"output_types":["assistant responses","code execution results","retrieved documents"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_11","uri":"capability://automation.workflow.configuration.driven.agent.instantiation.and.customization","name":"configuration-driven agent instantiation and customization","description":"Langroid uses configuration objects (dataclasses) to define agent behavior, LLM settings, tool registration, and vector store configuration. Agents are instantiated from configs, enabling declarative agent definition without code changes. Configs can be loaded from files, environment variables, or code, providing flexibility for different deployment scenarios.","intents":["Define agent behavior through configuration without code changes","Enable environment-specific agent customization","Support configuration versioning and rollback","Simplify agent instantiation and testing"],"best_for":["Teams managing multiple agent configurations","Applications requiring environment-specific customization","Organizations with non-technical stakeholders configuring agents"],"limitations":["Configuration schema is fixed; complex customizations may require code changes","No built-in configuration validation; invalid configs may fail at runtime","Configuration loading from files requires explicit parsing; no auto-discovery"],"requires":["Python 3.9+","Configuration files in supported format (YAML, JSON, Python dataclass)"],"input_types":["configuration files","environment variables","code-based config objects"],"output_types":["instantiated agents","validated configurations"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_12","uri":"capability://automation.workflow.error.handling.and.graceful.degradation.in.agent.workflows","name":"error handling and graceful degradation in agent workflows","description":"Langroid provides error handling mechanisms for agent failures, tool execution errors, and LLM API failures. Agents can catch exceptions, retry failed operations, and degrade gracefully when dependencies are unavailable. The framework supports custom error handlers and fallback strategies for different failure modes.","intents":["Handle LLM API failures and rate limiting gracefully","Retry failed tool executions with backoff strategies","Provide fallback responses when agents fail","Log and monitor agent errors for debugging"],"best_for":["Production applications requiring reliability","Teams building fault-tolerant multi-agent systems","Applications with external dependencies prone to failure"],"limitations":["Error handling is agent-specific; no framework-level error recovery","Retry logic is manual; no built-in exponential backoff or circuit breaker patterns","Error logging requires explicit configuration; no default error telemetry"],"requires":["Python 3.9+","Error handling code in agent implementations"],"input_types":["exceptions from LLM APIs","tool execution errors"],"output_types":["error messages","fallback responses","retry requests"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.pluggable.vector.stores","name":"retrieval-augmented generation with pluggable vector stores","description":"Langroid provides DocChatAgent and LanceDocChatAgent specialized agents that integrate vector stores for RAG. Agents can ingest documents, chunk them, embed them into vector databases (Lance, Pinecone, etc.), and retrieve relevant context for LLM prompts. The framework handles document processing, chunking strategies, and semantic search. Agents maintain conversation history while augmenting responses with retrieved document context, enabling knowledge-grounded conversations.","intents":["Build chatbots that answer questions grounded in document collections","Retrieve relevant context from large document sets for LLM prompts","Maintain conversation history while augmenting with semantic search results","Support multiple vector store backends without changing agent code"],"best_for":["Teams building document-based Q&A systems and chatbots","Applications requiring knowledge-grounded LLM responses","Organizations with large document collections needing semantic search"],"limitations":["Vector store integration requires explicit configuration; no auto-detection of available backends","Chunking strategies are configurable but not adaptive; no automatic optimization based on document type","Embedding quality depends on chosen embedding model; no built-in evaluation of retrieval relevance"],"requires":["Python 3.9+","Vector store (Lance, Pinecone, Weaviate, etc.) or local embedding model","Documents in supported formats (PDF, text, markdown)","Embedding model API key or local model"],"input_types":["documents (PDF, text, markdown)","user queries (text)","vector store configuration"],"output_types":["retrieved document chunks","augmented LLM prompts","grounded responses with citations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_3","uri":"capability://data.processing.analysis.specialized.domain.agents.for.sql.knowledge.graphs.and.tables","name":"specialized domain agents for sql, knowledge graphs, and tables","description":"Langroid provides pre-built specialized agents (SQLChatAgent, TableChatAgent, Neo4jChatAgent) that encapsulate domain-specific logic for querying databases, analyzing tables, and traversing knowledge graphs. These agents handle schema introspection, query generation, result interpretation, and error handling for their respective domains. Each agent type includes tools for schema exploration, query execution, and result formatting tailored to its domain.","intents":["Enable natural language queries against SQL databases","Analyze and query tabular data with natural language","Traverse and query knowledge graphs with semantic understanding","Automatically generate and execute domain-specific queries from user intent"],"best_for":["Teams building natural language interfaces to databases","Data analysis applications requiring semantic understanding of schemas","Knowledge graph applications needing conversational query interfaces"],"limitations":["SQL agent requires schema introspection; performance degrades with very large schemas (100+ tables)","Query generation quality depends on LLM capability; complex multi-join queries may require refinement","Neo4j agent requires Cypher query generation; no support for other graph query languages"],"requires":["Python 3.9+","Database connection (SQL) or graph database (Neo4j) or tabular data source","LLM with function calling support","Database credentials and network access"],"input_types":["natural language queries","database schema information","table/graph structure metadata"],"output_types":["SQL queries or Cypher queries","query results","natural language interpretations of results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_4","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Langroid abstracts LLM interactions through provider-agnostic classes (OpenAIGPT, AzureGPT, etc.) that implement a common interface for chat completion, streaming, and function calling. Agents can switch between providers by changing configuration without code changes. The framework handles API calls, token counting, rate limiting, and response parsing across different LLM APIs (OpenAI, Anthropic, Azure, local Ollama).","intents":["Switch between LLM providers without changing agent code","Support both cloud and local LLM deployments","Handle provider-specific API differences transparently","Enable cost optimization by switching providers based on task requirements"],"best_for":["Teams evaluating multiple LLM providers","Organizations with hybrid cloud/local LLM deployments","Cost-conscious teams needing provider flexibility"],"limitations":["Provider abstraction adds ~50-100ms overhead per request due to normalization layer","Not all LLM features are supported across all providers; advanced features may require provider-specific code","Token counting varies by provider; no unified token estimation across providers"],"requires":["Python 3.9+","API key for chosen provider (OpenAI, Anthropic, Azure, etc.) or local Ollama instance","Provider-specific configuration (model name, endpoint, etc.)"],"input_types":["chat messages (text)","system prompts","function schemas"],"output_types":["LLM responses (text)","streaming response chunks","function call requests"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_5","uri":"capability://planning.reasoning.structured.message.passing.with.chatdocument.protocol","name":"structured message passing with chatdocument protocol","description":"Langroid uses ChatDocument as a unified message format for all agent communication, containing sender metadata, content, tool calls, and routing information. Messages flow through agents via structured message-passing rather than direct function calls, enabling loose coupling and message inspection/logging. The protocol supports streaming, tool invocations, and delegation markers, providing a consistent communication layer across all agent interactions.","intents":["Enable loose coupling between agents through structured messages","Log and inspect all agent communications for debugging","Support message routing and delegation across agent networks","Enable streaming responses and incremental message processing"],"best_for":["Teams building complex multi-agent systems requiring observability","Applications needing message logging and audit trails","Developers debugging agent interactions and message flows"],"limitations":["Message serialization overhead adds ~10-20ms per message for large payloads","ChatDocument structure is fixed; custom message types require subclassing","No built-in message compression; large conversation histories consume significant memory"],"requires":["Python 3.9+","Understanding of ChatDocument structure and message routing"],"input_types":["user text","tool results","agent responses"],"output_types":["ChatDocument messages","serialized message logs","routed messages to agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_6","uri":"capability://automation.workflow.batch.processing.and.async.streaming.for.high.throughput.workloads","name":"batch processing and async streaming for high-throughput workloads","description":"Langroid supports batch processing of multiple queries and async/streaming response generation for high-throughput scenarios. Agents can process multiple messages concurrently, stream responses incrementally to clients, and handle backpressure. The framework provides async-first APIs for non-blocking I/O and integrates with async frameworks like FastAPI for real-time applications.","intents":["Process multiple queries concurrently without blocking","Stream LLM responses incrementally to clients","Build real-time chat applications with low latency","Handle high-throughput workloads with efficient resource utilization"],"best_for":["Teams building real-time chat applications","High-throughput batch processing scenarios","Applications requiring streaming responses to clients"],"limitations":["Async streaming requires client support for streaming responses; not all LLM providers support streaming equally","Batch processing throughput limited by LLM API rate limits; no built-in rate limit management","Concurrent agent execution may exceed memory limits with large agent networks"],"requires":["Python 3.9+ with async/await support","LLM provider supporting streaming (OpenAI, Anthropic)","Async-compatible web framework (FastAPI, etc.) for client integration"],"input_types":["multiple queries (batch)","streaming request parameters"],"output_types":["streaming response chunks","batch results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_7","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.tool.standardization","name":"mcp (model context protocol) integration for tool standardization","description":"Langroid integrates with the Model Context Protocol (MCP) standard, enabling agents to use MCP-compliant tools and resources. This allows agents to leverage a growing ecosystem of standardized tools without custom integration code. The framework handles MCP server communication, tool discovery, and invocation through the standard protocol.","intents":["Use standardized MCP tools without custom integration","Leverage ecosystem of MCP-compliant tools and resources","Enable tool interoperability across different LLM applications","Reduce custom tool development by using standard implementations"],"best_for":["Teams adopting MCP standard for tool interoperability","Organizations building tool ecosystems","Developers wanting to avoid tool lock-in"],"limitations":["MCP ecosystem is still emerging; limited tool availability compared to custom integrations","MCP server communication adds latency; no built-in caching of tool results","Tool discovery and capability negotiation requires MCP server support"],"requires":["Python 3.9+","MCP server implementation","MCP client library integration"],"input_types":["MCP tool definitions","MCP resource requests"],"output_types":["MCP tool results","resource content"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_8","uri":"capability://data.processing.analysis.document.ingestion.and.chunking.with.configurable.strategies","name":"document ingestion and chunking with configurable strategies","description":"Langroid provides document processing capabilities for ingesting various file formats (PDF, text, markdown, etc.) and chunking them for embedding and retrieval. The framework supports configurable chunking strategies (fixed-size, semantic, recursive) and handles document parsing, metadata extraction, and chunk overlap. Processed chunks are stored in vector databases for RAG applications.","intents":["Ingest documents in multiple formats for RAG applications","Configure chunking strategies based on document type and use case","Extract and preserve document metadata during processing","Prepare documents for semantic search and retrieval"],"best_for":["Teams building document-based Q&A systems","Organizations processing large document collections","Applications requiring fine-grained control over chunking"],"limitations":["Chunking strategies are static; no adaptive chunking based on document structure","PDF parsing quality varies by document complexity; scanned PDFs require OCR","No built-in deduplication of chunks; duplicate content may be stored in vector database"],"requires":["Python 3.9+","Document files in supported formats","Vector store for chunk storage"],"input_types":["PDF files","text files","markdown files","document URLs"],"output_types":["document chunks","chunk metadata","vector embeddings"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langroid--langroid__cap_9","uri":"capability://memory.knowledge.conversation.state.management.with.persistent.history","name":"conversation state management with persistent history","description":"Langroid agents maintain conversation history as part of their state, with support for persistent storage and retrieval. The framework handles message history management, context window optimization, and conversation resumption. Agents can save and load conversation state, enabling long-running applications and conversation continuity across sessions.","intents":["Maintain conversation history across multiple interactions","Resume conversations from previous sessions","Optimize context window usage by managing history size","Enable conversation analytics and audit trails"],"best_for":["Long-running chatbot applications","Applications requiring conversation continuity","Teams needing conversation audit trails"],"limitations":["History management is in-memory by default; requires explicit persistence configuration","Context window optimization is manual; no automatic summarization of old messages","Large conversation histories consume significant memory; no built-in archival strategy"],"requires":["Python 3.9+","Optional: persistent storage backend (database, file system)"],"input_types":["new messages","saved conversation state"],"output_types":["updated conversation history","serialized state for persistence"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API key (OpenAI, Anthropic, or local Ollama instance)","Task and Agent class definitions for each specialized role","LLM provider supporting function calling (OpenAI, Anthropic, or Ollama with function-calling support)","Tool definitions as ToolMessage subclasses","OpenAI API key","Assistant ID from OpenAI platform","Configuration files in supported format (YAML, JSON, Python dataclass)","Error handling code in agent implementations","Vector store (Lance, Pinecone, Weaviate, etc.) or local embedding model"],"failure_modes":["Message-passing overhead increases latency with agent count; no built-in optimization for large agent networks","Requires explicit task definition and agent configuration; no auto-discovery of agent capabilities","State management across agents relies on ChatDocument history; no built-in distributed state persistence","Tool schemas must be manually defined as dataclasses; no automatic introspection of arbitrary Python functions","Schema generation limited to JSON-serializable types; complex nested structures require custom serialization","No built-in retry logic for failed tool calls; requires explicit error handling in agent logic","OpenAI Assistants API has higher latency than direct API calls; not suitable for real-time applications","Limited customization of assistant behavior compared to custom agents","Thread management is handled by OpenAI; no local control over conversation state","Configuration schema is fixed; complex customizations may require code changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5636644274727767,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-05-02T00:20:24Z"},"community":{"stars":3999,"forks":370,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langroid--langroid","compare_url":"https://unfragile.ai/compare?artifact=langroid--langroid"}},"signature":"5jQeGfPBMcCvVGpWJvpnMsgzbteawbAQeC7kSCYdYlVfkq+zDwqteG9tVgJeCSnRHU9lMJuo50ktGJZzrMciAA==","signedAt":"2026-06-22T12:53:32.398Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langroid--langroid","artifact":"https://unfragile.ai/langroid--langroid","verify":"https://unfragile.ai/api/v1/verify?slug=langroid--langroid","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}