---

name: langroid description: "Use Langroid to build Python LLM agents, tools, tasks, RAG/document chat, provider configs, structured data agents, MCP integrations, Chainlit callbacks, and no-network smoke checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Langroid Repo Skill

Use this repo skill when a task names Langroid or asks for Python LLM agent workflows involving ChatAgent, Task, ToolMessage, providers, RAG, structured data agents, MCP, Chainlit, or Langroid optional extras.

Langroid is a Python framework for building LLM-powered agents that use tools, exchange messages, delegate tasks, retrieve from documents, and integrate with providers and external tool systems.

First checks

Read references/repo-provenance.md when you need to decide whether this skill matches a checkout or installed package version. For install and extras selection, read references/install-and-extras.md and references/optional-dependencies.md.

Minimal package check:

python - <<'PY'
import langroid as lr
import langroid.language_models as lm
print(lr.ChatAgent, lr.Task, lr.ToolMessage)
print(lm.OpenAIGPTConfig().chat_model)
PY

No-network skill helper:

python scripts/check_langroid_environment.py --json --optional

Route by task

User task shape	Read
Create a ChatAgent, define/enable ToolMessage, wire handlers, use Task, delegate subtasks, route recipients, configure done_sequences, test with MockLM, or debug tool/task loops	sub-skills/agents-tasks-tools/SKILL.md
Configure OpenAI-compatible models, Azure, Gemini, LiteLLM proxy, Ollama/local endpoints, Portkey, LangDB, HTTP clients, rotating keys, cached clients, embeddings, or model-name selection	sub-skills/llm-provider-config/SKILL.md
Build document QA/RAG with DocChatAgent, parse PDFs/DOCX/URLs/Markdown/DataFrames, tune chunking/retrieval/reranking, choose vector stores or embeddings, or debug empty retrieval	sub-skills/retrieval-doc-chat/SKILL.md
Use TableChatAgent, SQLChatAgent, Neo4jChatAgent, ArangoChatAgent, CSV-to-KG, schema metadata, database URIs, query validators, or dangerous-operation safety controls	sub-skills/data-sql-graph-agents/SKILL.md
Convert MCP servers to Langroid tools, choose async/decorator MCP patterns, enable search/file tools, wire Chainlit callbacks, HTML logging, quiet/status output, or external integration smoke checks	sub-skills/integrations-mcp-chainlit/SKILL.md

Operating defaults

• Prefer deterministic MockLM or config-construction checks before making live LLM/provider calls.
• Use the smallest optional extra needed for the workflow; do not install all unless broad demo coverage is explicitly required.
• Treat API keys, local LLM servers, MCP subprocess transports, Chainlit apps, vector DB services, SQL/graph databases, model downloads, OCR, and GPU execution as optional live-backend work that needs explicit prerequisites.
• Keep security defaults unless the environment is trusted: VectorStoreConfig.full_eval=False, TableChatAgentConfig.full_eval=False, and SQL/graph allow_dangerous_operations=False.
• Do not depend on original repo examples or tests at runtime. Use this skill's bundled references and scripts.

Bundled smoke scripts

Run these from this skill directory or pass their full path from another current working directory:

python scripts/check_langroid_environment.py --json --optional
python sub-skills/agents-tasks-tools/scripts/core_agent_smoke.py
python sub-skills/llm-provider-config/scripts/provider_config_smoke.py --compact
python sub-skills/retrieval-doc-chat/scripts/rag_config_smoke.py --json
python sub-skills/data-sql-graph-agents/scripts/data_agent_safety_smoke.py
python sub-skills/integrations-mcp-chainlit/scripts/mcp_tool_smoke.py

These helpers avoid provider calls, live database queries, long-running servers, network downloads, training, and destructive writes by default.

Cross-cutting troubleshooting

Start with references/troubleshooting.md for install/import/extras, provider credential boundaries, security defaults, and which sub-skill owns each failure surface. Use sub-skill troubleshooting files for workflow-specific symptoms and recovery.