langroid
AI Agents & AssistantsHarness LLMs with Multi-Agent Programming
Features
- Framework for building LLM‑powered multi‑agent applications in Python
- Extensible components: agents, vector stores, tools/functions
- Supports any LLM via simple abstractions (no LangChain dependency)
- Provides MCP tool adapter and optional Claude Code plugin
Recent releases
View all 27 releases →Path‑traversal + SQL file read fixes
- Adds Python 3.13 support; CI now tests against Python 3.11 and 3.13.
Full changelog
Python 3.13 support and Task.init() memory leak fix
Python 3.13 support
PR [#1011](https://github.com/langroid/langroid/pull/1011) bumps the supported Python range to >=3.10,<3.14 and adds Python 3.13 to the CI matrix alongside 3.11, so Langroid is now tested on both versions on every push.
pyproject.toml:requires-python = "<3.14,>=3.10".github/workflows/validate.yml: lint/test job now runs against["3.11", "3.13"]
Fix ObjectRegistry memory leak in Task.init()
PR [#1022](https://github.com/langroid/langroid/pull/1022) (originally [#1021](https://github.com/langroid/langroid/pull/1021)) fixes a memory leak in Task.init(). The method built a temporary ChatDocument solely to feed log_message(Entity.SYSTEM, ...), but ChatDocument.__init__ auto-registers every instance in the class-level ObjectRegistry, so each new Task left behind a system-message doc that was never freed. In long-running services that spawn many tasks (e.g. servers), this accumulated significantly over time.
The fix:
- explicitly removes the temporary doc via
ChatDocument.delete_id(...)after logging - renames the local to
system_message_temp_docto make its transient nature obvious - adds a regression test (
test_task_init_no_registry_leak) mirroring the PR #939 leak-check pattern, asserting that repeatedTask(...).init()calls leave zero leakedChatDocuments in the registry
Thanks to @alexagr for the catch and the original fix.
Fixes MiniMax context window size to 204,800 tokens (API limit), preventing unnecessary prompt truncation.
- MiniMax LLM provider with 7 models and up to 1M context
- OpenAI-compatible API with minimax/ prefix
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