langroid
Harness LLMs with Multi-Agent Programming
Source#
- Repository: langroid/langroid
- Last source update: 2026-03-01
- Last verified: 2026-03-01
Tags#
Integration notes#
Framework-level abstraction; derive bounded worker contracts from concrete tasks and APIs in docs/examples.
worker.md example#
Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.
---
id: langroid-repo-derived-worker
name: langroid Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/langroid/
source_repository: https://github.com/langroid/langroid
repository_default_branch: main
repository_language: Python
repository_license: MIT
repository_updated_at: 2026-03-01
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.95
derived_on: 2026-03-01
tags:
- agents
- ai
- chatgpt
- function-calling
- gpt
- gpt-4
---
# langroid Repo-Derived Worker
## Repo-derived summary
- Registry summary: Harness LLMs with Multi-Agent Programming
- Repository description: Harness LLMs with Multi-Agent Programming
- Stars (snapshot): 3,917
- Primary language: Python
- Worker mode classification: agent-orchestration-worker
## Extracted from
- https://github.com/langroid/langroid
- https://github.com/langroid/langroid/blob/main/README.md
- https://img.shields.io/pypi/v/langroid
- https://img.shields.io/pypi/dm/langroid
- https://github.com/langroid/langroid/actions/workflows/pytest.yml/badge.svg
## Evidence notes (from repository text)
- README summary paragraph: Documentation · Examples Repo · Discord · Contributing
- https://github.com/langroid/langroid/actions/workflows/pytest.yml/badge.svg](https://github.com/langroid/langroid/actions/workflows/pytest.yml)
- https://github.com/langroid/langroid/actions/workflows/docker-publish.yml/badge.svg](https://github.com/langroid/langroid/actions/workflows/docker-publish.yml)
- Python framework to easily build LLM-powered applications, from CMU and UW-Madison researchers.
- You set up Agents, equip them with optional components (LLM,
- vector-store and tools/functions), assign them tasks, and have them
## Installation hints found in README
- `pip install langroid`
- `pip install "langroid[hf-embeddings]"`
- `pip install "langroid[doc-chat]"`
- `pip install "langroid[db]"`
## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.
### Input schema
```json
{
"type": "object",
"additionalProperties": false,
"required": [
"run_id",
"task",
"context"
],
"properties": {
"run_id": {
"type": "string"
},
"task": {
"type": "string"
},
"context": {
"type": "object"
}
}
}
```
### Output schema
```json
{
"type": "object",
"additionalProperties": false,
"required": [
"run_id",
"status",
"result"
],
"properties": {
"run_id": {
"type": "string"
},
"status": {
"type": "string",
"enum": [
"ok",
"retryable_error",
"invalid_request",
"invalid_output"
]
},
"result": {
"type": "object"
}
}
}
```
### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use
## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.
How to use#
- Save this as a worker spec file (for example:
langroid-my-task.worker.md). - Replace the input/output schemas and purpose with your real bounded task.
- Enforce schema validation + timeout + retry policy in your runtime before production use.
Citation#
Reference URL: https://worker.md/registry/langroid/
Source URL: https://github.com/langroid/langroid