Migration
Python and TypeScript applications can adopt Agent RT incrementally through compatibility entry points, then move toward native runtime contracts when they need deeper control over tools, state, policy, or execution.
Compatibility surfaces
For common imports, migration can be as small as changing the module prefix. Keep the class or symbol you already use and point the import at Agent RT's compatibility layer. Migration field testing is ongoing; 65+ third-party repositories currently pass their test suites both before and after migration to Agent RT.
Python
from langchain_openai import ChatOpenAIfrom agent_rt.langchain_openai import ChatOpenAIfrom llama_index.llms.openai import OpenAIfrom agent_rt.llama_index.llms.openai import OpenAIfrom langchain_qdrant import QdrantVectorStorefrom agent_rt.langchain_qdrant import QdrantVectorStorefrom llama_index.vector_stores.qdrant import QdrantVectorStorefrom agent_rt.llama_index.vector_stores.qdrant import QdrantVectorStorefrom openai import AsyncOpenAIfrom agent_rt.openai import AsyncOpenAIfrom anthropic import AsyncAnthropicfrom agent_rt.anthropic import AsyncAnthropicfrom deepagents import create_deep_agentfrom agent_rt.deepagents import create_deep_agentfrom litellm import completionfrom agent_rt.litellm import completionfrom openrouter import OpenRouterfrom agent_rt.openrouter import OpenRouterTypeScript / JavaScript
import { ChatOpenAI } from "@langchain/openai";import { ChatOpenAI } from "agent-rt/@langchain/openai";import { OpenAI } from "@llamaindex/openai";import { OpenAI } from "agent-rt/@llamaindex/openai";import { QdrantVectorStore } from "@langchain/qdrant";import { QdrantVectorStore } from "agent-rt/@langchain/qdrant";import { QdrantVectorStore } from "@llamaindex/qdrant";import { QdrantVectorStore } from "agent-rt/@llamaindex/qdrant";import OpenAI from "openai";import OpenAI from "agent-rt/openai";import Anthropic from "@anthropic-ai/sdk";import Anthropic from "agent-rt/@anthropic-ai/sdk";import { createDeepAgent } from "deepagents";import { createDeepAgent } from "agent-rt/deepagents";import { OpenRouter } from "@openrouter/sdk";import { OpenRouter } from "agent-rt/@openrouter/sdk";Migrate with one command
Run one command in your project to rewrite every supported import:
uvx agent-rt migrate # Python
npx agent-rt migrate # TypeScript / JavaScriptIt runs your tests before and after the rewrite and keeps it only if nothing new fails: every test that fails afterwards must have failed before, in the same test at the same line. Otherwise every file is restored. It then prints a before-and-after card (tests, test time, peak memory, package size) and, for a GitHub repository, a pre-filled issue link to add it to the migration wall. Nothing is sent automatically. Install agent-rt (and pytest for Python) in the project first. Per-test comparison in TypeScript needs Vitest or Jest.
“Runs on Agent RT” badge
Maintainers whose repository runs on Agent RT can add this badge to their README: 
[](https://agent-rt.pro-genai.com/#migration)The badge is opt-in: we never open pull requests or issues asking repositories, including those on the migration wall, to add it.
Adopt in layers
Start at the compatibility entry point closest to the code you already have. Once behavior is stable, move runtime-sensitive concerns—tools, run limits, sandboxing, retrieval, memory, policy, and checkpoints—onto Agent RT-native contracts where the control boundary is explicit.
Supported compatibility themes
The migration layer covers common chat/completion entry points, streaming, tool history, OpenAI Responses continuation, file-search integration through Agent RT retrieval providers, web retrieval integration, reasoning/thinking controls, batch lifecycle compatibility, and read/query vector-store adapters for Chroma, Pinecone, Qdrant, Milvus, and Weaviate.
LangChain compatibility exposes vendor-shaped imports such as agent_rt.langchain_qdrant and agent-rt/@langchain/qdrant. LlamaIndex compatibility exposes agent_rt.llama_index.vector_stores.qdrant and agent-rt/@llamaindex/qdrant, including integration with VectorStoreIndex.from_vector_store() / fromVectorStore(). These adapters query existing collections; vendor write/admin lifecycle APIs remain outside the compatibility layer. See the vector database guide for the complete mapping.