Developer Platform for AI Agents

You write the agent.
We handle the
streaming.

The gap between an agent script and a production agent system is enormous. Reminix closes it with one line of code.

support_bot.pyyour code
from langchain import create_agent
from reminix_runtime import serve, memory, kb
 
agent = create_agent(
model="gpt-4o",
tools=[memory, kb, my_tools]
)
 
serve(agent)
terminalwhat you get
$ reminix agent invoke support-bot \
--prompt "How do I reset my password?"
 
✓ Agent responded (142 tokens)
tools: knowledge_base
 
Go to Settings → Security → Reset Password.
You'll receive a confirmation email within
a few minutes.

Ship agents, not infrastructure.

Deploying an agent on a generic PaaS means building all of this yourself. Reminix gives it to you out of the box.

Streaming infrastructure

SSE endpoints, backpressure handling, client reconnection. Built in.

Conversation state

Multi-turn sessions, message history, user-scoped persistence. Managed for you.

Tool orchestration

Your agent calls tools, we execute them, handle timeouts, and return results.

REST APIs & SDKs

Production endpoints + typed Python and TypeScript clients. Works with every agent.

Auth & secrets

API keys, environment variables, rate limiting. No DIY auth middleware.

Monitoring & logs

Request tracing, error tracking, latency metrics. Not another Datadog config.

Tools that would take weeks to build.

Memory, knowledge base, web search, and storage — managed services your agent can call with zero setup.

Memory

tools=["memory"]

User-scoped persistent memory across conversations. Your agent remembers context without you building a vector DB pipeline.

Knowledge Base

tools=["knowledge_base"]

Upload documents, we handle chunking, embedding, and retrieval. Project-scoped RAG without managing infrastructure.

Web Search & Fetch

tools=["web_search"]

Search the web and retrieve page content. No API keys to manage, no rate limits to handle.

KV Storage

tools=["kv_storage"]

Persistent key-value store for agent state, caches, and structured data. No database to provision.

Or deploy your own tools

tools/database.py
from reminix_runtime import tool
 
@tool
async def query_database(sql: str):
"""Query your database safely."""
validated = sanitize(sql)
result = await db.execute(validated)
return result.rows

Write tools in Python or TypeScript. Deploy once, use from any agent. Mix with built-in tools freely.

The same tools work across all agent patterns — chat, task, or workflow. Wire them up once, every agent can use them.

One line to production.

Wrap your existing agent with serve(). We handle everything else.

1

Write your agent

Use LangChain, Vercel AI, OpenAI, Anthropic, or the Reminix Runtime. Any framework, any model. Your code, your way.

2

Add serve(agent)

One line gives you production APIs, streaming, built-in tools, SDKs, and monitoring. No HTTP layer to write, no infra to configure.

3

Deploy

reminix deploy and you're live. Scaling, secrets, versions, rollbacks — all handled.

terminal

Prefer no code? Configure a native agent from the dashboard — system prompt, model, tools — and deploy in seconds.

Three patterns. Pick yours.

Chat, Task, or Workflow — each optimized for different problems. The platform handles the rest either way.

Chat

Multi-turn conversations with built-in state. Support bots, assistants, research agents.

Task

Stateless single-shot execution. Data processing, extraction, code analysis.

Workflow

Multi-step orchestration with branching, approvals, and parallel execution.

Learn more about agent patterns

Deploy your first agent in five minutes.

Free tier included. No credit card required.