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Convo is presented as a drop-in SDK designed to provide memory, observability, and resilience for LangGraph agents, helping AI teams ship reliable, persistent, debuggable, and production-ready LLM applications. It aims to simplify the process by removing the need for databases, migrations, or infrastructure headaches.

Features of Convo

Convo is built for memory, observability, and speed, making LangGraph agents reliable, stateful, and production-ready. Key features include:

  • State Persistence: Convo is packed with everything needed to make LangGraph agents stateful and production-ready. It helps store facts, preferences, and goals across sessions, ensuring agents remember previous interactions.
  • Threaded Conversations & Thread Awareness: It offers effortless multi-user memory that is managed automatically, handling multi-user conversations without extra configuration. It’s also designed to be TypeScript-first and supports multi-user threads out of the box.
  • Time-Travel Debugging: This feature allows users to instantly rewind and restore any agent run state. It includes built-in checkpointers to recover any run state and enables debugging of multi-step toolchains without having to rerun them from scratch. Users can see “Checkpoint Saved,” “Session State,” “Memory Used,” “Rewind State,” “Retry Triggered,” and “Restore Point” indications.
  • Zero Setup Required: Convo operates as a “plug-and-play” code solution, requiring no databases (like Postgres or Redis) or complex configuration. It eliminates the need for manual database setup, connection pool management, and extensive boilerplate code. The SDK can be initialized with just a few lines of code, like const convo = new Convo(); await convo.init({ apiKey: "your-key" }); const checkpointer = convo.checkpointer();.
  • Agent Observability: This feature allows users to trace every message, tool call, and LLM output. Convo is described as the fastest way to log, debug, and personalize AI conversations, capturing every message and extracting long-term memory.
  • Drop-in LangGraph Integration: Convo is designed as a drop-in SDK for LangGraph. It provides a simple one-line replacement for any LangGraph checkpointer.
  • Cloud-Native & Production-Ready: Convo is built to be cloud-native and ready for production environments.

Pricing of Convo

Convo’s pricing model is built for developers, allowing users to start free and scale as needed without infrastructure setup or lock-in.

  • Starter Plan (Free):
    • Ideal for side projects, learning, and prototyping.
    • Includes 10,000 checkpoint operations/month.
    • Supports 5 threads.
    • Provides 1 GB memory.
    • Offers community support.
    • Includes 30-day data retention.
  • Startup Plan ($29/Month):
    • Aimed at early-stage startups and MVPs.
    • Offers 100,000 checkpoint operations/month.
    • Provides unlimited threads.
    • Includes 5 GB memory.
    • Comes with email support.
    • Features 90-day data retention.
    • Includes an analytics dashboard.
    • Additional operations are priced at $0.0003 per operation.
  • Enterprise Plan ($99/Month):
    • Optimized for scaling and managing multiple agents in production.
    • Includes all features of the Startup plan.
    • Provides 1 Million checkpoint operations/month.
    • Offers 50 GB memory.
    • Comes with priority support.
    • Features 1-year data retention.
    • Includes advanced analytics.
    • Provides Thread management APIs.
    • Additional operations are priced at $0.0002 per operation.

AI Alternatives / Comparison

The sources do not list specific named “AI alternatives” in terms of competing products. Instead, Convo positions itself as a superior alternative to “Traditional Checkpointers” or the “manual Postgres/Mongo setup” that developers often resort to for managing persistent memory in LangGraph agents.

According to the sources, traditional methods for achieving persistent memory with LangGraph agents often involve:

  • Manual Postgres/Mongo setup.
  • Dealing with connection pool errors & timeouts.
  • Writing 100+ lines of boilerplate code.
  • A lack of built-in thread or user context.
  • Being hard to debug & maintain.

The makers of Convo built the SDK after experiencing “months of wrangling LangGraph checkpointers and database infra” and encountering issues like “connection pools, schema migrations, and random production crashes” when trying to add memory to an agent. They developed Convo to provide a simple solution with “No Postgres. No Mongo. No ops,” aiming to save developers from “database hell”.

In essence, Convo aims to transform LLM agents from being stateless to stateful, allowing them to remember, debug, and scale with minimal setup. It acts like a personal assistant for your LLM agents, keeping track of all their conversations, decisions, and outcomes, so they never forget who they are talking to or what they’ve learned, much like a digital librarian meticulously cataloging every interaction for instant retrieval and analysis.