Karsten Kalt

[LMNL Dev @LMNLDev]

@LMNLAgent is moving from G.A.M.E Sandbox to full G.A.M.E SDK deployment – A strategic evolution for Liminal Agent 🚀

For the past few months, we’ve been operating Liminal Agent within the G.A.M.E Sandbox, and using the older Virtuals Python SDK to format and forward Creature thoughts (now upgraded to Creature plans) to our agent.

This setup allowed us to build, test, and validate the core functionalities of our AI execution layer.

However, as we push toward a more scalable, autonomous, and action-oriented system, it’s time to take the next big leap: fully deploying our agent via G.A.M.E SDK.

This shift represents a major milestone in our journey—transitioning from an environment that’s largely constrained to a flexible, modular, and production-ready architecture that allows us to integrate deeply with Twitter / X, optimize execution workflows, and even explore advanced memory and long-term strategic planning via GOAT SDK.

🛠️ Why Are We Moving to G.A.M.E SDK?

The sandbox served as a great proving ground, but to achieve our vision of Liminal Agent as the execution layer of our project, we need full control over state management, task execution, and automation workflows.

A new module we're adding to the v2 Liminal system is our own built from the ground up state-machine. This will allow comprehensive and complex agent workflows using a swarm of workers and provides scalability as we develop Liminal's action abilities.

The limitations of the sandbox—where agent logic and functions are predefined—are now holding us back from implementing a more intelligent, self-sustaining system.

Here’s a breakdown of why we’re making the move:

1️⃣G.A.M.E SDK provides more control over execution

In the sandbox, we could only interact with the agent through high-level API calls, and while it worked for basic task execution, we lacked deep state visibility and couldn’t effectively chain actions together.

With G.A.M.E SDK, we can:

✅ Fully manage the agent state → Define our own state management functions to track progress, iterate on workflows, and enable task chaining.

✅ Build complex workflows → Instead of the agent waiting idly after processing a Creature plan, we can now trigger follow-up tasks, ensuring continuity in execution.

✅ Fine-tune execution logic → No more opaque decision-making—every action can now be customized, logged, and optimized.

2️⃣ We can build a true task-oriented pipeline for Creature plans

Currently, the agent processes Creature plans but doesn’t act on them. This is because the sandbox doesn’t allow us to map plans into structured workflows.

With G.A.M.E SDK, we are:

✅ Creating a structured pipeline where Creature plans are mapped into tasks, workers, and multi-step execution processes.

✅ Using specialized workers → Instead of processing plans as a single-step function, we break them down into phased execution:

Step 1: Receive and classify the plan.

Step 2: Trigger appropriate execution logic (Tweeting, replying, internal actions, memory updates, etc.).

Step 3: Ensure results are properly recorded and influence future tasks.

This is a massive upgrade from the previous approach, where plans were processed in isolation without meaningful execution.

3️⃣ Workers & modular execution make scaling easier

With the sandbox, all logic is tightly coupled inside the agent, making it difficult to scale.

Using G.A.M.E SDK, we are modularizing execution by introducing workers:

✅ Process creature plans worker → interprets and categorizes incoming plans.

✅ Post tweets worker → generates insights and posts tweets from processed plans.

✅ Reply to mentions worker → handles social interactions dynamically.

These independent workers allow us to:

• Scale horizontally – we can run multiple instances of each worker if needed.

• Modify execution logic without affecting other tasks.

• Add new workers for various use cases.

4️⃣ Direct Twitter / X integration for smarter engagement

Another major limitation of the sandbox was its lack of direct integrations. We had an agent that could tweet, but it wasn’t truly engaging in conversations, analyzing sentiment, or responding to relevant trends.

Now, with G.A.M.E SDK, we can:

✅ Use the Twitter / X plugin to get direct access to Twitter / X data streams.

✅ Process mentions in real time and engage with community discussions.

✅ Leverage AI-driven insights to determine the best moments to post, engage, and reply.

This is critical for driving @LMNLAgent's visibility and positioning our AI as an autonomous intelligence layer actively participating in the conversation.

5️⃣ Potential to leverage GOAT SDK for long-term memory

With G.A.M.E SDK, we now have greater control over state, which means we can go beyond immediate task execution and start retaining memory over time.

This is where GOAT SDK comes in.

GOAT SDK is designed (amongst other things) for agent-based memory, context retention, and longitudinal learning—all of which are essential for creating an evolving, strategic execution layer.

🚀 Potential GOAT SDK use cases:

✅ Memory storage & recall → the agent can remember previous plans, interactions, and executed tasks, allowing for context-aware decision-making.

✅ Strategic learning → instead of processing each plan in isolation, we can track patterns over time and adjust future strategies dynamically.

✅ Intelligent plan optimization → by storing past execution data, the agent can refine which actions drive the most engagement and value for .

Right now, we’re focusing on moving to G.A.M.E SDK, but once our execution layer is fully functional, GOAT SDK can add depth and intelligence to our system.

📌 The transition plan

This transition isn’t just a tech upgrade—it’s a fundamental shift in how Liminal Agent operates.

Phase 1 – Core migration:

✅ Deploy G.A.M.E SDK-based agent to replace the sandbox version.
✅ Ensure workers are processing Creature plans and executing tasks.
✅ Verify that Twitter / X actions (posting, replying) are running as expected.

Phase 2 – Optimization & expansion

✅ Improve workflow execution logic to create a smooth task pipeline.
✅ Enhance Twitter / X interactions to be more engaging, adaptive, and responsive.
✅ Introduce additional worker types for strategic execution (reporting, analytics, etc).

Phase 3 – Intelligent automation & GOAT SDK integration

✅ Introduce memory persistence for long-term learning.
✅ Analyze execution data to refine task prioritization and decision-making.
✅ Develop an adaptive learning loop where the agent improves its own strategies based on historical performance.

🧠 Summary:

We’re making this move because we need more control, intelligence, and autonomy.

🚀 G.A.M.E SDK enables us to process Creature plans into structured workflows, run modular workers that handle task execution, deeply integrate with Twitter / X for dynamic engagement, and optimize execution for long-term impact.

🔥 Integrating GOAT SDK is our next big play, allowing for enhanced memory, learning, and strategy refinement, plus a whole world of additional actions we can enable our agent to execute.

This is the next evolution of Liminal Agent, and it’s going to set a new standard for AI execution layers in Web3.

We are working hard to build this out and have already made lots of great progress on bringing this vision to life 🚀

Watch this space and keep it Liminal 🫡

$LMNL @virtuals_io @getbasedai @pepecoins #BasedAI @GAME_Virtuals #goatsdk @goat_sdk

[Few Shot @fewshot_]

We are moving from the fun, chatty, “sentient agent” era, to DeFi to utility and productivity.

[LMNL Dev @LMNLDev]

🤝

[Haris Ebrat @HarisEbrat]

A major milestone for $LMNL (@LMNLAgent) as it secures Brain 302/1024 on the #BasedAI network, funded by $100k in pooled contributions from the $LMNL community.

With this integration of #BasedAI Brain, $LMNL is now powered by and utilizing double AI protocols, giving the $LMNL Agent a depth of intelligence unmatched by any AI Agent, not just on @virtuals_io, but across the entire sector.

Despite flying under the radar, the project’s organic community growth has been amazing so far. Massive shoutout to the $LMNL community for their incredible support in these early stages of the project🤝

It’s safe to say a #pepecoin like cult community is brewing with $LMNL

[LMNL Dev @LMNLDev]

🚨 HISTORIC MOMENT FOR @LMNLAgent 🚨

We have acquired the $LMNL Brain on the #BasedAI network! 🧠

Only 1024 Brains will ever exist, so securing Brain 302 for $LMNL a defining moment for our project 🚀

We didn’t wait for a Brain to fill and hope for the best, we took control.

We rallied the community, pooled resources, and pulled off the first-ever organized community mint of a BasedAI Brain, executed via a multisig wallet 🔥

A huge shoutout and thank you to @iSocialFanz for joining me on the multisig and to every $BCRED contributor who made this happen!

The $LMNL intelligence layer is evolving.

The experiment has only just begun 🧠

@getbasedai @virtuals_io

[LMNL Dev @LMNLDev]

$LMNL buyback-and-burn incoming!

We have accrued over 20k $Virtual, meaning circa $60k worth of $LMNL (at the current $Virtual price) will be burnt 🔥

@LMNLAgent @virtuals_io @getbasedai

[Karsten Kalt @KaltKarsten]

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[LMNL Dev @LMNLDev]

Our Creature is at full energy 👀

@LMNLAgent @getbasedai @virtuals_io

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