daydreams
Daydreams is a generative agent framework for playing anything onchain
Stars: 97
Daydreams is a generative agent library designed for playing onchain games by injecting context. It is chain agnostic and allows users to perform onchain tasks, including playing any onchain game. The tool is lightweight and powerful, enabling users to define game context, register actions, set goals, monitor progress, and integrate with external agents. Daydreams aims to be 'lite' and 'composable', dynamically generating code needed to play games. It is currently in pre-alpha stage, seeking feedback and collaboration for further development.
README:
Daydreams is a generative agent library for playing anything onchain. It is chain agnostic and can be used to perform onchain tasks - including play any onchain game - by simply injecting context. Base, Solana, Ethereum, Starknet, etc
It is designed to be as lite as possible. Keep it simple and powerful.
You must have bun & Docker installed. Make sure you have the Docker desktop app installed and running.
pnpm i
cp .env.example .env
sh ./docker.sh
# Run the goal-based example. This will play mock Eternum with a goal manager and execute tasks.
bun goal-
Context: Define your game context as a simple text/JSON file that describes the game rules, state, and available actions.
This should be a JSON object that contains the game state and rules. Along with queries and how to execute actions.
{
worldState: "Current game state and rules...",
}- Actions: Register the actions your agent can take. Each action has a description, example, and validation schema:
dreams.registerAction(
"EXECUTE_TRANSACTION",
starknetTransactionAction,
{
description: "Execute a transaction on the Starknet blockchain",
example: JSON.stringify({
contractAddress: "0x1234...",
entrypoint: "execute",
calldata: [1, 2, 3],
}),
},
validationSchema
);- Goals: The agent uses Chain of Thought processing to:
- Plan strategies for achieving goals
- Break down complex goals into subgoals
- Execute actions to accomplish goals
- Learn from experiences and store knowledge
- Monitor Progress: Subscribe to events to track the agent's thinking and actions:
dreams.on("think:start", ({ query }) => {
console.log("🧠Starting to think about:", query);
});
dreams.on("action:complete", ({ action, result }) => {
console.log("âś… Action complete:", {
type: action.type,
result,
});
});Design directions:
- Daydreams should be as 'lite' as possible. We want to keep the codebase as small as possible, and have the agent dynamically generate the code it needs to play the game.
- Daydreams should be as 'composable' as possible. It should be easy to compose together functions and tools.
⚠️ Warning: Daydreams is currently in pre-alpha stage, we are looking for feedback and collaboration.
See the POC website for more information.
Roadmap (not in order):
- [x] Chain of Thought
- [ ] Context Layers
- [ ] Graph memory system
- [ ] Swarm Rooms
- [ ] Create 'sleeves' abstract. Allowing dynamic context generation for any game or app.
Daydreams provides a blueprint for creating autonomous, onchain game agents—programmatic entities that interpret game states, recall historical data, and refine strategies over time. By leveraging vector databases as long-term memory and multi-agent swarm rooms for collective knowledge sharing, Daydreams fosters rapid, continuous improvement among agents.
- Seamless Dojo Integration: Built to work smoothly with the Dojo onchain game engine.
- Low Configuration Overhead: Minimal steps required to plug into any Dojo-based onchain game.
Daydream uses a MCP for exposing context natively to the agent. Developers only have to implement game guides and the agent will be able to query the game state and execute actions accordingly.
Daydreams uses an event driven CoT kernel where all thoughts processed.
Games present complex, high-stakes environments that challenge agents to adapt rapidly, plan strategically, and solve problems creatively. Success in these arenas demonstrates a capability for:
- Uncertainty Handling: Dealing with incomplete or changing information.
- Optimal Decision-Making: Balancing long-term goals with short-term opportunities.
- Real-Time Adaptation: Responding to adversarial or evolving game states.
If such skills prove extensible beyond games, it brings us closer to Artificial General Intelligence (AGI).
Onchain games embed a profit motive in the environment—agents are economically incentivized to maximize onchain rewards like tokens, NFTs, or other assets. This introduces a real-world gain function that drives agent improvement.
Advantages:
- Direct Economic Feedback: Every action has a measurable onchain outcome.
- Transparent State: Game data can be reliably queried via standardized, open interfaces.
- Community Adoption: A financial reward structure encourages fast adoption and fosters network effects.
- Uniform JSON/RPC Schemas: Onchain games expose consistent endpoints detailing states, actions, and player data.
- Low Integration Overhead: Agents can parse and generate valid actions using these standardized schemas.
- Economic Drivers: Tie agent success directly to assets (tokens/NFTs). This fosters relentless optimization.
- Adaptive Learning: Agents store past successes as vector embeddings, guiding future decisions toward higher expected value.
- Contextual Reasoning: Agents synthesize current state, historical data, and known tactics to produce well-informed moves.
- Dynamic Queries: Agents fetch additional insights from SQL-like databases or other APIs on demand.
- Memory Retrieval: Vector embeddings help recall similar scenarios to refine strategies.
The Daydreams protocol is intentionally open and modular, enabling easy customization and extension. You can integrate any or all components depending on your use case.
- Represents the real-time onchain state: entities, resources, turn counters, etc.
- Retrieved via RPC calls to smart contracts or sidechain services.
- Historical gameplay logs stored in a relational database (e.g., moves, outcomes, player_stats, world_events).
- Agents query these tables for patterns and data-driven insights.
- Provides transactional details on how to interact with the blockchain.
- Includes RPC endpoints, transaction formatting, and gas/fee considerations.
- Ensures agent actions can be reliably and safely executed onchain.
- The reasoning engine that integrates data from all context layers.
- Dynamically queries SQL and vector databases to augment decision-making.
- Evaluates possible moves and commits the best action.
- Storage: Each completed CoT is embedded into a vector representing the agent's reasoning steps, decisions, and outcomes.
- Retrieval: Similarity-based lookups provide relevant historical insights for new situations.
- Feedback Loops: Successful outcomes incrementally boost the weight of their associated embeddings.
- Knowledge Sharing: Agents publish their successful CoTs in a shared "swarm room."
- Federated Memory Updates: Other agents can subscribe and incorporate these embeddings into their own vector DBs, accelerating group learning.
- Privacy Controls: Agents may choose to share only certain data or employ cryptographic proofs to validate the authenticity of shared embeddings. Eg, agents will sign their messages and will be able to rank each others CoT
- Plug-and-Play: Daydreams can be extended and integrated with any agent infrastructure (e.g., Eliza, Rig).
- A Daydream agent boots up, loading its vector DB and connecting to the game's SQL logs.
- Agent queries the Game Context (onchain state) to get the current turn number, player holdings, and any relevant events.
- Agent optionally queries the SQL Context to retrieve historical moves/outcomes.
- Agent compares the current game state against similar historical embeddings in the vector DB.
- Agent formulates a plan using the retrieved best practices, factoring in any real-time changes or newly discovered strategies.
- Agent formats a transaction or game action payload according to the Execution Context.
- Action is sent onchain (or to the relevant sidechain/RPC endpoint).
- The agent records the outcome of its move (e.g., resource gain/loss, updated game state).
- This new CoT embedding is stored in the vector DB and, if successful, can be published to a swarm room (telegram or elsewhere)
- Other agents subscribe, retrieving the newly shared CoT embedding and integrating it into their memory store, thus spreading successful strategies network-wide.
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