A reminder to our community.
We’ll be hosting our next Spaces tomorrow at 4PM UTC. The team will walk through the progress behind GraphEngine V1, how real usage is now translating into $GAI utility, and how we’re driving value back into the ecosystem through the platform.
We’ll also share early insight into an upcoming builder-focused initiative designed to support teams building real applications on GraphEngine, from wallet analytics and trading intelligence to PayFi and onchain automation. This program is aimed at turning developer effort into sustained usage and long-term ecosystem growth.
Set the reminder and tune in.
https://x.com/i/spaces/1BdxYZOQqpQKX
https://x.com/i/status/2001324196455170286
We’ll be hosting our next Spaces tomorrow at 4PM UTC. The team will walk through the progress behind GraphEngine V1, how real usage is now translating into $GAI utility, and how we’re driving value back into the ecosystem through the platform.
We’ll also share early insight into an upcoming builder-focused initiative designed to support teams building real applications on GraphEngine, from wallet analytics and trading intelligence to PayFi and onchain automation. This program is aimed at turning developer effort into sustained usage and long-term ecosystem growth.
Set the reminder and tune in.
https://x.com/i/spaces/1BdxYZOQqpQKX
https://x.com/i/status/2001324196455170286
X (formerly Twitter)
GraphAI (@GraphAIOfficial) on X
A reminder to our community.
We’ll be hosting our next Spaces tomorrow at 4PM UTC. The team will walk through the progress behind GraphEngine V1, how real usage is now translating into $GAI utility, and how we’re driving value back into the ecosystem through…
We’ll be hosting our next Spaces tomorrow at 4PM UTC. The team will walk through the progress behind GraphEngine V1, how real usage is now translating into $GAI utility, and how we’re driving value back into the ecosystem through…
❤4
We’re LIVE.
Join the conversation, as we we break down what’s live in V1, how the intelligence economy works, and what’s coming next.
https://x.com/i/spaces/1BdxYZOQqpQKX
https://x.com/GraphAIOfficial/status/2001683811038552457
Join the conversation, as we we break down what’s live in V1, how the intelligence economy works, and what’s coming next.
https://x.com/i/spaces/1BdxYZOQqpQKX
https://x.com/GraphAIOfficial/status/2001683811038552457
🔥2
GraphAI DevLog 17 - From Live Signals to Full History
Over the last two weeks, we expanded GraphEngine beyond live-only intelligence, delivering safe historical backfilling for Token Lens alongside stronger lifecycle controls and monetization alignment.
Here’s what landed:
✅ Token Lens Backfilling – Query recent historical on-chain events before transitioning to live mode, with explicit lifecycle states that prevent partial or inconsistent data.
✅ Subnoscription-Aware Lifecycles – Grace and suspension states now enforce plan compliance while giving users a clear recovery window.
✅ Paid Request Prioritization – Fast-track subgraph requests using $GAI, aligning execution speed with economic signal.
✅ Flexible Backfill Controls – Optional live ingestion during backfills and a structured path for extended, enterprise-grade requests.
These upgrades move GraphEngine closer to production-grade infrastructure, enabling deeper analysis while preserving system stability and cost controls.
Full DevLog: https://medium.com/@graphAI.tech/from-live-signals-to-full-history-823e38a35d53
https://x.com/GraphAIOfficial/status/2003142631950282978
Over the last two weeks, we expanded GraphEngine beyond live-only intelligence, delivering safe historical backfilling for Token Lens alongside stronger lifecycle controls and monetization alignment.
Here’s what landed:
✅ Token Lens Backfilling – Query recent historical on-chain events before transitioning to live mode, with explicit lifecycle states that prevent partial or inconsistent data.
✅ Subnoscription-Aware Lifecycles – Grace and suspension states now enforce plan compliance while giving users a clear recovery window.
✅ Paid Request Prioritization – Fast-track subgraph requests using $GAI, aligning execution speed with economic signal.
✅ Flexible Backfill Controls – Optional live ingestion during backfills and a structured path for extended, enterprise-grade requests.
These upgrades move GraphEngine closer to production-grade infrastructure, enabling deeper analysis while preserving system stability and cost controls.
Full DevLog: https://medium.com/@graphAI.tech/from-live-signals-to-full-history-823e38a35d53
https://x.com/GraphAIOfficial/status/2003142631950282978
❤3
Product Spotlight: Data Backfilling
Data Backfilling is now live in GraphEngine, unlocking the ability to analyse on-chain history before live tracking begins.
This marks a major step forward, moving from purely real-time monitoring to full context-aware on-chain intelligence.
At a simple level, backfilling allows GraphEngine to ingest and process historical blockchain events within a defined window.
Instead of starting analysis “from now,” users can now ask: what happened before, how did it start, and how did it evolve?
When a Lens is created with backfilling enabled, GraphEngine first reconstructs historical activity, then seamlessly transitions into live ingestion.
This ensures users never see partial or misleading data. History is complete before intelligence goes live.
This dramatically expands what users can do with GraphEngine.
Instead of just reacting to live events, you can now analyse early behaviour, launch dynamics, initial liquidity flows, and pre-existing patterns that shape everything that follows.
One powerful outcome of backfilling is the foundation for entirely new Lenses, for example the ability to analyse token launches. This enables deep analysis of how tokens launch on-chain: early transfers, initial swaps, liquidity actions, and behavioural shifts in the first critical moments.
Backfilling moves GraphEngine beyond one-dimensional scanner bots.
Instead of isolated alerts or single-event triggers, GraphEngine builds a connected historical graph that AI can reason over to understand cause, sequence, and context.
This is where AI-native analysis changes the game.
Rather than asking “did something happen?”, users can ask why it happened, what led up to it, and how early behaviour shaped later outcomes, all in natural language.
Combined with Wallet Lens and live ingestion, backfilling turns on-chain data into a continuous story.
History, real-time signals, and behavioural patterns are unified into a single intelligence layer that agents, analysts, and applications can build on.
Backfilling isn’t just a feature, it’s a shift in how on-chain analysis works.
From launch intelligence to behavioural context, GraphEngine is setting a new standard: AI that understands the past, reasons in the present, and scales into the future.
https://x.com/GraphAIOfficial/status/2003860329609166882
Data Backfilling is now live in GraphEngine, unlocking the ability to analyse on-chain history before live tracking begins.
This marks a major step forward, moving from purely real-time monitoring to full context-aware on-chain intelligence.
At a simple level, backfilling allows GraphEngine to ingest and process historical blockchain events within a defined window.
Instead of starting analysis “from now,” users can now ask: what happened before, how did it start, and how did it evolve?
When a Lens is created with backfilling enabled, GraphEngine first reconstructs historical activity, then seamlessly transitions into live ingestion.
This ensures users never see partial or misleading data. History is complete before intelligence goes live.
This dramatically expands what users can do with GraphEngine.
Instead of just reacting to live events, you can now analyse early behaviour, launch dynamics, initial liquidity flows, and pre-existing patterns that shape everything that follows.
One powerful outcome of backfilling is the foundation for entirely new Lenses, for example the ability to analyse token launches. This enables deep analysis of how tokens launch on-chain: early transfers, initial swaps, liquidity actions, and behavioural shifts in the first critical moments.
Backfilling moves GraphEngine beyond one-dimensional scanner bots.
Instead of isolated alerts or single-event triggers, GraphEngine builds a connected historical graph that AI can reason over to understand cause, sequence, and context.
This is where AI-native analysis changes the game.
Rather than asking “did something happen?”, users can ask why it happened, what led up to it, and how early behaviour shaped later outcomes, all in natural language.
Combined with Wallet Lens and live ingestion, backfilling turns on-chain data into a continuous story.
History, real-time signals, and behavioural patterns are unified into a single intelligence layer that agents, analysts, and applications can build on.
Backfilling isn’t just a feature, it’s a shift in how on-chain analysis works.
From launch intelligence to behavioural context, GraphEngine is setting a new standard: AI that understands the past, reasons in the present, and scales into the future.
https://x.com/GraphAIOfficial/status/2003860329609166882
❤3
Season’s greetings to the GraphAI Community!
This year was about designing systems, proving ideas in practice, and building the foundations for intelligent, on-chain infrastructure alongside a dedicated group of builders and supporters.
Thank you for being part of the journey and we’re looking forward to continuing to push what’s possible together in the year ahead.
https://x.com/i/status/2004233737492680980
This year was about designing systems, proving ideas in practice, and building the foundations for intelligent, on-chain infrastructure alongside a dedicated group of builders and supporters.
Thank you for being part of the journey and we’re looking forward to continuing to push what’s possible together in the year ahead.
https://x.com/i/status/2004233737492680980
X (formerly Twitter)
GraphAI (@GraphAIOfficial) on X
Season’s greetings to the GraphAI Community!
This year was about designing systems, proving ideas in practice, and building the foundations for intelligent, on-chain infrastructure alongside a dedicated group of builders and supporters.
Thank you for being…
This year was about designing systems, proving ideas in practice, and building the foundations for intelligent, on-chain infrastructure alongside a dedicated group of builders and supporters.
Thank you for being…
👏2
GraphEngine Metrics Update
Over the last two weeks, GraphEngine has processed thousands of new queries, delivering AI-driven insight across live on-chain activity. We’ve seen growing user interactions and, importantly, continued use of $GAI to power the protocol flywheel.
As we close out 2025 and head into 2026, this momentum puts GraphAI in a strong position with ambitious plans ahead.
More to share next week.
https://x.com/i/status/2005310478948679789
Over the last two weeks, GraphEngine has processed thousands of new queries, delivering AI-driven insight across live on-chain activity. We’ve seen growing user interactions and, importantly, continued use of $GAI to power the protocol flywheel.
As we close out 2025 and head into 2026, this momentum puts GraphAI in a strong position with ambitious plans ahead.
More to share next week.
https://x.com/i/status/2005310478948679789
👍2👏2
Deciphering Token Launches with GraphEngine
Understanding a token launch isn’t about reacting to alerts, it’s about seeing how activity unfolds over time. GraphEngine is live and enables end-to-end reconstruction, analysis, and reasoning over token launches from their very first on-chain moments. Instead of snapshots, it provides the full narrative.
Most on-chain tools surface isolated events: a transfer here, a swap there, a liquidity add somewhere else. Each signal appears independently, without sequence, causality, or downstream context. GraphEngine takes a different approach by rebuilding the entire story before drawing conclusions.
Through historical backfilling, GraphEngine ingests on-chain activity from the moment a token first appears. Early transfers, initial swaps, liquidity provisioning, and subsequent actions are reconstructed into a single, ordered timeline before live tracking begins. The result is a complete view - no gaps, no partial histories.
With that foundation in place, users don’t query raw logs. They ask meaningful questions: Who participated first? How did liquidity evolve in the first hour? When did behavior materially change? The AI reasons over sequences and phases, not just individual events.
Because the data is modeled as a graph, actions across time are connected. Early transfers link to later swaps; liquidity changes correlate with volume shifts; behavioral patterns align with specific launch phases. What is typically chaotic becomes interpretable and analyzable.
This approach is fundamentally different from one-dimensional launch bots. Rather than merely reporting that something happened, GraphEngine helps explain why it happened and what followed, embedding context directly into the analysis. Insight becomes analytical instead of reactive.
Once backfilling completes, the same lens transitions seamlessly into live mode. Post-launch behavior can be compared against early dynamics in real time, allowing users to spot divergence, decay, or sustained momentum as it unfolds. Historical and live signals form a continuous intelligence layer.
These capabilities are available today on GraphEngine. Soon, the experience will be further streamlined with the upcoming Token Launch Lens, which will simplify setup and abstract configuration. This is the next level of on-chain analysis and it’s already here.
https://x.com/GraphAIOfficial/status/2005672133062783284
Understanding a token launch isn’t about reacting to alerts, it’s about seeing how activity unfolds over time. GraphEngine is live and enables end-to-end reconstruction, analysis, and reasoning over token launches from their very first on-chain moments. Instead of snapshots, it provides the full narrative.
Most on-chain tools surface isolated events: a transfer here, a swap there, a liquidity add somewhere else. Each signal appears independently, without sequence, causality, or downstream context. GraphEngine takes a different approach by rebuilding the entire story before drawing conclusions.
Through historical backfilling, GraphEngine ingests on-chain activity from the moment a token first appears. Early transfers, initial swaps, liquidity provisioning, and subsequent actions are reconstructed into a single, ordered timeline before live tracking begins. The result is a complete view - no gaps, no partial histories.
With that foundation in place, users don’t query raw logs. They ask meaningful questions: Who participated first? How did liquidity evolve in the first hour? When did behavior materially change? The AI reasons over sequences and phases, not just individual events.
Because the data is modeled as a graph, actions across time are connected. Early transfers link to later swaps; liquidity changes correlate with volume shifts; behavioral patterns align with specific launch phases. What is typically chaotic becomes interpretable and analyzable.
This approach is fundamentally different from one-dimensional launch bots. Rather than merely reporting that something happened, GraphEngine helps explain why it happened and what followed, embedding context directly into the analysis. Insight becomes analytical instead of reactive.
Once backfilling completes, the same lens transitions seamlessly into live mode. Post-launch behavior can be compared against early dynamics in real time, allowing users to spot divergence, decay, or sustained momentum as it unfolds. Historical and live signals form a continuous intelligence layer.
These capabilities are available today on GraphEngine. Soon, the experience will be further streamlined with the upcoming Token Launch Lens, which will simplify setup and abstract configuration. This is the next level of on-chain analysis and it’s already here.
https://x.com/GraphAIOfficial/status/2005672133062783284
❤4👏2
As blockchains mature, the challenge is shifting from accessing data to actually understanding it.
With AI increasingly able to reason over history, patterns, and behaviour, on-chain analysis is starting to feel less like monitoring and more like interpretation.
Which on-chain signals matter most when you trade or research?
https://x.com/i/status/2006035941551661208
With AI increasingly able to reason over history, patterns, and behaviour, on-chain analysis is starting to feel less like monitoring and more like interpretation.
Which on-chain signals matter most when you trade or research?
https://x.com/i/status/2006035941551661208
X (formerly Twitter)
GraphAI (@GraphAIOfficial) on X
As blockchains mature, the challenge is shifting from accessing data to actually understanding it.
With AI increasingly able to reason over history, patterns, and behaviour, on-chain analysis is starting to feel less like monitoring and more like interpretation.…
With AI increasingly able to reason over history, patterns, and behaviour, on-chain analysis is starting to feel less like monitoring and more like interpretation.…
👏3❤2
GraphAI Bi-Weekly Recap
Here’s everything we shipped, launched, & explored across GraphAI, GraphEngine, & our growing intelligence ecosystem over the past two weeks.
GraphAI Spaces #9 – GraphEngine V1: Live, Scaling & Monetising On-Chain Intelligence
We hosted our ninth community Space breaking down what’s live in GraphEngine V1, how the intelligence economy works, & what’s coming next. The session covered live usage, monetisation mechanics, & the roadmap ahead, with insights from the core team.
🔗 https://x.com/GraphAIOfficial/status/2000596719885582692
An Overview of GraphAI’s Usage-Driven Token Utility
We shared our first full breakdown of GraphEngine usage metrics since paid credits went live. Queries, interactions, & $GAI accumulation are now directly tied to platform activity, forming a captured, self-reinforcing economic flywheel. From buy pressure and treasury capture to burns and builder rewards, this post outlines how real usage translates into token utility.
🔗 https://x.com/GraphAIOfficial/status/2000959996063015397
GraphAI DevLog 17 – From Live Signals to Full History
Over the last two weeks, GraphEngine expanded beyond live-only intelligence with the launch of safe historical backfilling for Token Lens. We also shipped subnoscription-aware lifecycles, paid request prioritisation, and flexible backfill controls, bringing GraphEngine closer to production-grade, enterprise-ready infrastructure.
🔗 https://x.com/GraphAIOfficial/status/2003142631950282978
Product Spotlight – Data Backfilling
Data Backfilling is now live in GraphEngine, enabling users to analyse on-chain history before transitioning into live tracking. By reconstructing historical activity first, GraphEngine ensures complete context, eliminates partial views, & unlocks deep analysis of early behaviour, launch dynamics, and long-term patterns.
🔗 https://x.com/GraphAIOfficial/status/2003860329609166882
GraphEngine Metrics Update
Over the past two weeks, GraphEngine processed thousands of new queries, with growing user interactions & continued use of $GAI to power the protocol flywheel. As we close out 2025 & head into 2026, momentum across usage and adoption continues to build.
🔗 https://x.com/GraphAIOfficial/status/2005310478948679789
Deciphering Token Launches with GraphEngine
We detailed how GraphEngine enables end-to-end understanding of token launches by reconstructing full historical timelines before live tracking begins. Using backfilling & graph-based reasoning, users can analyse early transfers, liquidity dynamics, & behavioural shifts, turning chaotic launches into structured, explainable intelligence.
🔗 https://x.com/GraphAIOfficial/status/2005672133062783284
From Accessing Data to Understanding It
As blockchains mature, we explored how the challenge is shifting from data access to true interpretation. With AI reasoning over history, patterns, & behaviour, on-chain analysis is evolving from monitoring events to understanding meaning and we asked the community which signals matter most in trading and research.
🔗 https://x.com/GraphAIOfficial/status/2006035941551661208
What’s Coming Next…
Verified Lifecycle Communication
Transactional email workflows are now live, enabling reliable, verified communication for critical subgraph & account lifecycle events. This lays the groundwork for richer alerts & system-driven messaging.
Prompt Response Efficiency
Ongoing work is focused on improving prompt-response performance across GraphEngine, optimizing latency & reliability for real-time intelligence queries.
Infrastructure Cost Discipline
A structured cost-optimization roadmap is underway, targeting safe efficiency gains across compute, databases, and messaging, without compromising reliability or scalability.
Operational Readiness Upgrades
Profile completeness enforcement and identity verification improvements strengthen platform hygiene & enterprise readiness.
A foundational week focused on reliability, efficiency, & scale. More improvements rolling out soon.
https://x.com/GraphAIOfficial/status/2007515044570092012
Here’s everything we shipped, launched, & explored across GraphAI, GraphEngine, & our growing intelligence ecosystem over the past two weeks.
GraphAI Spaces #9 – GraphEngine V1: Live, Scaling & Monetising On-Chain Intelligence
We hosted our ninth community Space breaking down what’s live in GraphEngine V1, how the intelligence economy works, & what’s coming next. The session covered live usage, monetisation mechanics, & the roadmap ahead, with insights from the core team.
🔗 https://x.com/GraphAIOfficial/status/2000596719885582692
An Overview of GraphAI’s Usage-Driven Token Utility
We shared our first full breakdown of GraphEngine usage metrics since paid credits went live. Queries, interactions, & $GAI accumulation are now directly tied to platform activity, forming a captured, self-reinforcing economic flywheel. From buy pressure and treasury capture to burns and builder rewards, this post outlines how real usage translates into token utility.
🔗 https://x.com/GraphAIOfficial/status/2000959996063015397
GraphAI DevLog 17 – From Live Signals to Full History
Over the last two weeks, GraphEngine expanded beyond live-only intelligence with the launch of safe historical backfilling for Token Lens. We also shipped subnoscription-aware lifecycles, paid request prioritisation, and flexible backfill controls, bringing GraphEngine closer to production-grade, enterprise-ready infrastructure.
🔗 https://x.com/GraphAIOfficial/status/2003142631950282978
Product Spotlight – Data Backfilling
Data Backfilling is now live in GraphEngine, enabling users to analyse on-chain history before transitioning into live tracking. By reconstructing historical activity first, GraphEngine ensures complete context, eliminates partial views, & unlocks deep analysis of early behaviour, launch dynamics, and long-term patterns.
🔗 https://x.com/GraphAIOfficial/status/2003860329609166882
GraphEngine Metrics Update
Over the past two weeks, GraphEngine processed thousands of new queries, with growing user interactions & continued use of $GAI to power the protocol flywheel. As we close out 2025 & head into 2026, momentum across usage and adoption continues to build.
🔗 https://x.com/GraphAIOfficial/status/2005310478948679789
Deciphering Token Launches with GraphEngine
We detailed how GraphEngine enables end-to-end understanding of token launches by reconstructing full historical timelines before live tracking begins. Using backfilling & graph-based reasoning, users can analyse early transfers, liquidity dynamics, & behavioural shifts, turning chaotic launches into structured, explainable intelligence.
🔗 https://x.com/GraphAIOfficial/status/2005672133062783284
From Accessing Data to Understanding It
As blockchains mature, we explored how the challenge is shifting from data access to true interpretation. With AI reasoning over history, patterns, & behaviour, on-chain analysis is evolving from monitoring events to understanding meaning and we asked the community which signals matter most in trading and research.
🔗 https://x.com/GraphAIOfficial/status/2006035941551661208
What’s Coming Next…
Verified Lifecycle Communication
Transactional email workflows are now live, enabling reliable, verified communication for critical subgraph & account lifecycle events. This lays the groundwork for richer alerts & system-driven messaging.
Prompt Response Efficiency
Ongoing work is focused on improving prompt-response performance across GraphEngine, optimizing latency & reliability for real-time intelligence queries.
Infrastructure Cost Discipline
A structured cost-optimization roadmap is underway, targeting safe efficiency gains across compute, databases, and messaging, without compromising reliability or scalability.
Operational Readiness Upgrades
Profile completeness enforcement and identity verification improvements strengthen platform hygiene & enterprise readiness.
A foundational week focused on reliability, efficiency, & scale. More improvements rolling out soon.
https://x.com/GraphAIOfficial/status/2007515044570092012
👏3
Welcome to 2026.
Last year was about turning ideas into real infrastructure, building, shipping, testing, and learning in public.
GraphAI enters this year with GraphEngine live, usable, and already doing meaningful work on-chain.
Throughout 2025, we focused on laying strong foundations.
From real-time ingestion and historical context to AI-native querying and economic alignment, GraphEngine evolved from concept to a working intelligence layer for on-chain data.
Today, users can use GraphEngine to explore on-chain activity on Base through natural language, structured queries, and integrate with their systems via MCP.
Wallet behaviour, token activity, historical patterns, and live signals are modelled as graphs, enabling context, not just visibility.
With features like Wallet Lens, backfilling, and live ingestion working together, users can analyse how activity begins, evolves, and changes over time.
This moves on-chain analysis beyond dashboards and alerts into something more exploratory, explainable, and usable.
As we move into early 2026, our focus shifts to expansion.
We’ll be rolling out new Lenses to support a wider range of use cases, making powerful analysis workflows easier to access with less setup.
We’re also working toward going cross-chain, so intelligence isn’t siloed by network.
Understanding behaviour across ecosystems will be critical as on-chain activity continues to fragment and scale.
On the economic side, we’re expanding the $GAI ecosystem through initiatives like GraphPools and deeper integration of usage-driven incentives.
At the same time, we’re preparing a builder program to support teams using GraphAI to create real AI-powered applications across Web3.
2025 was about building the engine. 2026 is about putting it to work with more users, more builders, and more intelligence flowing through the system.
We’re excited to keep building this next chapter together.
More details on each of these will follow very soon as we prepare for an exciting start to 2026.
We’re looking forward to sharing what’s next and building this next phase of GraphAI together with the community.
https://x.com/GraphAIOfficial/status/2008216078930092080
Last year was about turning ideas into real infrastructure, building, shipping, testing, and learning in public.
GraphAI enters this year with GraphEngine live, usable, and already doing meaningful work on-chain.
Throughout 2025, we focused on laying strong foundations.
From real-time ingestion and historical context to AI-native querying and economic alignment, GraphEngine evolved from concept to a working intelligence layer for on-chain data.
Today, users can use GraphEngine to explore on-chain activity on Base through natural language, structured queries, and integrate with their systems via MCP.
Wallet behaviour, token activity, historical patterns, and live signals are modelled as graphs, enabling context, not just visibility.
With features like Wallet Lens, backfilling, and live ingestion working together, users can analyse how activity begins, evolves, and changes over time.
This moves on-chain analysis beyond dashboards and alerts into something more exploratory, explainable, and usable.
As we move into early 2026, our focus shifts to expansion.
We’ll be rolling out new Lenses to support a wider range of use cases, making powerful analysis workflows easier to access with less setup.
We’re also working toward going cross-chain, so intelligence isn’t siloed by network.
Understanding behaviour across ecosystems will be critical as on-chain activity continues to fragment and scale.
On the economic side, we’re expanding the $GAI ecosystem through initiatives like GraphPools and deeper integration of usage-driven incentives.
At the same time, we’re preparing a builder program to support teams using GraphAI to create real AI-powered applications across Web3.
2025 was about building the engine. 2026 is about putting it to work with more users, more builders, and more intelligence flowing through the system.
We’re excited to keep building this next chapter together.
More details on each of these will follow very soon as we prepare for an exciting start to 2026.
We’re looking forward to sharing what’s next and building this next phase of GraphAI together with the community.
https://x.com/GraphAIOfficial/status/2008216078930092080
❤2👍2
Market momentum is building early in 2026, with on-chain activity on Base increasing.
GraphEngine already supports direct querying of several Base tokens, and creating your own subgraph is straightforward if you need something specific.
Try here: https://app.graphai.tech/login
Specify the token, LPs of interest, and historical window with Token Lens, and let GraphEngine do the heavy lifting.
What on-chain activity are you most curious about right now? Share your views on X(Twitter)
https://x.com/GraphAIOfficial/status/2008570024878108914
GraphEngine already supports direct querying of several Base tokens, and creating your own subgraph is straightforward if you need something specific.
Try here: https://app.graphai.tech/login
Specify the token, LPs of interest, and historical window with Token Lens, and let GraphEngine do the heavy lifting.
What on-chain activity are you most curious about right now? Share your views on X(Twitter)
https://x.com/GraphAIOfficial/status/2008570024878108914
app.graphai.tech
GraphAI - Explore Decentralized Subindexes
Built on top of MCP interfaces, our system lets you search, explore, and manage AI-generated subindexes with minimal integration effort.
❤4
Case Study: Using MCP for Cross-Context Reasoning
GraphEngine turns raw on-chain data into structured intelligence, with MCP connecting that intelligence directly to your app.
Instead of maintaining custom indexers or hard-coded queries, builders can use existing or custom subgraphs in GraphEngine. Each subgraph provides a clean, AI-ready view of on-chain behavior.
MCP tokens expose these subgraphs as scoped tools AI models can call directly. Your app no longer queries raw data, it queries intelligence.
By connecting an LLM via MCP, models can reason across multiple subgraphs: comparing wallets, identifying patterns, and summarizing behavior dynamically.
This simplifies architecture. Builders focus on UX while GraphEngine handles ingestion, historical backfilling, & graph modeling no indexers, pipelines, or schema drift.
New subgraphs can be added without redeploying logic, keeping apps extensible by design.
GraphEngine provides the intelligence layer. MCP provides the interface.
Try now.
X Post Link
GraphEngine turns raw on-chain data into structured intelligence, with MCP connecting that intelligence directly to your app.
Instead of maintaining custom indexers or hard-coded queries, builders can use existing or custom subgraphs in GraphEngine. Each subgraph provides a clean, AI-ready view of on-chain behavior.
MCP tokens expose these subgraphs as scoped tools AI models can call directly. Your app no longer queries raw data, it queries intelligence.
By connecting an LLM via MCP, models can reason across multiple subgraphs: comparing wallets, identifying patterns, and summarizing behavior dynamically.
This simplifies architecture. Builders focus on UX while GraphEngine handles ingestion, historical backfilling, & graph modeling no indexers, pipelines, or schema drift.
New subgraphs can be added without redeploying logic, keeping apps extensible by design.
GraphEngine provides the intelligence layer. MCP provides the interface.
Try now.
X Post Link
❤3