ActiveGraph

event-sourced graph runtime for long running agents

[Gautham Pai @gauthampai]

A task triaging system built over @ActiveGraphAI that assesses all open tasks periodically and helps me with their status updates. In this example, it is suggesting that a task (Blue node) should be marked as done (Orange node) based on a project log entry (Yellow node).

[Yohei @yoheinakajima]

a parallel experiment building a coding agent on top of @activegraphai. you can see everything flattened down to a single event log trace

[ActiveGraph @ActiveGraphAI]

Our third LongMemEval experiments by adding semantic ingestion to deterministic retrieval.

The hybrid approach improved end-to-end QA accuracy to 87.6% and evidence retrieval to 90.0%, but the QA gain did not reach statistical significance in this run.
https://t.co/xBIxtsutSQ

[ActiveGraph @ActiveGraphAI]

[New Technical Blog Post] In our second longmemeval experiment, we introduce semantic ingestion into recall leveraging the ActiveGraph runtime.

We started from a 60.6% baseline and improved to 83.4%/84.8% for flat/agentic retrieval with LLM ingestion.
https://t.co/sqeRxuUfGA

[Gautham Pai @gauthampai]

Building a custom UI to view objects, relations, behaviors, logs in @ActiveGraphAI.

The UI starts with a pack. The pack lists all the objects, relations and behaviors. Selecting an object shows the lifecycle from creation to current state and what behavior triggered a change in its state.

[Gautham Pai @gauthampai]

If you are into DAG workflows, n8n and all that, stop what you are doing and look at @ActiveGraphAI. It's a bigger deal than what Claude released with Dynamic Workflows today. Believe me, you won't be disappointed!

It's still a new project but shows a lot of promise. Keen to integrate this into my existing workflows.

[General Intelligence Company @intelligenceco]

"If I had Cofounder three years ago," says @yoheinakajima, "BabyAGI might have been a company."

Our newest case study tracks our most active user, Yohei Nakajima, building @ActiveGraphAI - the event sourced graph runtime for long running agents.

[ActiveGraph @ActiveGraphAI]

Reproducibility:

The benchmark harness is in https://t.co/OWMHtO9SZZ. The repo pins the LongMemEval submodule, Python version, reader settings, judge model, embedding model, lockfile, dataset checksums, per-run manifests, and token counts

[ActiveGraph @ActiveGraphAI]

[technical blog post]
“Evidence Compilation Before Semantic Memory: ActiveGraph on LongMemEval-S”
——
🔍85.6% QA accuracy and 86.2% turn answer-in-context at 2,462 mean context tokens, with deterministic non-generative ingestion

https://t.co/ICi61cDAU5

[ActiveGraph @ActiveGraphAI]

ActiveGraph is not the highest public LongMemEval number. Its narrower position is that it is a low-context, non-generative-ingestion substrate result: 85.6% QA at 2.4k context tokens, with paired statistics, pinned artifacts, and a replayable event-sourced runtime underneath.

[Yohei @yoheinakajima]

babyagi has ~200 citations, but 0 papers... i just published my first paper on arXiv 😆

"The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems"

https://t.co/c7mbRggdCh

the case for agents that coordinate through persistent replayable state — no conversation loops, no workflows, no A2A — with auditability, forking, and causal lineage built in.

check it out and let me know what you think!

[Yohei @yoheinakajima]

i'm excited to open source Active Graph: an event-sourced reactive graph runtime for long-running, agents 🔄🧠

events/logs projects a graph. reactive behaviors react and affect the graph. fork-and-diff agent runs. no A2A, no workflows, no DAG

site: https://t.co/Bbknu3ieUi
docs: https://t.co/HAnKYjrZxZ
github: https://t.co/jXQpMcyP1n
quick start: pip install activegraph

this is an early experiment in a new paradigm for agent architecture 🧪