apprentice-labs/activegraph-gbrain-bridge

★ 21PythonAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((repo))
    What it does
      Bridges two AI libraries
      Reads from GBrain
      Dry-run writes only
      Fixture test helpers
    Tech stack
      Python
      ActiveGraph
      GBrain
    Use cases
      AI agent memory
      Local dev testing
      Integration demos
    Audience
      AI agent developers
      Python library users
    Maturity
      Proof of concept
      Live writes pending
      Compatibility matrix

mindmap root((repo)) What it does Bridges two AI libraries Reads from GBrain Dry-run writes only Fixture test helpers Tech stack Python ActiveGraph GBrain Use cases AI agent memory Local dev testing Integration demos Audience AI agent developers Python library users Maturity Proof of concept Live writes pending Compatibility matrix

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Things people build with this

USE CASE 1

Give an AI agent access to a structured knowledge store (GBrain) while recording everything it does via ActiveGraph.

USE CASE 2

Test and develop AI agent integrations locally using fixture data, without needing live network calls to GBrain or a language model.

USE CASE 3

Query a GBrain knowledge schema and propose changes in dry-run mode for human review before anything is applied.

USE CASE 4

Explore how to wire together durable AI memory and runtime activity logging as a starting point for a custom agent system.

Tech stack

PythonActiveGraphGBrain

Getting it running

Difficulty · moderate Time to first run · 30min

Install as a Python library. Demos run fully offline using fixture data, no GBrain, LLM, or external service needed to start. Check the compatibility matrix for supported GBrain versions before using with a live instance.

In plain English

This repository is a compatibility bridge between two Python libraries called ActiveGraph and GBrain. Both relate to building AI agent systems, and the bridge connects them while keeping their responsibilities separate. GBrain handles durable, persistent knowledge: structured notes and documents stored in files, retrieval indexes, typed relationships between concepts, facts, timelines, and data ingestion jobs. Think of it as a long-term memory and knowledge store for an AI agent. ActiveGraph handles the runtime side: recording what an agent actually did, logging events, tracking cause-and-effect chains between steps, replaying past runs, and enforcing policy rules before allowing certain actions. Together, the two libraries give an AI agent both a place to store what it knows and a record of how it used that knowledge. This bridge package, installable as a Python library, provides a typed client interface for reading from GBrain, querying its schema, and proposing changes back to it in a dry-run mode. A dry-run means the proposed change is submitted for review rather than applied directly. Production live writes to GBrain are not yet supported. The bridge also provides fixture and test helpers so developers can work with the system without calling real external services, making it possible to develop and test locally using recorded responses. The demos included in the repository run entirely without live network calls to GBrain, a language model, or any external deployment. They are designed to prove the integration mechanics using fixture data. The repository notes that GBrain versions not listed in its compatibility matrix are unverified, and it explicitly does not claim endorsement by either upstream project. The README describes this as a proof-of-concept for a launch, and a STATUS.md file in the repository gives the current maturity and support state. Several planned features are listed as not yet implemented, including exposing usable tools via the ActiveGraph tools interface.

Copy-paste prompts

Prompt 1

I'm using the activegraph-gbrain-bridge Python package. Show me how to set up the typed GBrain client, query its schema, and submit a dry-run change proposal.

Prompt 2

Using activegraph-gbrain-bridge fixtures, write a local test that simulates reading structured knowledge from GBrain and logging the agent's actions in ActiveGraph, no live network calls.

Prompt 3

Explain the difference between what GBrain and ActiveGraph each handle in this bridge, and show me a minimal Python example that wires them together.

Prompt 4

I want to build an AI agent that stores long-term knowledge in GBrain and tracks its reasoning steps in ActiveGraph. Using activegraph-gbrain-bridge as a starting point, what does the architecture look like and what gaps do I need to fill myself?

Prompt 5

Show me how to use the test helpers in activegraph-gbrain-bridge to mock GBrain responses so I can develop my AI agent integration without hitting any external services.

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