JacBrain

A project memory for AI agents building with Jac.

Keep useful knowledge. Find what matters. Check it with the compiler.

Try it · How it works · Roadmap

The idea

An AI coding agent often has to look up the same language rules and work through the same errors across sessions. JacBrain gives it a place to keep and find that knowledge.

It connects notes, code, compiler errors, and candidate fixes in a knowledge graph: a collection of records linked by how they relate. When an agent starts a task, JacBrain returns a small selection of relevant records for that project and Jac version.

The goal is to build effectively with Jac using less repeated explanation and fewer reference tokens. JacBrain supplies reusable language knowledge even when an agent does not reliably know Jac. It does not retrain the agent.

Related MCP server: MCP Memory Keeper

How it works

flowchart LR
    A[Save notes and code] --> B[Find context for a task]
    B --> C[Agent proposes code]
    C --> D[Jac compiler checks it]
    D --> E[Keep the result]
    E --> B

For example, an agent working on an offer-search walker can ask for related notes and code. JacBrain returns matching records with their sources. The agent can then submit a candidate snippet to the real Jac compiler and keep the result for later retrieval.

A compiler pass means the snippet compiles. Tests are still needed to show that it behaves correctly.

Try it

You need Python 3.11+. This first example needs no Jac installation, API key, or extra Python packages. These commands work in PowerShell or Bash:

git clone https://github.com/CosmonautJones/jacbrain.git
cd jacbrain

# Save a sample note and code file
python -m jacbrain ingest examples/walker-note.md --project demo
python -m jacbrain ingest examples/walker-pattern.jac --project demo

# Ask for relevant context
python -m jacbrain context "walker Offer traversal" --project demo

You’ll get JSON containing matching records, their sources, and validation status. Data stays in .jacbrain/brain.sqlite3 on your machine. Choose only nonsecret files to ingest.

Next: connect a coding agent and enable compiler checks →

Load Jac's actual reference guides

With Jac 0.37.23 available, import its bundled knowledge once:

python -m jacbrain sync-guides
python -m jacbrain context "walker with one typed report after traversal" --project my-app

This imports the installed guides, splits them into source-linked sections, and connects their explicit references. Task queries can then use that language knowledge alongside your project's notes. Re-run the import to refresh it. See the Windows setup if Jac runs in WSL.

Where it stands

Early working foundation. You can use the local tools today; the complete learning loop is still being built.

The persistent service currently uses Python and SQLite. The Jac nodes, edges, and walkers form a separate runnable graph model. “Learning” here means keeping evidence across sessions, not training an AI model.

First measured result

In a four-task pilot, selected context used 73% fewer reference tokens than curated complete guides. Both approaches passed all four compiler and behavior checks. Total observed model input fell by 16%, including tool overhead.

That is an encouraging small result, not proof of general savings. Graph and plain section retrieval returned identical context, so the graph itself has not yet shown an advantage. Read the experiment and its limits →

Explore further

Jac already provides MCP tools and code-context queries. JacBrain builds on that work and explores memory across tasks. Our prior-art review covers related projects and the questions we still need to test.

Built by Travis Jones, inspired by working with Jac on the M-Local team project. Independent of the Jac/Jaseci maintainers. MIT licensed.