Object-Spatial Programming#
Learn Jac's unique graph-based programming paradigm with nodes, edges, and walkers.
Prerequisites
- Completed: Installation
- Recommended: What Makes Jac Different (gentler introduction)
- Time: ~45 minutes
This is the hands-on tutorial
This page teaches OSP by building things step by step. For the complete, authoritative specification of node/edge/walker syntax, see the OSP reference.
Graph Persistence Between Runs
jac run persists graph state to a .jac/ directory. If you run an example multiple times, you may see duplicate nodes or NodeAnchor ... is not a valid reference! errors. To start fresh, clean the .jac/ directory: jac clean --all
What is Object-Spatial Programming?#
Traditional OOP: Objects exist in isolation. You call methods to bring data to computation.
Object-Spatial Programming (OSP): Objects exist in a graph with explicit relationships. You send computation (walkers) to data.
graph TD
subgraph oop["Traditional OOP"]
Obj["Object<br/>.method()"]
end
subgraph osp["Object-Spatial Programming"]
N1["Node"] -->|Edge| N2["Node"]
W1(["Walker visits<br/>and operates"]) -.-> N1
W2(["Walker moves<br/>to connected nodes"]) -.-> N2
end
Nodes: Objects in Space#
Nodes are classes that can be connected in a graph.
node Person {
has name: str;
has age: int;
def greet() -> str {
return f"Hi, I'm {self.name}!";
}
}
with entry {
# Create nodes (just like regular objects)
alice = Person(name="Alice", age=30);
bob = Person(name="Bob", age=25);
# Use them like regular objects
print(alice.greet()); # Hi, I'm Alice!
print(bob.age); # 25
}
Key point: Nodes are full-featured classes with methods, inheritance, etc. The graph capability is dormant until you connect them.
Connecting Nodes#
Use the ++> operator to connect nodes:
node Person {
has name: str;
}
with entry {
alice = Person(name="Alice");
bob = Person(name="Bob");
carol = Person(name="Carol");
# Connect to root (the default starting node)
root ++> alice;
root ++> bob;
# Connect alice to carol
alice ++> carol;
}
This creates a graph:
graph TD
root --> alice
root --> bob
alice --> carol
Edges: Named Relationships#
Edges can carry data and have types:
node Person {
has name: str;
}
edge Knows {
has since: int; # Year they met
has strength: str; # "close", "acquaintance"
}
with entry {
alice = Person(name="Alice");
bob = Person(name="Bob");
# Connect with a typed edge
alice +>: Knows(since=2020, strength="close") :+> bob;
}
A typed edge stores its own data on the connection itself, so "Alice knows Bob since 2020" lives on the edge rather than on either node. For the full set of connection operators (generic, typed, bidirectional) and typed edge endpoints, see the Edges reference.
Querying the Graph#
Use spatial operators to navigate:
node Person {
has name: str;
}
with entry {
alice = Person(name="Alice");
bob = Person(name="Bob");
carol = Person(name="Carol");
root ++> alice;
alice ++> bob;
alice ++> carol;
# Query connections from root
people = [root -->]; # All nodes connected to root
print(len(people)); # 1 (alice)
# Query from alice
friends = [alice -->]; # [bob, carol]
# Filter by type
only_people = [root -->][?:Person];
}
The same edge-reference bracket syntax does a lot: filter by edge type ([src ->:Knows:->]), filter results by node type ([?:Person]), filter by attribute ([?age >= 18]), and chain hops for friends-of-friends ([alice ->:Knows:-> ->:Knows:->]). For the complete query grammar, see the Object Spatial Queries reference.
Walkers: Mobile Computation#
Naming: Root (capitalized) is the type of the root node, used in ability declarations like can X with Root entry. root (lowercase) is the built-in instance, used in expressions like root ++> node or root spawn Walker().
Walkers are objects that traverse the graph and execute abilities at each node.
node Person {
has name: str;
has visited: bool = False;
}
walker Greeter {
can start with Root entry {
visit [-->]; # Visit nodes connected to root
}
can greet with Person entry {
print(f"Hello, {here.name}!");
here.visited = True;
}
}
with entry {
alice = Person(name="Alice");
bob = Person(name="Bob");
root ++> alice;
alice ++> bob;
# Spawn walker at root
root spawn Greeter();
}
Output:
Walkers need explicit traversal
A walker spawned at root starts there but doesn't automatically visit connected nodes. You must include a can start with Root entry { visit [-->]; } ability to begin traversal. Without it, the walker sits at root and its node-specific abilities never fire.
Wait, why only Alice? Because the walker visits root first (via start), then visits Alice (via greet), but doesn't continue to Bob. The walker needs to be told to continue traversing.
Walker Traversal with visit#
Use visit to continue to connected nodes:
node Person {
has name: str;
}
walker Greeter {
can start with Root entry {
visit [-->]; # Start by visiting nodes connected to root
}
can greet with Person entry {
print(f"Hello, {here.name}!");
visit [-->]; # Continue to all connected nodes
}
}
with entry {
alice = Person(name="Alice");
bob = Person(name="Bob");
carol = Person(name="Carol");
root ++> alice;
alice ++> bob;
alice ++> carol;
root spawn Greeter();
}
Output:
Walker Context Variables#
Inside a walker ability, here is the current node and self is the walker instance. (visitor is the mirror image -- it only exists inside node abilities, where it names the visiting walker.) For the full table of which special reference is valid in which context, see Special References.
node Room {
has name: str;
}
walker Explorer {
has rooms_visited: int = 0;
can explore with Room entry {
self.rooms_visited += 1;
print(f"In {here.name}, visited {self.rooms_visited} rooms");
visit [-->];
}
}
Reporting Results#
Use report to collect results from walkers:
node Person {
has name: str;
has age: int;
}
walker FindAdults {
can start with Root entry {
visit [-->];
}
can check with Person entry {
if here.age >= 18 {
report here; # Add to results
}
visit [-->];
}
}
with entry {
root ++> Person(name="Alice", age=30);
root ++> Person(name="Bob", age=15);
root ++> Person(name="Carol", age=25);
result = root spawn FindAdults();
print(f"Found {len(result.reports)} adults");
for person in result.reports {
print(f" - {person.name}");
}
}
Output:
The report keyword does two things: prints the value to the console, and stores it in result.reports for programmatic access.
Stopping Early with disengage#
Use disengage to immediately stop a walker's traversal -- useful when you've found what you're looking for:
node Person {
has name: str;
has age: int;
}
walker FindFirst {
can start with Root entry {
visit [-->];
}
can check with Person entry {
if here.age >= 18 {
report here;
disengage; # Stop immediately -- don't visit more nodes
}
visit [-->];
}
}
with entry {
root ++> Person(name="Alice", age=15);
root ++> Person(name="Bob", age=25);
root ++> Person(name="Carol", age=30);
result = root spawn FindFirst();
print(f"First adult: {result.reports[0].name}");
}
Output:
Without disengage, the walker would continue visiting Carol. With it, the walker stops as soon as it finds Bob. This is especially useful in search walkers where you need to find and modify a specific node (like toggling or deleting a task by ID).
Entry Points#
Walkers can have different entry points:
node Person {
has name: str;
has age: int;
}
walker DataProcessor {
has data: str;
# Runs when spawned at root
can start with Root entry {
print("Starting from root");
visit [-->];
}
# Runs when visiting a Person node
can process with Person entry {
print(f"Processing {here.name}");
visit [-->];
}
# Runs only at the spawn location -- a generic `with entry`
# does NOT fire on every visited node
can default with entry {
print("At spawn location");
visit [-->];
}
}
For the complete event-clause forms (typed entries, exit abilities, walker-type unions), see Node Entry/Exit Abilities.
Practical Example: Social Network#
node User {
has username: str;
has bio: str = "";
}
edge Follows {
has since: str;
}
walker FindFollowers {
can find with User entry {
# Find all users who follow this user
followers = [<-:Follows:<-];
for follower in followers {
report follower;
}
}
}
walker FindMutualFollows {
can find with User entry {
following = [here ->:Follows:->];
followers = [here <-:Follows:<-];
for user in following {
if user in followers {
report user; # Mutual follow!
}
}
}
}
with entry {
alice = User(username="alice");
bob = User(username="bob");
carol = User(username="carol");
root ++> alice;
root ++> bob;
root ++> carol;
# Alice follows Bob, Bob follows Alice (mutual)
alice +>: Follows(since="2024") :+> bob;
bob +>: Follows(since="2024") :+> alice;
# Carol follows Alice
carol +>: Follows(since="2024") :+> alice;
# Find Alice's followers
result = alice spawn FindFollowers();
print("Alice's followers:");
for user in result.reports {
print(f" - {user.username}");
}
# Find Alice's mutual follows
result = alice spawn FindMutualFollows();
print("Alice's mutual follows:");
for user in result.reports {
print(f" - {user.username}");
}
}
Output:
User(username='bob', bio='')
User(username='carol', bio='')
Alice's followers:
- bob
- carol
User(username='bob', bio='')
Alice's mutual follows:
- bob
Running as an API#
The same graph code becomes a REST API:
node Todo {
has title: str;
has done: bool = False;
}
walker add_todo {
has title: str;
can create with Root entry {
new_todo = here ++> Todo(title=self.title);
report new_todo;
}
}
Every public walker becomes an endpoint: its has properties become the request body and its report values become the response. See Walkers as REST APIs for the full treatment.
Note
main.jac is the default entry point. If your file has a different name (e.g., app.jac), pass it explicitly: jac start app.jac.
# Run as API server
jac start
# Call via HTTP
curl -X POST http://localhost:8000/walker/add_todo \
-H "Content-Type: application/json" \
-d '{"title": "Learn OSP"}'
When to Use Functions vs Walkers#
Jac gives you two ways to expose server logic: def:pub functions and walker types. The short version: def:pub functions are great for simple, stateless CRUD and quick prototyping, while walkers shine for graph traversal, per-user data isolation, and production apps where state accumulates across nodes. For the full side-by-side comparison, see When to Use Walkers vs Functions in the reference.
Rule of thumb
Start with def:pub to prototype quickly. Switch to walkers when you need authentication, per-user data isolation, or multi-step graph traversal.
The AI Day Planner Tutorial uses def:pub in the early parts, then refactors to walkers in Part 6 -- showing exactly when and why to make the switch.
Key Takeaways#
| Concept | Purpose |
|---|---|
node |
Objects that live in a graph |
edge |
Typed relationships between nodes |
walker |
Mobile computation that traverses the graph |
++> |
Connect nodes |
[-->] |
Query connections |
visit |
Continue walker traversal |
report |
Collect results from walker |
disengage |
Stop walker traversal immediately |
here |
Current node in walker |
spawn |
Start walker at a node |
Next Steps#
Continue Learning:
- Testing - Test your nodes and walkers
- AI Integration - Add LLM capabilities
- AI Day Planner Tutorial - Build a complete app with OSP concepts
Reference:
- Graph Operations - Complete edge/node operator reference
- Walker Responses - Understanding
.reportspatterns - Part III: OSP - Full language reference