S13Code

S13Code is the standalone Session 13 agent runtime. It implements a live task graph, scoped and provenance-bearing memory, Rohan's semantic chunking V2, and Agent2Agent interoperability. It asks glc_v3 for model completions over HTTP and never owns provider credentials.

What runs where

Service	Default address	Responsibility
glc_v3	http://127.0.0.1:8111	Models, keys, routing and channels
S13Code HTTP	http://127.0.0.1:8113	Graph, memory, documents and JSON-RPC A2A
S13Code gRPC	127.0.0.1:8114	Official A2A gRPC service
Ollama	http://127.0.0.1:11434	Phi-4 segmentation and Nomic embeddings

Requirements

• Python 3.11 or newer
• uv
• A running glc_v3
• A running Ollama with phi4 and nomic-embed-text

ollama pull phi4
ollama pull nomic-embed-text
ollama serve

Install and run

Unzip glc_v3, S13Code, and S13Proof beside one another. Start glc_v3 first. Then, from this directory:

uv sync

export GLC_BASE_URL=http://127.0.0.1:8111
export S13_GATEWAY_PROVIDER=gemini
export S13_SANDBOX_ROOT="$PWD/sandbox"
export S13_CHUNK_MODEL=phi4:latest
export S13_LIVE_SEMANTIC_CHUNKING=1

uv run s13code serve

State is written under ~/.s13code by default. Set S13_DATA_DIR to use another directory.

Check both services:

curl http://127.0.0.1:8111/healthz
curl http://127.0.0.1:8113/healthz
curl http://127.0.0.1:8113/readyz
curl http://127.0.0.1:8113/.well-known/agent-card.json

Run a prompt

curl -s http://127.0.0.1:8113/v1/agent/runs \
  -H 'Content-Type: application/json' \
  -d '{
    "tenant_id": "course",
    "project_id": "s13",
    "user_id": "student-01",
    "agent_id": "assistant",
    "prompt": "Say hello."
  }'

The response contains the final answer, graph nodes and edges, ordered graph events, and provider/agent assignments. Inspect a persisted run with:

curl http://127.0.0.1:8113/v1/agent/runs/<run-id>

Index the sample corpus

The five files under sandbox/papers/ are fixed .txt fixtures for semantic chunking and retrieval proofs.

curl -s http://127.0.0.1:8113/v1/agent/runs \
  -H 'Content-Type: application/json' \
  -d '{
    "tenant_id": "course",
    "project_id": "papers",
    "user_id": "student-01",
    "prompt": "Index every .txt file under papers/. Confirm how many chunks were indexed in total."
  }'

Document ingestion is versioned and atomic: source preparation, semantic boundaries, exact spans, Nomic embeddings, and visibility succeed together or roll back together.

Architecture

• s13code/core/live_graph/: durable graph state, patches, event replay and bounded parallel execution
• s13code/core/memory/: scope checks, provenance, contradiction history, semantic chunking and FAISS retrieval
• s13code/core/a2a_adapter/: Agent Cards, JSON-RPC, SSE/push, official gRPC and trust checks
• s13code/gateway.py: the only S13Code → glc_v3 seam
• s13code/runtime.py: joins graph, memory, tools and model calls into an inspectable run
• tests/: executable invariants and regression cases

Test before opening a pull request

uv run ruff check .
uv run pytest -q

cd ../S13Proof
uv sync
uv run pytest -q

Student contribution

Fork the official theschoolofai/S13Code repository linked from Axiom, create a branch, implement one meaningful extension, and open one pull request against that repository. Do not open the Session 13 pull request against theschoolofai/glc_v3.

Add one subsection to this README in the same pull request. It must contain:

1. the user-visible capability,
2. the exact prompt or API request,
3. the graph and ordered event trace,
4. the actual final result,
5. evidence and provider/agent assignments,
6. the adversarial failure and its fix,
7. commands that reproduce the result from a fresh checkout.

Do not commit .env, credentials, personal memory, generated databases, unrestricted local paths, benchmark output containing private data, or provider responses containing secrets. Use synthetic identities in every proof.

License

MIT. See LICENSE.