efficient-agent-protocol 0.1.7

High-performance multi-agent framework for stateful batched execution.

Efficient Agent Protocol (EAP)

Status: Experimental (pre-1.0). APIs and schema may change. See STABILITY.md and ROADMAP.md for guarantees and planned milestones. Latest stable release: v0.1.7

Efficient Agent Protocol is a local-first framework for multi-step tool workflows. It stores large outputs as pointer-backed state (ptr_*) and runs dependency-aware DAG steps in parallel.

Who This Is For

• Python developers building local-first agent/tool orchestration
• Teams that need pointer-backed state and execution trace visibility

Not ideal yet for:

• strict long-term API compatibility requirements before v1.0
• non-technical users expecting zero-configuration onboarding

What You Get

• Pointer-based state to keep prompts small
• Parallel DAG execution with retries and validation
• Built-in chat UI (Streamlit) with trace + data inspection
• Conversation memory (full/window/summary)
• Pluggable pointer storage backends (SQLite, Redis, PostgreSQL)

Quickstart (GitHub-first)

Requirements:

• Python 3.9+

1. Install

git clone https://github.com/GenieWeenie/efficient-agent-protocol.git
cd efficient-agent-protocol
pip install -e .

2. Configure

cp .env.example .env

Minimum variables:

EAP_BASE_URL=http://localhost:1234
EAP_MODEL=nemotron-orchestrator-8b
EAP_API_KEY=not-needed

3. Smoke test

python -m examples.01_minimal

4. Run dashboard

pip install streamlit pandas
streamlit run app.py

5. Use it

• Open http://localhost:8501
• In Agent Chat, ask for a task
• Check Data Inspector for pointer payloads
• Check Execution Trace for step timing/retries/errors

Programmatic Example

from eap.protocol import StateManager
from eap.environment import AsyncLocalExecutor, ToolRegistry
from eap.environment.tools import read_local_file, READ_FILE_SCHEMA
from eap.agent import AgentClient

state_manager = StateManager()
registry = ToolRegistry()
registry.register("read_local_file", read_local_file, READ_FILE_SCHEMA)
executor = AsyncLocalExecutor(state_manager, registry)

architect = AgentClient(
    base_url="http://localhost:1234",
    model_name="nemotron-orchestrator-8b",
    provider_name="local",
)

manifest = registry.get_agent_manifest()
macro = architect.generate_macro("Read README.md and summarize setup steps", manifest)
# asyncio.run(executor.execute_macro(macro))

Common Commands

python3 -m pytest -q
pre-commit run --all-files
python3 scripts/migrate_state_db.py --db-path agent_state.db --dry-run
python3 scripts/export_metrics.py --db-path agent_state.db --output metrics/latest.json
python3 -m build

Docs

• STABILITY.md
• ROADMAP.md
• docs/v1_contract.md
• docs/release_notes_template.md
• docs/benchmarks.md
• docs/release.md
• docs/v1_stabilization_checklist.md
• docs/migrations.md
• docs/observability.md
• docs/maintainer_runbook.md
• SECURITY.md
• CONTRIBUTING.md
• GitHub roadmap board: https://github.com/users/GenieWeenie/projects/1
• docs/configuration.md
• docs/architecture.md
• docs/tools.md
• docs/workflow_schema.md
• docs/storage_lifecycle.md
• docs/storage_backends.md
• docs/sdk_contract.md
• docs/distributed_execution.md
• docs/troubleshooting.md
• docs/eap_proof_sheet.md