KOAN

Knowledge-Oriented Agent Network

A context graph-native framework for intelligent, grounded, and traceable AI agents.

KOAN connects LLM-powered agents to property graph databases, so every decision an agent makes is recorded as a traceable node in a decision graph. This enables precedent search, policy governance, gap detection, and full audit trails across multi-agent systems.

Features

• Unified LLM Client -- OpenAI and Anthropic with automatic retry, streaming, and cost tracking
• Structured Output -- Parse LLM responses into typed Pydantic models
• Tool System -- @tool decorator with automatic JSON schema generation
• Agent Orchestration -- ReAct agents, sequential chains, parallel fan-out, supervisor, and orchestrator patterns
• Streaming -- Token-by-token streaming for agents, orchestrators, and SSE endpoints
• MCP Integration -- Build and connect to Model Context Protocol servers
• Context Graphs -- Trace every agent decision into Neo4j, Memgraph, or FalkorDB
• Graph-Augmented Reasoning -- Agents read past decisions, entity history, and policies from the graph before reasoning, then write new traces back (closed loop)
• Self-Organizing Policies -- Policies emerge automatically from repeated tool patterns, gate available tools, and retire when feedback turns negative
• Feedback-Weighted Precedents -- Past decisions carry feedback scores so agents learn which approaches worked and which didn't
• Precedent Search -- Find past decisions for the same entity, with confidence and outcome data
• Policy Governance -- Link decisions to policies, detect ungoverned gaps
• Memory -- Thread-scoped conversation history (SQLite), vector memory (ChromaDB), forkable threads with checkpoint/rewind (time travel)
• Observability -- OpenTelemetry tracing, structured logging, per-call cost tracking
• CLI -- koan verify, koan init, koan mcp dev, koan graph init

Install

pip install koan-ai

With LLM providers:

pip install koan-ai[llm]          # OpenAI + Anthropic
pip install koan-ai[openai]       # OpenAI only
pip install koan-ai[anthropic]    # Anthropic only

With context graphs:

pip install koan-ai[graph-neo4j]     # Neo4j
pip install koan-ai[graph-memgraph]  # Memgraph
pip install koan-ai[graph-falkordb]  # FalkorDB

Everything:

pip install koan-ai[all]

Note: The PyPI package is koan-ai, but the import name is koan:

import koan
from koan.agents import ReactAgent

Quickstart

1. Basic LLM Call

import asyncio
from koan.llm.client import LLMClient
from koan.llm.config import LLMConfig
from koan.types import Message

async def main():
    async with LLMClient() as client:
        config = LLMConfig(provider="openai", model="gpt-4o-mini")
        messages = [Message(role="user", content="What is KOAN?")]
        response = await client.complete(config, messages)
        print(response.text)

asyncio.run(main())

2. Agent with Tools

import asyncio
from koan.agents import AgentConfig, ReactAgent
from koan.llm.client import LLMClient
from koan.tools import ToolRegistry, tool

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"22C and sunny in {city}"

async def main():
    async with LLMClient() as client:
        registry = ToolRegistry()
        registry.add(get_weather)

        agent = ReactAgent(
            "weather-bot",
            client=client,
            config=AgentConfig(
                provider="openai",
                model="gpt-4o-mini",
                system_prompt="Use tools to answer questions.",
            ),
            tools=registry,
        )

        result = await agent.run("What's the weather in Tokyo?")
        print(result.output)

asyncio.run(main())

3. Multi-Agent Orchestrator

from koan.agents import Orchestrator, ReactAgent, RuleRouter

orchestrator = Orchestrator(
    agents={
        "finance": finance_agent,
        "hr": hr_agent,
    },
    router=RuleRouter({
        "stock": "finance", "price": "finance",
        "employee": "hr", "pto": "hr",
    }),
)

result = await orchestrator.run("What is the stock price of AAPL?")

4. Decision Tracing with Context Graphs

from koan.graph import DecisionTracer, GraphConfig, create_graph_client

config = GraphConfig(provider="falkordb", uri="redis://localhost:6379")

async with create_graph_client(config) as graph:
    async with DecisionTracer(graph, run_id="r-1", agent_id="bot", thread_id="t-1") as tracer:
        decision_id = await tracer.record_decision(
            description="Classified as billing inquiry",
            decision_type="classification",
            confidence=0.95,
            reasoning="Keywords: invoice, payment",
        )
        await tracer.record_action(
            decision_id=decision_id,
            action_type="tool_call",
            tool_name="lookup_billing",
        )
        await tracer.record_outcome(status="success", result="Resolved")

5. Streaming Agent Events

from koan.agents import StreamingReactAgent, ToolCallStart, TextDelta, AgentComplete

agent = StreamingReactAgent("bot", client=client, config=config, tools=registry)

async for event in agent.run_stream("What is 17 + 28?"):
    if isinstance(event, TextDelta):
        print(event.text, end="", flush=True)
    elif isinstance(event, ToolCallStart):
        print(f"\n[calling {event.tool_name}...]")
    elif isinstance(event, AgentComplete):
        print(f"\nDone ({event.tool_calls_made} tool calls)")

Architecture

koan/
├── llm/          # Unified LLM client (OpenAI, Anthropic)
├── output/       # Structured output parsing (Pydantic)
├── prompts/      # Jinja2 prompt templates
├── streaming/    # Token streaming, SSE helpers
├── tools/        # @tool decorator, registry, execution
├── memory/       # Thread store (SQLite), vector memory (ChromaDB)
├── agents/       # ReAct, Chain, Parallel, Orchestrator, Supervisor
├── mcp/          # MCP server builder + client
├── graph/        # Context graphs (Neo4j, Memgraph, FalkorDB)
├── telemetry/    # OpenTelemetry, structured logging, cost tracking
└── cli/          # Developer CLI (verify, init, mcp dev, graph init)

Context Graph Schema

Agent -EXECUTED-> Run -CONTAINS-> Decision -LED_TO-> Action
                   |                  |
                   +-PRODUCED-> Outcome  |-GOVERNED_BY-> Policy
                                         |-REFERENCED-> Entity
                                         +-CITED_PRECEDENT-> Decision

Graph Database Setup

Start a graph database with Docker:

# FalkorDB (Redis protocol)
docker compose -f docker/docker-compose.graph.yml --profile falkordb up -d

# Neo4j (Bolt protocol)
docker compose -f docker/docker-compose.graph.yml --profile neo4j up -d

# Memgraph (Bolt protocol)
docker compose -f docker/docker-compose.graph.yml --profile memgraph up -d

CLI

koan verify          # Check API keys and installed packages
koan init [path]     # Scaffold a new KOAN project
koan mcp dev <file>  # Run an MCP server in dev mode
koan graph init      # Initialize graph database schema

Examples

See the examples/ directory for working demonstrations:

Example	Description
01_basic_completion.py	Simplest LLM call
02_structured_output.py	Pydantic model as output
03_streaming_chat.py	Token-by-token streaming
04_tool_agent.py	Agent with custom tools
05_multi_agent.py	Orchestrator + sub-agents
09_streaming_tool_agent.py	Streaming agent with tools
10_streaming_orchestrator.py	Streaming multi-agent
12_supervisor.py	Supervisor pattern
13_mcp_server_client.py	MCP server + client
15_supervisor_mcp_agents.py	Per-agent MCP servers
16_context_graph.py	Full context graph lifecycle
17_graph_augmented_agent.py	Graph-augmented agent (closed loop)
18_feedback_and_calibration.py	Human feedback and confidence calibration

Demos

Eight interactive demo apps (FastAPI + browser UI) showcasing KOAN in action:

Demo	Port	Features
Demo 1 — Simple Chat	8001	ReactAgent, tools, SQLite threads, SSE streaming
Demo 2 — Graph Chat	8002	GraphAugmentedAgent, feedback, context graph
Demo 3 — Multi-Agent	8003	Orchestrator, RuleRouter, 3 agents
Demo 4 — Full Stack	8004	Multi-agent + graph, evaluation, calibration
Demo 5 — Self-Organizing	CLI	Full policy lifecycle: emergence → gating → retirement
Demo 6 — Legal Assistant	8006	Case lookup, document search, motion drafting
Demo 7 — Medical Triage	8007	Patient vitals, labs, medication & allergy checks
Demo 8 — Investment Advisor	8008	Portfolio risk, trade execution, market data

Demos 5-8 demonstrate the self-organizing policy lifecycle across different domains. See demos/ for setup instructions and demos/TEST_QUESTIONS.md for copy-paste test scripts.

Development

# Install uv
curl -Ls https://astral.sh/uv/install.sh | sh

# Clone and setup
git clone https://github.com/koan-framework/koan.git
cd koan
uv sync --group dev

# Run tests
uv run pytest                                      # Unit tests (no external deps)
uv run pytest -m integration                       # Integration tests (needs API keys)
uv run pytest -m graph                             # Graph tests (needs Docker)

# Lint and type check
uv run ruff check src/
uv run pyright src/

License

MIT