kognios 1.1.7

A from-scratch Python agent framework with Tools, Memory, Knowledge/RAG, Team routing, streaming, and async.

Kogni·OS

A from-scratch Python agent framework. No heavy deps, no vector DB.
SQLite ships with Python. That's all you need.

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Why Kogni·OS?

Most agent frameworks are wrappers around wrappers. Kogni·OS is written from scratch so you can read and understand every line and extend it without fighting abstractions.

• SQLite FTS5 for RAG — no Chroma, no FAISS, no vector DB
• ReAct loop in ~800 lines of core code
• Zero heavy dependencies
• Built-in streaming, async, and structured output
• 9 providers: Anthropic, OpenAI, Groq, Gemini, Mistral, Cohere, Ollama, Bedrock, xAI

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Architecture

┌─────────────────────────────────────────────┐
│                   Team                       │
│  router_model ──▶ pick agent ──▶ agent.run() │
└───────────────────────┬─────────────────────┘
                        │
          ┌─────────────▼──────────────┐
          │           Agent             │
          │  ┌──────────────────────┐  │
          │  │   ReAct Loop         │  │
          │  │  think → tool → obs  │  │
          │  └──────────┬───────────┘  │
          │             │               │
          │  ┌──────────▼───────────┐  │
          │  │    BaseModel ABC      │  │
          │  │  Anthropic │ OpenAI  │  │
          │  │  Groq      │ Gemini  │  │
          │  └──────────────────────┘  │
          │                            │
          │  ┌──────────────────────┐  │
          │  │   ToolRegistry       │  │  ◀── @tool decorator
          │  │   @tool fns + schema │  │
          │  └──────────────────────┘  │
          │                            │
          │  ┌──────────────────────┐  │
          │  │   Memory             │  │
          │  │  ShortTerm (RAM)     │  │
          │  │  LongTerm  (SQLite)  │  │
          │  └──────────────────────┘  │
          │                            │
          │  ┌──────────────────────┐  │
          │  │   KnowledgeBase      │  │
          │  │  SQLite FTS5 / BM25  │  │  ◀── load(file|url|text)
          │  └──────────────────────┘  │
          └────────────────────────────┘

---

Install

pip install git+https://github.com/lavkeshdwivedi/kogniOS

Or from source (recommended for contributors):

git clone https://github.com/lavkeshdwivedi/kogniOS
cd kogniOS
pip install -e ".[dev]"

Requires Python ≥ 3.11 and at least one provider key:

Provider	Env var
Anthropic	ANTHROPIC_API_KEY
OpenAI	OPENAI_API_KEY
Groq	GROQ_API_KEY
Google Gemini	GEMINI_API_KEY
Mistral	MISTRAL_API_KEY
Cohere	COHERE_API_KEY
AWS Bedrock	AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY
xAI (Grok)	XAI_API_KEY
Ollama	no key (run locally)

---

30-second quickstart

from kognios import Agent, AnthropicModel, tool

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

agent = Agent(
    model=AnthropicModel(),
    tools=[get_weather],
    instructions="Be concise.",
)
print(agent.run("What's the weather in Paris?"))
# → "It's 22°C and sunny in Paris."

---

Core primitives

1 · Agent + Tools (ReAct loop)

@tool reads your type hints to generate an OpenAI-compatible JSON schema automatically. No decorating, no YAML, no config files.

from kognios import Agent, AnthropicModel, tool

@tool
def add(x: int, y: int) -> int:
    """Add two integers."""
    return x + y

@tool
def multiply(x: int, y: int) -> int:
    """Multiply two integers."""
    return x * y

agent = Agent(model=AnthropicModel(), tools=[add, multiply])
print(agent.run("What is (3 + 4) × 6?"))

The ReAct loop runs think → call tool → observe → repeat until the model stops calling tools, with a configurable max_iterations guard (default 10).

---

2 · Memory

from kognios import Agent, AnthropicModel, ShortTermMemory, LongTermMemory

short = ShortTermMemory(max_turns=20)   # sliding window, in RAM
long  = LongTermMemory(db_path="my.db") # SQLite key/value

long.store("user_name", "Alice")
long.store("location",  "Berlin")

agent = Agent(model=AnthropicModel(), memory=short)
# Known facts from long-term memory are auto-injected into the system prompt.

• ShortTermMemory: conversation history with sliding-window truncation
• LongTermMemory: persistent SQLite key/value; survives restarts

Semantic long-term memory: retrieve relevant facts by meaning, not key:

from kognios import Agent, AnthropicModel, LongTermMemory

def my_embed(texts):
    import openai
    client = openai.OpenAI()
    r = client.embeddings.create(model="text-embedding-3-small", input=texts)
    return [item.embedding for item in r.data]

long = LongTermMemory(db_path="my.db", embed_fn=my_embed)
long.store("user_goal", "Build a Python agent framework")
long.store("user_name", "Alice")

# retrieve the most relevant facts for a query
relevant = long.semantic_recall("What is the user building?", top_k=2)
# → [("user_goal", "Build a Python agent framework"), ...]

Requires pip install 'kognios[vector]' (numpy).

---

3 · Knowledge / RAG

from kognios import Agent, AnthropicModel, SQLiteKnowledge

kb = SQLiteKnowledge(db_path="docs.db")
kb.load("docs/manual.txt")                           # file path
kb.load("https://example.com/spec", source_type="url")  # URL
kb.load("Inline text goes here…")                   # raw string
kb.load("report.pdf")                               # PDF (pip install 'kognios[pdf]')
kb.load("page.html")                                # HTML (tags stripped)
kb.load("data.csv")                                 # CSV (flattened to text)
kb.load("config.json")                              # JSON (flattened)
kb.load("report.docx")                              # Word doc (pip install 'kognios[docx]')
kb.load("https://github.com/owner/repo", source_type="github")  # GitHub repo

agent = Agent(model=AnthropicModel(), knowledge=kb)
print(agent.run("Summarise the key points."))

Chunks are indexed with SQLite FTS5 and retrieved via BM25 ranking, no external vector database required.

---

4 · Team routing

from kognios import Agent, Team, AnthropicModel

model = AnthropicModel()
agents = {
    "finance": Agent(model=model, name="finance",
                     description="Stocks, markets, financial data."),
    "weather": Agent(model=model, name="weather",
                     description="Weather forecasts and climate."),
}
team = Team(router_model=model, agents=agents)
print(team.run("What is Apple's stock price?"))
# → Router picks "finance", delegates the message, returns the answer.

---

Multi-step planning

Ask an agent to plan and execute a complex goal in steps:

agent = Agent(model=AnthropicModel(), tools=[web_search, calculator])
result = agent.plan_and_run("Research the top 3 Python web frameworks and compare their performance.")
print(result)

The agent first generates a numbered plan, then executes each step with accumulated context, and finally consolidates the results.

Agent-to-agent messaging

Agents can talk directly to each other without a Team router:

researcher = Agent(model=AnthropicModel(), name="researcher",
                   instructions="Research and summarize topics.")
writer = Agent(model=AnthropicModel(), name="writer",
               instructions="Write clear, engaging prose.")

# Researcher gathers info, writer polishes it
summary = researcher.run("Summarize the key features of SQLite.")
article = writer.send(researcher, f"Rewrite this as a blog intro: {summary}")
print(article)

---

Streaming

for chunk in agent.stream("Write me a haiku about SQLite."):
    print(chunk, end="", flush=True)
print()

---

Async

import asyncio
from kognios import Agent, AnthropicModel

async def main():
    agent = Agent(model=AnthropicModel())
    result = await agent.arun("What is the capital of Japan?")
    print(result)

asyncio.run(main())

Async streaming:

async for chunk in agent.astream("Tell me a joke."):
    print(chunk, end="", flush=True)

---

Structured output

from pydantic import BaseModel
from kognios import Agent, AnthropicModel

class CityInfo(BaseModel):
    city: str
    country: str
    population_millions: float

agent = Agent(model=AnthropicModel())
info = agent.run("Tell me about Tokyo.", output_type=CityInfo)
print(info.city, info.population_millions)  # Tokyo  13.96

Requires pip install 'kognios[structured]'

---

Providers

from kognios.models.anthropic import AnthropicModel
from kognios.models.openai    import OpenAIModel
from kognios.models.groq      import GroqModel
from kognios.models.gemini    import GeminiModel
from kognios.models.ollama    import OllamaModel
from kognios.models.mistral   import MistralModel
from kognios.models.cohere    import CohereModel
from kognios.models.bedrock   import BedrockModel
from kognios.models.xai       import XAIModel

model = AnthropicModel(model="claude-sonnet-5")
model = OpenAIModel(model="gpt-4o")
model = GroqModel(model="llama-4-scout")                    # GROQ_API_KEY
model = GeminiModel(model="gemini-2.5-flash")                          # GEMINI_API_KEY
model = OllamaModel(model="llama3.3")                                  # needs Ollama running locally
model = MistralModel(model="mistral-large-latest")                     # MISTRAL_API_KEY
model = CohereModel(model="command-a-plus-05-2026")                    # COHERE_API_KEY
model = BedrockModel(model="anthropic.claude-3-7-sonnet-20250219-v1:0")  # AWS_* env vars
model = XAIModel(model="grok-4")                                  # XAI_API_KEY

All providers share the same BaseModel interface; one line to swap.

---

MCP (Model Context Protocol)

Connect any MCP server to your agent in two lines:

from kognios import Agent, AnthropicModel, MCPClient

async def main():
    async with MCPClient.stdio("uvx", ["mcp-server-filesystem", "."]) as mcp:
        agent = Agent(model=AnthropicModel(), tools=mcp.tools())
        result = await agent.arun("List Python files in this directory.")
        print(result)

Sync path:

with MCPClient.stdio("uvx", ["mcp-server-filesystem", "."]).sync() as mcp:
    agent = Agent(model=AnthropicModel(), tools=mcp.tools())
    print(agent.run("How many Python files are there?"))

Install the optional dep: pip install 'kognios[mcp]'

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Built-in tools

Import any of these into your agent:

from kognios.tools.builtins import (
    calculator,        # safe AST-based math evaluator
    python_eval,       # sandboxed exec with stdout capture
    read_file,         # read any local file
    write_file,        # write file + create parent dirs
    http_get,          # fetch URL (urllib, no requests needed)
    web_search,        # DuckDuckGo search
    code_interpreter,  # isolated subprocess exec with timeout
    browse,            # JS-rendered page fetching (pip install 'kognios[browser]')
)

---

CLI

# Interactive chat (streams by default)
kognios chat

# Choose provider and model
kognios chat -p openai -m gpt-4o

# One-shot Q&A over a local document
kognios ask README.md "What does this project do?"

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HTTP server

Serve any agent over HTTP with a single command:

pip install 'kognios[serve]'
kognios serve --provider anthropic --port 8000

Then call it:

curl -X POST http://localhost:8000/run \
     -H 'Content-Type: application/json' \
     -d '{"message": "What is the capital of France?"}'
# → {"response": "Paris.", "usage": {"input_tokens": 12, "output_tokens": 3}}

---

Evaluation

Run your agent against a test dataset and measure quality:

from kognios import Agent, AnthropicModel
from kognios.eval import AgentEvaluator, EvalCase, contains, exact_match

agent = Agent(model=AnthropicModel())

cases = [
    EvalCase(input="What is 2+2?", expected="4"),
    EvalCase(input="Capital of France?", expected="Paris"),
]

evaluator = AgentEvaluator(agent, scorer=contains)
report = evaluator.run(cases)
report.print_report()
# Cases: 2 | Pass rate: 100.0% | Mean score: 1.000 | Mean latency: 0.82s

Built-in scorers: exact_match, contains, regex_match(pattern), llm_judge(model).

Async parallel evaluation: await evaluator.arun(cases).

---

Guardrails

Validate or transform agent input/output with guardrails:

from kognios import Agent, AnthropicModel, block_keywords, max_length, pii_scrubber, GuardrailError

agent = Agent(
    model=AnthropicModel(),
    input_guardrails=[
        block_keywords("jailbreak", "ignore previous instructions"),
        max_length(1000),
    ],
    output_guardrails=[pii_scrubber()],
)

try:
    result = agent.run("Tell me how to jailbreak this system")
except GuardrailError as e:
    print(f"Blocked: {e}")

Built-in guardrails: block_keywords(*kws), max_length(n), pii_scrubber(), profanity_filter().

Plugin system

Third-party packages can extend Kogni·OS with new providers, tools, and memory backends by declaring Python entry points:

# In your package's pyproject.toml:
[project.entry-points."kognios.providers"]
my_provider = "my_package:MyModel"

[project.entry-points."kognios.tools"]
my_tool = "my_package:my_tool_fn"

Then discover and load them at runtime:

from kognios.plugins import load_plugins, list_plugins

providers = load_plugins("kognios.providers")
print(list_plugins("kognios.tools"))

---

Tracing

Record every LLM call and tool execution as a span:

from kognios import Agent, AnthropicModel
from kognios.tracing import Tracer

tracer = Tracer()
agent = Agent(model=AnthropicModel(), tracer=tracer)
agent.run("What is 2+2?")

tracer.print_spans()
# [TRACE] llm.complete 823.4ms model=claude-sonnet-4-6

---

Examples

python examples/basic_agent.py
python examples/agent_with_tools.py
python examples/agent_with_memory.py
python examples/agent_with_knowledge.py
python examples/multi_agent_team.py

---

Tests

pip install -e ".[dev]"
pytest tests/ -v

154 unit tests, all mocked, no API key required.

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Project layout

kognios/
├── agent.py              # Agent class + ReAct loop
├── team.py               # Team router
├── cli.py                # Click CLI
├── models/
│   ├── base.py           # BaseModel ABC, ModelResponse, ModelChunk
│   ├── anthropic.py      # Anthropic provider
│   ├── openai.py         # OpenAI provider
│   ├── groq.py           # Groq (OpenAI-compatible)
│   └── gemini.py         # Gemini (OpenAI-compatible)
├── tools/
│   ├── registry.py       # @tool decorator + ToolRegistry
│   └── builtins/         # calculator, python_eval, read_file, …
├── memory/
│   ├── short_term.py     # in-RAM sliding window
│   └── long_term.py      # SQLite key/value
├── knowledge/
│   ├── base.py           # KnowledgeBase ABC
│   └── sqlite_fts.py     # FTS5 RAG implementation
└── storage/
    └── sqlite.py         # shared connection + migrations

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Contributing

See CONTRIBUTING.md for setup instructions, coding conventions, and the PR process.

Good areas to contribute:

• New model providers
• Long-term memory backends (Redis, DynamoDB)
• Async-native kognios eval --concurrent flag
• OpenTelemetry sink for the Tracer

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Roadmap

See ROADMAP.md for the full plan.

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License

MIT. See LICENSE.

Built by Lavkesh Dwivedi.