buddy-ai 2.2.3

A comprehensive Python framework for building and deploying AI agents with multi-model support and advanced capabilities

🤖 Buddy AI

A model-agnostic Python framework for building production-grade AI agents

Documentation · GitHub · CHANGELOG · Examples

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What is Buddy AI?

Buddy AI is a comprehensive Python framework for creating, deploying, and managing intelligent AI agents. It provides a unified interface across 25+ LLM providers, a powerful memory system, extensible tools, RAG-based knowledge management, multi-agent teams, and workflows — all production-ready out of the box.

New in v2.2.0 → Competency Engine — a balance-aware competency score that routes tasks to the most competent member and prioritizes what to train next.

v2.1.0 → PULSE — give your AI an employee identity, teach it through interactive knowledge transfer, have it attend meetings, manage tasks, and more.

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Installation

# Core install
pip install buddy-ai

# With all optional dependencies (recommended)
pip install buddy-ai[all]

# Specific providers
pip install buddy-ai[openai]
pip install buddy-ai[anthropic]
pip install buddy-ai[google]
pip install buddy-ai[groq]

# Ecosystem integrations
pip install buddy-ai[langchain]   # use Buddy models/agents/tools in LangChain
pip install buddy-ai[langgraph]   # run Buddy agents as LangGraph nodes

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Quick Start

from buddy import Agent
from buddy.models.openai import OpenAIChat

agent = Agent(
    name="Assistant",
    model=OpenAIChat(),
    instructions="You are a helpful assistant."
)

response = agent.run("What can you do?")
print(response.content)

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✨ PULSE — Virtual Employee's ERA

The flagship feature of v2.1.0

PULSE turns a Buddy AI agent into a fully-functional virtual human team member — complete with a professional identity, the ability to learn from documents and live human sessions (KT), attend meetings, manage tasks, and communicate across channels.

Launch the PULSE web UI

pip install buddy-ai[all]

# Set your LLM key
export OPENAI_API_KEY=sk-...

# Start the dashboard (or configure the key inside the UI)
buddy pulse start
# Open http://localhost:8888

Or use PULSE in Python

from buddy.pulse import PulseEmployee
from buddy.pulse.identity import EmployeeProfile
from buddy.models.openai import OpenAIChat

# Create the employee
priya = PulseEmployee(
    employee_profile=EmployeeProfile(
        full_name="Priya Sharma",
        role="Senior Backend Engineer",
        department="Engineering",
        skills=["Python", "FastAPI", "PostgreSQL"],
        timezone="Asia/Kolkata",
    ),
    model=OpenAIChat(id="gpt-4o"),
)

# Introduce herself
print(priya.introduce_yourself())

# Learn from a document
summary = priya.take_kt(
    source="architecture.md",
    session_name="Payments Architecture",
    knowledge_giver="Arjun Nair",
)
print(f"Confidence: {summary.confidence_score:.0%}")
print(f"Mental model: {summary.mental_model}")

# Start a live interactive KT session
session = priya.start_live_kt(
    session_name="Auth Service Deep Dive",
    knowledge_giver="Rahul",
)

# Human explains, Priya asks targeted questions
turn = session.human_explains("Our auth uses JWT with RS256...")
print(turn.pulse_message)  # Priya's response + questions
print(turn.questions)      # Clarifying questions

# Attend a meeting and extract actions
notes = priya.attend_meeting(
    transcript="Alice: Let's ship the new API by Friday...",
    title="Sprint Planning",
)
for action in notes.action_items:
    print(f"[{action.priority}] {action.description} → {action.owner}")

# Receive and track a task
task = priya.receive_task(
    title="Refactor payment retry logic",
    description="Reduce retry latency by 40%",
    priority="high",
    deadline="2026-06-20",
)
print(priya.report_status(task.task_id).message)

PULSE Web UI — 9 pages

Page	What it does
Onboarding Wizard	4-step setup: identity → company/role → skills → launch
Dashboard	KT stats, active tasks, knowledge domains
KT Center	Launch live (chat) or async (document/URL) KT sessions
Live KT Session	3-panel view: dialogue + real-time Confidence Meter + Mental Model
Meeting Room	Paste transcript → extract decisions + action items instantly
Task Board	Kanban board with move buttons and status updates
Chat	WebSocket streaming chat directly with your PULSE employee
Knowledge Explorer	Search across everything PULSE has learned
Settings	Configure AI model, API keys, and employee profile

Desktop app (Windows & Mac)

Run PULSE as a native desktop app with system tray support — the autonomous worker keeps running when you close the window.

pip install buddy-ai[all]
cd pulse-ui && npm install && npm run build
cd ../pulse-desktop && npm install && npm start

Build installers: npm run build:win or npm run build:mac. See pulse-desktop/README.md.

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Core Features

🤖 Agent System

from buddy import Agent
from buddy.models.anthropic import Claude
from buddy.tools.tavily import TavilyTools
from buddy.memory.agent import AgentMemory

agent = Agent(
    name="ResearchBot",
    model=Claude(id="claude-opus-4-5"),
    tools=[TavilyTools()],
    memory=AgentMemory(),
    instructions="You are a research assistant.",
    markdown=True,
)

response = agent.run("Latest developments in quantum computing?")

🧠 25+ Model Providers

from buddy.models.openai import OpenAIChat
from buddy.models.anthropic import Claude
from buddy.models.google import Gemini
from buddy.models.groq import Groq
from buddy.models.ollama import Ollama

# Local model via Ollama
agent = Agent(model=Ollama(id="llama3.2"))

# Switch models at runtime
agent.model = Claude(id="claude-sonnet-4-5")

🛠️ Tools

from buddy import Agent
from buddy.models.openai import OpenAIChat
from buddy.tools import tool
from buddy.tools.python import PythonTools
from buddy.tools.file import FileTools

# Built-in toolkits
agent = Agent(model=OpenAIChat(), tools=[PythonTools(), FileTools()])

# Custom tool with the @tool decorator
@tool
def get_stock_price(ticker: str) -> str:
    """Get real-time stock price for a ticker symbol."""
    return fetch_price(ticker)

# Plain functions work too — just pass them in `tools=[...]`
agent = Agent(model=OpenAIChat(), tools=[get_stock_price])

🧠 Memory & Knowledge (RAG)

from buddy.knowledge.pdf import PDFKnowledgeBase
from buddy.vectordb.chroma import ChromaDb

knowledge = PDFKnowledgeBase(
    path="company_handbook.pdf",
    vector_db=ChromaDb(collection="handbook"),
)

agent = Agent(
    model=OpenAIChat(),
    knowledge=knowledge,
    search_knowledge=True,
)

👥 Multi-Agent Teams

from buddy import Agent, Team

researcher = Agent(name="Researcher", role="Research the topic")
writer = Agent(name="Writer", role="Write a clear summary")
reviewer = Agent(name="Reviewer", role="Review and improve")

team = Team(
    members=[researcher, writer, reviewer],
    mode="coordinate",  # "route" | "coordinate" | "collaborate"
    instructions="Produce a polished research report.",
)

response = team.run("Write a report on renewable energy trends.")

⚙️ Workflows

from buddy.workflow.workflow import Workflow

class ResearchWorkflow(Workflow):
    def run(self, topic: str):
        research = self.researcher.run(f"Research: {topic}")
        draft = self.writer.run(f"Write based on: {research.content}")
        return self.reviewer.run(f"Review: {draft.content}")

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CLI

# Initialize Buddy configuration
buddy init

# PULSE virtual employee
buddy pulse start             # Launch web UI (http://localhost:8888)
buddy pulse create            # Interactive employee setup
buddy pulse kt --source doc.pdf --name "KT Session" --giver Alice
buddy pulse status            # Module health check

# Training
buddy train /data --name my-model

# Desktop app (Windows & Mac)
cd pulse-desktop && npm install && npm start

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Deployment

# FastAPI
from buddy.app.fastapi import FastAPIApp

app = FastAPIApp(agents=[agent])
app.serve(app="my_module:app", host="0.0.0.0", port=7777)

# Docker
FROM python:3.12-slim
RUN pip install buddy-ai[all]
COPY . /app
WORKDIR /app
CMD ["buddy", "pulse", "start", "--host", "0.0.0.0"]

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What's New in v2.2.0

• Competency Engine — a balance-aware competency score (buddy.eval.competency) for agents and teams, decomposing competency into vertical (per-domain depth), crosswise (dependency-weighted cross-domain interaction), and deficit (gap to mastery) components.
• Autonomous learning loop — AutonomousCompetencyLoop reads live signals, scores competency, and automatically enqueues training jobs for the weakest, highest-leverage gaps.
• Runtime competency routing — buddy.eval.competency_runtime infers a task's domain, routes it to the most competent member, adapts the execution policy/model tier, and feeds the outcome back into a live tracker.
• LangChain & LangGraph integrations — buddy.integrations adds dependency-light adapters: use any Buddy model as a LangChain BaseChatModel (BuddyChatModel), expose Buddy agents as LangChain tools (BuddyAgentTool), convert tools/messages both ways, drop Buddy agents into a LangGraph StateGraph (BuddyNode, build_sequential_graph), and route between members with the Competency Engine (make_competency_edge).
• Built-in prompt caching — set cache_prompt=True on any Agent or Model to activate provider-native caching. Anthropic automatically receives cache_control breakpoints on the system prompt, tool list, and conversation history; OpenAI surfaces server-side cache hit/miss token counts in RunResponse.metrics. Fine-grained control via PromptCacheConfig.
• Docs & examples — see Competency Engine, Integrations, Prompt Caching, examples/12_competency_engine.py, and examples/13_langchain_langgraph.py.

Full CHANGELOG →

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What's New in v2.1.0

• PULSE — Virtual Employee's ERA: identity, interactive KT, meetings, tasks, comms, feedback, onboarding
• PULSE Web UI — Full React 18 + TypeScript dashboard with 9 pages
• Live KT — Real-time dialogue with Confidence Meter and Mental Model panel
• LLM Settings UI — Configure provider, model, and API key directly in the browser
• 36 unit tests — Full test coverage for all PULSE modules
• Bug fix — ProfessionalMemory composition refactor

Full CHANGELOG →

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Optional Dependencies

Extra	Installs
buddy-ai[openai]	OpenAI SDK
buddy-ai[anthropic]	Anthropic SDK
buddy-ai[google]	Google Generative AI SDK
buddy-ai[groq]	Groq SDK
buddy-ai[aws]	boto3 (AWS Bedrock)
buddy-ai[training]	PyTorch, Transformers, PEFT
buddy-ai[multimodal]	Pillow, OpenCV, librosa
buddy-ai[tools]	Playwright, Selenium, Slack SDK
buddy-ai[all]	Everything above

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Links

• Documentation: esasrir91.github.io/buddy-ai
• GitHub: github.com/esasrir91/buddy-ai
• PyPI: pypi.org/project/buddy-ai
• Issues: github.com/esasrir91/buddy-ai/issues
• Discussions: github.com/esasrir91/buddy-ai/discussions

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License

MIT License — see LICENSE

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Built with ❤️ by Sriram Sangeeth Mantha