Over the last several months, I have been building and developing Praval, a Pythonic agentic AI framework for multi-agent system development. Praval takes inspiration from coral ecosystems, where specialized agents interact as peers to create emergent intelligence.

Why I Built Praval

  1. Non-hierarchical, emergent systems
    Maintainable agentic systems are still rare; most frameworks assume a manager–worker hierarchy. Praval is explicitly designed for ecosystems of peers, where intelligence emerges from many specialized agents interacting rather than from one "boss agent" orchestrating everything.

  2. Agent-to-agent communication as a first-class concern
    In many frameworks, communication is a bolt-on capability. In Praval, Reef, the communication substrate, is core to the design: agents send and receive structured messages called spores.

  3. Native memory instead of bolt-on vector stores
    Memory in most frameworks is an afterthought. Praval ships with a multi-layered memory system powered by ChromaDB, integrated into the agent lifecycle rather than glued on later. Praval also supports Qdrant.

  4. Self-documenting, sensible defaults
    I wanted a framework where the default configuration already gives you observability, memory, and reasonable behaviors, without needing to wire every subsystem by hand.

  5. Observability and operability from day one
    Praval embraces OpenTelemetry and structured logging so that distributed multi-agent systems can be monitored, traced, and debugged like modern microservices.

Core Concepts in Praval

At a high level, Praval gives you:

  • Agents – Python functions decorated with @agent() that become autonomous workers in your ecosystem.
  • Spores – Structured messages carrying knowledge (spore.knowledge) between agents.
  • Reef – The communication substrate where agents broadcast and listen for spores.
  • Memory – Multi-layered storage (short-term, long-term, episodic, semantic) with ChromaDB integration.
  • Tools – Decorator-based functions that agents can call to interact with external systems.
  • Observability – Built-in tracing and logging via OpenTelemetry-compatible outputs.

The README has a detailed overview of these ideas, but I'll sketch a compact tour here.

Getting Started: Installation

Praval is published on PyPI:

pip install praval

For memory-enabled agents:

pip install praval[memory]

For all features (secure messaging, extra storage backends, etc.):

pip install praval[all]

You'll need at least one LLM provider key (OpenAI, Anthropic, or Cohere). Praval looks for these in your environment. I've tested the framework extensively with OpenAI, and it works well with multiple OpenAI models.

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export COHERE_API_KEY="sk-cohere-..."

# Praval-specific defaults for model selection
export PRAVAL_DEFAULT_PROVIDER="openai"
export PRAVAL_DEFAULT_MODEL="gpt-4o-mini"

Here, PRAVAL_DEFAULT_PROVIDER selects OpenAI as the primary LLM provider, and PRAVAL_DEFAULT_MODEL sets the default OpenAI model used by chat() and other helpers. You can override these in code or per-agent configuration if needed.

Example: A Simple Agent Ecosystem

Here is a minimal multi-agent example inspired by the Praval README and the praval-ai website. Three agents—researcher, analyst, and writer—collaborate via spores on the Reef:

import time
from praval import agent, chat, broadcast, start_agents, get_reef

@agent("researcher", responds_to=["query"])
def researcher(spore):
    """I research topics deeply."""
    topic = spore.knowledge.get("topic", "AI")
    findings = chat(f"Research: {topic}")
    broadcast({"type": "analysis_request", "data": findings})

@agent("analyst", responds_to=["analysis_request"])
def analyst(spore):
    """I analyze data for insights."""
    insights = chat(f"Analyze: {spore.knowledge.get('data', '')}")
    broadcast({"type": "report", "insights": insights})

@agent("writer", responds_to=["report"])
def writer(spore):
    """I create polished reports."""
    report = chat(f"Write: {spore.knowledge.get('insights', '')}")
    print(f"Report generated:\\n{report}")

if __name__ == "__main__":
    start_agents(
        researcher,
        analyst,
        writer,
        initial_data={"type": "query", "topic": "multi-agent AI systems"},
    )

    # Allow agents time to exchange spores and complete LLM calls
    time.sleep(5)

    # Gracefully shut down the reef
    get_reef().shutdown(wait=True)

What's notable here is not just the brevity, but the lack of orchestration glue. Agents are functions, communication is implicit via spores, and the Reef takes care of message delivery.

Architecture

Praval is built around a few core ideas that make it flexible and powerful:

Praval Architecture Diagram

Feature Highlights

1. Decorator-Based Agents

Praval agents are plain Python functions decorated with @agent():

from praval import agent, chat

@agent("summarizer", responds_to=["summarize"])
def summarizer_agent(spore):
    text = spore.knowledge["text"]
    summary = chat(f"Summarize this: {text}")
    return {"summary": summary}

This keeps the API surface small and familiar, and it scales well across teams: every agent is just a normal function plus a bit of metadata.

2. Reef: Native Agent-to-Agent Communication

Reef is Praval's communication layer. Agents exchange spores—structured JSON-like messages:

from praval import agent, broadcast

@agent("notifier", responds_to=["build_complete"])
def notifier(spore):
    status = spore.knowledge["status"]
    print(f"Build completed with status: {status}")

# Somewhere else in your ecosystem:
broadcast({"type": "build_complete", "status": "success"})

Under the hood, Reef can run in-memory for simple setups, or use backends like RabbitMQ for distributed, enterprise-grade deployments.

3. Multi-Layered Memory with ChromaDB

Praval ships with a memory system that supports:

  • Short-term working memory
  • Long-term vector memory via ChromaDB
  • Episodic experience tracking
  • Semantic knowledge storage

You can enable memory using the appropriate extras (praval[memory]) and then configure it with environment variables or code. The memory abstractions are documented here.

4. Observability with OpenTelemetry

Observability is built in. You can view recent traces in the console or export them to your observability stack:

from praval import agent, chat
from praval.observability import show_recent_traces, export_traces_to_otlp

@agent("researcher")
def research_agent(spore):
    return {"findings": chat(spore.knowledge["topic"])}

if __name__ == "__main__":
    # Run some agents, then:
    show_recent_traces(limit=10)
    export_traces_to_otlp("http://localhost:4318/v1/traces")

Because this is built on OpenTelemetry, you can plug Praval into systems like Jaeger, Zipkin, or DataDog without custom bridging code.

5. Tooling: Giving Agents External Capabilities

Praval has a decorator-based tool system so agents can call out to external services:

from praval.tools import tool
from praval import agent

@tool("web_search", description="Search the web", shared=True)
def search_web(query: str) -> str:
    # Replace this with your actual search integration
    return f"Pretend search results for: {query}"

@agent("researcher")
def research_agent(spore):
    query = spore.knowledge.get("query", "Praval multi-agent framework")
    results = search_web(query)
    return {"results": results}

This pattern keeps your agents composable and testable: tools are just functions you can unit-test independently.

How I'm Using Praval

Praval has already powered a few experimental applications:

  • Praval Deep Research – a microservices-based research assistant that orchestrates multiple agents to perform deep literature and web analysis.
  • Praval Analytics – an agentic BI demo application that combines data exploration with conversational interfaces.

These apps are still evolving, but they validate the idea that "agentifying" Python workflows becomes much simpler with Praval. For simple multi-agent projects, an in-memory Reef inside a single container is enough. For more demanding workloads, a RabbitMQ-backed Reef and proper observability give you a production-grade path.

Building Praval: Process and Learnings

Building Praval has been an exciting experience. I used Claude Code extensively for much of the development and engineering. The pace of development was faster than I expected, but not without challenges. Designing Praval forced me to think deeply about:

  • The needs of agents in real-world applications
  • The tradeoffs between simplicity and power in API design
  • How to bake in memory, observability, and security without overwhelming users

I relied on diagrams and architecture sketches before writing code, then iterated quickly with tests and example applications. Building apps like Deep Research and Analytics on early Praval builds revealed plenty of rough edges, which I've been smoothing out through the 0.7.x releases.

Acknowledgements

Praval would not have been possible without support and encouragement from a small group of family and friends. Meera has been enthusiastic and keen about Praval, and has supported me in all ways possible, as I developed this framework over the last several months. Akas has often discussed with me about how he might use it in his own apps. I want to extend special thanks to Bargava for his remarkable support and encouragement—especially at a time when I felt I had hit a wall. He rekindled my interest in Praval and has been a true champion of this project.

What's Next and How to Get Involved

Praval is under active development, and there is a lot more to do: stronger patterns for large ecosystems, more features, bug fixes, performance improvements, more out-of-the-box examples, and deeper integrations with production observability and deployment tools.

If you are interested in building multi-agent systems in Python, I would love your feedback:

Try it out, build something cool with it, and let me know what works well and what can be improved! I would love to hear your thoughts and feedback.