BeeAI: IBM's Open-Source Multi-Agent Framework (2026 Guide for Founders)
BeeAI is IBM's open-source framework for building production-grade multi-agent AI systems. Hosted by the Linux Foundation, it uses the Agent Communication Protocol (ACP) to let agents from different frameworks work together. Here's what founders need to know.
What Is BeeAI?
BeeAI is an open-source platform that provides a centralized place to discover, run, and share AI agents across frameworks. The AI agents (called "bees") connect to an LLM and can access tools to respond to user queries and perform tasks.
Key components:
- BeeAI Framework: Build production-grade agents in Python or TypeScript
- BeeAI Platform: Run and orchestrate agents with a UI or CLI
- Agent Communication Protocol (ACP): Standard for agents to communicate across frameworks
Why "Bee"?
Like bees in a hive, BeeAI agents work together, each with specialized roles, communicating to accomplish complex tasks. A single bee can't build a hive, but thousands working together create something remarkable.
Why BeeAI Matters for Founders
2026 is the year multi-agent systems move from research to production. BeeAI provides:
Framework Agnostic
Agents from different frameworks (LangChain, AutoGPT, custom) can work together via ACP protocol.
Production Ready
OpenTelemetry observability, dynamic workflows, and enterprise-grade stability for real workloads.
Open Governance
Linux Foundation governance ensures transparency and community-driven development. No vendor lock-in.
Plug Any LLM
Works with Ollama, OpenAI, Anthropic, and other providers. Swap models without rewriting agents.
Agent Communication Protocol (ACP)
ACP is the standard that makes multi-agent systems work. Created by IBM BeeAI in early 2025, it's now governed by the Linux Foundation.
Why ACP Matters
Without a standard protocol, agents from different frameworks can't talk to each other. ACP solves this:
- REST-based: Lightweight, uses familiar web principles
- No special SDK: Any language/framework can implement it
- Multi-modal: Supports text, images, audio, video, and binary data
- Standardized discovery: Agents can find and understand each other's capabilities
ACP vs MCP
Anthropic's Model Context Protocol (MCP) connects AI to tools and data sources. ACP connects agents to each other. They're complementary - use MCP for tool integration, ACP for agent orchestration.
Getting Started with BeeAI
Installation
# Install BeeAI CLI
pip install beeai-cli
# Or with npm for TypeScript
npm install -g @i-am-bee/beeai-cli
# Start the BeeAI platform
beeai platform start
# List available agents in the catalog
beeai agent list
Your First Agent (Python)
from beeai import Agent, tool
# Define a tool the agent can use
@tool
def search_database(query: str) -> str:
"""Search the customer database"""
# Your database logic here
return f"Found 3 results for: {query}"
# Create an agent
agent = Agent(
name="customer-support-bee",
description="Handles customer inquiries",
tools=[search_database],
model="gpt-4" # Or "claude-3", "ollama/llama3", etc.
)
# Run the agent
response = agent.run("Find orders for customer john@example.com")
print(response)
Multi-Agent Workflow
from beeai import Agent, Swarm
# Create specialized agents
researcher = Agent(
name="researcher-bee",
description="Finds information from web and databases",
tools=[web_search, database_query]
)
analyst = Agent(
name="analyst-bee",
description="Analyzes data and generates insights",
tools=[data_analysis, chart_generation]
)
writer = Agent(
name="writer-bee",
description="Creates reports and summaries",
tools=[document_writer]
)
# Create a swarm that coordinates them
swarm = Swarm(
agents=[researcher, analyst, writer],
orchestration="dynamic" # Agents decide task order
)
# Execute a complex task
result = swarm.run("""
Research competitors in the AI agent space,
analyze their pricing and features,
and create a competitive analysis report.
""")
BeeAI vs Other Agent Frameworks
| Feature | BeeAI | LangGraph | AutoGPT | CrewAI |
|---|---|---|---|---|
| Multi-framework agents | Yes (ACP) | No | No | No |
| Open governance | Linux Foundation | LangChain Inc | Community | CrewAI Inc |
| Python support | Yes | Yes | Yes | Yes |
| TypeScript support | Yes | Yes | No | No |
| Agent marketplace | Built-in catalog | No | Plugins | No |
| Enterprise observability | OpenTelemetry native | LangSmith | Basic | Basic |
| LLM flexibility | Any provider | Any provider | OpenAI focused | Any provider |
Pre-Built Agents in BeeAI Catalog
BeeAI Platform includes a catalog of ready-to-use agents:
- Aider: Pair programmer for editing code in your repo
- GPT-Researcher: Autonomous agent for gathering and organizing research
- Web Navigator: Browses and extracts information from websites
- Document Processor: Reads and analyzes PDFs, Word docs, spreadsheets
- SQL Agent: Queries databases and generates reports
# Use a pre-built agent from the catalog
beeai agent run gpt-researcher \
--query "What are the top AI agent frameworks in 2026?"
# Or programmatically
from beeai.catalog import get_agent
researcher = get_agent("gpt-researcher")
result = researcher.run("Analyze Tesla's Q4 2025 earnings")
Production Features
Dynamic Workflows with Decorators
from beeai import workflow, parallel, retry
@workflow
def contract_analysis(contract_text: str):
# Run these in parallel
@parallel
def analyze():
legal_review = legal_agent.run(contract_text)
financial_review = finance_agent.run(contract_text)
risk_assessment = risk_agent.run(contract_text)
return legal_review, financial_review, risk_assessment
results = analyze()
# Synthesize with retry on failure
@retry(max_attempts=3)
def synthesize():
return synthesis_agent.run(f"Combine these analyses: {results}")
return synthesize()
Declarative Orchestration (YAML)
# workflow.yaml
name: customer-onboarding
agents:
- name: data-collector
model: gpt-4
tools: [form_parser, id_verification]
- name: risk-scorer
model: claude-3
tools: [credit_check, fraud_detection]
- name: account-creator
model: gpt-4
tools: [database_write, email_sender]
flow:
- step: collect
agent: data-collector
input: $customer_data
- step: score
agent: risk-scorer
input: $collect.output
condition: $collect.success == true
- step: create
agent: account-creator
input: $score.output
condition: $score.risk_level < "high"
Observability with OpenTelemetry
from beeai import Agent
from beeai.observability import setup_telemetry
# Enable tracing and metrics
setup_telemetry(
service_name="my-ai-service",
exporter="jaeger", # or "datadog", "newrelic", etc.
endpoint="http://localhost:14268/api/traces"
)
# All agent runs are now traced automatically
agent = Agent(name="traced-bee", model="gpt-4")
agent.run("Process this request") # Trace sent to Jaeger
Founder Use Cases
1. Customer Support Automation
Deploy a swarm of agents: one handles FAQs, another processes refunds, another escalates to humans. All coordinated via ACP.
2. Research and Analysis
Combine GPT-Researcher for data gathering, a custom analyst agent for insights, and a writer agent for reports.
3. Code Review Pipeline
Multiple specialized agents review different aspects: security, performance, style, documentation.
4. Sales Intelligence
Agents that research prospects, analyze competitors, and draft personalized outreach.
Getting Help
- GitHub: github.com/i-am-bee/beeai-framework
- Documentation: IBM Research BeeAI
- Discord: BeeAI community server
- IBM Support: Enterprise support available through IBM
Bottom Line for Founders
BeeAI is the right choice if you need:
- Multi-agent systems: Coordinate specialized agents for complex workflows
- Framework flexibility: Mix agents from different sources via ACP
- Enterprise readiness: OpenTelemetry, Linux Foundation governance, IBM backing
- No vendor lock-in: Open source, any LLM provider, standard protocols
If 2025 was the year of single AI agents, 2026 is when multi-agent systems go into production. BeeAI gives you the infrastructure to build them.
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