38.8K stars but only 1.45M PyPI/month — DL/star ratio of 37 is the 3rd most inflated in the category. $5.4M funding (PitchBook). Full-stack offering: agents + teams + workflows + AgentOS control plane. Performance claims (10,000x faster instantiation, 50x less memory) are single-source and unverified. All HN submissions appear to be from the same user (likely founder).
Where it wins
38.8K GitHub stars — top 3 by stars in category
Full-stack offering: agents, teams, workflows, AgentOS control plane
$5.4M funding (PitchBook)
Active development — last push 2026-03-19
Where to be skeptical
DL/star ratio of 37 — 3rd most inflated in category
All HN submissions appear from same user (likely founder) — self-promotional pattern
Performance claims (10,000x faster, 50x less memory) are single-source, unverified
No independently confirmed enterprise customers
Editorial verdict
Include with caveats. High star count but inflated DL/star ratio and self-promotional HN pattern. No independently confirmed enterprise customers. Full-stack offering is ambitious but evidence is thin. Re-evaluate upward if independent enterprise evidence surfaces.
Source
Related
Claude Code
98Anthropic's official agentic coding CLI. v2.1.81 (Mar 20) shipped `--bare`, smarter worktree resume, and improved MCP OAuth while the repo crossed 82,204 stars and logged ~14 commits/week across 10+ maintainers. Terminal-native, tool-use-driven, with deep file system + shell access, #1 SWE-bench Pro standardized (45.89%), ~4% of GitHub public commits (SemiAnalysis), $2.5B annualized revenue. 8M+ npm weekly downloads. Opus 4.6 with 1M context.
LangGraph
95#1 Python agent framework by production evidence — 40.2M PyPI downloads/month, Fortune 500 deployments (LinkedIn, Uber, Replit, Elastic, Klarna, Cloudflare, Coinbase), ~400 LangGraph Platform companies, LangSmith rated best-in-class observability. Stable v1.x API, model-agnostic, MCP support.
Pydantic AI
95#3 Python agent framework by downloads — 15.6M PyPI/month. Built by the Pydantic team. Runtime type enforcement is a genuine differentiator no other framework offers. V1 shipped with Temporal integration for durable execution and Logfire observability. Emerging pattern: 'Pydantic AI for agent logic, LangGraph for orchestration' (ZenML).
AutoGen (Microsoft)
95⚠️ MAINTENANCE MODE — Microsoft officially confirmed bug fixes and security patches only, no new features (VentureBeat 2026-02-19). 55.9K stars but only 1.57M PyPI/month — DL/star ratio of 28, the most inflated among active frameworks. Being replaced by Microsoft Agent Framework (AutoGen + Semantic Kernel merge, GA targeted ~Q2 2026). Teams on AutoGen should plan migration.
Public evidence
DL/star ratio of 37 vs LangGraph's 1,516. Star count significantly outpaces actual deployment. Community interest outpaces production adoption.
Modest funding relative to category peers. Enough runway to iterate but not enterprise-scale backing.
Raw GitHub source
Constrained peek so you can sanity-check the source material without leaving the site.
Agno is the runtime for agentic software. Build agents, teams, and workflows. Run them as scalable services. Monitor and manage them in production.
| Layer | What it does |
|---|---|
| Framework | Build agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations. |
| Runtime | Serve your system in production with a stateless, session-scoped FastAPI backend. |
| Control Plane | Test, monitor, and manage your system using the AgentOS UI. |
Build a stateful, tool-using agent and serve it as a production API in ~20 lines.
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
from agno.os import AgentOS
from agno.tools.mcp import MCPTools
agno_assist = Agent(
name="Agno Assist",
model=Claude(id="claude-sonnet-4-6"),
db=SqliteDb(db_file="agno.db"),
tools=[MCPTools(url="https://docs.agno.com/mcp")],
add_history_to_context=True,
num_history_runs=3,
markdown=True,
)
agent_os = AgentOS(agents=[agno_assist], tracing=True)
app = agent_os.get_app()
Run it:
export ANTHROPIC_API_KEY="***"
uvx --python 3.12 \
--with "agno[os]" \
--with anthropic \
--with mcp \
fastapi dev agno_assist.py
In ~20 lines, you get:
Connect to the AgentOS UI to monitor, manage, and test your agents.
http://localhost:8000).https://github.com/user-attachments/assets/75258047-2471-4920-8874-30d68c492683
Open Chat, select your agent, and ask:
What is Agno?
The agent retrieves context from the Agno MCP server and responds with grounded answers.
https://github.com/user-attachments/assets/24c28d28-1d17-492c-815d-810e992ea8d2
You can use this exact same architecture for running multi-agent systems in production.
Agentic software introduces three fundamental shifts.
Traditional software receives a request and returns a response. Agents stream reasoning, tool calls, and results in real time. They can pause mid-execution, wait for approval, and resume later.
Agno treats streaming and long-running execution as first-class behavior.
Traditional systems execute predefined decision logic written in advance. Agents choose actions dynamically. Some actions are low risk. Some require user approval. Some require administrative authority.
Agno lets you define who decides what as part of the agent definition, with:
Traditional systems are designed to be predictable. Every execution path is defined in advance. Agents introduce probabilistic reasoning into the execution path.
Agno builds trust into the engine itself:
Agno runs in your infrastructure, not ours.