Agno (formerly Phidata): The Practical Guide to Production-Ready, Memory-Rich Agents That Actually Ship.

Build production-ready AI agents in 2026 with Agno (ex-Phidata): memory-rich workflows, SQL/vector DB RAG, fast tool use—plus patterns you can ship with confidence.

Real code patterns for agent memory, SQL + vector retrieval, and pragmatic ops—without turning your stack into a science fair.

Let’s be honest: most “agent frameworks” are great at demos and less great at 3 a.m. when your on-call is debugging why the agent forgot the customer’s account ID… again.

Agno is trending because it leans into the unsexy stuff that makes agents real: durable memory, database awareness, and production ergonomics. The docs explicitly show agents with SQLite-backed persistence and “learning” modes (automatic or agentic) so your agent can remember across sessions.

We’re going to walk through:

  • A minimal agent
  • Adding durable memory (the “wait… you remember me?” moment)
  • RAG with a vector DB (without duct-taping five abstractions together)
  • SQL-aware patterns (safe, scoped, and audit-friendly)
  • Practical tips for VPs and dev leads: observability, cost, evals, and rollouts
  • A few honest comparisons (LangChain/LangGraph, CrewAI, AutoGen)

And yes, we’ll keep it fun. But not “clown-car fun.” More like “we ship on Fridays but with feature flags” fun.

Table of contents

  1. What Agno is (and what it’s not)
  2. Setup (fast path)
  3. Your first agent (baseline)
  4. Memory that persists (SQLite + learning modes)
  5. RAG that doesn’t hate you (Chroma example)
  6. SQL-aware agents (patterns that won’t get you pwned)
  7. Real implementation tips (latency, cost, reliability)
  8. Comparisons: Agno vs LangChain/LangGraph vs CrewAI/AutoGen
  9. Key takeaways

1 What Agno is (and what it’s not)

Agno’s core pitch: build agents that are production-ready, with memory and DB connectivity as first-class concerns. Agno positions this as a differentiator for RAG + database workflows.

What it’s not:

  • It’s not “one prompt to rule them all.”
  • It won’t replace good system design.
  • It will make persistence + memory workflows less painful than hand-rolling them.

2 Setup

Agno’s docs recommend uv and show installing agno openai sqlalchemy chromadb. 

uv venv --python 3.12
source .venv/bin/activate
uv pip install -U agno openai sqlalchemy chromadb
export OPENAI_API_KEY="sk-..."   # use a secret manager in real deployments

3 Your first agent (baseline)

Straight from Agno docs, the minimal agent looks like this: 

from agno.agent import Agent
from agno.models.openai import OpenAIResponses

agent = Agent(model=OpenAIResponses(id="gpt-5.2"))  # example model id

agent.print_response(
    "Hi! I'm Alice. I work at Anthropic as a research scientist.",
    stream=True
)

Tip: Treat the model id as configuration. In prod, we keep it in env/config so we can swap models without redeploying the universe.

4 Memory that persists (SQLite + learning modes)

This is where Agno starts feeling “production-shaped.”

Option A: Always learn (automatic extraction)

Agno docs show adding a SQLite DB and enabling learning=True, plus add_history_to_context=True so the agent can recall prior context. 

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    add_history_to_context=True,
    learning=True,
    markdown=True,
)

if __name__ == "__main__":
    user_id = "user@example.com"

    agent.print_response(
        "Hi! I'm Alice. I prefer concise responses without too much explanation.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )

    # New session, same user
    agent.print_response(
        "What do you know about me?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )

Option B: Agentic learning

If you want tighter control, Agno supports an agentic mode where the model decides what to store (and you can watch the tool calls).

When to use which:

  • Automatic learning: internal copilots, low-risk personalization, faster iteration
  • Agentic learning: regulated domains, strict governance, “no surprises” memory policies

VP-level note: This is where you define your data retention and PII policy. Memory is a feature and a liability.

5 RAG that doesn’t hate you

Agno’s “learned knowledge” example shows a Knowledge object with a Chroma vector DB and an embedder, wired into learning so knowledge can transfer across sessions/users.

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses
from agno.vectordb.chroma import ChromaDb, SearchType

knowledge = Knowledge(
    name="Agent Learnings",
    vector_db=ChromaDb(
        name="learnings",
        path="tmp/chromadb",
        persistent_client=True,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    add_history_to_context=True,
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(mode=LearningMode.AGENTIC),
    ),
    markdown=True,
)

Practical RAG tips that save real time

  • Chunking: start with doc-structure-aware chunking (headings > naive fixed-size).
  • Hybrid retrieval: if your corpus is messy, hybrid search often beats “pure vector vibes.”
  • Grounding: require citations in responses to reduce “confident nonsense.”

6 SQL-aware agents

Agno’s docs position “database-aware agents” as a core strength.
But “agent + SQL” can go wrong fast if you don’t set boundaries.

The safe pattern

We generally recommend a 3-step flow:

  1. Generate a candidate SQL query (no execution)
  2. Validate (allowlist tables/columns, block DDL/DML, enforce LIMIT)
  3. Execute with a read-only role + audit logging

If we do nothing else, we do this. Your database is not a playground.

7 Real implementation tips (for devs and AI VPs)

Reliability

  • Add timeouts + retries around model calls.
  • Build fall-through behavior: if RAG retrieval fails, respond with “I don’t have enough context” and request a source.
  • Use staged rollouts: shadow mode → limited cohort → full traffic.

Cost & latency

  • Route easy requests to a cheaper model; reserve the heavy model for hard queries.
  • Cache embeddings and frequently accessed retrieval results.
  • Measure tokens per request and set budget alarms.

Governance

  • Decide what memory is allowed to store (PII? secrets? customer contracts?).
  • Store memory encrypted at rest; restrict access (least privilege).
  • Keep an explicit deletion workflow (“forget me” that actually forgets).

8 Comparisons

Agno vs LangChain/LangGraph

  • LangChain/LangGraph: very flexible “agent plumbing,” huge ecosystem, but you often assemble persistence/memory patterns yourself.
  • Agno: opinionated “agent product” ergonomics—durable memory and DB-backed workflows are front-and-center.

Agno vs CrewAI / AutoGen

  • CrewAI/AutoGen shine for multi-agent choreography and experimentation.
  • Agno shines when you want a single agent (or system) that behaves like it belongs in production: remembers, retrieves, and integrates cleanly.

9 Key takeaways

  • Agno’s real differentiator is operational: memory + DB integration patterns that reduce glue code.
  • Start small: baseline agent → persistent memory → RAG → SQL.
  • Treat performance claims as benchmarks, not guarantees. Validate on your stack.
  • The “production-ready” bar is governance + reliability + observability—not just “it answered my question once.”

— Cohorte Team
February 09, 2026.