semantica/docs/integrations/agno.md

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Agno Integration	Wire Semantica's semantic intelligence stack into Agno multi-agent teams via five focused components.	robot

Five drop-in components that bring Semantica's KG, vector memory, and decision intelligence into any Agno agent or team.

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Installation

# Core integration
pip install "semantica[agno]"

# With a graph store backend
pip install "semantica[agno,graph-neo4j]"
pip install "semantica[agno,graph-falkordb]"

# Full stack
pip install "semantica[agno,graph-neo4j,vectorstore-pgvector]"

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Components at a Glance

Class	Agno Primitive	Semantica Backing
AgnoContextStore	AgentMemory(db=…)	AgentContext + VectorStore
AgnoKnowledgeGraph	Agent(knowledge=…)	ContextGraph + KG pipeline
AgnoDecisionKit	Agent(tools=[…])	DecisionQuery, CausalChainAnalyzer, PolicyEngine
AgnoKGToolkit	Agent(tools=[…])	NERExtractor, RelationExtractor, Reasoner
AgnoSharedContext	Team-level	Shared ContextGraph across agents

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1. AgnoContextStore

Replaces Agno's flat conversation storage with a hybrid vector + context graph memory store. Implements agno.memory.db.base.MemoryDb.

from agno.agent import Agent
from agno.memory import AgentMemory
from agno.models.openai import OpenAIChat

from semantica.context import ContextGraph
from semantica.vector_store import VectorStore
from integrations.agno import AgnoContextStore

store = AgnoContextStore(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
    graph_expansion=True,
    session_id="user_session_42",
)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    memory=AgentMemory(db=store),
    description="A financially aware assistant with persistent decision intelligence.",
)

agent.print_response("Recommend a portfolio allocation for a risk-averse investor.")

Method	Description
upsert_memory()	Store text in AgentContext (vector index + graph node)
read_memories()	Hybrid retrieval: vector similarity + graph hop expansion
record_decision()	Record a structured decision with reasoning and outcome
find_precedents()	Return semantically similar historical decisions

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2. AgnoKnowledgeGraph

Gives Agno agents a queryable ContextGraph instead of a flat document store. Ingested documents pass through the full Semantica extraction pipeline.

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

from semantica.kg import GraphBuilder
from semantica.semantic_extract import NERExtractor, RelationExtractor
from integrations.agno import AgnoKnowledgeGraph

kg = AgnoKnowledgeGraph(
    graph_builder=GraphBuilder(),
    ner_extractor=NERExtractor(),
    relation_extractor=RelationExtractor(),
)

kg.load("regulatory_docs/", recursive=True)
kg.load(texts=["Basel IV capital requirements apply from January 2026."])

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=kg,
    search_knowledge=True,
)

Ingestion pipeline:

parse → NER → relation extract → graph build → vector index

Search (multi-hop GraphRAG):

vector retrieval → entity lookup → graph hop expansion → context injection

ctx = kg.get_graph_context("Basel IV")
# Returns a text summary of the entity's immediate neighbourhood

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3. AgnoDecisionKit

Exposes Semantica's decision intelligence as native Agno tools.

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

from semantica.context import AgentContext
from integrations.agno import AgnoDecisionKit

ctx = AgentContext(decision_tracking=True)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[AgnoDecisionKit(context=ctx)],
    show_tool_calls=True,
)

agent.print_response("Should we approve this mortgage application?")

Tool	Description
record_decision	Record a decision with reasoning, outcome, and confidence
find_precedents	Search for similar past decisions
trace_causal_chain	Trace causal chain of a decision
analyze_impact	Assess downstream influence of a decision
check_policy	Validate decision against policy rules
get_decision_summary	Summarise decision history by category

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4. AgnoKGToolkit

Lets agents actively build and query the context graph during reasoning.

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from integrations.agno import AgnoKGToolkit

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[AgnoKGToolkit()],
    show_tool_calls=True,
)

Tool	Description
extract_entities	Extract named entities from text
extract_relations	Extract relationships between entities
add_to_graph	Add entities / relations to the context graph
query_graph	Query the graph (natural-language or Cypher)
find_related	Find concepts related to a given entity
infer_facts	Apply rules to infer new facts from the graph
export_subgraph	Export a subgraph as RDF / JSON-LD

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5. AgnoSharedContext

A single ContextGraph shared across an Agno Team. Each agent gets a role-scoped view via bind_agent().

from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat

from semantica.context import ContextGraph
from semantica.vector_store import VectorStore
from integrations.agno import AgnoSharedContext, AgnoDecisionKit, AgnoKGToolkit

shared = AgnoSharedContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
)

research_agent = Agent(
    name="Researcher",
    model=OpenAIChat(id="gpt-4o"),
    memory=shared.bind_agent("researcher"),
    tools=[AgnoKGToolkit(context=shared)],
)

decision_agent = Agent(
    name="Analyst",
    model=OpenAIChat(id="gpt-4o"),
    memory=shared.bind_agent("analyst"),
    tools=[AgnoDecisionKit(context=shared)],
)

team = Team(
    name="Research & Decision Team",
    agents=[research_agent, decision_agent],
    mode="coordinate",
)

# Record a team-level decision
decision_id = shared.record_decision(
    category="strategy",
    scenario="Expand to EU market",
    reasoning="Strong demand signals from Q1 survey",
    outcome="approved",
    confidence=0.87,
    agent_role="cfo",
)

precedents = shared.find_precedents("market expansion")
insights   = shared.get_shared_insights()

Memories written by one agent are immediately visible to all other agents in the team. Each agent's writes are tagged with their role for independent filtering.

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API Reference

from integrations.agno import (
    AgnoContextStore,    # MemoryDb implementation
    AgnoKnowledgeGraph,  # AgentKnowledge implementation
    AgnoDecisionKit,     # Decision intelligence Toolkit
    AgnoKGToolkit,       # Knowledge graph Toolkit
    AgnoSharedContext,   # Team-level shared context
    AGNO_AVAILABLE,      # bool — True if agno is installed
)

All five classes are usable without agno installed — they carry the full Semantica API and degrade gracefully.

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See Also

AgentContext and ContextGraph backing the integration. KG construction used by AgnoKnowledgeGraph. Configure LLM providers for Agno agents. Vector backend for AgnoContextStore.