- Introduction
- Architectural Philosophy
- High-Level System Architecture
- Agent Architecture
- Agentic Context Architecture
- Agent History Management
- Storage Architecture
- Reasoning Architecture
- Memory and State Management
- Session Management
- Knowledge Architecture
- Agent Collaboration Architecture
- Event-Driven Architecture
- Performance and Scalability Design
- Integration Architecture
- Security and Privacy Design
Agno is a full-stack framework designed for building Multi-Agent Systems with a focus on performance, modularity, and progressive capability enhancement. The framework is architected around the principle of five distinct levels of agentic capability, providing developers with a clear progression path from simple tool-using agents to complex multi-agent workflows with sophisticated reasoning and collaboration capabilities.
The design philosophy centers on creating a unified abstraction layer that allows developers to work with multiple AI model providers (including language models, vision models, and multi-modal models) while maintaining consistent behavior and performance characteristics. Agno's architecture emphasizes modularity, allowing components to be composed and configured declaratively while maintaining high performance through careful resource management and asynchronous execution patterns.
Agno models agents as digital people with human-like capabilities. Each agent is a complete individual with their own personality, memory, skills, and ability to form relationships - not just a collection of functions.
Think Like Humans: Agents have a "brain" (AI models) that handles reasoning, creativity, and decision-making across text, images, and audio. They develop consistent personalities and communication styles.
Remember Like Humans: Agents have personal memory systems that store experiences, learn from mistakes, and build wisdom over time. Each agent becomes unique through their accumulated experiences.
Work Like Humans: Agents use tools to accomplish tasks, access knowledge bases for information, and maintain ongoing relationships through sessions. They demonstrate initiative, creativity, and emotional intelligence.
Collaborate Like Humans: Agents form working relationships, understand group dynamics, and adapt their communication style based on context and the people they're working with.
Agno directly mirrors human capabilities:
- Brain → AI Models: Cognitive processing and reasoning
- Memory → Memory Systems: Personal experiences and learning
- Skills → Tools: Abilities to interact with the world
- Education → Knowledge: Access to information and training
- Experience → Storage: Accumulated wisdom and patterns
- Relationships → Teams: Ongoing interactions and context
Like human professional development, agents progress through capability levels:
- Basic Agency - Fundamental skills and tools (entry-level worker)
- Knowledge-Aware - Access to specialized information (educated professional)
- Memory-Enabled - Learning and experience accumulation (experienced professional)
- Collaborative - Effective teamwork and delegation (team member)
- Orchestrated - Leadership and process coordination (manager)
Human-like Authenticity: Agents should feel like genuine digital colleagues, not automated tools.
Performance First: Sub-microsecond instantiation and minimal memory usage for responsive interactions.
Cognitive Flexibility: Support for different AI models and specialized strengths, like humans with different talents.
Social Composability: Mix and match capabilities to create agents with specific personalities and skills for different roles.
Authentic Asynchrony: Operations are designed to be non-blocking not just for performance, but to enable natural conversation flow and multi-tasking capabilities that mirror human interaction patterns.
Agno's architecture is fundamentally centered around the Agent as an autonomous entity, similar to how a person functions with their cognitive abilities and social relationships. Each Agent integrates various capabilities as part of their "personality" and "skills."
graph TB
subgraph Individual_Agent["Individual Agent"]
Brain["Brain (AI Models)"]
Memory["Personal Memory"]
Tools["Available Tools"]
Knowledge["Knowledge Access"]
Sessions["Active Sessions"]
end
subgraph Model_Providers["Model Providers (Brain Options)"]
OpenAI["OpenAI (GPT, Vision, Audio)"]
Anthropic["Claude (Text, Vision)"]
Ollama["Local Models"]
Others["Other AI Models"]
end
subgraph Tool_Ecosystem["Tool Ecosystem"]
WebTools["Web Tools"]
DatabaseTools["Database Tools"]
APITools["API Tools"]
CustomTools["Custom Tools"]
end
subgraph Knowledge_Sources["Knowledge Sources"]
VectorDB["Vector Databases"]
Documents["Document Stores"]
APIs["External APIs"]
WebContent["Web Content"]
end
subgraph Storage_Systems["Storage Systems"]
SessionStore["Session Storage"]
MemoryStore["Memory Storage"]
StateStore["State Storage"]
end
Brain --> Memory
Brain --> Tools
Brain --> Knowledge
Memory --> Sessions
Brain --> OpenAI
Brain --> Anthropic
Brain --> Ollama
Brain --> Others
Tools --> WebTools
Tools --> DatabaseTools
Tools --> APITools
Tools --> CustomTools
Knowledge --> VectorDB
Knowledge --> Documents
Knowledge --> APIs
Knowledge --> WebContent
Memory --> SessionStore
Memory --> MemoryStore
Sessions --> StateStore
Agno provides integrated capabilities that each Agent can possess and utilize based on their configuration and needs. The Agent serves as the central coordinator that brings together cognitive processing (models), memory systems, tool capabilities, and knowledge access into a cohesive autonomous entity.
The data flow in Agno follows a clear pattern from user input through agent reasoning to tool execution and response generation. When a user submits a message, it first passes through the message processing pipeline, which handles multimodal content, applies any necessary transformations, and creates the internal Message representation.
sequenceDiagram
participant User
participant Agent
participant Memory
participant Knowledge
participant Model
participant Tools
participant Storage
User->>Agent: Submit Message
Agent->>Memory: Retrieve Context
Agent->>Knowledge: Search Relevant Info
Agent->>Agent: Assemble Context
Agent->>Model: Send Context
Model-->>Agent: Response + Tool Calls
alt Tool Calls Present
Agent->>Tools: Execute Tools (Parallel)
Tools-->>Agent: Tool Results
Agent->>Model: Send Tool Results
Model-->>Agent: Final Response
end
Agent->>Memory: Update Session
Agent->>Storage: Persist State
Agent-->>User: Return Response
The Agent then constructs the full context for the AI model, which includes the system message derived from the agent's configuration, any relevant knowledge retrieved from the knowledge base, memory content from previous interactions, and the current user message (which may include text, images, audio, or other modalities). This context assembly process is designed to be fast and cache-friendly.
The constructed context is then sent to the appropriate AI model through the Model abstraction layer. The model's response is parsed into a standardized format that includes both content and any tool calls that the model wants to make.
If tool calls are present, they are executed through the tool execution layer, which handles parameter validation, actual tool invocation, and result formatting. Tool execution can be parallelized when multiple tools are called simultaneously, and the results are collected and formatted for inclusion in the next model interaction.
The final response is then processed through the response pipeline, which handles formatting, streaming if requested, and any necessary post-processing before being returned to the user.
The Agent in Agno is designed as a complete autonomous entity, analogous to a person with cognitive capabilities, memory, skills, and the ability to form relationships. Rather than being a collection of separate components, an Agent integrates all these capabilities into a cohesive identity.
graph TB
subgraph Agent_Core["Agent Core"]
Brain["Cognitive Core<br/>(AI Models: LLM, Vision, Multi-modal)"]
Identity["Personal Identity<br/>(Configuration & State)"]
Reasoning["Reasoning Engine<br/>(Step-by-step thinking)"]
end
subgraph Agent_Capabilities["Agent Capabilities"]
Memory["Memory Systems<br/>(Personal & Session)"]
Tools["Tool Access<br/>(Action capabilities)"]
Knowledge["Knowledge Base<br/>(Information access)"]
Context["Contextual Awareness<br/>(Situational understanding)"]
end
subgraph Agent_Storage["Agent Storage"]
SessionStore["Session Storage<br/>(Conversations & state)"]
PersistentStore["Persistent Storage<br/>(Long-term data)"]
History["History Management<br/>(Conversational continuity)"]
end
subgraph Agent_Flow["Agent Interaction Flow"]
Input["User Input"]
ContextAssembly["Context Assembly"]
ModelInteraction["Model Interaction"]
ToolExecution["Tool Execution"]
MemoryUpdate["Memory Update"]
Response["Response Generation"]
end
Brain --> Identity
Identity --> Reasoning
Reasoning --> Memory
Memory --> Tools
Tools --> Knowledge
Knowledge --> Context
Context --> SessionStore
SessionStore --> PersistentStore
PersistentStore --> History
Input --> ContextAssembly
ContextAssembly --> Brain
Brain --> ModelInteraction
ModelInteraction --> ToolExecution
ToolExecution --> MemoryUpdate
MemoryUpdate --> Response
Context --> ContextAssembly
Memory --> ContextAssembly
History --> ContextAssembly
Knowledge --> ContextAssembly
Cognitive Core: Each Agent has a "brain" powered by AI models (including language models, vision models, and multi-modal models) that serves as their reasoning and decision-making center. Just as humans can develop different cognitive strengths, agents can be configured with different models optimized for specific thinking patterns and modalities. The cognitive core integrates with the reasoning engine to enable systematic problem-solving and thoughtful decision-making.
Personal Identity: Agents maintain consistent personalities, preferences, and approaches to problem-solving across interactions. This identity emerges from their configuration, accumulated experiences, and learned behaviors, all of which are preserved through the storage and memory systems.
Adaptive Behavior: Like humans, agents learn and adapt from their experiences. They adjust their approaches based on what works, remember successful strategies through the memory system, and avoid repeating mistakes by learning from their history.
Social Capabilities: Agents are inherently social entities capable of forming working relationships, understanding context in group settings, and collaborating effectively with both humans and other agents. Their contextual awareness and memory systems enable them to maintain relationship continuity.
Multi-Modal Processing: Agents can process and generate content across multiple modalities, including text, images, audio, and video. This capability enables agents to understand visual information, process spoken language, and generate appropriate responses in the most suitable format for the task.
Integrated Component Interaction: The Agent's execution model flows naturally from thought to action, seamlessly integrating:
- Context Assembly: Gathering relevant situational information from multiple sources and modalities
- Memory Recall: Accessing both short-term conversational history and long-term user memories
- Knowledge Access: Retrieving relevant information from knowledge bases
- Reasoning Process: Engaging systematic thinking when problems require it
- Tool Usage: Executing actions through available tools
- Storage Management: Persisting important information across interactions
- History Tracking: Maintaining conversational continuity and learning from past interactions
Agentic Context in Agno represents the dynamic, task-specific information that agents use to understand their current situation and make informed decisions. Unlike static configuration or persistent memory, context is fluid and adaptive, changing based on the immediate needs of each interaction.
The context system is designed around the principle that agents should have access to relevant situational information without being overwhelmed by irrelevant data. This leads to a selective, intelligent context assembly process that provides agents with precisely the information they need for their current task.
graph TB
subgraph Context_Sources["Context Sources"]
UC["User Context<br/>(Current Request)"]
SC["Situational Context<br/>(Environment State)"]
HC["Historical Context<br/>(Recent Interactions)"]
DC["Domain Context<br/>(Specialized Knowledge)"]
end
subgraph Context_Processing["Context Processing"]
CA["Context Assembly"]
CF["Context Filtering"]
CP["Context Prioritization"]
CR["Context Resolution"]
end
subgraph Context_Integration["Context Integration"]
SP["System Prompt"]
UP["User Prompt"]
TI["Tool Instructions"]
RM["Reasoning Memory"]
end
subgraph Context_Types["Context Types"]
Static["Static Context<br/>(Configuration)"]
Dynamic["Dynamic Context<br/>(Runtime Data)"]
Computed["Computed Context<br/>(Derived Information)"]
External["External Context<br/>(API Data)"]
end
UC --> CA
SC --> CA
HC --> CA
DC --> CA
CA --> CF
CF --> CP
CP --> CR
CR --> SP
CR --> UP
CR --> TI
CR --> RM
Static --> UC
Dynamic --> SC
Computed --> HC
External --> DC
Context assembly in Agno is an intelligent process that occurs before each agent interaction. The system evaluates what contextual information would be most valuable for the current task and assembles it into a coherent context package.
Static Context includes configuration-based information that rarely changes, such as agent personality, role definitions, and basic instructions. This context is typically defined when agents are created and provides consistent behavioral guidelines.
Dynamic Context encompasses real-time information that changes with each interaction, such as current time, user location, system state, or external API data. This context is resolved immediately before agent execution to ensure freshness.
Computed Context involves derived information that's calculated based on other context sources, such as user mood analysis, task complexity assessment, or relevance scoring of historical interactions.
External Context integrates information from outside systems, such as current weather, stock prices, or user's calendar information. This context type requires careful management of API calls and caching to maintain performance.
# Context configuration and usage
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
context={
"user_timezone": get_user_timezone(),
"current_weather": lambda: get_weather_api(),
"business_hours": check_business_hours,
"user_preferences": UserPreferences()
},
add_context=True, # Include context in user prompt
resolve_context=True # Resolve functions before execution
)Context in Agno has well-defined scopes and lifecycles that determine when context is created, updated, and discarded. Run-scoped context exists only for the duration of a single agent execution and is discarded afterward. This type of context is ideal for temporary calculations or one-time API calls.
Session-scoped context persists across multiple interactions within a user session. This context type is useful for maintaining user preferences, temporary settings, or ongoing task state that should be remembered during a conversation.
Agent-scoped context is associated with a specific agent instance and persists as long as the agent exists. This context typically includes agent configuration, learned behaviors, and long-term operational parameters.
Global context is shared across all agents in a deployment and typically includes system-wide configuration, shared resources, or common reference data.
Context handling includes comprehensive security and privacy protections, particularly important given that context often contains sensitive user information or real-time data about user activities.
Context data is encrypted in transit and at rest when it contains sensitive information. Access to context is controlled through the same authentication and authorization mechanisms used for other agent resources.
Privacy controls allow users to specify what types of contextual information agents can access and use. This includes opt-out mechanisms for location data, calendar access, or other potentially sensitive context sources.
Context logging and audit trails provide transparency into what contextual information agents access and how it's used, supporting both debugging and compliance requirements.
Agent History in Agno is designed as a sophisticated system for managing conversational context and maintaining continuity across interactions. Unlike simple chat logs, the history system is intelligent about what information to preserve, how to present it to agents, and when to compress or summarize historical content.
The history system recognizes that different types of historical information have different relevance patterns. Recent interactions are typically most relevant and should be immediately accessible, while older interactions may need to be summarized or indexed for selective retrieval.
graph TB
subgraph History_Layers["History Layers"]
IH["Immediate History<br/>(Last 3-5 exchanges)"]
RH["Recent History<br/>(Current session)"]
EH["Extended History<br/>(Previous sessions)"]
AH["Archived History<br/>(Long-term storage)"]
end
subgraph History_Processing["History Processing"]
HC["History Compression"]
HS["History Summarization"]
HI["History Indexing"]
HR["History Retrieval"]
end
subgraph History_Integration["History Integration"]
ML["Message List"]
CP["Context Prompt"]
MS["Memory System"]
KS["Knowledge System"]
end
subgraph History_Types["History Types"]
CT["Conversation Turns"]
TC["Tool Call History"]
RT["Reasoning Traces"]
EC["Error Contexts"]
end
IH --> HC
RH --> HS
EH --> HI
AH --> HR
HC --> ML
HS --> CP
HI --> MS
HR --> KS
CT --> IH
TC --> RH
RT --> EH
EC --> AH
The conversational history system maintains a detailed record of user interactions while providing intelligent mechanisms for presenting relevant historical context to agents. The system automatically manages history size to prevent context window overflow while preserving the most important historical information.
Immediate History consists of the most recent message exchanges and is always included in agent context. This history is preserved exactly as it occurred, providing precise context for ongoing conversations.
Session History encompasses the full conversation within a session but may be compressed or summarized when it becomes too large for efficient processing. The compression process preserves key information while reducing token usage.
Extended History covers interactions from previous sessions and is selectively retrieved based on relevance to current conversations. This history is typically summarized and indexed for efficient search and retrieval.
# History configuration
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
add_history_to_messages=True, # Include history in context
num_history_runs=5, # Number of recent exchanges to include
read_chat_history=True, # Enable history reading tools
search_previous_sessions_history=True, # Cross-session history access
num_history_sessions=3 # Number of previous sessions to search
)The history system includes sophisticated tagging and filtering capabilities that prevent historical information from being recursively included and enable intelligent selection of relevant historical context.
Messages from history are tagged with from_history=True to prevent them from being processed as new input in subsequent interactions. This tagging system ensures that agents don't become confused about the temporal order of interactions.
History filtering enables agents to focus on specific types of historical information, such as only tool call results, only user questions, or only successful task completions. This filtering capability is particularly useful for specialized agents that need specific types of historical context.
Beyond conversational history, Agno maintains detailed records of tool calls, their parameters, results, and any errors that occurred. This tool call history serves multiple purposes: debugging tool-related issues, learning from successful tool usage patterns, and providing context for complex multi-step tool sequences.
The tool call history system tracks not just what tools were called, but also the reasoning behind tool calls, the effectiveness of tool usage, and patterns in how tools are combined to accomplish tasks.
# Tool history access
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
read_tool_call_history=True, # Enable tool history access
tools=[web_search, calculator, file_manager]
)
# Tool history is automatically available to agents
response = agent.run("What calculations did I ask you to do yesterday?")As conversations grow long, the history system automatically applies compression and summarization techniques to maintain relevant context while staying within computational and token limits.
Lossless Compression removes redundant information, standardizes formatting, and eliminates unnecessary metadata while preserving all essential content. This compression is applied to recent history to maximize the amount of precise context that can be included.
Lossy Summarization creates condensed versions of older historical content, preserving key decisions, outcomes, and user preferences while discarding detailed conversational flow. This summarization is applied to extended history to maintain long-term context awareness.
Semantic Indexing creates searchable representations of historical content that enable retrieval of relevant historical information based on current conversation topics, even from very old interactions.
The storage architecture in Agno is designed around the principle of appropriate persistence for different types of data, with careful consideration of performance, reliability, and scalability requirements. Rather than using a single storage solution, Agno employs a multi-tier storage strategy that matches storage characteristics to data requirements.
The storage system recognizes that agent-related data has diverse characteristics: some data is accessed frequently and needs to be fast, other data is critical and needs to be durable, and some data is large and needs to be cost-effective to store. This diversity leads to a sophisticated storage architecture that can optimize for different data patterns.
graph TB
subgraph Storage_Tiers["Storage Tiers"]
MemoryTier["Memory Tier<br/>(Redis, In-Memory)"]
FastTier["Fast Tier<br/>(SQLite, Local SSD)"]
DurableTier["Durable Tier<br/>(PostgreSQL, MySQL)"]
ArchiveTier["Archive Tier<br/>(S3, Blob Storage)"]
end
subgraph Data_Types["Data Types"]
SessionData["Session Data"]
AgentConfig["Agent Configuration"]
UserMemories["User Memories"]
KnowledgeBase["Knowledge Base"]
HistoryLogs["History Logs"]
Artifacts["Files & Artifacts"]
end
subgraph Storage_Backends["Storage Backends"]
SQLite["SQLite"]
PostgreSQL["PostgreSQL"]
MongoDB["MongoDB"]
Redis["Redis"]
S3["AWS S3"]
GCS["Google Cloud Storage"]
FileSystem["Local File System"]
end
subgraph Access_Patterns["Access Patterns"]
HighFreq["High Frequency<br/>(< 10ms)"]
MediumFreq["Medium Frequency<br/>(< 100ms)"]
LowFreq["Low Frequency<br/>(< 1s)"]
Batch["Batch Access<br/>(Background)"]
end
SessionData --> MemoryTier
AgentConfig --> FastTier
UserMemories --> DurableTier
KnowledgeBase --> DurableTier
HistoryLogs --> ArchiveTier
Artifacts --> ArchiveTier
MemoryTier --> Redis
FastTier --> SQLite
DurableTier --> PostgreSQL
DurableTier --> MongoDB
ArchiveTier --> S3
ArchiveTier --> GCS
HighFreq --> MemoryTier
MediumFreq --> FastTier
LowFreq --> DurableTier
Batch --> ArchiveTier
Agent session storage is designed to provide fast, reliable persistence for active user sessions while supporting the complex data relationships that sessions entail. Sessions contain conversation history, user preferences, temporary state, and references to related data like memories and knowledge.
The session storage system supports both structured data (user preferences, session metadata) and unstructured data (conversation content, file attachments). This dual nature requires a storage system that can efficiently handle both types while maintaining strong consistency and fast access.
Session Lifecycle Management includes automatic cleanup of expired sessions, archival of old but potentially valuable session data, and migration of session data between storage tiers based on access patterns.
# Storage configuration for agents
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
storage=SqliteStorage(
table_name="agent_sessions",
db_file="agent_data.db"
),
# Alternative storage backends
# storage=PostgresStorage(connection_string="postgresql://..."),
# storage=RedisStorage(host="localhost", port=6379),
# storage=MongoDBStorage(connection_string="mongodb://...")
)Agno supports multiple storage backends to accommodate different deployment scenarios, from single-machine development environments to large-scale distributed deployments. Each storage backend is implemented through a common interface that abstracts storage operations while allowing backend-specific optimizations.
SQLite Storage provides a lightweight, file-based storage solution ideal for development, testing, and single-machine deployments. SQLite storage includes automatic schema management and supports the full range of agent data types.
PostgreSQL Storage offers enterprise-grade reliability and performance for production deployments. PostgreSQL storage includes advanced features like full-text search, JSON querying, and horizontal scaling through read replicas.
Redis Storage provides ultra-fast access for high-performance scenarios where sub-millisecond response times are critical. Redis storage is typically used for session data and frequently accessed memories.
MongoDB Storage supports flexible schema evolution and complex document structures, making it ideal for deployments with diverse data requirements or frequent schema changes.
Cloud Storage Integration includes support for AWS S3, Google Cloud Storage, and Azure Blob Storage for archival data and large file storage.
Data persistence in Agno follows carefully designed strategies that balance durability, performance, and cost. Write-through caching ensures that critical data is immediately persisted while maintaining fast read access through memory or fast storage tiers.
Asynchronous persistence allows non-critical data updates to be queued and persisted in the background, reducing latency for user-facing operations while ensuring eventual consistency.
Backup and recovery mechanisms are built into the storage architecture, with support for point-in-time recovery, incremental backups, and cross-region replication for disaster recovery scenarios.
Data migration capabilities enable moving data between storage backends as requirements change, supporting scenarios like moving from development SQLite to production PostgreSQL without data loss.
Storage performance optimization in Agno includes multiple strategies applied at different levels of the storage stack. Connection pooling minimizes the overhead of database connections by reusing existing connections across multiple agent operations.
Query optimization includes automatic query plan analysis, index recommendations, and query caching for frequently executed queries. The storage layer monitors query performance and provides insights for optimization.
Batch operations group multiple related storage operations into single transactions, reducing network round-trips and improving overall throughput for bulk operations.
Storage tiering automatically moves data between storage tiers based on access patterns, keeping frequently accessed data in fast storage while moving cold data to cost-effective archival storage.
The reasoning architecture in Agno is designed to augment agent capabilities with structured thinking processes that mirror human problem-solving approaches. Rather than relying solely on the AI model's immediate response, the reasoning system breaks down complex problems into manageable steps, documents the thinking process, and arrives at more thoughtful and accurate conclusions.
The reasoning system recognizes that different types of problems benefit from different reasoning approaches. Some problems require systematic analysis, others benefit from creative exploration, and still others need careful verification of facts and logic. This recognition leads to a flexible reasoning architecture that can adapt its approach based on the problem type and context.
graph TB
subgraph Reasoning_Types["Reasoning Types"]
Analytical["Analytical Reasoning<br/>(Step-by-step logic)"]
Creative["Creative Reasoning<br/>(Exploration & ideation)"]
Critical["Critical Reasoning<br/>(Verification & validation)"]
Collaborative["Collaborative Reasoning<br/>(Multi-perspective)"]
end
subgraph Reasoning_Process["Reasoning Process"]
ProblemAnalysis["Problem Analysis"]
StepGeneration["Step Generation"]
StepExecution["Step Execution"]
Validation["Result Validation"]
Synthesis["Synthesis"]
end
subgraph Reasoning_Tools["Reasoning Tools"]
ThinkingTools["Thinking Tools"]
VerificationTools["Verification Tools"]
ExplorationTools["Exploration Tools"]
ReflectionTools["Reflection Tools"]
end
subgraph Reasoning_Models["Reasoning Models"]
PrimaryModel["Primary Model<br/>(Main reasoning)"]
VerificationModel["Verification Model<br/>(Fact checking)"]
CreativeModel["Creative Model<br/>(Ideation)"]
CriticModel["Critic Model<br/>(Evaluation)"]
end
Analytical --> ProblemAnalysis
Creative --> StepGeneration
Critical --> StepExecution
Collaborative --> Validation
ProblemAnalysis --> ThinkingTools
StepGeneration --> VerificationTools
StepExecution --> ExplorationTools
Validation --> ReflectionTools
ThinkingTools --> PrimaryModel
VerificationTools --> VerificationModel
ExplorationTools --> CreativeModel
ReflectionTools --> CriticModel
The reasoning process in Agno follows a structured pipeline that can be configured to match the complexity and requirements of different tasks. The process begins with problem analysis, where the system identifies the type of reasoning required and develops an approach strategy.
Problem Decomposition breaks complex problems into smaller, manageable components that can be reasoned about independently. This decomposition process considers the relationships between components and ensures that the reasoning process addresses all aspects of the original problem.
Step-by-Step Execution works through each component systematically, documenting reasoning at each step and building toward a comprehensive solution. Each step includes explicit reasoning about why particular approaches were chosen and what evidence supports the conclusions.
Validation and Verification checks the logical consistency of reasoning steps, verifies facts against available knowledge sources, and identifies potential gaps or errors in the reasoning process.
# Reasoning configuration
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
reasoning=True, # Enable reasoning mode
reasoning_model=OpenAIChat(id="gpt-4o-mini"), # Optional separate model
reasoning_min_steps=1, # Minimum reasoning steps
reasoning_max_steps=10, # Maximum reasoning steps
)
# Reasoning agent automatically engages for complex queries
response = agent.run(
"Analyze the market trends for renewable energy and recommend investment strategies"
)Advanced reasoning scenarios in Agno can leverage multiple AI models with different strengths for different aspects of the reasoning process. This multi-model approach recognizes that different models may excel at different types of thinking and modalities.
Primary Reasoning Model handles the main reasoning process, working through problems step by step and documenting the thinking process.
Verification Model specializes in fact-checking, logical validation, and identifying potential errors or gaps in reasoning.
Creative Model focuses on generating alternative approaches, brainstorming solutions, and exploring novel perspectives.
Critic Model evaluates reasoning quality, identifies weaknesses, and suggests improvements to the reasoning process.
The reasoning system is designed to be transparent and auditable, providing detailed visibility into how agents arrive at their conclusions. Reasoning traces capture the complete reasoning process, including initial problem analysis, each reasoning step, evidence considered, and final synthesis.
This transparency serves multiple purposes: it helps users understand and trust agent conclusions, enables debugging of reasoning errors, supports compliance requirements, and facilitates learning from successful reasoning patterns.
Reasoning traces are preserved as part of the conversation history and can be referenced in future interactions to maintain consistency and build upon previous reasoning work.
The reasoning system includes several optimizations to balance reasoning quality with performance requirements. Adaptive Step Selection adjusts the number of reasoning steps based on problem complexity, using fewer steps for simple problems and more steps for complex analysis.
Parallel Reasoning Paths explore multiple approaches simultaneously when appropriate, allowing the system to compare different reasoning strategies and select the most promising approach.
Reasoning Caching stores successful reasoning patterns and reuses them for similar problems, reducing computation time while maintaining reasoning quality.
Early Termination stops the reasoning process when sufficient confidence is reached, avoiding unnecessary computation while ensuring thorough analysis for complex problems.
Agno's multi-agent collaboration is designed around natural human relationship patterns. Just as people form different types of working relationships based on context and needs, agents establish various collaboration models:
graph TB
subgraph "Handoff Relationships"
Generalist["Generalist Agent"]
Specialist1["Finance Specialist"]
Specialist2["Research Specialist"]
Specialist3["Writing Specialist"]
Generalist -->|"Delegate Complex Tasks"| Specialist1
Generalist -->|"Delegate Research"| Specialist2
Generalist -->|"Delegate Writing"| Specialist3
Specialist1 -->|"Return Results"| Generalist
Specialist2 -->|"Return Results"| Generalist
Specialist3 -->|"Return Results"| Generalist
end
subgraph "Team Relationships"
TeamLead["Team Leader"]
WebAgent["Web Research Agent"]
DataAgent["Data Analysis Agent"]
ReportAgent["Report Writing Agent"]
SharedGoal["Shared Team Goal"]
TeamLead <--> SharedGoal
WebAgent <--> SharedGoal
DataAgent <--> SharedGoal
ReportAgent <--> SharedGoal
WebAgent <--> DataAgent
DataAgent <--> ReportAgent
end
subgraph "Supervision Relationships"
Supervisor["Supervisor Agent"]
Junior1["Junior Agent 1"]
Junior2["Junior Agent 2"]
QualityControl["Quality Control"]
Supervisor -->|"Assign Tasks"| Junior1
Supervisor -->|"Assign Tasks"| Junior2
Junior1 -->|"Submit Work"| QualityControl
Junior2 -->|"Submit Work"| QualityControl
QualityControl -->|"Review & Feedback"| Supervisor
end
subgraph "Network Relationships"
Hub["Hub Agent"]
Peer1["Domain Expert 1"]
Peer2["Domain Expert 2"]
Peer3["Domain Expert 3"]
Hub <--> Peer1
Hub <--> Peer2
Hub <--> Peer3
Peer1 <--> Peer2
Peer2 <--> Peer3
Peer1 <--> Peer3
end
Handoff Relationships mirror how people delegate specialized tasks to experts. A generalist agent identifies when a task requires specialized knowledge and hands it off to an expert agent, maintaining context throughout the process.
Team Relationships represent collaborative groups working toward shared goals, similar to project teams where members have different roles but contribute to common objectives.
Supervision Relationships establish hierarchical structures where experienced agents guide and review the work of less experienced ones, ensuring quality and providing learning opportunities.
Network Relationships create peer-to-peer connections where agents with different expertise areas can consult each other, forming dynamic knowledge networks.
Agent communication in Agno mimics natural human communication patterns. When agents collaborate, they exchange rich contextual information similar to how people brief each other on tasks and share knowledge.
Context Handoff: When delegating tasks, agents provide complete situational awareness - the original user request, work completed so far, specific requirements, and expectations. This ensures seamless transitions without information loss.
Knowledge Sharing: Agents can share discoveries, insights, and learned information with each other, building collective intelligence while maintaining individual expertise.
Status Updates: Collaborative agents provide progress updates and request assistance when needed, maintaining transparency and enabling dynamic coordination.
Result Integration: Agents combine their individual contributions into cohesive responses, synthesizing different perspectives and expertise areas into unified outcomes.
Just as human teams develop shared understanding and common knowledge, agent collaborations maintain collective context that all participants can access and contribute to.
Collective Memory: Teams of agents maintain shared memories of their collaborative experiences, decisions made, and lessons learned, building institutional knowledge over time.
Common Understanding: Agents develop shared vocabularies and working methods through repeated collaboration, improving efficiency and reducing miscommunication.
Distributed Awareness: Each agent maintains awareness of other team members' capabilities, current tasks, and availability, enabling intelligent coordination and resource allocation.
Conflict Resolution: When agents have different perspectives or conflicting information, they use structured discussion and evidence-based reasoning to reach consensus, similar to human team problem-solving processes.
Agno's state management architecture is built around the principle of explicit state boundaries with clear ownership and access patterns. Rather than maintaining global state that all components can access freely, the system defines specific state scopes with well-defined interfaces for state access and modification.
graph TB
subgraph "State Scopes"
AS["Agent State<br/>(Configuration)"]
SS["Session State<br/>(Conversation Context)"]
WS["Workflow State<br/>(Process Progress)"]
GS["Global State<br/>(System Config)"]
end
subgraph "Persistence Levels"
Transient["Transient<br/>(Memory Only)"]
SemiPersistent["Semi-Persistent<br/>(Local Storage)"]
Durable["Durable<br/>(Database)"]
end
subgraph "Consistency Models"
Strong["Strong Consistency"]
Eventual["Eventual Consistency"]
Optimistic["Optimistic Concurrency"]
end
AS --> Strong
AS --> SemiPersistent
SS --> Eventual
SS --> Durable
WS --> Strong
WS --> Durable
GS --> Strong
GS --> Transient
Agent State encompasses the configuration and runtime state of individual agents. This state is primarily immutable configuration established at agent creation time, with minimal mutable runtime state. Agent state is designed to be serializable, enabling agents to be persisted and restored across process boundaries.
Session State represents the evolving context of user interactions. This state includes conversation history, user preferences that have been learned during the session, and any temporary context that needs to persist across message exchanges within a session.
Workflow State manages the progression through multi-step processes. This state includes the current step, completed steps, any branching decisions, and accumulated results from previous steps. Workflow state is designed to be durable and recoverable, supporting long-running processes that might span hours or days.
The state persistence strategy in Agno is designed around the principle of appropriate persistence levels for different types of state. Transient state that doesn't need to survive process restarts is kept in memory for performance. Semi-persistent state that should survive process restarts but can be recreated if necessary is stored in fast local storage. Durable state that must never be lost is stored in reliable persistent storage with appropriate backup strategies.
The persistence layer abstracts over different storage backends, allowing deployments to choose appropriate storage solutions based on their scale, reliability, and performance requirements. Small deployments might use SQLite for simplicity, while large-scale deployments can use distributed databases for reliability and performance.
State serialization is handled through a pluggable serialization system that can adapt to different storage backend requirements. The default serialization uses JSON for human readability and debugging, but binary formats can be used when performance is critical.
State consistency is maintained through careful transaction boundaries and consistency models appropriate to each state type. Agent configuration state uses strong consistency since it rarely changes and errors would be immediately apparent. Session state uses eventual consistency with conflict resolution since multiple agents might update it simultaneously.
Recovery mechanisms are built into the state management system to handle various failure scenarios. Agent state can be reconstructed from configuration, session state includes checkpointing and rollback capabilities, and workflow state supports resumption from any completed step.
Session management in Agno is designed around a clear lifecycle model that supports both short-lived interactions and long-running conversations. Sessions are created explicitly when users begin interactions and can persist across multiple process restarts and deployments.
stateDiagram-v2
[*] --> Created: User Initiates
Created --> Active: Initialize Context
Active --> Active: Message Exchange
Active --> Paused: Temporary Halt
Paused --> Active: Resume Session
Active --> Terminated: Explicit End
Active --> Timeout: Inactivity
Timeout --> Terminated: Cleanup
Terminated --> [*]: Resources Released
state Active {
[*] --> Processing
Processing --> ToolExecution: Tools Called
ToolExecution --> Processing: Results Ready
Processing --> MemoryUpdate: Context Changed
MemoryUpdate --> Processing: Memory Saved
Processing --> Response: Complete
Response --> [*]: Message Sent
}
Session creation involves establishing user identity, initializing session-specific context, and setting up any necessary resources like memory stores or knowledge base connections. The session creation process is designed to be fast and reliable, as it directly impacts user experience.
During active sessions, the system maintains conversation context, tracks user preferences and learned information, and manages any session-specific resources. The session management system is designed to handle concurrent access from multiple agents or processes while maintaining consistency.
Session termination can be explicit through user action or implicit through timeout mechanisms. The termination process includes cleanup of temporary resources, persistence of important session data, and proper resource deallocation.
One of Agno's key architectural features is the ability to maintain context across session boundaries. This capability is essential for building agents that can maintain long-term relationships with users and learn from interactions over time.
Cross-session context preservation involves identifying information that should persist beyond individual sessions and storing it in appropriate persistent storage. This includes user preferences, learned facts about users, and significant interaction history.
The system provides mechanisms for users to control what information persists across sessions, supporting privacy preferences and regulatory requirements. Users can request deletion of stored information or modification of what types of information are retained.
Session isolation ensures that information from one user's sessions cannot inadvertently leak into another user's sessions. This isolation is maintained through careful access control mechanisms and clear separation of user-specific data.
The session management system includes authentication and authorization mechanisms that verify user identity and control access to session data. These mechanisms are designed to integrate with external authentication systems while providing secure defaults for simple deployments.
The knowledge architecture in Agno is designed around the principle that agents should have access to relevant, up-to-date information without requiring manual curation of every piece of knowledge they might need. This leads to a system that can automatically ingest, process, and retrieve information from diverse sources while maintaining high relevance and accuracy.
The knowledge base is designed as a multi-modal system that can handle text documents, structured data, images, and other content types. This design recognizes that useful knowledge comes in many forms and that agents need to work with diverse information types to be truly useful.
Knowledge ingestion is designed to be both automatic and controllable, allowing systems to continuously update their knowledge while providing mechanisms for controlling what information is included and how it's processed. The ingestion pipeline includes deduplication, quality assessment, and content normalization steps.
The integration with vector databases is a core architectural component that enables semantic search and retrieval across large knowledge bases. The design abstracts over different vector database implementations while providing a consistent interface for knowledge storage and retrieval.
graph TB
subgraph "Knowledge Sources"
PDFs["PDF Documents"]
URLs["Web Pages"]
Databases["Databases"]
APIs["External APIs"]
end
subgraph "Processing Pipeline"
Chunking["Document Chunking"]
Embedding["Embedding Generation"]
Indexing["Vector Indexing"]
Metadata["Metadata Extraction"]
end
subgraph "Vector Databases"
LanceDB["LanceDB"]
ChromaDB["ChromaDB"]
Pinecone["Pinecone"]
Qdrant["Qdrant"]
Weaviate["Weaviate"]
end
subgraph "Search Types"
Semantic["Semantic Search"]
Hybrid["Hybrid Search"]
Filtered["Filtered Search"]
Similarity["Similarity Search"]
end
PDFs --> Chunking
URLs --> Chunking
Databases --> Chunking
APIs --> Chunking
Chunking --> Embedding
Chunking --> Metadata
Embedding --> Indexing
Indexing --> LanceDB
Indexing --> ChromaDB
Indexing --> Pinecone
Indexing --> Qdrant
Indexing --> Weaviate
LanceDB --> Semantic
ChromaDB --> Hybrid
Pinecone --> Filtered
Qdrant --> Similarity
Weaviate --> Semantic
Vector embeddings are generated using configurable embedding models, allowing deployments to choose appropriate models based on their performance, accuracy, and cost requirements. The system supports multiple embedding models simultaneously, enabling different embedding strategies for different types of content.
Search and retrieval operations support multiple search types including semantic similarity, hybrid search combining semantic and keyword approaches, and filtered search that combines vector similarity with metadata constraints. These search modes can be combined and configured to optimize for different use cases.
One of Agno's innovative features is agentic knowledge management, where agents can autonomously update and maintain their knowledge bases based on interactions and discoveries. This capability transforms knowledge bases from static repositories into dynamic, evolving systems.
Agents can identify gaps in their knowledge during conversations and take action to fill those gaps through research, user queries, or external data source integration. This process is designed to be transparent to users while continuously improving agent capabilities.
Quality control mechanisms ensure that agent-generated knowledge additions meet appropriate standards for accuracy and relevance. These mechanisms include confidence scoring, source tracking, and review processes for significant knowledge updates.
Knowledge retrieval is optimized for the specific patterns of agent interactions, which typically involve retrieving small amounts of highly relevant information quickly rather than exhaustive search across large datasets. The retrieval system is designed to minimize latency while maximizing relevance.
Caching strategies are employed at multiple levels to improve retrieval performance. Frequently accessed knowledge is cached in memory, common queries are cached with their results, and embedding calculations are cached to avoid redundant computation.
The retrieval system includes mechanisms for learning from agent feedback about knowledge relevance, gradually improving retrieval quality through usage patterns and explicit feedback signals.
Agno's event-driven architecture provides comprehensive observability and extensibility throughout the system. The event system is designed around strongly-typed events that capture significant occurrences during agent execution, from high-level user interactions down to individual tool calls and reasoning steps.
graph LR
subgraph "Event Sources"
Agent["Agent Events"]
Tools["Tool Events"]
Memory["Memory Events"]
Model["Model Events"]
Session["Session Events"]
end
subgraph "Event Processing"
Validation["Event Validation"]
Enrichment["Context Enrichment"]
Filtering["Event Filtering"]
Routing["Event Routing"]
end
subgraph "Event Handlers"
Logging["Logging System"]
Metrics["Metrics Collection"]
Monitoring["Real-time Monitoring"]
Analytics["Analytics Engine"]
Alerts["Alert System"]
end
subgraph "External Systems"
OpenTelemetry["OpenTelemetry"]
Prometheus["Prometheus"]
DataDog["DataDog"]
Custom["Custom Systems"]
end
Agent --> Validation
Tools --> Validation
Memory --> Validation
Model --> Validation
Session --> Validation
Validation --> Enrichment
Enrichment --> Filtering
Filtering --> Routing
Routing --> Logging
Routing --> Metrics
Routing --> Monitoring
Routing --> Analytics
Routing --> Alerts
Metrics --> OpenTelemetry
Monitoring --> Prometheus
Analytics --> DataDog
Alerts --> Custom
Events are designed to be both human-readable for debugging and machine-processable for automated monitoring and analysis. Each event includes sufficient context to understand its significance within the broader agent execution flow.
The event system supports both synchronous and asynchronous event processing, allowing for real-time monitoring and response as well as batch processing for analytics and system optimization.
Event processing in Agno follows a pipeline architecture where events flow through a series of processing stages. The first stage handles immediate event validation and enrichment, adding context and metadata that might be useful for downstream processors.
Subsequent stages can filter, aggregate, transform, or route events based on configurable rules. This design enables different deployment environments to implement appropriate event processing strategies without modifying core agent code.
The event processing pipeline is designed to be fault-tolerant, with mechanisms for handling processing failures without losing events or disrupting agent execution. Event processing failures are themselves captured as events, providing visibility into system health.
The event system serves as the foundation for comprehensive monitoring and observability capabilities. Events provide the raw data for metrics, logging, tracing, and alerting systems that help operators understand system behavior and performance.
Integration with external monitoring systems is provided through configurable event exporters that can send events to systems like OpenTelemetry, Prometheus, or custom monitoring solutions. These integrations are designed to be lightweight and non-intrusive to agent performance.
The observability system includes both operational metrics focused on system health and performance, and business metrics focused on agent effectiveness and user satisfaction. This dual focus supports both operational excellence and continuous improvement of agent capabilities.
Performance optimization in Agno is approached systematically across multiple dimensions. Startup Performance is optimized through lazy loading of components, minimal object initialization at agent creation time, and careful management of import dependencies to reduce Python import overhead.
Runtime Performance optimization focuses on minimizing the overhead of agent execution cycles. This includes efficient message processing, optimized context assembly, and careful memory management to avoid garbage collection pressure during agent execution.
I/O Performance is optimized through extensive use of asynchronous operations, connection pooling for external services, and intelligent caching strategies that reduce redundant network calls and computation.
Agno's concurrency architecture is built around Python's asyncio framework, providing efficient handling of I/O-bound operations that are common in agent workloads. The design carefully separates CPU-bound operations that benefit from process-level parallelism from I/O-bound operations that benefit from async concurrency.
graph TB
subgraph "Concurrency Models"
AsyncIO["AsyncIO Event Loop"]
ThreadPool["Thread Pool"]
ProcessPool["Process Pool"]
TaskQueue["Task Queue"]
end
subgraph "Operation Types"
IOBound["I/O Bound<br/>(API Calls, DB)"]
CPUBound["CPU Bound<br/>(Embeddings, ML)"]
Mixed["Mixed Operations"]
end
subgraph "Execution Patterns"
Sequential["Sequential"]
Parallel["Parallel"]
Pipeline["Pipeline"]
Batch["Batch Processing"]
end
subgraph "Resource Management"
ConnectionPool["Connection Pooling"]
RateLimit["Rate Limiting"]
BackPressure["Back Pressure"]
CircuitBreaker["Circuit Breaker"]
end
IOBound --> AsyncIO
CPUBound --> ProcessPool
Mixed --> ThreadPool
AsyncIO --> Parallel
ThreadPool --> Pipeline
ProcessPool --> Batch
Parallel --> ConnectionPool
Pipeline --> RateLimit
Batch --> BackPressure
ConnectionPool --> CircuitBreaker
Tool execution is designed to maximize parallelism, with independent tool calls executed concurrently and results collected efficiently. This approach significantly reduces response times for agents that make multiple tool calls.
The concurrency design includes careful resource management to prevent resource exhaustion under high load, with configurable limits on concurrent operations and graceful degradation strategies when limits are approached.
Horizontal scalability is supported through stateless agent design that enables agents to be distributed across multiple processes or machines without requiring complex coordination mechanisms. Session state and knowledge bases can be shared across multiple agent instances through appropriate storage backends.
The architecture supports both scale-up patterns for single-machine deployments and scale-out patterns for distributed deployments. This flexibility allows deployments to choose appropriate scaling strategies based on their performance requirements and operational constraints.
Caching strategies are designed to be effective at scale, with configurable cache layers that can reduce load on shared resources like language model APIs and knowledge bases. Cache invalidation strategies ensure that cached data remains fresh while minimizing cache misses.
Agno's integration architecture provides multiple interfaces for different types of integrations. The Python API provides the most direct and feature-complete interface for applications written in Python. This API is designed to be intuitive and follows Python conventions while providing access to all framework capabilities.
graph TB
subgraph "Client Interfaces"
PythonAPI["Python API"]
RestAPI["REST API"]
GraphQL["GraphQL API"]
WebSocket["WebSocket"]
CLI["Command Line"]
end
subgraph "Protocol Support"
HTTP["HTTP/HTTPS"]
WS["WebSocket"]
gRPC["gRPC"]
MessageQueue["Message Queues"]
end
subgraph "Integration Patterns"
Webhook["Webhooks"]
Polling["Polling"]
Streaming["Streaming"]
Batch["Batch Processing"]
end
subgraph "Authentication"
APIKey["API Keys"]
OAuth["OAuth 2.0"]
JWT["JWT Tokens"]
SAML["SAML"]
end
subgraph "External Systems"
Databases["Databases"]
CloudServices["Cloud Services"]
Microservices["Microservices"]
ThirdPartyAPIs["Third Party APIs"]
end
PythonAPI --> HTTP
RestAPI --> HTTP
GraphQL --> HTTP
WebSocket --> WS
CLI --> gRPC
HTTP --> Webhook
WS --> Streaming
gRPC --> Batch
MessageQueue --> Polling
Webhook --> APIKey
Streaming --> OAuth
Batch --> JWT
Polling --> SAML
APIKey --> Databases
OAuth --> CloudServices
JWT --> Microservices
SAML --> ThirdPartyAPIs
REST API integration is provided through FastAPI-based routes that expose agent capabilities over HTTP. These APIs are designed to be stateless and cacheable, supporting high-performance web applications and microservice architectures.
Real-time integration is supported through WebSocket connections that enable streaming agent responses and bi-directional communication. This capability is essential for interactive applications that need to provide real-time feedback to users.
Integration with external systems is handled through a plugin architecture that allows new integrations to be added without modifying core framework code. Standard integrations include databases, message queues, external APIs, and monitoring systems.
Authentication and authorization integration supports multiple strategies including API keys, OAuth flows, and integration with enterprise identity systems. These integrations are designed to be secure by default while supporting the flexibility needed for diverse deployment environments.
Data integration patterns support both push and pull models for external data sources. Push integration allows external systems to send data to agents through webhooks or message queues, while pull integration allows agents to actively retrieve data from external sources.
The deployment architecture supports diverse deployment patterns from single-machine installations to distributed cloud deployments. Container-based deployment is supported through provided Docker images and Kubernetes manifests.
Configuration management is designed to support different deployment environments through environment-specific configuration files and environment variable overrides. This approach enables the same agent code to be deployed across development, staging, and production environments with appropriate configuration changes.
Resource management capabilities allow deployments to configure resource limits and optimization strategies appropriate to their hardware and scaling requirements. This includes memory limits, connection pool sizes, and caching configurations.
Security in Agno is implemented through defense-in-depth principles with security considerations integrated throughout the architecture rather than added as an afterthought. Authentication and authorization are built into the core session management system, ensuring that access control is enforced consistently across all framework capabilities.
Input validation and sanitization are implemented at multiple levels to prevent injection attacks and other input-based vulnerabilities. Tool execution includes sandboxing capabilities to limit the potential impact of malicious or buggy tools.
Secure communication is enforced for all external integrations, with TLS encryption required for network communications and secure credential storage for API keys and other sensitive configuration.
Privacy protection is built into the memory and knowledge management systems, with mechanisms for controlling what information is stored, how long it's retained, and who can access it. Users have control over their data with capabilities for viewing, modifying, and deleting stored information.
Data minimization principles guide the design of data collection and storage, ensuring that only necessary information is retained and that stored information serves clear purposes. Automated data retention policies help ensure that old data is properly cleaned up.
Anonymization and pseudonymization capabilities support use cases where data analysis is needed but individual privacy must be protected. These capabilities are particularly important for deployments that need to comply with privacy regulations.
The framework includes comprehensive audit logging that captures all significant system events in a tamper-evident format. These logs support compliance requirements and security investigations while maintaining performance under normal operating conditions.
Compliance frameworks support common regulatory requirements including GDPR, HIPAA, and SOC 2. The framework provides configuration options and operational procedures that help deployments achieve compliance with these standards.
Regular security assessments and vulnerability management processes are integrated into the framework development lifecycle, ensuring that security issues are identified and addressed promptly. The framework includes mechanisms for security updates that can be deployed without disrupting running agent systems.
This design document provides a comprehensive overview of Agno's architecture, emphasizing the thoughtful design decisions that enable the framework to provide powerful agent capabilities while maintaining high performance and operational excellence. The architecture's modular design and clear separation of concerns enable developers to build sophisticated agentic systems while providing the flexibility needed for diverse deployment scenarios and use cases.