<!-- source: https://deepwiki.com/MervinPraison/PraisonAI/2-core-agent-system -->

# Core Agent System

- api.md
- src/praisonai-agents/praisonaiagents/agent/agent.py
- src/praisonai-agents/praisonaiagents/llm/llm.py

This document covers the fundamental agent architecture in PraisonAI, including the `Agent` class, LLM integration, tool execution, and core functionality that powers individual agents. This system provides the building blocks for creating intelligent agents that can process instructions, execute tools, and interact with users.

For information about agent orchestration and multi-agent coordination, see [Multi-Agent Orchestration](/MervinPraison/PraisonAI/2.3-llm-integration). For memory and knowledge management details, see [Memory and Knowledge Systems](/MervinPraison/PraisonAI/3-execution-and-control). For safety mechanisms, see [Safety and Validation](/MervinPraison/PraisonAI/4-memory-and-knowledge-systems). For comprehensive tool integration, see [Tool Systems](/MervinPraison/PraisonAI/5-tool-systems).

## Core Architecture Overview

The core agent system is built around the `Agent` class ([agent/agent.py155-1695](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L155-L1695)), which provides the foundation for individual AI agents. The system emphasizes **lazy loading** and **zero overhead** - features are only initialized when used, reducing startup time from ~420ms to ~20ms in silent mode.

### Agent Class Structure

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```

**Lazy Loading Pattern:** Heavy dependencies like `rich.console.Console`, `HookRunner`, and `StreamEventEmitter` are only imported and initialized when their properties are first accessed ([agent/agent.py24-94](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L24-L94)). This is achieved through property decorators that initialize private attributes (`__hook_runner`, `__stream_emitter`) on first access.

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py155-175](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L155-L175) [src/praisonai-agents/praisonaiagents/agent/agent.py24-94](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L24-L94) [src/praisonai-agents/praisonaiagents/agent/agent.py1278-1315](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1278-L1315)

## Agent Initialization and Lifecycle

The `Agent` class supports two initialization patterns: **instruction-based** (simplified) and **role-based** (traditional). The initialization process includes configuration resolution, feature setup, and lazy resource allocation.

### Agent Initialization Flow

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```

**Consolidated Parameters System:** The `Agent` class uses a unified parameter resolution system where simple inputs (bool, string, dict) are automatically converted to typed config objects using `resolve()` ([config/param_resolver.py125-386](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/config/param_resolver.py#L125-L386)). For example:

- output="verbose" -> OutputConfig(verbose=True, markdown=True, ...)
- memory=True -> MemoryConfig() with defaults
- memory="chromadb://./storage" -> MemoryConfig(backend="chromadb", ...)

**Fast Path Optimization:** Common cases like `output=None` or `memory=False` skip the full resolver and use cached defaults, reducing initialization overhead ([agent/agent.py621-900](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L621-L900)).

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py399-1370](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L399-L1370) [src/praisonai-agents/praisonaiagents/config/param_resolver.py125-386](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/config/param_resolver.py#L125-L386) [src/praisonai-agents/praisonaiagents/config/loader.py1-100](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/config/loader.py#L1-L100)

## Agent Execution Methods

The `Agent` class provides multiple execution entry points for different use cases:

### Execution Method Architecture

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```

**Method Comparison Table:**

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py1952-3339](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1952-L3339)

## Agent Identity and Display

The agent system includes a sophisticated identity management system for display and tracking:

### Agent Identity System

```

```

**Identity Features:**

- display_name property returns either self.name or f"Agent {self._agent_index}" ( agent/agent.py 1867-1875 )
- _agent_counter is a class-level counter incremented for each unnamed agent ( agent/agent.py 157 )
- agent_id is lazily generated using shortuuid when first accessed ( agent/agent.py 1877-1887 )
- Instruction-based agents skip "Agent Info" panel when name=None to reduce clutter

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py1108-1136](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1108-L1136) [src/praisonai-agents/praisonaiagents/agent/agent.py1867-1887](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1867-L1887)

## LLM Integration Layer

The agent system supports multiple LLM integration patterns through the `LLM` class wrapper around LiteLLM, enabling access to 100+ model providers.

### LLM Integration Decision Tree

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```

**LLM Class Architecture:**

The `LLM` class ([llm/llm.py91-1656](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/llm/llm.py#L91-L1656)) provides:

- LiteLLM Integration: Unified interface to 100+ providers via litellm.completion() ( llm/llm.py 1530-1656 )
- Model Capabilities: Auto-detection of features like web search, prompt caching, streaming ( llm/llm.py 684-722 )
- Tool Calling: Support for native tool calling and Ollama fallbacks ( llm/llm.py 1443-1529 )
- Streaming: Configurable streaming with tool call support ( llm/llm.py 1256-1297 )
- Caching: Prompt caching for Anthropic, OpenAI, Deepseek ( llm/llm.py 710-722 )
- Rate Limiting: Automatic retry on rate limit errors with exponential backoff ( llm/llm.py 556-683 )

**LLM Configuration Patterns:**

**OpenAI Client Property (Lazy):**

```

```

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py1139-1273](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1139-L1273) [src/praisonai-agents/praisonaiagents/llm/llm.py91-722](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/llm/llm.py#L91-L722) [src/praisonai-agents/praisonaiagents/agent/agent.py1755-1768](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1755-L1768)

## Tool Execution System

The agent system supports multiple tool formats through automatic schema generation and a unified execution interface. Tools can be functions, classes, MCP servers, or pre-formatted OpenAI tool definitions.

### Tool Execution Flow

```

```

### Tool Format Resolution

```

```

**Tool Definition Generation (_generate_tool_definition):**

The system automatically generates OpenAI function schemas from Python callables ([agent/agent.py269-398](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L269-L398)):

1. Signature Inspection: Uses inspect.signature() to extract parameters
2. Type Mapping: Converts Python types to JSON schema types (int -> "integer", list -> "array")
3. Docstring Parsing: Extracts parameter descriptions from Args: section
4. Required Detection: Marks parameters without defaults as required
5. Special Tool Support: LangChain: Uses tool.run method if hasattr(tool, 'run') CrewAI: Uses tool._run method if hasattr(tool, '_run') MCP: Calls tool.to_openai_tool() for pre-formatted definitions

**Tool Execution (execute_tool):**

The `execute_tool()` method ([agent/agent.py1556-1653](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L1556-L1653)) handles tool invocation:

1. Tool Lookup: Searches self.tools list for matching function name
2. MCP Detection: Checks hasattr(tool, 'call_tool') for MCP instances
3. Async Handling: Uses asyncio.run() for async MCP calls
4. Error Handling: Wraps exceptions in error dictionaries
5. Result Formatting: Converts results to strings or structured dicts

**Tool Caching:**

Formatted tool definitions are cached in `_formatted_tools_cache` dict with max size of 100 to avoid repeated schema generation ([agent/agent.py1363-1365](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/agent/agent.py#L1363-L1365)).

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py269-398](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L269-L398) [src/praisonai-agents/praisonaiagents/agent/agent.py1479-1555](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1479-L1555) [src/praisonai-agents/praisonaiagents/agent/agent.py1556-1653](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L1556-L1653)

## Memory and Knowledge Integration

The agent system integrates with memory and knowledge management systems to provide persistent context and retrieval-augmented generation (RAG) capabilities.

### Memory and Knowledge Integration

```

```

**Memory and Knowledge Features:**

- self.chat_history list for conversation context (line 458)
- Knowledge processing with user_id and agent_id scoping (lines 500-502)
- Optional memory dependency with ImportError handling (lines 318-326)
- File, URL, and string content support in knowledge processing (lines 543-551)

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py318-326](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L318-L326) [src/praisonai-agents/praisonaiagents/agent/agent.py491-553](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L491-L553) [src/praisonai-agents/praisonaiagents/agent/agent.py458](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L458-L458)

## Safety and Validation

The core agent system includes built-in safety mechanisms through guardrails and human approval systems.

### Safety and Guardrail System

```

```

**Guardrail System Components:**

- Function-based guardrails with signature validation (lines 561-590)
- LLM-based guardrails with description strings (lines 593-596)
- Retry mechanism with max_guardrail_retries parameter (lines 676-722)
- Integration with TaskOutput validation (lines 680-684)
- GuardrailResult return type for standardized responses

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py555-724](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L555-L724)

## Configuration and Initialization

### Agent Configuration Reference

### Environment Variables

The agent system respects several environment variables for configuration:

- OPENAI_MODEL_NAME : Default LLM model
- OPENAI_BASE_URL : Custom API endpoint
- OPENAI_API_KEY : API authentication
- PRAISON_CONFIG_DIR : Configuration directory

Sources: [src/praisonai-agents/praisonaiagents/agent/agent.py337-508](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/agent/agent.py#L337-L508) [src/praisonai-agents/praisonaiagents/llm/llm.py87-207](https://github.com/MervinPraison/PraisonAI/blob/ab80807b/src/praisonai-agents/praisonaiagents/llm/llm.py#L87-L207)
