GKP Agent (Generated Knowledge Prompting)

The GKP Agent enhances its reasoning by generating relevant knowledge before answering queries. This approach, inspired by Liu et al. 2022, is particularly effective for tasks requiring commonsense reasoning and factual information. The agent consists of three main components:

Knowledge Generator — Creates relevant factual information
Reasoner — Uses generated knowledge to form answers
Coordinator — Synthesizes multiple reasoning paths into a final answer

Architecture

API Reference

GKPAgent

Parameter	Type	Default	Description
`agent_name`	`str`	`"gkp-agent"`	Name identifier for the agent
`model_name`	`str`	`"openai/o1"`	LLM model to use for all components
`num_knowledge_items`	`int`	`6`	Number of knowledge snippets to generate per query

Method	Parameters	Returns
`process(query)`	`query: str`	`Dict[str, Any]` — full processing results
`run(task)`	`task: str`	`str` — the final answer to a single task
`__call__(task)`	`task: str`	`str` — alias for `run(task)`

run() only accepts a single task string (it internally calls the private _run([task])[0]). There is no public batch-processing method that accepts a list of tasks; process each task with a separate run() call if you need to handle multiple queries.

KnowledgeGenerator

Parameter	Type	Default	Description
`agent_name`	`str`	`"knowledge-generator"`	Name identifier
`model_name`	`str`	`"openai/o1"`	Model for knowledge generation
`num_knowledge_items`	`int`	`2`	Number of knowledge items to generate per query

Method	Parameters	Returns
`generate_knowledge(query)`	`query: str`	`List[str]` — generated knowledge statements

Reasoner

Parameter	Type	Default	Description
`agent_name`	`str`	`"knowledge-reasoner"`	Name identifier
`model_name`	`str`	`"openai/o1"`	Model for reasoning

Method	Parameters	Returns
`reason_and_answer(query, knowledge)`	`query: str`, `knowledge: str`	`Dict[str, str]` — explanation, confidence, answer

Example

from swarms import GKPAgent

agent = GKPAgent(
    agent_name="gkp-agent",
    model_name="claude-sonnet-4-6",
    num_knowledge_items=6,
)

query = "What are the implications of quantum entanglement on information theory?"

result = agent.run(query)

print(f"Query: {query}")
print(f"Answer: {result}")

Best Practices

Knowledge Generation: Set appropriate number of knowledge items based on query complexity
Reasoning Process: Ensure diverse reasoning paths for complex queries. Validate confidence levels.
Coordination: Review coordination logic for complex scenarios. Validate final answers against source knowledge.

Performance Considerations

Processing time increases with number of knowledge items
Complex queries may require more knowledge items
Consider caching frequently used knowledge
Monitor token usage for cost optimization

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

GKP Agent (Generated Knowledge Prompting)

Architecture

API Reference

GKPAgent

KnowledgeGenerator

Reasoner

Example

Best Practices

Performance Considerations

​Architecture

​API Reference

​GKPAgent

​KnowledgeGenerator

​Reasoner

​Example

​Best Practices

​Performance Considerations

Architecture

API Reference

GKPAgent

KnowledgeGenerator

Reasoner

Example

Best Practices

Performance Considerations