Documentation Index
Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
Use this file to discover all available pages before exploring further.
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(queries, detailed_output?) | queries: List[str], detailed_output: bool | Union[List[str], List[Dict[str, Any]]] |
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.agents.gkp_agent import GKPAgent
agent = GKPAgent(
agent_name="gkp-agent",
model_name="claude-sonnet-4-6",
num_knowledge_items=6,
)
queries = [
"What are the implications of quantum entanglement on information theory?",
]
results = agent.run(queries)
for i, result in enumerate(results):
print(f"\nQuery {i+1}: {queries[i]}")
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.
- 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
