61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
from letta_client import Letta
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client = Letta(base_url="http://localhost:8283")
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# list available models
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models = client.models.list_llms()
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for model in models:
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print(f"Provider {model.model_endpoint_type} model {model.model}: {model.handle}")
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# list available embedding models
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embedding_models = client.models.list_embedding_models()
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for model in embedding_models:
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print(f"Provider {model.handle}")
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# openai
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openai_agent = client.agents.create(
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model="openai/gpt-4o-mini",
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embedding="openai/text-embedding-3-small",
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# optional configuration
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context_window_limit=16000,
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embedding_chunk_size=300,
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)
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# Azure OpenAI
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azure_openai_agent = client.agents.create(
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model="azure/gpt-4o-mini",
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embedding="azure/text-embedding-3-small",
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# optional configuration
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context_window_limit=16000,
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embedding_chunk_size=300,
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)
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# anthropic
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anthropic_agent = client.agents.create(
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model="anthropic/claude-sonnet-4-20250514",
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# note: anthropic does not support embeddings so you will need another provider
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embedding="openai/text-embedding-3-small",
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# optional configuration
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context_window_limit=16000,
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embedding_chunk_size=300,
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)
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# Groq
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groq_agent = client.agents.create(
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model="groq/llama-3.3-70b-versatile",
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# note: groq does not support embeddings so you will need another provider
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embedding="openai/text-embedding-3-small",
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# optional configuration
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context_window_limit=16000,
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embedding_chunk_size=300,
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)
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# Ollama
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ollama_agent = client.agents.create(
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model="ollama/thewindmom/hermes-3-llama-3.1-8b:latest",
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embedding="ollama/mxbai-embed-large:latest",
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# optional configuration
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context_window_limit=16000,
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embedding_chunk_size=300,
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)
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