agentrank 0.1.2

Temporal-aware embedding model for AI agent memory retrieval. The first embedder that understands WHEN memories happened.

🧠 AgentRank

The first embedding model that understands WHEN memories happened.

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Why AgentRank?

Standard embeddings (OpenAI, Cohere, MiniLM) treat "yesterday" and "6 months ago" identically. For AI agent memory, this breaks everything.

AgentRank adds:

• Temporal embeddings — 10 learnable time buckets so the model understands recency
• Memory type embeddings — Distinguishes events, preferences, and instructions
• 21% better retrieval on agent memory benchmarks

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Installation

pip install agentrank

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Quick Start

from agentrank import AgentRankEmbedder

# Load model
model = AgentRankEmbedder.from_pretrained("vrushket/agentrank-base")

# Encode with temporal context
embeddings = model.encode(
    texts=["User prefers Python for backend development"],
    temporal_info=[7],        # 7 days ago
    memory_types=["semantic"] # It's a preference
)

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Models

Model	Params	Use Case	HuggingFace
AgentRank-Base	149M	Best quality	vrushket/agentrank-base
AgentRank-Small	33M	Fast inference	vrushket/agentrank-small

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Benchmarks

Model	MRR	Recall@5
AgentRank-Base	0.65	99.6%
AgentRank-Small	0.64	97.4%
all-mpnet-base-v2	0.54	79.6%
all-MiniLM-L6-v2	0.53	75.2%

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Works Great With

CogniHive — Multi-agent memory system with "who knows what" routing

pip install cognihive

Together: CogniHive routes questions to the right agent, AgentRank retrieves the right memories.

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Links

• HuggingFace Models: huggingface.co/vrushket
  • agentrank-base
  • agentrank-small
• GitHub: github.com/vmore2/AgentRank-base
• CogniHive: pypi.org/project/cognihive

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Contact

• Author: Vrushket More
• Email: [e-mail]
• Issues: GitHub Issues

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License

Apache 2.0 — Free for commercial use.