Use it when your goal in databases and data is clear and you can limit it to the data and actions it actually needs.
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SKILLoptionalHermes Optional Skills
Pinecone Research
Agent RAG and long-term memory with Pinecone
Last registry verification2026-08-18v1.0.0immuhammadfurqan
What does it add to Hermes?
Agent RAG and long-term memory with Pinecone
Pinecone Research is a skill related to databases and data. It gives the agent a structured way to read or query data. Some tools may also write data, so clear limits matter.
This plain-language explanation is based on the publisher description. The original text remains visible for verification.
Do not add it merely to experiment when Hermes already has a simpler path, or when you cannot review its source and permissions.
Best for users who want a repeatable way of working inside Hermes.
Use a test database and a read-only account, then request a simple query that changes no records.
Original publisher description
Agent RAG and long-term memory with Pinecone
Data source
This entry was indexed from Hermes Optional Skills. Our explanation interprets the type and domain without inventing a capability not present upstream.
Security review
The source is official or editorially reviewed, but you still need to review permissions and version compatibility.
Inspect, install, then test.
- 01Open the source
Match the publisher, license, and description to your need. Check the real update history.
- 02Review permissions and secrets
Never paste a secret value into this site. Use environment-variable names and grant the smallest scope.
- 03Copy setup only after review
The controls below copy text. They do not execute commands on your device.
- 04Test with a non-sensitive task
Inspect the visible tools, then exclude write or delete tools you do not need.
Review the command, then copy it.
hermes skills install pinecone-researchHermes Belarabi does not execute this command. Installation happens on your device and remains subject to Hermes scanning and your review.
Exactly what Hermes loads when this skill runs.
Reproduced from the official documentation. Read it before enabling the skill: this text becomes the agent's instructions.
Agent RAG and long-term memory with Pinecone.
Skill metadata
A lookup table. Do not read it all; find the row that applies to you.
| Source | Optional — install with hermes skills install official/research/pinecone-research |
| Path | optional-skills/research/pinecone-research |
| Version | 1.0.0 |
| Author | immuhammadfurqan |
| License | MIT |
| Dependencies | pinecone-client, langchain-pinecone |
| Platforms | linux, macos, windows |
| Tags | RAG, Pinecone, Memory, Research, Vector Database, Agent, Retrieval |
Reference: full SKILL.md
Explains the idea itself. Read it slowly; the later sections build on it.
Use Pinecone as a retrieval-augmented generation (RAG) backend for agent conversations: persist embeddings, retrieve relevant context from past sessions, and build long-term memory.
When to use this skill
Explains the idea itself. Read it slowly; the later sections build on it.
Use when:
- Building agent RAG pipelines with Pinecone as the vector store
- Need persistent long-term memory across agent sessions
- Combining retrieval with agent tool use
- Researching or prototyping semantic search workflows
Use the mlops/pinecone skill instead when:
- Need a general Pinecone reference (index management, CRUD, hybrid search)
- Working on production infrastructure without agent integration
Quick start
Ordered, practical steps. Run one and confirm it worked before moving on.
Setup
pip install pinecone-client langchain-pinecone langchain-openaiSet your API key:
export PINECONE_API_KEY="your-api-key"Basic RAG pipeline
from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
# Initialize Pinecone
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
# Create or connect to index
index_name = "agent-memory"
if index_name not in [i.name for i in pc.list_indexes()]:
pc.create_index(
name=index_name,
dimension=1536,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
# Build vector store
vectorstore = PineconeVectorStore.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(),
index_name=index_name,
)
# Retrieve relevant context
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
results = retriever.invoke("What did the agent discuss yesterday?")Namespace-based session memory
# Store per-session memory
vectorstore = PineconeVectorStore(
index=pc.Index(index_name),
embedding=OpenAIEmbeddings(),
namespace=f"session-{session_id}",
)
# Query across all sessions (no namespace filter)
all_memory = PineconeVectorStore(
index=pc.Index(index_name),
embedding=OpenAIEmbeddings(),
)
results = all_memory.similarity_search("relevant query", k=10)Best practices
Explains the idea itself. Read it slowly; the later sections build on it.
- Namespace by session or user — isolate data for multi-tenant agents
- Batch upserts — 100–200 vectors per batch for efficiency
- Metadata filtering — tag vectors with session ID, timestamp, topic
- Prune old memory — delete stale namespaces to control costs
- Use serverless — auto-scaling, pay-per-use pricing
Resources
Explains the idea itself. Read it slowly; the later sections build on it.
- Pinecone Docs: https://docs.pinecone.io
- LangChain Integration: https://python.langchain.com/docs/integrations/vectorstores/pinecone
- Free Tier: 1 index, 100K vectors (1536 dimensions)