Agent RAG and long-term memory with Pinecone.

Skill metadata


Source	Optional — install with `mibyan 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

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

Pinecone Research — Agent RAG & Long-Term Memory

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

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

Setup

pip install pinecone-client langchain-pinecone langchain-openai

Set 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

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

Pinecone Docs: https://docs.pinecone.io
LangChain Integration: https://python.langchain.com/docs/integrations/vectorstores/pinecone
Free Tier: 1 index, 100K vectors (1536 dimensions)

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Pinecone Research — Agent RAG and long-term memory with Pinecone

Skill metadata

Reference: full SKILL.md

Pinecone Research — Agent RAG & Long-Term Memory

When to use this skill

Quick start

Setup

Basic RAG pipeline

Namespace-based session memory

Best practices

Resources

​Skill metadata

​Reference: full SKILL.md

​Pinecone Research — Agent RAG & Long-Term Memory

​When to use this skill

​Quick start

​Setup

​Basic RAG pipeline

​Namespace-based session memory

​Best practices

​Resources

Skill metadata

Reference: full SKILL.md

Pinecone Research — Agent RAG & Long-Term Memory

When to use this skill

Quick start

Setup

Basic RAG pipeline

Namespace-based session memory

Best practices

Resources