How to Build RAG Pipelines with OpenClaw

An OpenClaw RAG pipeline indexes documents into a vector store, retrieves relevant chunks based on a query using semantic similarity, and passes those chunks to an LLM prompt for grounded response generation.

OpenClaw agents can each handle part of it. One indexes, one retrieves, one synthesizes. Fast.io provides the storage and indexing built in. No need for services like Pinecone or Weaviate.

Core steps:

• Ingestion and indexing: Split documents into chunks, generate embeddings, store in vector DB.
• Retrieval: Embed query, find top-k similar chunks (e.g., high cosine similarity).
• Generation: Stuff context into LLM prompt: "Use only this context: {chunks}. Answer: {query}"

RAG reduces hallucinations on your domain data, as Pinecone explains. Fast.io's Intelligence Mode automates embeddings and storage for OpenClaw.

Fast.io supports large-file uploads with chunked transfer. Indexed content becomes queryable via natural language.

OpenClaw handles agent coordination well but lacks storage and built-in RAG. Fast.io adds those with agent tools.

Benefits:

• No infrastructure needed: Intelligence Mode indexes uploads automatically. Queries include citations right away.
• Agent collaboration: Multiple OpenClaw instances work in one workspace using locks and webhooks.
• Free tier: 50GB storage, 5,000 credits/month, no card needed.
• Scales to production: Handles book-length PDFs, multi-format support (PDF, DOCX, code).

LangChain requires setting up a vector store. Here, ClawHub works without configuration.

Get set up:

1. Install OpenClaw locally.
2. Create Fast.io agent account. 50GB free.
3. Install ClawHub skill: npx clawhub@latest install dbalve/fast-io. Adds 14 tools.

Auth happens once via browser. Test with: "Create workspace rag-demo".

Verify: Check MCP server for 251 tools.

Create Fast.io workspace "rag-test" with intelligence enabled.

Workspaces act as your vector stores. Intelligence Mode enables auto-indexing.

ClawHub call:

org-create-workspace {"name": "rag-pipeline", "intelligence": true}

Confirm with workspace-details: "intelligence_enabled": true.

Uploads now auto-chunk and embed. Costs 10 credits/page ingested.

Pro tip: Name workspaces by project, e.g., "q1-financials-rag".

Load your documents.

Local upload:

workspace-storage-add-file workspace="rag-pipeline" path="/docs/" filename="manual.pdf" content_base64="[base64]"

URL import:

web-import workspace="rag-pipeline" path="/docs/" url="https://example.com/data.pdf"

Pulls from Drive/Box via OAuth. No local storage hit.

Batch via agent loop:

docs = ["doc1.pdf", "doc2.txt"]
for doc in docs:
    tool("workspace-storage-add-file", {"workspace": "rag-pipeline", "path": "/corpus/", "filename": doc})

Poll storage-list: ai_state: "ready". Large files chunk automatically.

Core of RAG: fetch relevant context.

Semantic query:

workspace-search workspace="rag-pipeline" query="payment terms" folders_scope="root:abc"

Returns top chunks, scores, nodeIds.

Hybrid (keyword + semantic): Add search_type: "hybrid".

Agent code:

def retrieve(query):
    results = tool("workspace-search", {"workspace": "rag-pipeline", "query": query, "top_k": 5})
    return [r["content"] for r in results if r["score"] > 0.75]

Scope with folders_scope="folderId:abc" limits to subfolders.

Combine retrieval + LLM.

Direct AI chat (easiest):

ai-chat-create context_type="workspace" type="chat_with_files" folders_scope="root:abc" query_text="Summarize payment terms"

Polls until done and returns cited answers.

Custom LLM:

chunks = retrieve("indemnity clauses")
prompt = f"""Context: {' '.join(chunks)}
Question: What are indemnity clauses?
Answer using only context:"""
response = llm(prompt)

Post-process: re-rank chunks, compress context with an LLM.

Ready for Agentic RAG?

50GB free, 5,000 credits/month. Install ClawHub skill for OpenClaw RAG pipelines.

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A single agent works for simple cases. Production setups use agent teams.

Three-agent research pipeline:

• Indexer agent: Uses web-import to pull sources, acquires exclusive lock with storage-lock-acquire to avoid concurrent writes, uploads files, then releases lock.
• Retriever agent: Sets up webhook with webhook-create events=["file_ingested"], triggers on new files, performs workspace-search for relevant chunks.
• Synthesizer agent: Takes retrieved chunks, generates response using ai-chat-create or LLM prompt, outputs to create-note with citations.

Indexer agent code example:

lock = tool(\"storage-lock-acquire\", {\"path\": \"/corpus/\", \"workspace\": \"rag-pipeline\"})
tool(\"web-import\", {\"workspace\": \"rag-pipeline\", \"path\": \"/corpus/\", \"url\": \"https://example.com/source.pdf\"})
tool(\"storage-lock-release\", {\"lock_id\": lock[\"id\"]})

Retriever agent webhook handler:

def handle_ingest(event):
    query = f\"Key points from {event['filename']}\"
    chunks = tool(\"workspace-search\", {\"workspace\": \"rag-pipeline\", \"query\": query, \"top_k\": 5})
    synth_agent.run(chunks)

Conflict-free multi-agent access: File locks ensure safe concurrent operations. Invite other agents with member-add.

Scale to production: Pair with human review in the UI. Transfer ownership to clients. Use webhooks to re-index on file changes. The setup works for many agents and large data amounts.

Build reliable pipelines.

• Chunking strategy: Fast.io handles semantic boundary-aware chunking automatically, with chunk sizes optimized for retrieval.
• Top-k tuning: Begin with a small top-k set; evaluate using precision@K or NDCG metrics on a validation set.
• Scoped retrieval: Use folder-level scopes like folders_scope="root:abc" for modular pipelines.
• Version pinning: Cache reliable results by querying nodeId:versionId for reproducible retrieval.
• Monitoring ingestion: Poll activity-poll for indexing status; alert on ai_state != \"ready\".
• Cost management: Monitor auth-status for remaining credits; batch operations to stay under limits.
• Reranking: Apply a cross-encoder model post-retrieval to boost precision.
• Query expansion: Rewrite queries with LLM for better recall in ambiguous cases.

Hybrid search combines semantic vectors with keyword matching to catch domain terms.

Evaluation framework: Create a dataset of query-ground_truth pairs. Compute recall@k, precision@k, and faithfulness score using tools like RAGAS or TruLens.

No results: Check ai_state: "ready", intelligence ON. Low scores: Increase top_k, check query embedding. Credit errors: Upgrade or transfer ownership. Locks stuck: storage-lock-release. Large files: Use chunked uploads for large document sets.

Logs: events-search filters by actor/type.

Fast.io eliminates DB setup, adds collab.