magpie-search 1.1.0

Local-first search tool for Claude Code session transcripts — hybrid keyword + semantic search, with optional multi-source (federated) trust-tagged search.

Magpie Search

A federated search engine — a search engine an AI reaches for when it needs to find something true to reason over.

---

Ever had your computer reboot on you, or a power outage hit mid-session? Every thread your agent was holding — gone. Now you have the tool to get it back. Never forget what your agent lost again. Magpie indexes everything your AI has ever worked through, locally, so a crash is a hiccup instead of amnesia.

What Magpie is

A normal search engine looks in one place. Magpie takes one question and fans it across everything that matters at once — the AI's entire conversation history, the files on the machine, a structured knowledge graph, a vector store, the live web, even YouTube — and pulls the answer back from wherever it actually lives. Six sources, one call.

And it searches each one the right way. It can grep for an exact string or regex when you know the precise token — a file path, an error, a line of code. It can search by keyword. It can search by meaning, so it finds the thing even when the words don't match. It can do all of that at once.

Then it does the part that makes it trustworthy: it fuses everything into a single ranked answer, and every result carries a trust tier — fact > reference > lead > stale. The solid sources rise, the loose ones are marked as leads to verify, duplicates collapse, and it's all trimmed to fit so it never floods the AI's context. Ask it to go deep and it expands one question into many, reads the pages, and tells you how many independent sources agree — a full research sweep without an army of agents.

It runs entirely on the machine. No server, no account, no telemetry. The AI's transcripts and files never leave. It plugs into whatever AI is running over MCP, so the agent can reach all six sources the instant it needs them.

It is a tool for an AI.

What's inside

At its core is a local index of the AI's transcripts: a SQLite database with two structures built side by side —

• an FTS5 full-text index (BM25 keyword ranking), and
• a vector index (sqlite-vec) of 384-dim embeddings produced locally by a small all-MiniLM-L6-v2 model.

Everything is redacted at ingest — a scrubber strips ~30 classes of secrets (keys, tokens, private keys, connection strings) before a single byte hits the index.

On top of that index sit the five search modes:

Mode	What it does
grep	literal / regex match (exact tokens: paths, errors, code)
lexical	FTS5 / BM25 keyword
semantic	embedding K-NN, cosine distance in the vector index
hybrid	lexical + semantic fused by RRF
rerank	hybrid, then a cross-encoder (jina-reranker) re-scores each candidate

Around that sits the federation layer — the part that makes it federated:

• A provider plugin system. Six backends (transcripts, files, knowledge graph, vector, web, YouTube), each returns Hit objects tagged with a trust tier.
• A fan-out: one query goes to all providers concurrently (≤8 workers), each with a 5-second timeout that fails open — a slow source contributes nothing rather than blocking the call.
• Trust-weighted RRF fusion — Reciprocal Rank Fusion where each source's rank is multiplied by its trust weight (fact ×3, reference ×2, lead ×1, stale ×0.3), damping constant 60. This is the math that merges six heterogeneous sources into one honest ranking.
• Cross-source dedup by content hash — the same fact found in three places collapses to one hit, tagged with where else it appeared (corroboration).
• A token-budget trim, so the merged set never overflows the calling AI's context.

And it exposes all of this to an AI over an MCP server — the tools it hands an agent are exactly: search, recent, session, list_sessions, stats, reindex. Note what's not in that list: nothing that writes an answer.

Why that is not RAG

RAG = Retrieval-Augmented Generation. It's a two-stage pipeline, and the defining stage is the second one: a retriever finds chunks → they're stuffed into a prompt → a language model generates the prose answer. The "G" is the whole point of the name; without a generator writing the answer, it isn't RAG.

Magpie has no G:

1. There is no generator anywhere in the search path. Nothing in Magpie composes a natural-language answer. The closest thing to a model — the cross-encoder reranker — outputs a relevance number per result and reorders the list. It scores; it never writes a sentence.
2. It stops at "here are the ranked hits." A RAG owns the prompt assembly and the model call. Magpie returns the fused, trust-ranked results and hands them back through MCP. What the AI does next — whether it even generates anything — is the AI's job, outside Magpie.
3. Its retriever is more than a RAG's retriever, not less. A textbook RAG retriever is one vector store: embed the query, top-k by cosine, done. Magpie's retrieval is six sources, five modes, trust-weighted fusion, cross-source dedup. It's a far more capable "R" — but it's still only the R.

Plug Magpie into an AI and the pair can form a RAG — Magpie is the R, the AI you bring is the G. But Magpie by itself ships only the R, and a stronger R than usual. It finds and ranks the truth; it never generates the answer.

Deep web search — research breadth without the token bill

The expensive part of "deep research" is reasoning, and the multi-agent approach pays for it N times over — one full LLM context per agent, often millions of tokens for a single question. But reasoning doesn't need to fan out; one capable model already in context can synthesize. Only the searching needs breadth — and searching the web is pure retrieval, zero LLM tokens.

magpie-search deepweb is built on that asymmetry. It fires several sub-queries at the web in parallel, fuses them by trust-weighted RRF + dedup-by-URL into one compact, token-budget-trimmed source set, optionally reads the top pages' text (still token-free), and reports how many independent domains corroborate the result — an agent-free version of the verification a research swarm pays agents to do.

So you get the breadth, page-reading, and corroboration of a multi-agent deep search, but your model only pays for a single synthesis pass over a trimmed result set — thousands of tokens where a swarm spends millions.

# one question, several angles, read the top pages — all token-free retrieval
magpie-search deepweb "the question" --q "another angle" --q "a third angle" --thorough

The model in your loop then does one synthesis pass over the merged, corroborated set. That's the whole saving: the breadth is free, you pay only for the answer.

---

Install

pip install magpie-search

Or install the latest straight from source (pulls all dependencies):

pip install "git+https://github.com/xfloukiex-lab/magpie-search.git"

Optional — add the local-LLM features (the cross-encoder reranker runs on the base install; the session summarizer needs Ollama):

# 1. Install Ollama (free, runs entirely locally) — https://ollama.com/download
# 2. Pull the model magpie-search uses
ollama pull phi3.5

Python 3.10+ on Windows, macOS, and Linux.

Quickstart

magpie-search index                               # build the index (incremental)
magpie-search search "that retry backoff thing"   # keyword search
magpie-search search --mode hybrid "..."          # keyword + semantic, fused
magpie-search search --mode rerank "..."          # + cross-encoder rerank
magpie-search stats                               # sanity-check the index

Connect it to your AI (MCP)

Magpie speaks the Model Context Protocol, so any MCP-capable agent can call it. Point your client at the bundled server:

// e.g. an MCP client config
{
  "mcpServers": {
    "magpie": { "command": "magpie-search-mcp" }
  }
}

The agent then has search, recent, session, list_sessions, stats, and reindex available — federated, trust-ranked, context-budgeted.

CLI reference

Command	What
magpie-search index	Incremental indexing pass over ~/.claude/projects/
magpie-search search "q"	Search — --mode grep|lexical|semantic|hybrid|rerank
magpie-search recent --n 30	Latest 30 messages of the newest session
magpie-search session SESSION-ID	Full transcript of one session
magpie-search list	Recent sessions
magpie-search stats	Index size, last-indexed time, row counts
magpie-search backup	Back up ~/.claude/projects/ to a configurable destination

Add --help to any command for full options.

Python API

import magpie_search

results = magpie_search.search("retry backoff", mode="hybrid", k=5)
for h in results["hits"]:
    print(h["trust"], h["source"], h["snippet"])

# LLM features (needs Ollama + phi3.5)
import magpie_search.llm
ranked  = magpie_search.llm.search_rerank(query="retry backoff", k=3, pool=10)
summary = magpie_search.llm.summarize(session_id="abc-123", n_messages=80)

Backup

magpie-search backup copies your transcript tree to a destination of your choice — a local folder (default, zero config), a remote SSH target (NAS / home server), or a remote SSH target with VM boot/suspend. Configure it in ~/.magpie-search/backup.env:

MAGPIE_SEARCH_BACKUP_SSH_HOST=[e-mail]
MAGPIE_SEARCH_BACKUP_SSH_DEST=~/claude-transcripts/

Useful flags: --dry-run, --no-suspend, --show-config. Backup copies; it never deletes originals.

Configuration

Everything is environment-variable driven with sensible defaults.

Var	Default	What
MAGPIE_SEARCH_HOME	~/.magpie-search	Data directory (DB, models, logs)
MAGPIE_SEARCH_MODELS_DIR	$MAGPIE_SEARCH_HOME/models	fastembed model cache
MAGPIE_SEARCH_OLLAMA_HOST	http://localhost:11434	Ollama server URL
MAGPIE_SEARCH_TOKENIZER	heuristic	Set to tiktoken for precise budget counting
MAGPIE_SEARCH_AUDIT_LOG	$MAGPIE_SEARCH_HOME/llm-audit.jsonl	Per-call audit log

The summarizer passes through a 6-probe guardrail stack (length, proper-noun-safety, identifier-safety, refusal-drift, semantic-grounding, self-verify); all six must pass for trust: clean. Any failure suppresses the summary and returns trust: degraded — quiet over wrong. Raw messages stay accessible via magpie-search session SESSION-ID.

Privacy

Magpie Search is a local tool. No server, no account, no telemetry, no analytics, no auto-update, no crash reporter. Your transcripts, the index, the audit log, the model cache, and the backups all live on your machine. The only network calls it ever makes are: your local Ollama server (LLM features), your own backup target (only when you run backup), and a one-time model download from Hugging Face on first run. Verify it yourself with tcpdump, Wireshark, or a network-blocked sandbox.

Scheduling

Run magpie-search index (and optionally backup) on a schedule. Ready-made units live in installers/ for systemd (Linux), launchd (macOS), and Task Scheduler (Windows).

Troubleshooting

• "rsync not on PATH" — falls back to scp -r. On Windows, install Git for Windows, which ships rsync.
• Search returns nothing — run magpie-search stats; if last_indexed_at is null, run magpie-search index.
• Summarizer always degraded — that's the false-positive guard working as designed. Raw transcripts remain available via session SESSION-ID.

---

About

Magpie Search is built by VektorGeist LLC.

We build local-first tools for people who run their own AI. Magpie is the search core; Aviary is our agent platform.

• Website: vektorgeist.com
• Contact: [e-mail]
• Issues & contributions: open an issue or PR on this repository.

License

Licensed under the Apache License 2.0 — see LICENSE. Copyright © 2026 VektorGeist LLC.

"Magpie Search" and the magpie mark are trademarks of VektorGeist LLC. The code is open under Apache-2.0; the brand and name are reserved.