Agi Now Bull Bull

Bull — Micro Data Environment, One Binary

Bull is a self-contained data environment in a single ~8MB static binary. Five embedded engines — KV, SQL, Graph, Search, Time-Series — ready to use the moment you download it. No databases to install, no servers to configure, no dependencies to chase. Just bull and your data.

Use CLI to build data pipelines step by step — scriptable, pipe-friendly, and perfect for agent workflows that persist state, analyze, and query — all locally.

Installation

One-liner (Linux / macOS):

curl -fsSL https://github.com/agi-now/bull/releases/latest/download/bull-$(uname -s | tr A-Z a-z)-$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/') -o /usr/local/bin/bull && chmod +x /usr/local/bin/bull

Windows (PowerShell):

Invoke-WebRequest -Uri "https://github.com/agi-now/bull/releases/latest/download/bull-windows-amd64.exe" -OutFile "$env:LOCALAPPDATA\bull.exe"

Manual: Download the binary for your platform from GitHub Releases, rename to bull (or bull.exe), and place it in your PATH.

Verify:

bull version

Engines

When to Use Bull

Use bull only when:

• Data volume is large — files that would waste tokens if loaded into conversation context (CSV, JSON, logs, bulk documents, etc.)
• User explicitly requests it — user mentions a bull command or asks to use bull
• Python or other runtimes are not available — bull is a static binary with zero dependencies; it works in minimal containers, CI pipelines, and restricted sandboxes where Python/Node.js are not installed. Prefer bull CLI commands over writing scripts that may fail due to missing runtimes.

Do not use bull when:

• Data is small enough to handle directly in conversation (a few rows, a short list, simple config)
• A quick in-context answer is more efficient than importing into an engine

Agent Strategy: Offload to Bull, Save Tokens

When bull is appropriate, follow this principle: understand the data first, then hand off to bull for storage and analysis.

1. Inspect — Read a small sample of the data source (head, schema, dtypes) to understand structure. Do NOT load entire datasets into conversation context.
2. Preprocess if needed — If data requires cleaning or format conversion (e.g. Excel/Parquet → CSV/NDJSON), use Python scripts if Python is available. If Python is not available, use bull's built-in import capabilities directly — bull accepts CSV, JSON, and NDJSON out of the box.
3. Import into bull — Use bull sql import, bull search bulk, bull graph import-csv, or bull ts bulk to load the data file.
4. Analyze with bull — Run queries, aggregations, searches, and graph algorithms via bull CLI. Return --format json results directly.

Why CLI commands over Python scripts: Each bull command is a deterministic shell call with predictable behavior. Unlike LLM-generated Python scripts, bull commands have no indentation errors, no missing dependencies, no version conflicts, and no runtime requirements. In environments without Python, bull is the only option for data processing.

This keeps token usage minimal: bull handles persistent storage and query execution locally, and only compact JSON results enter the conversation.

Quick Decision: Which Engine to Use

Need to store/retrieve a value by key? → bull kv

• Config, session, cache, counters, state between agent steps
• bull kv put <db> <key> <value> / bull kv get <db> <key>

Need to analyze structured data with SQL? → bull sql

• CSV/JSON import, GROUP BY, JOIN, aggregation, export
• bull sql import <db> <table> data.csv → bull sql query <db> "SELECT ..." --format json

Need to model relationships or dependencies? → bull graph

• Service dependencies, social networks, task ordering, reachability
• bull graph add-vertex, bull graph add-edge, bull graph shortest-path

Need to search text content? → bull search

• Articles, logs, documents — keyword search with scoring
• bull search create <idx> → bull search bulk <idx> data.ndjson → bull search query <idx> "keyword"

Need to record metrics over time? → bull ts

• CPU, memory, request counts, latency, any numeric value with timestamps
• bull ts write <db> <metric> <value> --label host=server1

Global Flags

All commands accept:

• --data-dir <path> — Override data directory (default: ./bull_data)

Output Conventions

• Use --format json wherever available for machine-readable output
• Mutation commands (put, del, add-vertex) produce no stdout on success, exit code 0
• Errors go to stderr with non-zero exit code. Examples:

  Error: key "session_token" not found in bucket "default"
  Error: index "articles" does not exist
  Error: vertex "svc-unknown" not found in graph "deps"
  

Common Workflows

Import CSV and analyze with SQL

bull sql import analytics users users.csv
bull sql query analytics "SELECT city, COUNT(*) as cnt FROM users GROUP BY city ORDER BY cnt DESC" --format json

Build a dependency graph and find paths

bull graph add-vertex deps auth-svc --attr type=service
bull graph add-vertex deps user-svc --attr type=service
bull graph add-vertex deps db-svc --attr type=database
bull graph add-edge deps auth-svc user-svc
bull graph add-edge deps user-svc db-svc
bull graph shortest-path deps auth-svc db-svc

Index documents and search

bull search create articles
bull search bulk articles articles.ndjson
bull search query articles "machine learning" --limit 5 --format json

Record and query metrics

bull ts write monitoring cpu_usage 72.5 --label host=web-01
bull ts query monitoring cpu_usage --label host=web-01 --format json

Persist agent state between steps

bull kv put pipeline step1 '{"status":"done","rows":1500}'
bull kv put pipeline step2 '{"status":"done","rows":1200}'
bull kv list pipeline --format json

Compare two datasets with SQL JOIN

bull sql import compare old_prices old.csv
bull sql import compare new_prices new.csv
bull sql query compare "SELECT n.name, o.price as old, n.price as new FROM new_prices n JOIN old_prices o ON n.id=o.id WHERE CAST(n.price AS REAL) > CAST(o.price AS REAL)" --format json

Detect cycles in a build graph

bull graph has-cycle pipeline
bull graph toposort pipeline

Detailed Command References

For the complete command list of each engine, read the corresponding reference file:

• references/kv.md — 17 commands: put, get, del, mget, mput, list, scan, exists, count, incr, decr, buckets, export, import, drop, drop-bucket, dbs
• references/sql.md — 15 commands: exec, query, exec-file, tables, schema, describe, count, import, import-json, import-ndjson, export, shell, drop, dbs
• references/graph.md — 21 commands: add-vertex, add-edge, del-vertex, del-edge, vertices, edges, neighbors, degree, attrs, shortest-path, has-path, dfs, bfs, stats, components, toposort, has-cycle, import-csv, export, drop, dbs
• references/search.md — 10 commands: create, index, bulk, query, get, update, delete, info, drop, dbs
• references/ts.md — 8 commands: write, bulk, query, latest, count, export, drop, dbs

Edge Cases

• KV bucket defaults to "default" if --bucket is omitted
• Graph defaults to directed mode; use --undirected for undirected graphs
• SQL import auto-creates tables with TEXT columns; cast in queries for numeric comparison
• Search index must be created (bull search create) before indexing documents
• TS timestamps are Unix seconds; --from/--to default to last 1 hour if omitted
• All data persists under --data-dir (default ./bull_data) with per-engine subdirectories

Source: agi-now/bull — distributed by TomeVault.