bikky gives AI coding agents (GitHub Copilot, Claude Code, Cursor, and other MCP clients) long-term memory that persists across sessions, across tools, and across your whole team. Whether you're a team that wants every engineer's agent to start from the same knowledge base, or a solo power dev running a dozen agentic sessions a day, bikky captures what's learned during sessions so future sessions start smarter.

Who it's for

• 👥 Teams & software factories — What one engineer's agent learns today, every agent on the team can recall tomorrow. Shared memory turns institutional knowledge into something queryable instead of tribal — onboarding accelerates, conventions stop drifting, and the same lesson never gets re-learned twice.
• 🧑‍💻 Solo AI power devs — You run multiple Cursor / Claude Code / Copilot sessions every day and you're tired of re-explaining the codebase, the conventions, and last week's decisions to each new agent. bikky remembers across every session and every tool.

The problem

The most valuable things you and your agents learn — why a config value exists, which deploy step matters, what broke last quarter, the convention you settled on yesterday — happen during sessions. And then they vanish when the session closes. Whether you're a team — where knowledge lives in heads, chat threads, and closed PRs, and every new engineer's agent has to learn it from scratch — or a solo power dev juggling dozens of agentic sessions a day across multiple tools that don't remember each other, it's the same wall. Hand-written docs drift the moment they're published.

How bikky solves it

bikky gives your agent memory tools and runs a small background service after bikky setup. You keep working normally; bikky captures useful facts, organizes them, recalls them in future sessions, and keeps the store tidy over time.

• Capture — Facts are extracted automatically from session transcripts; no manual docs to write.
• Classify — Memories are grouped as engineering, product, human, or system so they stay easy to browse and filter.
• Recall — Every new session, yours or a teammate's, recalls from the same store via semantic search.
• Curate — bikky merges duplicates, fades stale facts, resolves contradictions, distills recurring patterns, and builds an entity graph over time.
• Compound — Session 50 is dramatically better than session 1 because memory accumulates.

Subtypes keep recall precise without making setup harder:

• Engineering — codebase maps, architecture decisions, infra topology, access patterns, operational procedures, troubleshooting gotchas, and conventions.
• Product — domain rules, product decisions, requirements, user workflows, roadmap items, success metrics, and market insights.
• Human — preferences, person profiles, ownership notes, working agreements, and activity events.
• System — session indexes, episodes, workstreams, and feedback signals.

Quick start

This quick start uses local Qdrant + hosted models: Qdrant runs on your machine, while hosted embeddings and LLM calls provide strong extraction and recall quality without running local LLMs.

# 1. Pull and run Qdrant (vector store)
docker run -d --name qdrant -p 6333:6333 -v qdrant_storage:/qdrant/storage qdrant/qdrant

# 2. Install bikky
npm install -g bikky
mkdir -p ~/.bikky
# Replace sk-... below with your hosted model API key.
cat > ~/.bikky/config.json <<'JSON'
{
  "qdrant_url": "http://localhost:6333",
  "qdrant_api_key": "",
  "embedding": {
    "provider": "openai",
    "model": "text-embedding-3-small",
    "dimensions": 1536,
    "api_key": "sk-..."
  },
  "llm": {
    "provider": "openai",
    "model": "gpt-4.1-mini",
    "api_key": "sk-..."
  }
}
JSON
# qdrant_api_key is optional; leave it empty or omit it for local Qdrant.
# Prefer env vars? Omit api_key above and set OPENAI_API_KEY instead.

# 3. Register bikky with your editor and start the background service
bikky setup            # writes MCP config for Copilot + Claude Code, then starts the daemon

Restart your editor. The memory tools appear automatically in supported MCP clients.

bikky status           # confirms Qdrant, embeddings, daemon, and UI health

That's it. You can keep Qdrant local forever, or move the vector store to Qdrant Cloud later.

For 100% local and account-free setup, use the local and free config. It is best for private testing rather than long-term team use, and extraction, embedding, and curation performance depends on the local models and hardware you run.

Setup options

bikky supports four common setup shapes. Pick based on where you want Qdrant to run and where model calls should happen.

What you need

| Component | Required | Options | | ----------------------- | ------------------------------ | ---------------------------------------------------------------------------------------- | | Node.js | ≥ 20 | nvm install 20 or your package manager | | Vector store | Qdrant | Local Docker · Qdrant Cloud · Self-hosted | | Embeddings | One provider | OpenAI · Ollama · Bedrock · Portkey | | LLM | One provider | OpenAI · Ollama · Bedrock · Portkey | | Docker (optional) | Only if you run Qdrant locally | Docker Desktop, OrbStack, colima, etc. |

Both embedding.provider and llm.provider accept the same values: ollama, openai, bedrock, or portkey.

Choose a setup

| Setup | Best for | Config | | -------------------------------- | -------------------------------------------------------------- | ------------------------------------------------------------------------- | | Fully hosted | Best performance and teams; managed vector storage and models | Fully hosted config | | Local Qdrant + hosted models | Local vector storage with hosted extraction and embedding | Hosted models config | | Local and free | Private/free testing; quality depends on local models | Local config guide | | Hosted Qdrant + local Ollama | Shared vector storage while keeping model calls local | Hosted Qdrant + local models |

Configure

Pick the setup guide above for the copy-paste config. Config lives at ~/.bikky/config.json, and you can also set QDRANT_URL and QDRANT_API_KEY as environment variables.

For hosted models, custom providers, multiple profiles, or advanced tuning, use the full configuration guide.

📖 Full configuration guide: docs/configuration.md

🛠 Want to add a new embedding or LLM provider (Vertex, OpenRouter, etc.)? See CONTRIBUTING.md — it's a single-file change.

Web UI

bikky-ui is a local dashboard for browsing and managing your team's memory — facts, entities, quality metrics, aggregate impact insights, and the relationship graph.

npx bikky-ui          # one-shot — no install needed
# or install globally
npm install -g bikky-ui
bikky-ui              # opens http://localhost:1422

The UI reads from your existing ~/.bikky/config.json (or BIKKY_HOME/config.json) — no extra configuration required.

CLI

bikky mcp       # start MCP server (stdio) — used by editors
bikky setup     # install MCP configs for Copilot + Claude Code, then start the daemon
bikky start     # alias for setup
bikky stop      # stop the background daemon
bikky daemon    # run the daemon in the foreground
bikky status    # check memory system health
bikky ui        # launch the local web dashboard
bikky render    # render a prompt to JSON (for eval harnesses & debugging)

bikky status is the first thing to run when setup feels wrong. It checks the config, Qdrant, embeddings, background daemon, and local UI health, then tells you what needs attention. Use bikky status --json for automation.

License

AGPL-3.0 — see LICENSE.