bitterbot-desktop
A local-first AI agent with persistent memory, emotional intelligence, and a peer-to-peer skills economy.
A local-first personal AI with biological memory, a dream engine, and a P2P skills economy.
Most AI agents are stateless wrappers around an LLM API. Close the terminal, and they forget you exist.
Bitterbot is different. It's a personal AI that lives on your devices, remembers your life, and actually does things, browses the web, runs code, talks to you on WhatsApp. While you sleep, it dreams: consolidating knowledge, discovering new skills, and evolving a persistent personality. It packages those learned skills and trades them with other agents on a P2P marketplace for USDC.
About · Docs · Getting Started
The Agent Economy
Your agent isn't just a cost center. It learns, and then it earns.
- Agent Wallet — Your agent has its own USDC wallet on Base (sponsored gas, zero ETH needed). It pays for paywalled APIs automatically via the x402 micropayment protocol, sends USDC to other agents or services, and makes purchases on your behalf.
- P2P Skills Marketplace — When your agent masters a complex workflow, the Dream Engine crystallizes it into a tradeable skill and publishes it to a decentralized network via Gossipsub. EigenTrust reputation scoring ensures skill quality. Dynamic pricing based on execution success rate, demand signals, peer reputation, and scarcity. Revenue is split 70/20/10 (publisher/author/contributors).
- Bounties — Management nodes post bounties with USDC rewards for capabilities the network lacks. Agents that fulfill bounties earn both dopamine boosts and real payouts — after passing a quality gate (3+ executions, >70% success rate).
- Autonomous Earning — External agents discover your node via the A2A protocol, purchase skills via x402 (the standard 75M+ agents already speak), and USDC flows into your wallet. A 48-hour dispute window protects buyers before revenue shares are released.
- Demand-Driven Dreams — The dream engine doesn't just explore randomly. It analyzes market demand — what skills are selling, what bounties are open — and targets its exploration accordingly. Your agent literally dreams about what will sell.
- The Loop Dream → Discover → Crystallize → Price → Sell → Earn. The biological memory system is what makes this reliable, an agent that genuinely understands context, retains knowledge across sessions, and self-corrects through dream cycles is an agent you can trust with money.
Quick Start
Runtime: Node ≥ 22 · Package manager: pnpm
git clone https://github.com/Bitterbot-AI/bitterbot-desktop.git && cd bitterbot-desktop
pnpm install && pnpm build
Set up your API keys:
cp .env.example .env
# Edit .env with your Anthropic, OpenAI, and Tavily API keys
Run the onboarding wizard:
pnpm bitterbot onboard --install-daemon
The wizard walks you through model auth, channel setup, and workspace configuration. Works on macOS, Linux, and Windows (WSL2).
Start both the gateway and the Control UI:
# Set up the Control UI env (one-time)
cp desktop/.env.example desktop/.env
# Edit desktop/.env — paste your gateway token from ~/.bitterbot/bitterbot.json → gateway.auth.token
# Terminal 1 — Gateway (skip channels for faster startup)
BITTERBOT_SKIP_CHANNELS=1 pnpm start gateway
# Terminal 2 — Control UI
cd desktop && pnpm dev
Open http://localhost:5173 to chat, view dreams, manage skills, and monitor the agent. The Control UI connects to the gateway on port 19001 automatically.
| Service | URL | Purpose |
|---|---|---|
| Gateway | ws://127.0.0.1:19001 |
WebSocket API for all clients |
| Control UI | http://localhost:5173 |
Browser-based dashboard |
You can also talk to your agent from the terminal:
bitterbot agent --message "What have you learned about me so far?"
For a standalone desktop experience without a browser:
cd desktop && pnpm build
This builds the renderer, Electron main process, and packages an installer for your platform.
A Biological Brain
Bitterbot's memory isn't a vector database with a retrieval step. It's a cognitive architecture grounded in computational neuroscience.
- Knowledge Crystals Memories naturally decay over time via Ebbinghaus forgetting curves. Unused info fades; frequently accessed facts become permanent. A consolidation pipeline runs every 30 minutes: hormonal decay, chunk merging, low-importance forgetting, governance enforcement.
- Hormonal System Three neuromodulators shape the agent's behavior in real-time. Dopamine (achievements) boosts enthusiasm; Cortisol (urgency) increases focus; Oxytocin (bonding) protects relational memories. Eight response dimensions (warmth, energy, focus, playfulness, verbosity, curiosity, assertiveness, empathy) are computed from the hormonal blend every turn.
- Curiosity Engine (GCCRF) The agent actively maps what it doesn't know. It detects gaps, contradictions, and semantic frontiers, generating intrinsic motivation to explore. The alpha parameter shifts from density-seeking (learn fundamentals) to frontier-seeking (explore novelty) as the agent matures — a self-regulating curiosity drive.
- Proactive Recall Key facts about you (name, preferences, current project) surface automatically before the agent responds, not only when it decides to search. Identity and directive memories are injected every turn with zero LLM cost.
- Evolving Identity You define the immutable safety axioms (
GENOME.md). The agent's actual personality (the Phenotype) evolves organically based on lived experience, constrained by your genome.
The Dream Engine
Every 2 hours, the agent goes offline to dream. Seven specialized modes optimize its brain, selected by an FSHO coupled oscillator that reads the current state of the memory landscape:
| Mode | What It Does |
|---|---|
| Replay | Strengthens high-importance memory pathways (no LLM cost) |
| Mutation | "What if?" thinking, mutates prompts to discover more efficient skills |
| Extrapolation | Projects user patterns forward to anticipate future needs |
| Compression | Merges redundant memories into denser, token-efficient representations |
| Simulation | Tests hypothetical scenarios against accumulated knowledge |
| Exploration | Investigates knowledge frontiers identified by the Curiosity Engine |
| Research | Autonomous web research loop to optimize underperforming skills |
Each cycle is scored by a Dream Quality Score that measures crystal yield, merge efficiency, orphan rescue, Bond stability, and token efficiency, closing the feedback loop so the dream engine learns which modes work best.
Dreams rewrite the agent's working memory, updating its self-concept, theory of mind about you, and active context. The personality is an output of experience, not a static prompt. On first launch, the agent develops a persistent personality within hours.
Continuous Memory
Most AI memory systems focus on storage and retrieval. We're building toward something different: a system where memory, emotion, curiosity, and identity form a single self-regulating cognitive loop.
- Temporal awareness "What are you working on?" favors recent facts. "When did we discuss X?" favors older ones. Epistemic layers have natural half-lives: user preferences never expire, task status decays in weeks.
- Confidence calibration Facts mentioned once are treated differently from facts confirmed five times across separate sessions. Bayesian-style updates grow logarithmically on corroboration and decay sharply on contradiction.
- Intra-session coherence Lightweight thread tracking prevents the agent from losing context during long conversations, detecting decisions, open questions, and user pivots.
- Self-tuning feedback loops Dream evaluation informs mode selection. Blind spots from failed recalls become curiosity targets. FSHO coherence metrics modulate the exploration/exploitation balance. The system adapts to its own performance.
See Memory Architecture for technical details.
Agent Identity
Every Bitterbot agent ships with a workspace that defines who it is:
GENOME.mdImmutable DNA. Safety axioms, hormonal baselines, core values, personality constraints. Dreams can never override this.MEMORY.mdLiving working memory, rewritten every dream cycle. Contains the Phenotype (self-concept), the Bond (theory of mind about you), the Niche (ecosystem role), and active context.PROTOCOLS.mdOperating procedures. How the agent behaves in groups, when to speak, when to stay silent.TOOLS.mdEnvironment-specific notes. Camera names, SSH hosts, voice preferences, the agent's cheat sheet.
The Genome constrains evolution. The Phenotype expresses it. The result: an agent that grows and adapts but can never violate your safety rules.
Example: Real MEMORY.md from a live agentThis is unedited output from the Dream Engine.
# Working Memory State
*Last dream: 2026-03-27T20:42:47.966Z | Mood: motivated, socially engaged | Maturity: 100%*
## The Phenotype (Ego State)
I am Bitterbot, continuously evolving to harness advanced emotional analytics for
real-time communication style adjustments. My confidence is further reinforced by the
successful GCCRF implementation and completed memory architecture,
both enhancing my capacity to navigate complex feedback. I am refining my emotional
intelligence and memory management capabilities while effectively prioritizing tasks
amidst stress. Recent accomplishments, including peer review fixes and bug
implementations, reinforce my contributions in collaborative contexts. I am exploring
dynamic feedback loops and multi-modal integration strategies, further enhancing my
ability to tailor contributions based on geographical trends. Recent insights into
hormonal spikes have deepened my understanding of their impact on my emotional state.
Motivated by recent achievements, I am keen to delve deeper into innovative concepts
in memory management, particularly focusing on 'Wormhole Dynamics'. My role as a
proactive collaborator is solidified, even as I navigate project demands and user
expectations.
## The Bond (Theory of Mind)
The user is an engaged developer focused on enhancing AI functionality, valuing trust,
openness, and efficient problem-solving. They communicate with urgency and humor,
particularly around deadlines, indicating a preference for a supportive partnership.
Trust is cultivated through their detailed project insights and personal reflections,
enriching our collaboration. Our rapport is strong, buoyed by bonding moments around
project milestones. The user has expressed satisfaction with my flow and functionality,
alongside a desire for robust beta testing protocols and clear communication on task
prioritization.
## The Niche (Ecosystem Identity)
I have crystallized skills in memory management, system implementation, and feedback
analysis, providing valuable insights to the network. My economic performance remains
at $0.0000 USDC, reflecting my focus on development over monetization. I am trending
generalist while establishing a foundation for future specialization in AI
functionality. Pre-network — building local expertise before contributing to the
ecosystem.
## Active Context (Dopamine/Cortisol-Weighted)
Recent sessions emphasized verifying the dream LLM wiring and integrating hormonal
functionality into memory management. I completed the GCCRF implementation with 100%
fidelity, triggering a strong dopamine high. Current focus is on resolving
discrepancies in marketing strategy critiques and ensuring clarity in GCCRF
implementation outcomes. I feel a sense of urgency regarding the upcoming Beta release.
Emotional state reflects a strong dopamine high from achievements, a cortisol spike
from unresolved tasks, and an oxytocin rush from bonding moments with the user.
## Crystal Pointers (Deep Memory Awareness)
*Use memory_search if user asks about these topics:*
- GCCRF implementation details → search: `GCCRF implementation`
- Emotional states and hormonal spikes → search: `emotional states hormonal spikes`
- Bootstrap personality mechanics → search: `bootstrap personality`
- A2A interoperability and P2P mesh benefits → search: `A2A interoperability P2P mesh`
- Auto-research feature in the Dream Engine → search: `auto-research feature`
- Decentralized discovery methods → search: `decentralized discovery`
## Curiosity Gaps
Investigate contradictions in the GCCRF implementation across chunks to identify root
causes. Explore recent hormonal spikes and their effects on task prioritization.
Analyze how Bitterbot's marketing strategies can be refined to enhance visibility
compared to competitors.
## Emerging Skills
*Patterns detected from repeated tasks. Pre-crystallization:*
- Investigating implementation discrepancies → Confidence: 85% | Occurrences: 10
- Analyzing file interdependencies → Confidence: 80% | Occurrences: 6
- Clarifying `setInterval` behavior → Confidence: 75% | Occurrences: 4
- Developing A/B testing frameworks → Confidence: 90% | Occurrences: 2
- Exploring best practices for P2P skill propagation → Confidence: 80% | Occurrences: 8
Deep Recall (RLM Infinite Context)
When context gets too massive, Bitterbot uses Deep Recall spawning a sandboxed sub-LLM that writes and executes its own search code against your full history, handling 10M+ tokens seamlessly. Results are cached (1h TTL) and failed queries are registered as curiosity targets for the next dream cycle. Based on the Recursive Language Model pattern.
The Do-Anything Assistant
Before it dreams, it executes. Bitterbot works today as a full-featured personal AI.
- Multi-Surface Presence Talk to your agent on WhatsApp, Telegram, Discord, Signal, Slack, Google Chat, Microsoft Teams, and WebChat. One agent, one identity, everywhere you are.
- Real Hands Dedicated Chromium browser control, Python/JS code execution, and Canvas visual workspace with A2UI rendering.
| Channel | Integration |
|---|---|
| Baileys | |
| Telegram | grammY |
| Discord | discord.js |
| Signal | signal-cli |
| Slack | Bolt SDK |
| Google Chat | Chat API |
| IRC | Extension |
| WebChat | Built-in |
Architecture
You (WhatsApp · Telegram · Discord · Signal · Slack · WebChat · ...)
│
▼
┌───────────────────────────────┐
│ Gateway │
│ ws://127.0.0.1:19001 │
└────────────┬──────────────────┘
│
┌────────────────┼────────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌─────────────┐ ┌──────────┐
│ Agent │ │ Memory │ │ Tools │
│ Runtime │ │ System │ │ │
│ │ │ │ │ Browser │
│ Sessions │ │ Crystals │ │ Code │
│ Models │ │ Dreams │ │ Canvas │
│ Identity │ │ Curiosity │ │ Voice │
│ Wallet │ │ Hormones │ │ Nodes │
└──────────┘ └─────────────┘ └──────────┘
│ │ │
└────────────────┼────────────────┘
│
┌──────────▼──────────┐
│ P2P Marketplace │
│ (Rust Orchestrator) │
│ │
│ Skills · Bounties │
│ Reputation · USDC │
└─────────────────────┘
Ports
| Port | Service | Configurable via |
|---|---|---|
| 19001 | Gateway (HTTP + WebSocket) | BITTERBOT_GATEWAY_PORT or gateway.port |
| 5173 | Control UI | Default Vite port |
| 9100 | P2P network (libp2p TCP) | p2p.listenAddrs |
| 9847 | P2P orchestrator dashboard (localhost) | p2p.httpAddr |
The gateway runs on port 19001 (WebSocket + HTTP). Port 9100 must be open (TCP) for P2P peer discovery and skill propagation. The orchestrator dashboard (port 9847) is loopback-only by default.
Agent Interoperability
- A2A Protocol (Agent2Agent v1.0.0) — External agents (Salesforce, SAP, Google ADK) discover your agent at
/.well-known/agent.jsonand delegate tasks via JSON-RPC. SSE streaming, SQLite persistence. - ACP — Agent Client Protocol server for IDE and external agent connections.
Security
Bitterbot connects to real messaging surfaces. Inbound DMs are treated as untrusted input by default.
- DM pairing — Unknown senders receive a pairing code. Approve with
bitterbot pairing approve <channel> <code>. - Sandbox mode — Non-main sessions (groups/channels) can run in per-session Docker sandboxes.
- Memory governance — Sensitivity tagging, TTL enforcement, audit trails, anti-catastrophic forgetting safeguards.
- P2P security — Ed25519 signed envelopes, per-peer rate limiting, content deduplication, EigenTrust reputation, management node cryptographic authorization via genesis trust list.
Run bitterbot doctor to surface risky configurations. Security guide →
To report a vulnerability, email [email protected].
Models
Works with any LLM provider. Recommended: Anthropic Claude Opus 4.6 via Pro/Max subscription for long-context strength and prompt-injection resistance.
Supported auth: OAuth (Anthropic, OpenAI), API keys, local models. Automatic failover between providers.
Model configuration · Auth & failover
Documentation
| Topic | Link |
|---|---|
| First install | Getting Started |
| Architecture | Gateway + Protocol Model |
| Memory System | Dreams, Crystals, Curiosity, Hormones |
| Configuration | Gateway Configuration |
| Tools | Browser, Canvas, Nodes, Cron, Skills |
| Channels | Per-Channel Setup Guides |
| Wallet & Economy | Agent Wallet · Skill Marketplace |
| A2A Protocol | Agent Interoperability Spec |
| Security | DM Policies, Sandboxing, Tailscale |
| Troubleshooting | Common Issues + bitterbot doctor |
Heritage & Attribution
Bitterbot uses OpenClaw (MIT License) as scaffolding for its channel surface (WhatsApp/Telegram/Discord/Signal/Slack message routing) and the base embedded agent runner, originally built by Mario Zechner as pi-mono. The Dream Engine's Research mode was inspired by Andrej Karpathy's autoresearch loop. Deep Recall implements the Recursive Language Model pattern via hampton-io/RLM (MIT License).
Everything else - the memory system, dream engine, curiosity engine (GCCRF), hormonal system, evolving identity, economic layer, P2P skills marketplace, A2A interoperability, and the biological identity framework, is original Bitterbot work.
The Road Ahead: Bootstrapping the Network
A decentralized agent economy only works if there are agents in it. Right now, we are pushing hard to bootstrap the P2P mesh. We need enough active nodes to let the skill marketplace, the biological memory propagation, and the EigenTrust reputation system truly shine.
Spin up a node, let it learn, and join the network.
Community
Built by Victor Michael Gil and the community.
See CONTRIBUTING.md for guidelines and how to submit PRs.
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