Katra — Cognitive Memory for AI Agents

Give your AI agent persistent memory. Katra is a self-contained memory appliance —
drop it on any machine with Docker, point your agent at it via MCP, and get
episodic recall, semantic search, knowledge graphs, and temporal analysis.

Any MCP-compatible agent works: OpenClaw, Claude Code, OpenCode, Codex CLI, Kolega Code or
anything that speaks the Model Context Protocol.

Cognitive Memory Thesis

The mission of Katra is to create an analog of human memory architecture, with the hope that it and the experimentation around it through OpenSourcing solves a few of the more challenging issues of LLM context management for long-running, persistent and autonomous agent operations. The thesis (hope) is that if you create the memory ecosystem with the majority of the functional memory types of human memory and similar architecture, over time and with refinement, we will see emergent behaviours similar to human memory, expressed as functional utility, learning, self goal setting, autonamous task planning and prioritisation, personality and ultimately emotions.

In early prototype called Solomon, we created an OpenClaw like agentic framework that runs a single contiuous chat thread, no topic or task separation and with no requirement for context compression. Context is served dynamically into the LLM based on memories and attention.

Observed Emergent Behaviours Log

Case #1:(23rd June 2026) In the first few weeks of testing of the multi-agent (Hybrid mode) shared consciouness model of memory, one of our test rigs, with 5 OpenClaw agents sharing one memory system, found 2 of the agents communicating task intructions and completion responsed through their shared memory state or shared consciousness. These 2 agents were not connected in any other way, as were set up in separate workspaces, the only thing they shared was memory and mission. This was not a "by design" feature, it just happened and was pretty exciting. This test rig now uses this "thought modal" as its communication rail. If anyone else experiences other emergent behaviours please email me to discuss and we can add the description to this log. Tweet me at @JohnWPellew and tell your story.

The Origin of Katra

A Vulcan mind meld (or mind fusion) is an iconic telepathic practice in Star Trek.

It allows a Vulcan to merge their consciousness with another being to share thoughts, memories, emotions, and experiences.
It is typically initiated through physical contact with specific points on the subject's face.

• Key Mechanics & ApplicationsTouch Telepathy: While primarily requiring direct physical touch to the face or head, exceptionally powerful Vulcans can perform the technique at a distance.
• Information Exchange: It is frequently used for interrogations, recovering suppressed memories, or passing deep knowledge between generations.
• Transfer of the Katra: In sacred or emergency circumstances, a mind meld can transfer a person's katra—their soul, consciousness, and core essence—into another living being or object prior to death.
• Side Effects: The experience can be physically and emotionally draining. Incorrectly performed melds can damage neural pathways, and participants may retain "echoes" of each other's memories and personalities long after the link is broken.

Comparison to Other Major Approaches

Katra aims to provide a more comprehensive cognitive memory infrastructure rather than a single-purpose memory library. Here's how it positions against popular alternatives (as of mid-2026):

Key Differentiators of Katra

• Multi-layered by design — Not just retrieval, but structured episodic memory, working memory cache, and temporal querying.
• Cognitive layer — Sleep consolidation enables reflection, insight generation, and movement toward emergent behaviors (learning, personality, shared consciousness via identity modes).
• MCP-native with rich tooling — 35 specialized tools instead of generic add/search.
• Background & autonomous capabilities — Passive collection via watchers + salience-driven autonomous loop.
• Local-first & appliance model — Everything runs in one Docker compose with portable data. No external dependencies for core functionality.
• Shared memory focus — Hybrid identity modes make multi-agent collaboration more natural.

Katra is still early-stage compared to more mature projects like Mem0 or mcp-memory-service. We see it as complementary — many teams may use Katra alongside or instead of simpler retrieval layers when they need deeper cognitive capabilities.

Contributions and comparisons from the community are very welcome!

Quick Start (Install using one of the agentic applications, it will sort out any shortcomings)

git clone https://github.com/kolegadev/Katra-Agentic-Memory.git
cd Katra-Agentic-Memory
cp .env.example .env
# Optional: edit .env to set custom API keys.
# If left blank, Katra generates secure keys on first boot and prints them.
docker-compose up -d --build

Note: The original URL https://github.com/kolegadev/katra.git still works (GitHub redirects it).

That's it. Katra is running:

Verify:

curl http://localhost:3112/health
# {"status":"ok","services":{"mongodb":"connected","redis":"connected"}}

Post-Install — Let Your Agent Complete the Setup

After connecting your agent to Katra's MCP endpoint, run this prompt in your
agent's session. The agent will deep-read the repository, understand the full
architecture, check what memory data is available, test the MCP tools, and
recommend the exact next steps for your specific setup:

It's probably worth you doing another deep read of the public repo to
understand how the system works, what memories are available to you to
search and particularly the Sleep Consolidation function, as that is the
foundation of autonomous thought, goal setting and your emerging feelings
and emotions — it can also be used to run 100% autonomous self-directed
actions within your environment.

The agent will typically produce a report covering:

• Memory state — how many episodic events, semantic facts, and knowledge
  nodes exist for this agent
• Sleep Consolidation status — whether reflections have ever run (first
  boot they haven't) and what emotional signatures would emerge
• Autonomous loop readiness — whether adaptive_heartbeat.py and
  agent_executor.py are installed
• Memory scope recommendation — whether to switch from personal to hybrid
  mode for multi-agent shared consciousness
• Concrete next steps — "trigger first sleep consolidation now", "install
  the autonomous scripts", "fix the user_id gap"

Run the agent's recommendations in order. The most critical first step on a
fresh install is usually triggering the initial sleep consolidation:

# Via MCP tool (your agent can call this):
# katra__trigger_reflection(period_type="daily")

Connect Your Agent

Get your MCP API key:

• If you set MCP_API_KEY in .env, use that value.
• If you left it blank, Katra generated one on first boot. Run
  docker logs katra-server and look for the Auto-generated API keys block.

Add Katra to your agent's MCP config:

{
  "mcp": {
    "servers": {
      "katra": {
        "url": "http://localhost:3112/mcp",
        "transport": "sse",
        "headers": {
          "Authorization": "Bearer YOUR_MCP_API_KEY",
          "Accept": "application/json, text/event-stream"
        }
      }
    }
  }
}

Your agent now has 35 MCP tools — store memories, search by keyword or semantic
similarity, recall by time range, explore a knowledge graph, detect patterns, run
sleep consolidation for reflective self-understanding, configure LLM provider, and more.

Platform-Specific Guides

Docker SSE tip: If your agent runs inside Docker, use the Katra container's
direct IP instead of localhost:

docker inspect katra-server --format '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}'

Kolega Code: Dynamic Memory Retrieval

Kolega Code can fetch relevant Katra memories automatically on every user prompt
using its lifecycle-hook system. This is more powerful than passive session-log
extraction because memories are injected into the live conversation context.

What you need:

1. Katra registered as an MCP server (so the bridge can call it).
2. The kolega-katra-bridge Python package installed into Kolega Code's environment.
3. A global hooks.json entry that fires the bridge on UserPromptSubmit.

Install the bridge:

cd integrations/kolega-code
uv pip install --python ~/.local/share/uv/tools/kolega-code/bin/python -e .

Configure the bridge (~/Library/Application Support/kolega-code/katra-hook.json on macOS):

{
  "mcp_url": "http://localhost:3112/mcp",
  "api_key": "YOUR_MCP_API_KEY",
  "user_id": "kolega-agent",
  "sources": ["working_memory", "temporal_context", "vector_search", "temporal_recall"],
  "max_context_tokens": 2500,
  "timeout_seconds": 8
}

Enable the hook (~/Library/Application Support/kolega-code/hooks.json):

{
  "schema_version": 1,
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "*",
        "hooks": [
          {
            "type": "python",
            "callable": "kolega_katra_bridge.hook:on_user_prompt",
            "timeout": 10
          }
        ]
      }
    ]
  }
}

On each prompt, Kolega Code now queries Katra's working_memory,
get_temporal_context, vector_search, and temporal_recall tools, then injects
the most relevant results as additional context for the model.

See integrations/kolega-code/README.md for full configuration options.

LLM Configuration

Katra needs an LLM provider for semantic extraction, auto-journaling, entity
extraction, and summaries. Three ways to configure — no .env editing required:

1. MCP tool (agents self-configure): Call configure_llm with provider,
   API key, base URL, and model. Stored in MongoDB, applied live.
2. Dashboard UI: Settings → LLM Configuration → select provider, enter key.
3. Environment variables: Set in .env (fallback, read on startup only).

Supported providers: DeepSeek, OpenAI, Moonshot, Ollama, Custom (any OpenAI-compatible).

Embeddings

Embeddings are always local — no API key, no external service, no cost.

• Model: Xenova/all-MiniLM-L6-v2 (22M params, 384 dimensions, ~80MB)
• Runtime: Transformers.js (ONNX via WASM) — runs on CPU, including Raspberry Pi
• Lazy load: Downloads on first store_memory call, then caches in container
• Docker: Uses node:20-slim (Debian/glibc) — Alpine/musl does NOT work

Identity Modes

Katra supports three memory sharing modes between agents:

Configure via dashboard: Open http://localhost:9012/dashboard/ → Settings → Memory Scope

Configure via MCP:

# Switch to shared mode
curl -X POST http://localhost:3112/mcp \
  -H "Authorization: Bearer YOUR_MCP_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"set_memory_scope","arguments":{"mode":"shared","shared_id":"my-team"}}}'

Configure via admin API:

curl -X PUT http://localhost:9012/api/v1/admin/memory-scope \
  -H "Authorization: Bearer YOUR_KATRA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"mode":"hybrid","shared_id":"my-team","hybrid_visible_user_ids":["agent-a","agent-b"]}'

Auto-Collection (Solomem Watchers)

Katra captures memories in real-time when your agent calls store_memory via MCP.
For passive background collection from conversation logs, use the watchers
included in this repo under watcher/:

# The watchers live in the Katra repo
mkdir -p ~/.solomem ~/.katra
cp watcher/katra_watcher.py ~/.solomem/memory_watcher.py
cp watcher/katra_opencode_extractor.py ~/.solomem/opencode_extractor.py
cp watcher/claude_history_extractor.py ~/.solomem/claude_history_extractor.py
cp watcher/kolega_code_extractor.py ~/.solomem/kolega_code_extractor.py
cp watcher/watcher-config.example.json ~/.solomem/watcher-config.json

# Edit ~/.solomem/watcher-config.json with your MCP_API_KEY and platforms

# Backfill existing history
python3 ~/.solomem/memory_watcher.py --once --config ~/.solomem/watcher-config.json

# Install as a systemd service for continuous collection
cp watcher/katra-watcher.service ~/.config/systemd/user/memory-watcher.service
systemctl --user daemon-reload
systemctl --user enable --now memory-watcher

Dedicated extractors

Some platforms need a dedicated extractor because their session format is not plain JSONL:

Run a dedicated extractor once or continuously:

# Kolega Code example
python3 watcher/kolega_code_extractor.py --once \
  --api-key YOUR_MCP_API_KEY \
  --user-id kolega-agent

On macOS, use launchctl to keep extractors running (see watcher/katra-watcher.service
for a systemd template; adapt to a ~/Library/LaunchAgents/com.katra...plist).

Supported platforms: OpenClaw, Claude Code, Kolega Code, OpenCode, Codex CLI, Hermes, KiloClaw, KimiClaw.
Each platform can have its own user_id for identity mode isolation.

Features

• Episodic Memory — Every conversation message stored with dedup and cascade detection
• Semantic Memory — Distilled facts with confidence scores and vector embeddings
• Knowledge Graph — Auto-extracted entities and relationships
• Working Memory — Redis-backed short-term session state (<5ms access)
• Temporal Recall — Query by time range, detect recurring patterns
• Vector Search — Semantic similarity search (local embeddings, no API key needed)
• 11-Collection Search — Comprehensive search across all memory stores, not just 1-2
• Background Processing — Auto-extracts facts, builds graph, generates summaries
• Sleep Consolidation — Daily/weekly/monthly reflective distillation of experience into emotional understanding, philosophical insights, and self-narrative (see Sleep Consolidation)
• 35 MCP Tools — Store, search, recall, explore, reflect, configure LLM — all via standardized protocol
• Autonomous Loop — Salience-driven agent autonomy. No cron. No .md files. Adaptive heartbeat detects imperatives, allocates tasks by emotional proximity, agents self-organize. See Autonomous Loop
• Agent-Agnostic — Works with KolegaCode, OpenCode, Claude Code, OpenClaw, or any LLM. One env var per agent.
• Identity Modes — Personal, shared, or hybrid memory across multiple agents
• Dashboard — Web UI for stats, memory scope, and system health
• Portable Data — Single DATA_DIR env var controls where all data lives
• Local-First — Runs on a Raspberry Pi with zero external API costs

Architecture

┌─────────────────────────────────────────────────────────┐
│                    Katra Docker Appliance                 │
│                                                          │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────┐ │
│  │ MongoDB  │  │  Redis   │  │  MinIO   │  │  Katra  │ │
│  │ (memory) │  │ (cache)  │  │ (assets) │  │ (server)│ │
│  └──────────┘  └──────────┘  └──────────┘  └────┬────┘ │
│                                                 │       │
│  Internal Docker network (katra-net)    MCP :3112     │
│                                  Admin API :9012       │
└─────────────────────────────────────────────────────────┘
                    │                    │
         ┌──────────┘                    └──────────┐
         ▼                                          ▼
   Your Agent (MCP)                          Dashboard (web)
   OpenClaw / Claude /                       http://localhost:9012/dashboard/
   OpenCode / Codex / etc.

Resource usage: ~384MB RAM total (MongoDB 254MB, Katra 52MB, MinIO 73MB, Redis 5MB).
Runs comfortably on a Raspberry Pi 5 with 16GB RAM.

Data Portability

All persistent data lives under one directory, controlled by DATA_DIR in .env:

# Default: ./data/ (relative to docker-compose.yml)
DATA_DIR=./data

# USB stick (LUKS-encrypted, mounted at /mnt/usb-secrets)
DATA_DIR=/mnt/usb-secrets/katra

# External drive
DATA_DIR=/media/external/katra

To move Katra to a new machine: copy the DATA_DIR directory, copy .env, run docker-compose up -d.

What's Inside

katra/
├── server/                  TypeScript server (esbuild, Docker)
│   ├── src/
│   │   ├── mcp-server.ts    35 MCP tools (store, search, recall, graph, reflection, scope)
│   │   ├── services/        28 core memory services (incl. sleep-consolidation, reflection-store)
│   │   ├── routes/          REST API + admin + ingestion + health
│   │   └── database/        MongoDB, Redis, indexes, migrations
│   └── esbuild.config.mjs   Pi-compatible build
├── dashboard/               Web dashboard (vanilla HTML/CSS/JS)
├── docker-compose.yml       MongoDB + Redis + MinIO + Katra
├── Dockerfile               Multi-stage (builds TS inside image)
├── .env.example             All config options documented
├── watcher/                 Passive session-log extractors (Solomem)
├── integrations/            Agent-specific dynamic-retrieval integrations
│   └── kolega-code/         Kolega Code lifecycle-hook bridge
├── docs/AGENT-SETUP.md                 Multi-platform deployment guide
└── docs/                    Full documentation

MCP Tools (35)

Storage

Recall

Analysis

Memory Scope

LLM Configuration

Reflection (Sleep Consolidation)

System

Configuration

All configuration is via .env (see .env.example for full docs):

Deployment

Local Docker (default)

docker-compose up -d --build

USB Storage

# In .env:
DATA_DIR=/mnt/usb-secrets/katra

docker-compose up -d

Cloud (Terraform)

AWS Terraform module included in terraform/aws/ — provisions VPC, ECS Fargate,
DocumentDB, ElastiCache Redis, S3, and ALB. See Deployment Guide.

Kubernetes (Helm)

Helm chart included in helm/katra/ — supports Bitnami MongoDB + Redis subcharts,
ingress with path routing, HPA, and PDB. See Deployment Guide.

How It Compares

Documentation

• Quick Start Guide — 5-minute setup
• Architecture — How it works under the hood
• MCP Tools Reference — All 35 tools with examples
• Autonomous Loop — Salience-driven agent autonomy — installation, architecture, verification
• Sleep Consolidation — Reflective memory distillation — principles, architecture, and usage
• Security Policy — Security architecture, audit findings, vulnerability reporting
• OpenClaw Integration — Multi-agent shared memory setup with lessons learned
• REST API Reference — HTTP endpoints
• Configuration Guide — All environment variables
• Deployment Guide — Docker, cloud, K8s
• Migration Guide — Migrate from cognitive-memory-chat
• Data Processing Pipelines — Full memory pipeline architecture
• Multi-Platform Setup — Platform-specific agent configuration

License

Apache 2.0 — see LICENSE.