What it is
Mirix is a multi-agent personal assistant designed to track on-screen activities and answer user questions intelligently. By capturing real-time visual data and consolidating it into structured memories, Mirix transforms raw inputs into a rich knowledge base that adapts to your digital experiences.
AutoGineer overview, from the brain’s own files
Mirix is a memory server designed to provide AI agents with persistent, multi-modal memory. It functions by observing screen activity and processing natural language conversations, consolidating these inputs into six distinct memory components: Core, Episodic, Semantic, Procedural, Resource, and Knowledge Vault. The system uses a hybrid storage approach, leveraging PostgreSQL with pgvector for search and an optional Neo4j graph database for provenance tracking. Mirix includes an 'Auto-Dream' feature for periodic memory consolidation, deduplication, and refinement, ensuring that the agent's knowledge base remains structured and relevant over time.
How it works
Memory model: Hybrid
AutoGineer overview, from the brain’s own files
Mirix operates as a backend server that orchestrates six specialized sub-agents to manage different memory types. It captures user interactions and visual screen data, which are then processed and stored in a PostgreSQL database. The system supports full-text search via BM25 and vector similarity search. An optional graph memory layer can be enabled to map relationships between entities using Neo4j. The Auto-Dream functionality allows the system to review and merge memory entries, while the provided Python client enables agents to read from and write to these memory stores.
| Folder | Role | Files |
|---|---|---|
| .github | agent-config | 1 |
| assets | attachments | 3 |
| docs | docs | 5 |
| samples | examples | 16 |
| scripts | scripts | 34 |
| tests | tests | 132 |
| .vscode | folder | 2 |
| dashboard | folder | 35 |
| docker | folder | 3 |
| evals | folder | 53 |
| mirix | folder | 341 |
The agent writes
- mirix/schemas/ evidence: CLAUDE.md
- mirix/services/ evidence: CLAUDE.md
- mirix/server/rest_api.py evidence: CLAUDE.md
- mirix/client/remote_client.py evidence: CLAUDE.md
- mirix/orm/ evidence: CLAUDE.md
- mirix/agent/ evidence: CLAUDE.md
Requirements
Apps: Docker
Runtimes: Python >=3.10,<4.0
Databases: Neo4j, PostgreSQL, Redis, SQLite
API keys (by name): MIRIX_API_KEY
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)
Set it up
This brain runs code automatically: 2 scripts. Review Makefile, setup.py before opening the folder in Claude Code. No step below turns a hook on for you.
Makefile:$(POETRY) installsetup.py:python setup.py
- Not scanned by AutoGineerThe author’s README also describes installing it with Docker Compose. Read its install section: AutoGineer did not scan that path. https://github.com/Mirix-AI/MIRIX/blob/9077e4b229dd8f61c5703b431a97788c6b61acfb/README.md#quick-start
- Not scanned by AutoGineerAutoGineer could not scan everything it runs. https://pypi.org/project/mirix/
Created by Mirix AI. Published in Mirix-AI/MIRIX. Licence: Apache License 2.0.
Usage
AutoGineer overview, from the brain’s own files
- Initialize a meta-agent with specific memory components.
- Add conversation turns or tool-use logs to the memory store.
- Retrieve context-aware memories based on user queries.
- Trigger memory consolidation using the Auto-Dream endpoint.
- Enable graph-based provenance for complex entity relationships.
- “What did we discuss on MirixDB in last 4 days?”
- The agent retrieves relevant conversation history and memory blocks from the last four days to answer the user's question.
- “Deploy the service.”
- The agent records the tool call, captures any resulting errors or successful fixes, and stores this as procedural memory for future reference.
Documentation by Mirix AI
MIRIX - Multi-Agent Personal Assistant with an Advanced Memory System
Your personal AI that builds memory through screen observation and natural conversation
| 🌐 Website | 📚 Documentation | 📄 Paper | 💬 Discord
<!-- | [Twitter/X](https://twitter.com/mirix_ai) | [Discord](https://discord.gg/S6CeHNrJ) | -->
Key Features 🔥
Multi-Agent Memory System: Six specialized memory components (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault) managed by dedicated agents
Screen Activity Tracking: Continuous visual data capture and intelligent consolidation into structured memories
Privacy-First Design: All long-term data stored locally with user-controlled privacy settings
Advanced Search: PostgreSQL-native BM25 full-text search with vector similarity support
Graph Memory (optional): A provenance hypergraph over episodic and semantic memories in Neo4j, with n-ary facts that link entities through named roles and cite the memory rows they came from
Multi-Modal Input: Text, images, voice, and screen captures processed seamlessly
Results
Image: MIRIX-2.0 compared with other memory systems on user-centric and agent-centric tasks
MIRIX-2.0 compared with other memory systems. Each axis label gives the MIRIX-2.0 score; the value in parentheses is its lead over the strongest baseline on that axis, shown where MIRIX-2.0 leads.
(a) User-centric tasks (accuracy, %): conversational recall on LoCoMo and LongMemEval-S, and the document QA tracks of MemoryAgentBench (SHDocQA, MHDocQA, DetectiveQA). Baselines are A-Mem, Graphiti, MemoryBank, mem0 and HyperMem. LoCoMo uses graph memory with AutoDream; LongMemEval-S and document QA use graph memory alone.
(b) Agent-centric tasks (success rate, %): the six ALFWorld task types (Pick, Look, Clean, Heat, Cool, Pick Two) and five WebShop product categories (Fashion, Beauty, Electronics, Garden, Grocery), with GPT-6 Luna as the agent model. Baselines are no memory, AWM and ReasoningBank.
Quick Start
Step 1: Backend & Dashboard (Docker):
docker compose up -d --pull alwaysDashboard: http://localhost:5173
Step 2: Create an API key in the dashboard (http://localhost:5173) and set as the environmental variable MIRIX_API_KEY.
Step 3: Client (Python, mirix-client, https://pypi.org/project/mirix-client/):
pip install mirix-clientNow you are ready to go! See the example below:
from mirix import MirixClient
client = MirixClient(
api_key="your-api-key",
base_url="http://localhost:8531",
)
client.initialize_meta_agent(
config={
"llm_config": {
"model": "gemini-3.6-flash",
"model_endpoint_type": "google_ai",
"api_key": "your-api-key-here",
"model_endpoint": "https://generativelanguage.googleapis.com",
"context_window": 1_000_000,
},
"embedding_config": {
"embedding_model": "gemini-embedding-001",
"embedding_endpoint_type": "google_ai",
"api_key": "your-api-key-here",
"embedding_endpoint": "https://generativelanguage.googleapis.com",
"embedding_dim": 768,
},
"meta_agent_config": {
"agents": [
{
"core_memory_agent": {
"blocks": [
{"label": "human", "value": ""},
{"label": "persona", "value": "I am a helpful assistant."},
]
}
},
"resource_memory_agent",
"semantic_memory_agent",
"episodic_memory_agent",
"procedural_memory_agent",
"knowledge_vault_memory_agent",
],
},
}
)
client.add(
user_id="demo-user",
messages=[
{"role": "user", "content": [{"type": "text", "text": "The moon now has a president."}]},
{"role": "assistant", "content": [{"type": "text", "text": "Noted."}]},
],
session_id="sess-demo-001",
)
# Include tool activity when you want skills learned from the WORK PROCESS:
# tool errors, retries, and the fix that finally worked are the distiller's
# strongest signals. Pass tool results as role="tool" turns and/or a
# "tool_calls" list on assistant messages (OpenAI shape) — both are preserved
# in the session conversation store.
client.add(
user_id="demo-user",
messages=[
{"role": "user", "content": [{"type": "text", "text": "Deploy the service."}]},
{"role": "assistant", "content": "", "tool_calls": [
{"function": {"name": "kubectl_apply", "arguments": "{\"file\": \"deploy.yaml\"}"}}
]},
{"role": "tool", "name": "kubectl_apply", "content": "error: forbidden (missing RBAC)"},
{"role": "assistant", "content": [{"type": "text", "text": "Fixed the RBAC role and redeployed successfully."}]},
],
session_id="sess-demo-001",
)
memories = client.retrieve_with_conversation(
user_id="demo-user",
messages=[
{"role": "user", "content": [{"type": "text", "text": "What did we discuss on MirixDB in last 4 days?"}]},
],
limit=5,
)
print(memories)For more API examples, see samples/run_client.py.
Auto-Dream Memory Consolidation
MIRIX also exposes an auto-dream endpoint for explicit memory cleanup and consolidation. Auto-dream reviews existing memories, merges duplicates, resolves stale/conflicting entries where possible, and writes the result back through the memory tools.
REST endpoint:
POST /memory/auto_dream?user_id=demo-user
Content-Type: application/json
{
"mode": "experience",
"dry_run": false
}Supported mode values:
| Mode | Memory component(s) reviewed |
|---|---|
core | Core memory blocks |
episodic | Episodic memories |
semantic | Semantic memories |
resource | Resource memories |
procedural | Procedural memories |
knowledge | Knowledge Vault memories |
experience | Episodic, Semantic, and Knowledge Vault memories together |
Use dry_run: true to fetch counts and inspect what would be processed without applying updates. The optional model field can override the auto-dream agent model for that request. For procedural mode, pass the target meta_agent_id and optionally last_n_sessions to distill recent session experiences.
Python client example:
result = client.auto_dream(
user_id="demo-user",
mode="procedural",
meta_agent_id=meta_agent.id,
last_n_sessions=3,
dry_run=False,
)
print(result)Graph Memory
MIRIX can index episodic and semantic memories into a provenance hypergraph stored in Neo4j. Entities become anchors, each extracted fact becomes an n-ary frame whose edges name the role each anchor plays, and every frame cites the memory rows it came from. With graph memory on, episodic and semantic retrieval enters through the graph, and Auto-Dream merges duplicate anchors on the graph instead of rewriting stored memories.
Graph memory is off by default. To turn it on:
Start Neo4j 5.23 or later.
docker compose --profile graph up -dstarts Neo4j 5.26 with APOC; first setMIRIX_NEO4J_PASSWORD(at least 8 characters) in.env, because there is no default password.Install the Neo4j driver. It is in
requirements.txt; afterpip install -e ., runpip install "neo4j>=5.20,<6".Set
MIRIX_ENABLE_GRAPH_MEMORY=trueandMIRIX_NEO4J_PASSWORDto the password your Neo4j uses.MIRIX_NEO4J_URIandMIRIX_NEO4J_USERdefault tobolt://localhost:7687andneo4j.
With graph memory on, POST /memory/auto_dream refines the graph only unless the request sets graph_only to false. Evaluation settings and commands are in evals/GRAPH_MEMORY.md.
Upgrading an Existing Database
Fresh databases get their full schema automatically at startup. Existing databases require manual migrations when upgrading to a version that alters tables — startup creates missing tables but never adds columns to existing ones. If migrations are pending, the server refuses to start and names the exact scripts to run.
For the skill-based procedural memory release, run against your database (in this order):
psql "$MIRIX_PG_URI" -f scripts/migrate_procedural_to_skill.sql
psql "$MIRIX_PG_URI" -f scripts/migrate_add_message_session_id.sql
psql "$MIRIX_PG_URI" -f scripts/migrate_add_message_session_id_phase2.sql # outside a transaction (CREATE INDEX CONCURRENTLY)
psql "$MIRIX_PG_URI" -f scripts/migrate_add_conversation_message.sql
psql "$MIRIX_PG_URI" -f scripts/migrate_add_conversation_message_phase2.sql # outside a transaction
psql "$MIRIX_PG_URI" -f scripts/migrate_add_skill_experience.sql
psql "$MIRIX_PG_URI" -f scripts/migrate_add_agent_trigger_state.sql
psql "$MIRIX_PG_URI" -f scripts/migrate_backfill_procedural_scope.sql # backfill filter_tags['scope'] from each skill's owning clientFor the graph-memory release, which adds five provenance columns, also run:
psql "$MIRIX_PG_URI" -f scripts/migrate_add_graph_provenance_columns.sqlEach script is idempotent where possible and documents its own preconditions in its header. Run them once, then restart the server.
License
Mirix is released under the Apache License 2.0. See the LICENSE file for more details.
Contact
For questions, suggestions, or issues, please open an issue on the GitHub repository or contact us at [e-mail address on the upstream page](https://github.com/Mirix-AI/MIRIX/blob/9077e4b229dd8f61c5703b431a97788c6b61acfb/README.md)
Join Our Community
Connect with other Mirix users, share your thoughts, and get support:
💬 Discord Community
Join our Discord server for real-time discussions, support, and community updates: https://discord.gg/S6CeHNrJ
🎯 Weekly Discussion Sessions
We host weekly discussion sessions where you can:
Discuss issues and bugs
Share ideas about future directions
Get general consultations and support
Connect with the development team and community
📅 Schedule: Friday nights, 8-9 PM PST 🔗 Zoom Link: https://ucsd.zoom.us/j/96278791276
📱 WeChat Group
You can add the account ari_asm so that I can add you to the group chat.
Acknowledgement
We would like to thank Letta for open-sourcing their framework, which served as the foundation for the memory system in this project.
LICENSE
Shown as plain text.
Apache License
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- scripts/migrate_p16_agent_context_and_skill_feedback.sql (not served here)
- scripts/migrate_procedural_to_skill.sql (not served here)
- scripts/migrate_skill_experience_attempts.sql (not served here)
- scripts/migrate_skill_experience_pool.sql (not served here)
- scripts/migrate_skill_retrieval_fields.sql (not served here)
Advantages
- Supports multi-modal inputs including text, images, voice, and screen captures. evidence: README.md
- Provides a provenance hypergraph to link entities and cite memory sources. evidence: README.md
Limitations
- Existing databases require manual migration scripts when upgrading to versions that alter table schemas. evidence: README.md
- Requires manual configuration and setup of external dependencies like PostgreSQL and optional Neo4j. evidence: README.md
Security
Risk level: Low risk
This brain runs
Agent instructions that run or fetch something (12)
.cursorrules:User-defined tools run in `ToolExecutionSandbox` for security.cursorrules:Run tests with: `pytest tests/`.cursorrules:Run linters before committing: `ruff check .` and `pyright`.cursorrules:User-defined tools MUST run in `ToolExecutionSandbox`CLAUDE.md:docker-compose up -d # starts PostgreSQL (5432), Redis (6379), API (8531), Dashboard (5173)CLAUDE.md:docker-compose ps # verify all services healthyCLAUDE.md:pip install -e .CLAUDE.md:python scripts/start_server.py --port 8531CLAUDE.md:python scripts/start_server.py --port 8899 # Terminal 1CLAUDE.md:make format # ruff import sort + formatCLAUDE.md:make lint # ruff check + pyrightCLAUDE.md:make check # format + lint + test
Install scripts (2)
Makefile(make install):$(POETRY) installsetup.py:python setup.py
- Low risk: network-instructions (CLAUDE.md:31)
- Low risk: scripts-present (scripts/start_server.py)
- Low risk: scripts-present (setup.py)
Attribution and licence
- Created by
- Mirix AI
- First committed by
- @wangyu-ustc
- Declared authors
- Mirix AI (declared in pyproject.toml)
- Publisher
- MIRIX-AI (Community)
- Source
- Mirix-AI/MIRIX · commit 9077e4b
- Licence
- Apache License 2.0 (files served here)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Mirix AI.
Install from source
499 of its files are not served here, so there is no ZIP. Mirix-AI/MIRIX at 9077e4b