RecallCore is a revolutionary local-first, self-hosted AI memory layer that eliminates the frustrating "AI amnesia" problem plaguing modern development workflows. Think of it as a persistent hippocampus for your AI assistants — a dedicated knowledge repository that remembers every interaction, every codebase context, and every decision chain across sessions.
Unlike cloud-dependent alternatives, RecallCore runs entirely on your infrastructure, giving you complete data sovereignty while supercharging tools like GitHub Copilot, Cursor, Claude Desktop, and Cline with long-term contextual memory.
Every developer using AI coding assistants has experienced it: you spend 30 minutes building complex context in a conversation, explaining your architecture and preferences, only to have the AI forget everything the moment you start a new session or restart the IDE. This isn't just annoying — it's a productivity hemorrhage.
RecallCore acts as a neural bridge between your development sessions, creating a persistent knowledge graph that:
- Retains project-specific conventions and patterns across sessions
- Remembers your personal coding style and preferred frameworks
- Preserves critical debugging contexts so you never re-explain the same issue
- Builds institutional knowledge that grows smarter with every interaction
graph TD
A[Developer Tools] --> B[Plugin Adapters]
B --> C[Memory Orchestrator]
C --> D[Vector Store]
C --> E[Knowledge Graph]
C --> F[Session Cache]
D --> G[Local Embedding Engine]
E --> H[Relationship Mapper]
F --> I[Context Prioritizer]
G --> J[Model Agnostic Interface]
H --> J
I --> J
J --> K[OpenAI API]
J --> L[Claude API]
J --> M[Local LLMs]
K --> N[Response Augmenter]
L --> N
M --> N
N --> O[Memory Injection Layer]
O --> A
The architecture is designed as a closed-loop feedback system: every AI interaction flows through RecallCore, which extracts, indexes, and stores contextual information, then injects relevant memories back into subsequent prompts. This creates an ever-expanding knowledge base that requires zero manual curation.
- Automatic extraction of project structures, naming conventions, and architectural patterns
- Cross-session memory that spans days, weeks, or months
- Intelligent forgetting mechanisms to prevent context bloat
- Native plugins for GitHub Copilot, Cursor, Claude Desktop, and Cline
- Plugin SDK for custom integrations with any AI-powered development tool
- Real-time memory synchronization across all connected tools
- 100% local operation with no external dependencies
- Optional encrypted sync across multiple machines
- Complete data ownership — no telemetry, no cloud storage
- Sub-millisecond memory retrieval for real-time context injection
- Intelligent caching with LRU eviction and priority scoring
- Configurable memory retention policies based on relevance and recency
| Platform | Support Status | Performance Rating |
|---|---|---|
| 🪟 Windows 10/11 | Full Support | ⭐⭐⭐⭐⭐ |
| 🍎 macOS 13+ | Full Support | ⭐⭐⭐⭐⭐ |
| 🐧 Ubuntu 22.04+ | Full Support | ⭐⭐⭐⭐⭐ |
| 🐧 Debian 11+ | Full Support | ⭐⭐⭐⭐ |
| 🐧 Fedora 38+ | Full Support | ⭐⭐⭐⭐ |
| 🖥️ Arch Linux | Community Support | ⭐⭐⭐ |
| 🖥️ FreeBSD | Experimental | ⭐⭐ |
RecallCore seamlessly interfaces with the OpenAI API to enhance memory extraction and retrieval:
from recallcore import MemoryEngine
from openai import OpenAI
engine = MemoryEngine(
api_provider="openai",
api_key="sk-your-key-here",
model="gpt-4-turbo",
memory_threshold=0.75
)
# Every AI interaction is automatically contextualized
response = engine.query(
tool="cursor",
prompt="Refactor this authentication middleware",
context={"project": "microservice-auth", "language": "typescript"}
)The integration uses a semantic caching layer that reduces API costs by up to 40% while improving response relevance through memory injection.
For Claude API users, RecallCore provides deep integration with Anthropic's conversational model:
from recallcore import MemoryEngine
from anthropic import Anthropic
engine = MemoryEngine(
api_provider="claude",
api_key="sk-ant-your-key",
model="claude-3-opus",
compression_level="high"
)
# Memory persistence across Claude Desktop sessions
result = engine.process(
session_id="project-alpha-2026",
message="Continue the refactoring we discussed yesterday",
tool="claude-desktop"
)The Claude integration leverages conversation summarization to maintain context without exceeding token limits, preserving the essence of long development discussions.
# ~/.recallcore/config.yaml
version: "2.4"
engine:
memory_mode: "persistent"
storage_path: "/var/recallcore/data"
vector_dimension: 1536
similarity_metric: "cosine"
plugins:
- name: "copilot"
enabled: true
workspace: "/home/developer/projects"
context_depth: "full"
- name: "cursor"
enabled: true
workspace: "/home/developer/projects"
memory_injection_interval: 500ms
- name: "claude-desktop"
enabled: true
workspace: "/home/developer/notes"
session_persistence: true
api:
openai:
model: "gpt-4-turbo"
temperature: 0.3
max_tokens: 4096
claude:
model: "claude-3-opus"
temperature: 0.2
max_tokens: 8192
policies:
retention:
short_term: 7 days
medium_term: 30 days
long_term: 365 days
archive: "never"
relevance:
min_score: 0.65
boost_patterns:
- "architecture"
- "security"
- "performance"# Start the RecallCore daemon with verbose logging
recallcore start --verbose --config ~/.recallcore/config.yaml
# Output:
# [2026-03-15 10:23:45] INFO Starting RecallCore v2.4.0
# [2026-03-15 10:23:45] INFO Loading configuration from ~/.recallcore/config.yaml
# [2026-03-15 10:23:46] INFO Vector store initialized (1536 dimensions)
# [2026-03-15 10:23:46] INFO Knowledge graph loaded (12,847 nodes)
# [2026-03-15 10:23:46] INFO Plugin: copilot connected successfully
# [2026-03-15 10:23:46] INFO Plugin: cursor connected successfully
# [2026-03-15 10:23:46] INFO Plugin: claude-desktop connected successfully
# [2026-03-15 10:23:46] INFO API server listening on localhost:8080
# Query memory statistics
recallcore stats --format json
# Output:
# {
# "total_memories": 28471,
# "active_sessions": 3,
# "cache_hit_rate": 0.87,
# "average_retrieval_ms": 0.43,
# "storage_usage_mb": 847.2
# }
# Force memory consolidation (useful after large refactoring sessions)
recallcore consolidate --aggressive --deduplicateRecallCore ships with a responsive web dashboard built on modern web technologies that adapts seamlessly to any screen size:
- Desktop: Full-featured dashboard with real-time memory graphs, knowledge mining tools, and session playback
- Tablet: Condensed view with touch-optimized controls for on-the-go monitoring
- Mobile: Essential controls and notifications for checking memory health from your phone
The interface and documentation are available in 12 languages with ongoing community translations:
| Language | Status | Coverage |
|---|---|---|
| 🇺🇸 English | Complete | 100% |
| 🇪🇸 Spanish | Complete | 100% |
| 🇫🇷 French | Complete | 100% |
| 🇩🇪 German | Complete | 100% |
| 🇨🇳 Chinese (Simplified) | Complete | 100% |
| 🇯🇵 Japanese | Complete | 100% |
| 🇰🇷 Korean | Complete | 100% |
| 🇧🇷 Portuguese (Brazil) | Complete | 100% |
| 🇷🇺 Russian | Beta | 95% |
| 🇮🇹 Italian | Beta | 92% |
| 🇵🇱 Polish | Beta | 88% |
| 🇹🇷 Turkish | Alpha | 75% |
While RecallCore is self-hosted and designed for autonomy, we understand that even the best systems need human assistance. Our support infrastructure includes:
- Community Forum: Active discussion board with thousands of developers sharing configurations and workflows
- Discord Server: Real-time chat support with response times under 2 hours during business hours
- Knowledge Base: Comprehensive documentation with video tutorials and troubleshooting guides
- Priority Email: Premium support tier with 1-hour response guarantee (available with enterprise license)
- On-Premise Consultation: Dedicated support engineers for large-scale deployments (contact for pricing)
- Python 3.10+ or Node.js 18+
- 8GB RAM minimum (16GB recommended for large projects)
- 10GB free disk space for vector database
- One or more AI tool plugins (Copilot, Cursor, Claude Desktop, etc.)
# Clone the repository (once available)
git clone https://github.com/recallcore/recallcore
# Navigate to the directory
cd recallcore
# Install dependencies
pip install -r requirements.txt
# Run the setup wizard
recallcore setup --interactive
# Start the engine
recallcore start# Pull the image
docker pull recallcore/recallcore:latest
# Run with persistent storage
docker run -d \
--name recallcore \
-p 8080:8080 \
-v /path/to/data:/var/recallcore/data \
-v /path/to/config:/etc/recallcore \
recallcore/recallcore:latestImportant Notice: RecallCore is an independent, open-source project and is not affiliated with, endorsed by, or sponsored by GitHub, Microsoft, OpenAI, Anthropic, Cursor, or any other third-party tool provider. The names "GitHub Copilot," "Cursor," "Claude Desktop," "OpenAI," and "Claude" are trademarks of their respective owners.
Upon installation, RecallCore operates entirely on your local machine. No data is transmitted externally unless you explicitly configure cloud-based features. The developers of RecallCore are not responsible for any data loss, security breaches, or legal issues arising from the use of this software. Users are responsible for complying with the terms of service of any third-party tools they integrate with RecallCore.
This software is provided "as is," without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
Privacy Note: RecallCore does not collect telemetry, analytics, or usage data. If you choose to enable automatic updates, a simple version check will be performed against the release server. No personal information is ever transmitted.
RecallCore is released under the MIT License. You are free to use, modify, and distribute this software for any purpose, commercial or non-commercial, provided that the original copyright notice and permission notice are included in all copies or substantial portions of the software.
We welcome contributions from the community! Whether you're fixing bugs, adding features, improving documentation, or translating the interface, your help makes RecallCore better for everyone.
Check out our Contributing Guide for detailed instructions on:
- Setting up the development environment
- Coding standards and conventions
- Testing requirements
- Pull request process
| Quarter | Feature | Status |
|---|---|---|
| Q1 2026 | Multi-modal memory (images, diagrams) | ✅ Complete |
| Q2 2026 | Team collaboration mode | 🔄 In Progress |
| Q3 2026 | IDE plugin marketplace | 📅 Planned |
| Q4 2026 | Knowledge graph visualization | 📅 Planned |
Q: Does RecallCore work with local LLMs?
A: Yes! While optimized for OpenAI and Claude APIs, RecallCore supports any OpenAI-compatible API endpoint, including Ollama, LM Studio, and LocalAI.
Q: How much storage does memory require?
A: On average, 1MB of text content requires approximately 2.5MB of vector store space. A typical project with 6 months of history uses around 2-5GB.
Q: Can I use RecallCore without internet access?
A: Absolutely. RecallCore is designed for complete air-gapped operation. All processing, storage, and retrieval happen locally.
Q: How do I migrate from another memory system?
A: RecallCore includes an import tool that supports JSON, CSV, and Markdown formats. We also provide migration scripts for popular systems like MemGPT and LangChain.
Built with ❤️ by developers who hate repeating themselves. Copyright 2026 RecallCore Project.