memgpt_grounding
Science Score: 64.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
1 of 1 committers (100.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: biswesh456
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 7.86 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Letta (previously MemGPT)
[Homepage](https://letta.com) // [Documentation](https://docs.letta.com) // [Letta Cloud](https://forms.letta.com/early-access)
**👾 Letta** is an open source framework for building stateful LLM applications. You can use Letta to build **stateful agents** with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic. [](https://discord.gg/letta) [](https://twitter.com/Letta_AI) [](https://arxiv.org/abs/2310.08560) [](LICENSE) [](https://github.com/cpacker/MemGPT/releases) [](https://github.com/cpacker/MemGPT)[!NOTE] Looking for MemGPT? You're in the right place!
The MemGPT package and Docker image have been renamed to
lettato clarify the distinction between MemGPT agents and the API server / runtime that runs LLM agents as services.You use the Letta framework to create MemGPT agents. Read more about the relationship between MemGPT and Letta here.
⚡ Quickstart
The two main ways to install Letta are through pypi (pip) or via Docker:
* pip (guide below) - the easiest way to try Letta, will default to using SQLite and ChromaDB for the database backends
* Docker (guide here) - recommended for production settings, will default to using Postgres (+ pgvector) for the database backend
Step 1 - Install Letta using pip
sh
$ pip install -U letta
Step 2 - Set your environment variables for your chosen LLM / embedding providers
sh
$ export OPENAI_API_KEY=sk-...
For Ollama (see our full documentation for examples of how to set up various providers):
sh
$ export OLLAMA_BASE_URL=http://localhost:11434
Step 3 - Run the Letta CLI
You can create agents and chat with them via the Letta CLI tool (letta run):
sh
$ letta run
```
🧬 Creating new agent...
? Select LLM model: letta-free [type=openai] [ip=https://inference.memgpt.ai]
? Select embedding model: letta-free [type=hugging-face] [ip=https://embeddings.memgpt.ai]
-> 🤖 Using persona profile: 'sampov'
-> 🧑 Using human profile: 'basic'
-> 🛠️ 8 tools: sendmessage, pauseheartbeats, conversationsearch, conversationsearchdate, archivalmemoryinsert, archivalmemorysearch, corememoryappend, corememoryreplace
🎉 Created new agent 'InspiringSpinach'
Hit enter to begin (will request first Letta message)
💭 User engaged. Recognize as Chad. Acknowledge presence and establish connection. Explore interests and engage in meaningful discourse. 🤖 Hello, Chad. It's nice to meet you. I am keen on learning and exploring new ideas and concepts. Is there a specific topic you'd like to discuss today?
Enter your message: my name is Brad, not Chad...
💭 A discrepancy in identity. Reflect this change in core memory. Apologize for the oversight, and rectify the error. ⚡🧠 [function] updating memory with corememoryreplace First name: Chad → First name: Brad 💭 Apologize for the mistake in recognizing his name, and begin the conversation anew, addressing him by the correct name this time. 🤖 I apologize for the oversight, Brad. Let's restart. It's a pleasure to meet you, Brad. Is there a specific topic you'd like to discuss today?
Enter your message: ```
Step 4 - Run the Letta server
You can start the Letta API server with letta server (see the full API reference here):
sh
$ letta server
Initializing database...
Running: uvicorn server:app --host localhost --port 8283
INFO: Started server process [47750]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://localhost:8283 (Press CTRL+C to quit)
When you start the Letta API server, the ADE (Agent Development Environment) will be available on http://localhost:8283:

In Letta, all agents are stored/persisted in the same database, so the agents you create in the CLI are accessible via the API and ADE, and vice versa. Check out the quickstart guide on our docs for a tutorial where you create an agent in the Letta CLI and message the same agent via the Letta API.
🤗 How to contribute
Letta is an open source project built by over a hundred contributors. There are many ways to get involved in the Letta OSS project!
- Contribute to the project: Interested in contributing? Start by reading our Contribution Guidelines.
- Ask a question: Join our community on Discord and direct your questions to the
#supportchannel. - Report ssues or suggest features: Have an issue or a feature request? Please submit them through our GitHub Issues page.
- Explore the roadmap: Curious about future developments? View and comment on our project roadmap.
- Join community events: Stay updated with the event calendar or follow our Twitter account.
**Legal notices: By using Letta and related Letta services (such as the Letta endpoint or hosted service), you are agreeing to our privacy policy and terms of service.
Owner
- Name: Biswesh Mohapatra
- Login: biswesh456
- Kind: user
- Location: Paris
- Company: Inria
- Website: https://sites.google.com/view/biswesh-mohapatra/
- Twitter: bis1602
- Repositories: 8
- Profile: https://github.com/biswesh456
PhD student at INRIA Paris and affiliated with ENS(PSL)
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "Letta"
url: "https://github.com/letta-ai/letta"
preferred-citation:
type: article
authors:
- family-names: "Packer"
given-names: "Charles"
- family-names: "Wooders"
given-names: "Sarah"
- family-names: "Lin"
given-names: "Kevin"
- family-names: "Fang"
given-names: "Vivian"
- family-names: "Patil"
given-names: "Shishir G"
- family-names: "Stoica"
given-names: "Ion"
- family-names: "Gonzalez"
given-names: "Joseph E"
journal: "arXiv preprint arXiv:2310.08560"
month: 10
title: "MemGPT: Towards LLMs as Operating Systems"
year: 2023
GitHub Events
Total
- Member event: 1
- Push event: 3
- Pull request event: 1
- Create event: 3
Last Year
- Member event: 1
- Push event: 3
- Pull request event: 1
- Create event: 3
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| biswesh456 | b****a@i****r | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- AG2AI-Admin (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- builder latest build
- python 3.12.2-slim-bookworm build
- python 3.12.2-bookworm build
- 277 dependencies
- black ^24.4.2 develop
- ipdb ^0.13.13 develop
- ipykernel ^6.29.5 develop
- alembic ^1.13.3
- autoflake ^2.3.0
- black ^24.2.0
- chromadb >=0.4.24,<0.5.0
- composio-core ^0.5.34
- composio-langchain ^0.5.28
- datasets ^2.14.6
- demjson3 ^3.0.6
- docker ^7.1.0
- docstring-parser >=0.16,<0.17
- docx2txt ^0.8
- e2b-code-interpreter ^1.0.1
- fastapi ^0.104.1
- html2text ^2020.1.16
- httpx ^0.27.2
- httpx-sse ^0.4.0
- isort ^5.13.2
- jinja2 ^3.1.4
- langchain ^0.3.7
- langchain-community ^0.3.7
- llama-index ^0.11.9
- llama-index-embeddings-ollama ^0.3.1
- llama-index-embeddings-openai ^0.2.5
- locust ^2.31.5
- nltk ^3.8.1
- numpy ^1.26.2
- pathvalidate ^3.2.1
- pexpect ^4.9.0
- pg8000 ^1.30.3
- pgvector ^0.2.3
- pre-commit ^3.5.0
- prettytable ^3.9.0
- psycopg2 ^2.9.10
- psycopg2-binary ^2.9.10
- pyautogen 0.2.22
- pydantic >=2.7.4,<2.10.0
- pydantic-settings ^2.2.1
- pyhumps ^3.8.0
- pymilvus ^2.4.3
- pyright ^1.1.347
- pytest-asyncio ^0.23.2
- pytest-order ^1.2.0
- python <3.13,>=3.10
- python-box ^7.1.1
- python-multipart ^0.0.9
- pytz ^2023.3.post1
- pyyaml ^6.0.1
- qdrant-client ^1.9.1
- questionary ^2.0.1
- setuptools ^68.2.2
- sqlalchemy ^2.0.25
- sqlalchemy-json ^0.7.0
- sqlalchemy-utils ^0.41.2
- sqlmodel ^0.0.16
- tiktoken ^0.7.0
- tqdm ^4.66.1
- typer ^0.9.0
- uvicorn ^0.24.0.post1
- websockets ^12.0
- wikipedia ^1.4.0