getzep/graphiti
graphiti
Build Real-Time Knowledge Graphs for AI Agents
Usage guide
graphiti is an open-source project around agents, graph, llms with 28,097 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating graphiti for Python AI workflows.
- Comparing a GitHub project with 28,097 stars and current repository activity.
Pros
- graphiti has visible GitHub traction with 28,097 stars. Topics: agents, graph, llms.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
graphiti should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
graphiti architecture preview
graphiti's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, GitHub / Discord, and returns Grounded answers / search results.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://help.getzep.com/graphiti
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub / Discord
Tool adapters let the runtime act outside the model through GitHub / Discord.
GitHub, Discord
Output
Grounded answers / search results
The final result is an answer or ranked result grounded in retrieved context.
answer output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
graphiti has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/getzep/graphiti.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latestAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
Build Real-Time Knowledge Graphs for AI Agents
This is one of the documented reasons to evaluate graphiti before choosing a stack.
Focus area: agents
This is one of the documented reasons to evaluate graphiti before choosing a stack.
AI Agents project comparison
Compare graphiti with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official graphiti setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If Docker startup fails, check port conflicts, image pull permissions, and volume paths first.
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented graphiti example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
What is graphiti?
graphiti is an open-source ai agents project. Build Real-Time Knowledge Graphs for AI Agents
How do I install graphiti?
Start with the official README. The first detected setup step is: git clone https://github.com/getzep/graphiti.git.
Is graphiti beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can graphiti be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does graphiti need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt graphiti?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.