graphiti-core
A temporal graph building library
Decision gist · record as of 2026-08-14
Yes. Graphiti is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific problem—temporal context graphs for evolving agent memory—that traditional RAG and static knowledge graphs do not address well. The Apache-2.0 license is permissive. The main condition is that you must provision and manage a graph database backend (Neo4j, FalkorDB, or Neptune) separately; it is not a standalone solution.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or higher and a third-party graph database backend (Neo4j 5.26, FalkorDB 1.1.2, Amazon Neptune, or Neptune Analytics with OpenSearch).
- Low friction: pure Python wheel with seven runtime dependencies (neo4j, numpy, openai, posthog, pydantic, python-dotenv, tenacity).
- Active maintenance with recent releases and high repository engagement (29928 stars).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions — suitable for production deployments without licensing concerns.
last release 2026-07-27 (18 days) · last repo commit 2026-08-13 · 29,928 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,647,586 downloads/mo, #3,688 on PyPI
Alternatives
Verify before relying
pip install graphiti-core
from graphiti_core import Graphiti
from pydantic import BaseModel
class Person(BaseModel):
name: str
graph = Graphiti()
# Add entities and facts with temporal tracking
# Query across time and relationships- Specific performance characteristics (sub-second latency claim) and scalability limits for production workloads.
- Detailed setup and configuration complexity for different graph database backends.
- Whether the MCP server integration mentioned in the description is included in this package or requires separate installation.
What it is and what it does
Graphiti is a temporal graph framework that builds evolving context graphs for AI agents by continuously integrating user interactions, structured and unstructured data, and external information. Unlike static knowledge graphs or traditional RAG, it tracks when facts become true and when they are superseded, maintaining full temporal history and provenance to source episodes. Each entity, relationship, and fact has a validity window, allowing queries across time, meaning, and relationships.
The framework supports incremental updates without full recomputation, hybrid retrieval combining semantic embeddings with keyword search and graph traversal, and custom entity and relationship types defined via Pydantic models. It requires a pluggable graph database backend (Neo4j, FalkorDB, or Amazon Neptune) and integrates with OpenAI for embeddings and PostHog for telemetry. The design prioritizes real-time interaction and precise historical queries over batch processing, making it suitable for applications where context must evolve with every interaction.
Use it for
- Build AI agent memory systems that track how user preferences, relationships, and facts change over time with full audit trails.
- Query historical state of entities at any point in time without recomputing the entire graph.
- Combine semantic search with keyword matching and graph traversal to retrieve precise context for agent decision-making.
- Integrate continuously arriving structured and unstructured data into a coherent, queryable graph without batch reprocessing.
- Define custom entity and relationship types for domain-specific applications using Pydantic models.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Graphiti is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific problem—temporal context graphs for evolving agent memory—that traditional RAG and static knowledge graphs do not address well. The Apache-2.0 license is permissive. The main condition is that you must provision and manage a graph database backend (Neo4j, FalkorDB, or Neptune) separately; it is not a standalone solution.
Install
graphiti-core on PyPI
Before you install
Low friction: pure Python wheel with seven runtime dependencies (neo4j, numpy, openai, posthog, pydantic, python-dotenv, tenacity). Active maintenance with recent releases and high repository engagement (29928 stars).
Requires Python 3.10 or higher and a third-party graph database backend (Neo4j 5.26, FalkorDB 1.1.2, Amazon Neptune, or Neptune Analytics with OpenSearch).
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions — suitable for production deployments without licensing concerns.
Quickstart
pip install graphiti-core
from graphiti_core import Graphiti
from pydantic import BaseModel
class Person(BaseModel):
name: str
graph = Graphiti()
# Add entities and facts with temporal tracking
# Query across time and relationshipsVerify before relying
- Specific performance characteristics (sub-second latency claim) and scalability limits for production workloads.
- Detailed setup and configuration complexity for different graph database backends.
- Whether the MCP server integration mentioned in the description is included in this package or requires separate installation.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesneo4jnumpyopenaiposthogpydanticpython-dotenvtenacity |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,647,586 / month, #3,688 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: graphiti_core-0.29.3-py3-none-any.whl
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