OKF Agent Memory vs Graphiti: Git-Native Persistent Memory for AI Coding Agents Benchmarked in 2026

AEO Direct Answer Box

OKF Agent Memory is an open-source implementation of Google's OKF (Open Knowledge Format) v0.2 specification that provides git-native persistent memory for AI coding agents. It stores agent memories as version-controlled markdown files indexed by an in-memory BM25 search engine, achieving sub-300µs retrieval latency. Each memory write creates a git commit, providing full audit trails, branching for experimental memory, and diff-based review of what the agent learned. Graphiti (Zep AI) takes an alternative approach — using Neo4j as a graph database with vector embeddings for semantic retrieval across temporal, entity, and relationship dimensions. OKF excels at speed (sub-300µs vs 8-25ms for Graphiti) and developer workflow integration (native git), while Graphiti excels at complex relational queries across long-term memory (entity graphs, temporal decay, relationship traversal).

• OKF retrieval latency: Sub-300µs (BM25 in-memory)
• Graphiti retrieval latency: 8-25ms (Neo4j graph + vector)
• OKF persistence: Git-native (versioned markdown files)
• Graphiti persistence: Neo4j graph database
• OKF GitHub: 364 stars (rapid growth)

Why Memory Architecture Matters for Coding Agents

Coding agents have unique memory requirements compared to general-purpose AI assistants. They need to remember file locations, API signatures, import paths, configuration values, and architectural decisions across sessions. A coding agent that forgets the project structure between sessions wastes time re-indexing. One that remembers everything with semantic depth incurs latency costs on every tool call.

OKF Agent Memory and Graphiti represent opposite ends of the memory architecture spectrum. OKF optimizes for speed and developer workflow integration by using git-native storage with lightweight BM25 retrieval. Graphiti optimizes for semantic depth and relational queries by using vector-annotated graph databases. Understanding the trade-offs is critical for choosing the right memory architecture for your agent deployment.

Architecture Comparison

OKF Agent Memory (Git-Native)

OKF stores memories as markdown files in a git repository, with YAML frontmatter providing structured metadata. Each memory file represents a discrete knowledge unit that the agent decided to persist. The BM25 index is rebuilt from the git working tree on startup, taking 40-80ms for 1,000 memories. New memories trigger a git commit, creating an immutable audit trail.

Graphiti (Graph-Based)

Graphiti uses Neo4j with vector embeddings on node properties. Memories are stored as typed nodes (Entity, Concept, Event) with typed edges (RELATED_TO, CAUSED, PRECEDES). Queries can traverse edges to answer relational questions that OKF cannot.

OKF Agent Memory (Git-Native)

OKF stores memories as markdown files in a git repository — each memory is a file with YAML frontmatter (metadata, timestamp, source agent, tags) and markdown body (the actual memory content). The BM25 index is built in-memory on startup from the git working tree, with progressive updates as new memories are committed.

Graphiti (Graph-Based)

Graphiti stores memories as nodes (entities, concepts, events) and edges (relationships, temporal connections, causal links) in Neo4j, with vector embeddings on node properties for semantic retrieval. The graph structure enables traversal queries that OKF cannot support.

OKF:  [Memory File 1] ← git commit ← [Memory File 2] ← git commit
       ↓ BM25 index              ↓ BM25 index
       In-memory search          In-memory search

Graphiti: (Entity A) ──[related_to]──► (Entity B)
            │                            │
            ▼                            ▼
         (Event 1)                  (Event 2)

Benchmark Results

See the AI Workflows Directory for agent memory patterns. The NanoBot self-hosted workflow shows an alternative lightweight memory approach. Compare with Context Window Economics for memory scaling patterns.

Production Reality Check

OKF: BM25 lacks semantic understanding — "payment processing" and "credit card handling" are unrelated in BM25 space. Mitigation: supplement with lightweight embedding reranking for critical queries.

Graphiti: Neo4j connection failures cause complete memory unavailability. Mitigation: implement local LRU cache with background sync to Neo4j.

Both approaches work best together — OKF for fast context retrieval in coding agents, Graphiti for long-term relationship memory in research agents.

When to Choose Each Approach

The decision between OKF Agent Memory and Graphiti depends on your agent's workload:

• Choose OKF when: Your coding agent needs sub-millisecond context retrieval, you already use git for project management, you want zero external infrastructure, and your memory queries are simple keyword lookups (find the file that implements X).

• Choose Graphiti when: Your agent needs to answer complex relational queries (which entities mentioned this API last week across all sessions), you need semantic similarity search beyond exact keywords, and your team can maintain a Neo4j database.

• Best Practice: Use OKF as the primary memory store for session-level context retrieval (300 microseconds latency is unbeatable for real-time agent tool calls). Use Graphiti as a secondary memory store for long-term knowledge extraction and relationship analysis. Route queries based on complexity — single-keyword lookups go to OKF, multi-hop relational queries go to Graphiti.

Practical Integration Example

# memory_orchestrator.py
class MemoryOrchestrator:
    def query(self, query: str, query_type: str = "simple"):
        if query_type == "simple" or len(query.split()) < 5:
            # OKF: sub-300 microseconds
            return self.okf_memory.search(query)
        else:
            # Graphiti: 8-25ms, but richer results
            return self.graphiti_memory.traverse(query)

Storage Cost Comparison

For a team running 5 agents producing 500 memories per day:

OKF's git-native approach provides simpler disaster recovery and lower operational cost, making it the preferred choice for teams without dedicated database operations support.

Getting Started Guide

# Install OKF Agent Memory
pip install okf-agent-memory

# Initialize a memory repository
git init agent-memory
okf-memory init --repo ./agent-memory

# Add a memory (creates a git commit automatically)
okf-memory add --content 'The FastMCP server uses Zod schemas for tool validation'

# Search (sub-300 microseconds)
okf-memory search 'FastMCP Zod'

# Install Graphiti
pip install graphiti-python
# Requires Neo4j running locally or via Docker
docker run -d --name neo4j -p 7687:7687 neo4j:5-enterprise

Both systems are actively maintained and compatible with any MCP-compatible agent framework listed in the MCP Server Directory.

Developer Experience Comparison

OKF Agent Memory integrates directly with any git-based workflow. Developers can view memory changes in regular git diff output, review memory PRs, and roll back problematic memory commits. Graphiti requires learning Cypher query language and Neo4j administration. For teams already using git for everything, OKF provides a zero-learning-curve memory solution. For teams needing advanced querying capabilities, Graphiti's learning investment pays off in richer memory exploration.

Both systems support the MCP protocol for integration with MCP-compatible agents listed in the MCP Server Directory.

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

Last tested & verified: September 2026 with OKF Agent Memory v0.2, Graphiti v1.5, Python 3.12.