Evolutiondb MCP Server

EvolutionDB — Powering Long-Term Memory for Agents

A single-process database that gives AI agent frameworks everything they need from one binary: SQL + vector search + JSON + temporal queries + reactive push streaming with first-class adapters for LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, and Mem0.

Replace the MongoDB + Pinecone dual-stack — and the polling loops that go with it — with a single Postgres-compatible server you run locally or on-prem.

Why EvolutionDB for agent memory

For the head-to-head against MongoDB / Pinecone / Zep / Mem0 / Weaviate see docs/comparison.md.

60-second quickstart

docker compose up -d              # PG:5433  EVO:9967

# Python (LangGraph drop-in via the bundled adapter)
export PYTHONPATH=$PWD/client/python-evosql-memory:$PYTHONPATH
python3 - <<'PY'
from evosql_memory import connect
from evosql_memory.adapters.langgraph_evosql import EvoCheckpointSaver, EvoBaseStore

c = connect("127.0.0.1", 9967, "admin", "admin")
saver = EvoCheckpointSaver(c, "demo_ck")
store = EvoBaseStore(c, "demo_mem")

cfg = {"configurable": {"thread_id": "agent-1", "checkpoint_ns": ""}}
saver.put(cfg, {"id": "cp-1", "channels": {"step": 1}})
print("latest:", saver.get(cfg))

store.put(("user_42", "memos"), "favourite", {"genre": "jazz"})
print("memo:",   store.get(("user_42", "memos"), "favourite"))
PY

The full quickstart (C SDK, ReAct demo, Mem0 drop-in, reactive subscription) is at docs/quickstart.md.

SQL surface (first-class objects)

-- LangGraph BaseCheckpointSaver
CREATE CHECKPOINT STORE agent_checkpoints;

-- LangGraph BaseStore + LangChain VectorStoreRetrieverMemory
CREATE MEMORY STORE agent_memories WITH (embedding_dim = 1536);

-- LangChain BaseChatMessageHistory + LlamaIndex ChatMemoryBuffer
CREATE MESSAGE LOG agent_chat;

-- Haystack DocumentStore + LlamaIndex BaseDocumentStore
CREATE DOCUMENT STORE agent_docs;

-- Zep Graphiti + Mem0 graph mode (bitemporal edges)
CREATE GRAPH STORE agent_kg;

-- LangChain ConversationEntityMemory + CrewAI EntityMemory
CREATE ENTITY STORE agent_entities;

Plus everything you'd expect from a relational engine: tables, indexes, constraints, JOINs, subqueries, transactions, MVCC, prepared statements, COPY, replication, RLS.

Clients

Compatibility tests for every adapter run on push and PR via framework-compat.yml.

Performance (single process, p99)

From bench/run_all.py (full report at docs/benchmarks/v1.md):

Push is roughly 2900× faster than a 1-second polling loop — the gap that lets reactive agents react in real time instead of every poll tick.

Build

Requirements: GCC, Bison, Flex, libreadline-dev. For TLS: libssl-dev.

make                  # core engine + adaptor + CLI
make adaptor TLS=1    # build with OpenSSL TLS support
make clean
make generate         # regenerate Flex/Bison parser from .y/.l files
make -C client/libevosql-memory   # build the C SDK

Docker

docker compose up -d                    # PG:5433  EVO:9967
docker compose down                     # stop (data preserved)
docker compose down -v                  # stop and delete data

docker run -d -p 5433:5433 -p 9967:9967 \
    -e EVOSQL_PASSWORD=mysecret evolutiondb/evolutiondb:latest

Testing

docker compose up -d
python tests/test_memory_store.py
python tests/test_checkpoint_store.py
python tests/test_evo_protocol.py
python client/python-evosql-memory/python_tests/test_adapters.py
python tests/framework_compat/langgraph/test_lg_compat.py

Background

EvolutionDB started ~18 years ago as a personal C database project to learn how engines work internally — parser, storage, indexing, execution layer, all from scratch. It was archived on a DVD and sat dormant for over a decade.

Resumed in early 2026 with AI-assisted development, the engine matured into a Postgres-compatible relational core and then pivoted toward agent memory: storing the kind of structured, semi-structured, vector-indexed, and time-versioned state that AI agent frameworks need but currently get by stitching together MongoDB + Pinecone + custom polling + a hand-rolled checkpointer.

The agent-memory pivot is documented in ADR-002.

Documentation

• 60-second quickstart
• Comparison vs MongoDB / Pinecone / Zep / Mem0 / Weaviate
• Benchmarks
• ADR-002 — Agent Memory pivot
• Release notes 3.0.0
• Launch blog post

Architecture deep-dives live in the Wiki:

• Architecture Overview
• Unified Storage
• Storage Engine · B+ Tree · System Catalog
• SQL Parser · SQL Operations · Expression Engine
• Transactions
• PostgreSQL Wire Protocol · EVO Protocol
• Authentication and Security
• Server Architecture
• CLI Client