Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

Developer

HydraDB for

AI Coding Assistant Memory

Building autonomous software engineers means managing massive amounts of context. But relying on static CLAUDE.md files or standard vector databases leads to bloated prompts, context drift, and agents that hallucinate deprecated code. HydraDB provides a persistent, graph-native memory layer that tracks evolving architectural decisions, file change histories, and debugging sessions across your entire development stack. Stop manually curating text files and give your AI agents deterministic, just-in-time retrieval of the exact project constraints they need.

Start building

Book a demo

90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why autonomous coding agents keep losing context

Most AI developer tools rely on massive context windows or flat embedding searches. As a codebase grows, these approaches break down, forcing engineers to write complex glue code just to keep their agents aligned with current project standards.

Bloated static context files

Relying on CLAUDE.md, memory.md, or agents.md files creates an unsustainable engineering burden. As these files grow to hundreds of lines, they consume massive token budgets and trigger lost in the middle errors, causing agents to simply ignore critical architectural rules.

Bloated static context files

Bloated static context files

Temporal drift in codebases

Software architecture is dynamic. A library choice or design pattern that was correct six months ago might be an anti-pattern today. Standard vector databases flatten time, retrieving superseded Architectural Decision Records (ADRs) and feeding outdated constraints to your agent.

Temporal drift in codebases

Context fragmentation across tools

Developers frequently switch between Cursor, GitHub Copilot, custom CLI tools, and web-based AI coding assistants. Because these tools have siloed, transient memories, the agent loses track of what was already attempted, forcing the developer to re-explain their state in every new session.

Context fragmentation across tools

Lost debugging history

Coding is an iterative, multi-step process. When an agent lacks episodic memory, it forgets the file changes it just made or the terminal errors it just encountered. This traps autonomous agents in repetitive failure loops where they try the same broken solution multiple times.

Lost debugging history

// See It In Action //

An agent that actually remembers

Once your repository history, ADRs, and terminal sessions are ingested into HydraDB, it constructs a time-aware context graph. Before your LLM generates code, HydraDB retrieves the active architectural constraints and relevant debugging history, completely eliminating the need to stuff every rule into the prompt.

Live support session — HydraDB context active

Retrieval : 94ms

Implement the new user profile data fetching hook.

Developer

Coding agent request

Before generation

HydraDB recalled:

Old GraphQL approach (deprecated on Oct 10)

Oct 10 ADR update → Migrate all data fetching to REST with React Query

Developer (Alex) → Authored → PR #402 (Implemented React Query pattern for user dashboard on Oct 12)

CI/CD log from Oct 12 showing required error boundary wrapper for React Query hooks

I’ll implement the useUserProfile fetching hook using REST and React Query, adhering to the architecture update from October 10. I’ll wrap the hook in the standard error boundary you authored in PR #402 on October 12, which prevents the CI pipeline failures encountered during that dashboard migration.

// How Hydra Helps //

Context Infrastructure for Autonomous Software Engineers

HydraDB replaces brittle DIY RAG stacks and manual markdown file maintenance with a single infrastructure layer built for the complexity of AI-powered development workflows.

Persistent debugging memory

HydraDB stores episodic memory of long-horizon tasks. It tracks what an agent has already tried, which CI/CD errors were encountered, and how they were resolved. This allows agents to pause a session, switch tools, and resume debugging later without losing their train of thought.

Persistent debugging memory

Persistent debugging memory

Relationship-aware retrieval

Code doesn’t exist in a vacuum. HydraDB natively links pull requests, Jira tickets, documentation, and specific code snippets into a unified graph. When an agent looks at a piece of code, it can traverse the graph to understand why it was written and who approved it.

Relationship-aware retrieval

Temporal context

HydraDB uses a Git-style versioned temporal graph to manage knowledge updates. When an architectural standard changes, HydraDB appends the new state rather than destructively overwriting the old one. Your agent can reliably differentiate between active constraints and superseded patterns with 97.4% accuracy.

Temporal context

Structured context ingestion

HydraDB structures data from GitHub, GitLab, Jira and CLI environments at ingestion time. It automatically extracts and enriches PR histories, issue comments, and terminal errors.

Structured context ingestion

HydraDB structures data from GitHub, GitLab, Jira and CLI environments at ingestion time. It automatically extracts and enriches PR histories, issue comments, and terminal errors.

// Get Started //

Three steps to production-ready memory

HydraDB works with any LLM or agent framework. Replace your bloated markdown files with precise, structured context retrieval in minutes by connecting your repository and sending records to HydraDB’s ingestion endpoint.

Connect your repository and CLI sources

Use your existing Git provider APIs or CLI hooks to extract the architectural decisions, pull requests, and terminal logs you want your agent to remember.

import os
from hydradb import HydraDB

client = HydraDB(api_key=os.environ.get("HYDRA_KEY"))

# Retrieve an ADR and a recent PR using your Git provider's API
adr_content = github_client.get_file("docs/adr/004-use-react-query.md")
pr_data = github_client.get_pull_request(402)

import os
from hydradb import HydraDB

client = HydraDB(api_key=os.environ.get("HYDRA_KEY"))

# Retrieve an ADR and a recent PR using your Git provider's API
adr_content = github_client.get_file("docs/adr/004-use-react-query.md")
pr_data = github_client.get_pull_request(402)

import os
from hydradb import HydraDB

client = HydraDB(api_key=os.environ.get("HYDRA_KEY"))

# Retrieve an ADR and a recent PR using your Git provider's API
adr_content = github_client.get_file("docs/adr/004-use-react-query.md")
pr_data = github_client.get_pull_request(402)

Connect your repository and CLI sources

Use your existing Git provider APIs or CLI hooks to extract the architectural decisions, pull requests, and terminal logs you want your agent to remember.

import os
from hydradb import HydraDB

client = HydraDB(api_key=os.environ.get("HYDRA_KEY"))

# Retrieve an ADR and a recent PR using your Git provider's API
adr_content = github_client.get_file("docs/adr/004-use-react-query.md")
pr_data = github_client.get_pull_request(402)

Connect your repository and CLI sources

Use your existing Git provider APIs or CLI hooks to extract the architectural decisions, pull requests, and terminal logs you want your agent to remember.

import os
from hydradb import HydraDB

client = HydraDB(api_key=os.environ.get("HYDRA_KEY"))

# Retrieve an ADR and a recent PR using your Git provider's API
adr_content = github_client.get_file("docs/adr/004-use-react-query.md")
pr_data = github_client.get_pull_request(402)

Ingest and structure architectural context

Map your repository data into HydraDB’s typed fields. HydraDB’s Sliding Window Inference Pipeline automatically resolves entities like developers and tickets and links them into the context graph.

import json

# Ingest the ADR and PR history into HydraDB
client.context.ingest(
    type="knowledge",
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    app_knowledge=json.dumps([
        {
            "id": "adr_004",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "ADR-004: Migrate to REST with React Query",
            "type": "architectural_decision",
            "content": { "text": adr_content.text },
            "metadata": { "status": "Active", "date": "2023-10-10" }
        },
        {
            "id": "pr_402",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "PR #402: Implement React Query pattern",
            "type": "pull_request",
            "content": { "text": pr_data.diff },
            "metadata": { "author": pr_data.author, "merged": "2023-10-12" }
        }
    ])
)

import json

# Ingest the ADR and PR history into HydraDB
client.context.ingest(
    type="knowledge",
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    app_knowledge=json.dumps([
        {
            "id": "adr_004",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "ADR-004: Migrate to REST with React Query",
            "type": "architectural_decision",
            "content": { "text": adr_content.text },
            "metadata": { "status": "Active", "date": "2023-10-10" }
        },
        {
            "id": "pr_402",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "PR #402: Implement React Query pattern",
            "type": "pull_request",
            "content": { "text": pr_data.diff },
            "metadata": { "author": pr_data.author, "merged": "2023-10-12" }
        }
    ])
)

import json

# Ingest the ADR and PR history into HydraDB
client.context.ingest(
    type="knowledge",
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    app_knowledge=json.dumps([
        {
            "id": "adr_004",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "ADR-004: Migrate to REST with React Query",
            "type": "architectural_decision",
            "content": { "text": adr_content.text },
            "metadata": { "status": "Active", "date": "2023-10-10" }
        },
        {
            "id": "pr_402",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "PR #402: Implement React Query pattern",
            "type": "pull_request",
            "content": { "text": pr_data.diff },
            "metadata": { "author": pr_data.author, "merged": "2023-10-12" }
        }
    ])
)

Ingest and structure architectural context

import json

# Ingest the ADR and PR history into HydraDB
client.context.ingest(
    type="knowledge",
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    app_knowledge=json.dumps([
        {
            "id": "adr_004",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "ADR-004: Migrate to REST with React Query",
            "type": "architectural_decision",
            "content": { "text": adr_content.text },
            "metadata": { "status": "Active", "date": "2023-10-10" }
        },
        {
            "id": "pr_402",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "PR #402: Implement React Query pattern",
            "type": "pull_request",
            "content": { "text": pr_data.diff },
            "metadata": { "author": pr_data.author, "merged": "2023-10-12" }
        }
    ])
)

Ingest and structure architectural context

import json

# Ingest the ADR and PR history into HydraDB
client.context.ingest(
    type="knowledge",
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    app_knowledge=json.dumps([
        {
            "id": "adr_004",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "ADR-004: Migrate to REST with React Query",
            "type": "architectural_decision",
            "content": { "text": adr_content.text },
            "metadata": { "status": "Active", "date": "2023-10-10" }
        },
        {
            "id": "pr_402",
            "tenant_id": "engineering_org",
            "sub_tenant_id": "frontend_repo",
            "title": "PR #402: Implement React Query pattern",
            "type": "pull_request",
            "content": { "text": pr_data.diff },
            "metadata": { "author": pr_data.author, "merged": "2023-10-12" }
        }
    ])
)

Retrieve full context at inference time

Before your coding agent generates code or runs a terminal command, query HydraDB to retrieve the exact active constraints and relevant historical context, saving thousands of tokens per prompt.

# Retrieve structured project history before LLM generation
result = client.query(
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    query="Implement the new user profile data fetching hook.",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact, time-aware context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user="Implement the new user profile data fetching hook."
)

# Retrieve structured project history before LLM generation
result = client.query(
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    query="Implement the new user profile data fetching hook.",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact, time-aware context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user="Implement the new user profile data fetching hook."
)

# Retrieve structured project history before LLM generation
result = client.query(
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    query="Implement the new user profile data fetching hook.",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact, time-aware context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user="Implement the new user profile data fetching hook."
)

Retrieve full context at inference time

Before your coding agent generates code or runs a terminal command, query HydraDB to retrieve the exact active constraints and relevant historical context, saving thousands of tokens per prompt.

# Retrieve structured project history before LLM generation
result = client.query(
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    query="Implement the new user profile data fetching hook.",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact, time-aware context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user="Implement the new user profile data fetching hook."
)

Retrieve full context at inference time

Before your coding agent generates code or runs a terminal command, query HydraDB to retrieve the exact active constraints and relevant historical context, saving thousands of tokens per prompt.

# Retrieve structured project history before LLM generation
result = client.query(
    tenant_id="engineering_org",
    sub_tenant_id="frontend_repo",
    query="Implement the new user profile data fetching hook.",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact, time-aware context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user="Implement the new user profile data fetching hook."
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for Codebases

HydraDB is built for autonomous software engineers, multi-session coding assistants, and complex refactoring workflows where context must persist, relationships matter, and time changes what is true.

Features

HydraDB

Standard Vector Search

Cross-session memory

Persists debugging state, trial-and-error history, and workflow context across multiple sessions

Treats every prompt as an isolated query with no episodic continuity

Relationship-aware retrieval

Connects PRs, tickets, developers, and CI/CD logs in a traversable graph

Retrieves isolated code chunks based purely on semantic similarity

Temporal context

Distinguishes active architectural decisions from superseded ones via a versioned graph

Flattens time, frequently serving deprecated code patterns as current truth

Integration method

Structures context from Git, Jira, and CLI tools at ingestion via APIs

Requires complex custom chunking, parsing, and metadata filtering pipelines

Best fit

Autonomous software engineers, multi-session coding assistants, and complex refactoring workflows

Simple semantic search over static API documentation or readmes

Team hand-off summaries

Creates a shared account brief for every team

Creates fragmented hand-offs

Workflow triggers

Triggers updates when important signals change

Depends on humans noticing every signal

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Integration method

Best fit

Team hand-off summaries

Workflow triggers

HydraDB

Persists debugging state, trial-and-error history, and workflow context across multiple sessions

Connects PRs, tickets, developers, and CI/CD logs in a traversable graph

Distinguishes active architectural decisions from superseded ones via a versioned graph

Structures context from Git, Jira, and CLI tools at ingestion via APIs

Autonomous software engineers, multi-session coding assistants, and complex refactoring workflows

Creates a shared account brief for every team

Triggers updates when important signals change

Standard Vector Search

Treats every prompt as an isolated query with no episodic continuity

Retrieves isolated code chunks based purely on semantic similarity

Flattens time, frequently serving deprecated code patterns as current truth

Requires complex custom chunking, parsing, and metadata filtering pipelines

Simple semantic search over static API documentation or readmes

Creates fragmented hand-offs

Depends on humans noticing every signal

// What Teams Are Saying //

Trusted by teams building autonomous software engineers

Maintaining agents.md files became a nightmare as our codebase scaled. Replacing that manual process with HydraDB drastically reduced our LLM token costs and eliminated ‘lost in the middle’ hallucinations. Our coding agents finally respect our active architectural standards.

Marcus V.

CTO, AI-Native Developer Tools

Standard vector databases kept feeding our agents deprecated libraries because they appeared semantically similar to current tickets. HydraDB’s temporal graph fixed this immediately. Our agents now understand the difference between code we wrote yesterday and code we abandoned six months ago.

Sarah L.

Lead AI Engineer, Autonomous DevOps Platform

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

HydraDB is an object-store-native distributed graph database built in Rust, designed to serve as the context layer for AI systems. Unlike traditional graph databases, where storage is tied more closely to database servers or dedicated cluster volumes, HydraDB makes object storage itself the source of truth. This lets query nodes and indexers scale, restart, or be replaced independently without moving or replicating the graph. It also supports snapshot-consistent OpenCypher, GraphBLAS-accelerated traversal, Neo4j-compatible Bolt, and HTTP APIs.

Why use a graph database like HydraDB for AI agents?

A graph database like HydraDB gives AI agents something vector search alone cannot: relationships and state. Instead of retrieving isolated chunks that merely look similar to a query, an agent can follow explicit connections between people, projects, events, policies, documents, and past actions to understand how the current situation fits together.

When should I use HydraDB instead of a vector database?

Use a vector database when your main problem is semantic search over mostly static content: finding documents, chunks, tickets, or products that are similar to a query. Use HydraDB when your AI agent needs to understand how information connects and changes over time. If the agent needs to answer questions like who owns this project, what is blocking it, which policy applies to this customer, or what changed since the last session, similarity search alone is not enough. HydraDB lets the agent traverse entities and relationships and work with structured state instead of only retrieving nearby embeddings.

Does HydraDB work with GraphRAG?

Yes. HydraDB can be used as the graph database behind a GraphRAG system. GraphRAG is a retrieval approach rather than a specific database: it builds or uses a knowledge graph, then retrieves context by traversing relationships instead of relying only on vector similarity. Microsoft’s GraphRAG architecture is explicitly designed around a storage-agnostic knowledge model and allows custom storage and workflow implementations.

How does HydraDB improve retrieval for AI agents?

HydraDB improves retrieval by combining vector search with graph traversal, exact-match search, and temporal context. Instead of returning isolated similar chunks, it reconstructs the relationships, dependencies, and latest valid state around a query, which gives AI agents a smaller, more relevant set of evidence to reason over.

Can I use HydraDB with my existing AI stack?

Yes. HydraDB plugs into your existing AI stack through Neo4j-compatible Bolt and HTTP APIs. Keep your current models, agents, and retrieval pipeline, and add HydraDB as the graph layer for connected, stateful context.

How does HydraDB use context graphs for AI agents?

HydraDB turns fragmented agent memory into a connected context graph. This lets agents retrieve not only the relevant fact, but also how that fact relates to other entities, where it came from, and what the current state is, which gives multiple agents and workflows a consistent view of the same domain.

What AI use cases is HydraDB best suited for?

HydraDB is best for AI agents that need persistent, connected context such as support agents, coding agents, research agents, GraphRAG systems, and enterprise copilots. It is especially useful when the agent needs to remember history, follow relationships, and understand how state changes over time.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

X / Twitter

Discord

X / Twitter

Discord

Frequently Asked questions

What is AI agent memory for coding assistants?

AI agent memory gives coding assistants persistent context across development sessions. Instead of treating every coding task as a fresh interaction, the agent can remember previous debugging attempts, architectural decisions, file changes, errors, pull requests, and resolutions before making its next change.

Why isn't standard vector search enough for coding assistants?

Vector search retrieves code and documentation based primarily on semantic similarity. But coding tasks often depend on relationships and history: which architectural decision is currently active, which pull request introduced a pattern, which Jira issue caused a change, and which debugging attempts already failed. HydraDB connects these facts in a graph so coding agents can retrieve the relevant project history and relationships rather than only similar code chunks.

How does HydraDB help coding agents remember previous debugging sessions?

HydraDB stores persistent debugging memory across sessions. It can keep track of what the agent already tried, which files it changed, which terminal or CI/CD errors it encountered, and how those problems were eventually resolved. This allows an agent to resume long-running development tasks without repeatedly rediscovering the same context or trying the same failed solution again.

How does HydraDB handle codebases that change over time?

HydraDB maintains a versioned temporal graph of project context. When an architectural decision, dependency, or coding standard changes, the previous state can remain available instead of being overwritten. This helps coding agents distinguish current project rules from deprecated approaches and understand not only what the codebase looks like now, but how and why it changed.

How does HydraDB connect code with pull requests, issues, and documentation?

HydraDB uses relationship-aware retrieval to connect code snippets, files, pull requests, Jira tickets, architectural decisions, developers, documentation, and CI/CD history in the same graph. When an agent works on a piece of code, it can retrieve the surrounding context to understand why the code exists, which issue led to it, which changes affected it, and which project constraints still apply.

What development data can I bring into HydraDB?

You can bring repository history, pull requests, issues, architectural decision records, documentation, terminal logs, CI/CD errors, debugging sessions, and other structured or unstructured development context into HydraDB. HydraDB can then resolve entities and relationships across this information and organize it into persistent context that coding agents can retrieve later.

How can we bring engineering data into HydraDB?

HydraDB connectors continuously sync data from supported development and workplace tools into HydraDB. You can connect sources such as GitHub and Jira, while ingestion APIs and CLI hooks can be used for repository data, terminal sessions, custom tooling, and other engineering workflows. HydraDB can then structure pull requests, issues, documentation, errors, and related records into connected context for your coding agents. Learn more about HydraDB connectors.

Why is HydraDB a strong choice for building AI coding assistants?

Software development is stateful. The right code change often depends on what was tried before, which architectural decisions are active, why existing code was written, and how files, issues, pull requests, developers, and errors are connected. HydraDB is built for this kind of long-lived coding context. Its temporal graph keeps agents aligned with current project state, while fast graph traversal retrieves connected context without stuffing an entire repository history into the prompt. Its object-storage-native architecture also keeps growing engineering history affordable to retain while frequently accessed context remains fast.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

Why use a graph database like HydraDB for AI agents?

When should I use HydraDB instead of a vector database?

Does HydraDB work with GraphRAG?

How does HydraDB improve retrieval for AI agents?

Can I use HydraDB with my existing AI stack?

How does HydraDB use context graphs for AI agents?

What AI use cases is HydraDB best suited for?

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

X / Twitter

Discord