Is NocturnusAI a vector search or summarization tool?

No, it uses deterministic logical inference with backward chaining and truth maintenance, not vector search or summarization.

How does NocturnusAI reduce token usage?

It extracts structured facts and returns only the delta—what changed since the last turn—reducing tokens sent to the LLM by up to 10×.

What integrations does NocturnusAI support?

It integrates with LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Vercel AI SDK, and supports MCP clients like Claude Desktop and Cursor.

What is the license for NocturnusAI?

NocturnusAI is licensed under BUSL-1.1.

Nocturnusai

knowledgememoryreasoningcontext-compressionmcp

Deterministic reasoning engine that compresses AI agent context by extracting structured facts with logical inference and truth maintenance.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

We ran the install command below but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx nocturnusai

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "auctalis-nocturnusai": {
      "command": "uvx",
      "args": [
        "nocturnusai"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This server processes raw conversation turns into structured facts using an LLM, applies backward-chaining logical inference to identify relevant facts, and returns only the changes since the last turn. It reduces token usage in AI agent context, preserving truth and avoiding stale or hallucinated information. Use it to optimize context size for AI agents while maintaining logical consistency and proof chains.

Use cases

Compress AI agent conversation context by sending only changes

Maintain truth and retract outdated facts in agent memory

Integrate deterministic reasoning into LangChain or AutoGen workflows

Benchmark token usage reduction in multi-turn dialogs

Serve as an MCP-compatible context server for AI agents

Key features

Deterministic logical inference with proof chains

Truth maintenance with automatic fact retraction

Structured fact extraction from natural language turns

MCP server compatible with Claude Desktop, Cursor, Continue

Python and TypeScript SDKs for easy integration

Docker image for standalone deployment

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Nocturnusai.

Extracted Tool Capabilities

Deterministic logical inference with proof chains

Truth maintenance with automatic fact retraction

Structured fact extraction from natural language turns

MCP server compatible with Claude Desktop, Cursor, Continue

Python and TypeScript SDKs for easy integration

Docker image for standalone deployment

Documentation Overview

NocturnusAI

The context engineering engine for AI agents: send only what changed.

NocturnusAI — Context Engineering Engine for AI Agents

Before / After

python

# ❌ Without NocturnusAI — replay everything, every turn
messages = system_prompt + full_history + tool_outputs   # ~1,259 tokens/turn
response = llm(messages)                                 # $13,600/mo at scale

# ✅ With NocturnusAI — send only what changed
ctx = nocturnus.process_turns(raw_turns)                 # extract → infer → delta
messages = system_prompt + ctx.briefing_delta             # ~221 tokens/turn
response = llm(messages)                                 # $2,400/mo. Same accuracy.

The Numbers

Measured on live APIs. 15-turn product support conversation. Real usage.input_tokens counts. Run it yourself.

	Naive replay	RAG-optimized	NocturnusAI
Tokens per turn	~1,259	~800	~221
Cost per month (1K req/hr, Opus 4, $15/1M)	$13,600	$12,000	$2,400
Latency	high	medium	low
Truth-preserving	no	no	yes

Claude Opus 4: 5.7× reduction. Gemini 2.0 Flash: 10.0×. Full calculations.

Install

Terminal

pip install nocturnusai          # Python
npm install nocturnusai-sdk      # TypeScript
docker run -p 9300:9300 ghcr.io/auctalis/nocturnusai:latest  # Docker

Or use the setup wizard:

Terminal

curl -fsSL https://raw.githubusercontent.com/Auctalis/nocturnusai/main/install.sh | bash

Why Developers Star This Repo

Reproducible token reduction — benchmark in the repo, methodology published, run it against your own workload
Deterministic inference — same query, same result, every time. No embedding drift, no cosine similarity lottery
Truth maintenance — retract a fact, all derived conclusions auto-retract. No stale context, no hallucination on operational state
Plugs into existing stacks — LangChain, LlamaIndex, CrewAI, AutoGen, MCP, Vercel AI SDK, OpenAI Agents SDK, Mastra
Benchmarkable against naive replay — numbers derived, not invented. Every claim traces to a notebook cell

Framework Quickstarts

Framework	Integration	Link
LangChain / LangGraph	Drop-in `NocturnusContextProvider`, LangSmith trace pass-through	Docs
CrewAI	Task-scoped context per agent role	Docs
AutoGen	Context server callable by any agent	Docs
MCP	Spec-compliant server for Claude Desktop, Cursor, Continue	Config
OpenAI Agents SDK	Context middleware, no tool modifications	Docs
Vercel AI SDK	Edge-compatible adapter for Next.js, Nuxt, SvelteKit	Docs
Python SDK	`pip install nocturnusai`	Docs
TypeScript SDK	`npm install nocturnusai-sdk`	Docs

How It Works

Three steps. Every turn.

Extract — raw conversation turns → structured facts via LLM extraction
Infer — backward-chaining logical inference finds only the facts reachable from the agent's current goal
Return the delta — a briefingDelta containing only what changed since the last turn

This is not vector search. It is not summarization. It is deterministic inference on a logic engine — Hexastore indexing, backward chaining, and truth maintenance.

The Working Loop

LLM required for natural-language turns. The examples below send raw text turns through an LLM to extract structured facts. If you start the server without an LLM provider, natural-language turns will return zero facts. See Quick Start for setup options, or use predicate syntax (e.g., "customer_tier(acme_corp, enterprise)") which works without any LLM.

1. First reduction: `POST /context`

Terminal

curl -X POST http://localhost:9300/context \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{
    "turns": [
      "user: Customer says they are enterprise and blocked on SLA credits.",
      "tool: CRM says account is Acme Corp with a 2M ARR contract.",
      "agent: Last week support promised to review SLA eligibility.",
      "tool: Billing note says renewal is due next month."
    ],
    "maxFacts": 12
  }'

2. Goal-driven pass: `POST /memory/context`

Terminal

curl -X POST http://localhost:9300/memory/context \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{
    "goals": [{"predicate":"eligible_for_sla","args":["acme_corp"]}],
    "maxFacts": 12,
    "sessionId": "ticket-42"
  }'

3. Later turns: `POST /context/diff`

Terminal

curl -X POST http://localhost:9300/context/diff \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{"sessionId": "ticket-42", "maxFacts": 12}'

Returns only added and removed entries between snapshots.

4. End of thread: `POST /context/session/clear`

Terminal

curl -X POST http://localhost:9300/context/session/clear \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{"sessionId":"ticket-42"}'

Choose Your Surface

Python SDK

server.ts

from nocturnusai import SyncNocturnusAIClient

with SyncNocturnusAIClient("http://localhost:9300") as client:
    ctx = client.process_turns(
        turns=[
            "user: Customer says they are enterprise and blocked on SLA credits.",
            "tool: CRM says account is Acme Corp with a 2M ARR contract.",
        ],
        scope="ticket-42",
        session_id="ticket-42",
    )

    diff = client.diff_context(session_id="ticket-42", max_facts=12)
    client.clear_context_session("ticket-42")

    print(ctx.briefing_delta)

TypeScript SDK

server.ts

import { NocturnusAIClient } from 'nocturnusai-sdk';

const client = new NocturnusAIClient({
  baseUrl: 'http://localhost:9300',
  tenantId: 'default',
});

const ctx = await client.processTurns({
  turns: [
    'user: Customer says they are enterprise and blocked on SLA credits.',
    'tool: CRM says account is Acme Corp with a 2M ARR contract.',
  ],
  scope: 'ticket-42',
  sessionId: 'ticket-42',
});

const diff = await client.diffContext({ sessionId: 'ticket-42', maxFacts: 12 });
await client.clearContextSession('ticket-42');
console.log(ctx.briefingDelta);

MCP

config.json

{
  "mcpServers": {
    "nocturnus": {
      "url": "http://localhost:9300/mcp/sse",
      "transport": "sse"
    }
  }
}

Use the context tool each turn for a salience-ranked working set. Pair MCP with the HTTP context endpoints when you need goal-driven assembly and diffs.

What Lives Behind The Workflow

When you do need backend mechanics, NocturnusAI provides them:

Deterministic fact and rule storage
Backward-chaining inference with proof chains
Truth maintenance and contradiction handling
Temporal facts with ttl, validFrom, and validUntil
Multi-tenancy via X-Database and X-Tenant-ID
MCP, REST, Python SDK, TypeScript SDK, and CLI surfaces over the same engine

Quick Start

Docker (fastest)

Terminal

docker run -d --name nocturnusai -p 9300:9300 \
  --restart unless-stopped \
  -v nocturnusai-data:/data \
  ghcr.io/auctalis/nocturnusai:latest

Terminal

curl http://localhost:9300/health   # Verify it's running

Docker with Ollama (enables natural-language extraction)

Terminal

docker run -d --name nocturnusai -p 9300:9300 \
  --add-host=host.docker.internal:host-gateway \
  -e LLM_PROVIDER=ollama \
  -e LLM_MODEL=granite3.3:8b \
  -e LLM_BASE_URL=http://host.docker.internal:11434/v1 \
  -e EXTRACTION_ENABLED=true \
  ghcr.io/auctalis/nocturnusai:latest

From this repo

bash

make up-ollama && make smoke

CLI

bash

nocturnusai                                # Interactive REPL
nocturnusai -e "context 10"               # Salience-ranked working set
nocturnusai -e "compress"                 # POST /memory/compress
nocturnusai -e "cleanup 0.05"             # POST /memory/cleanup

Documentation

Full docs: nocturnus.ai

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

28d ago

Most recent push to the default branch.

Install check

Inconclusive

Didn't respond in our test window — often a slow first install.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Nocturnusai

Yes, an LLM is required to extract structured facts from natural-language turns; without an LLM provider, natural-language input returns zero facts.

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Technical Specs & Signals

CategoryKnowledge & Memory

PricingFree

10/10 checks healthy over the last 32d

52Quality signal: Good · 52/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools24/30

Adoption & activity5/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 23d ago via OSV.dev · nocturnusai (PyPI)

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Nocturnusai →Install in Claude Desktop Install in Cursor Install in VS Code

Nocturnusai

knowledgememoryreasoningcontext-compressionmcp

Deterministic reasoning engine that compresses AI agent context by extracting structured facts with logical inference and truth maintenance.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

We ran the install command below but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx nocturnusai

No response to initialize.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "auctalis-nocturnusai": {
      "command": "uvx",
      "args": [
        "nocturnusai"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

Use cases

Compress AI agent conversation context by sending only changes

Maintain truth and retract outdated facts in agent memory

Integrate deterministic reasoning into LangChain or AutoGen workflows

Benchmark token usage reduction in multi-turn dialogs

Serve as an MCP-compatible context server for AI agents

Key features

Deterministic logical inference with proof chains

Truth maintenance with automatic fact retraction

Structured fact extraction from natural language turns

MCP server compatible with Claude Desktop, Cursor, Continue

Python and TypeScript SDKs for easy integration

Docker image for standalone deployment

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Nocturnusai.

Extracted Tool Capabilities

Deterministic logical inference with proof chains

Truth maintenance with automatic fact retraction

Structured fact extraction from natural language turns

MCP server compatible with Claude Desktop, Cursor, Continue

Python and TypeScript SDKs for easy integration

Docker image for standalone deployment

Documentation Overview

NocturnusAI

The context engineering engine for AI agents: send only what changed.

NocturnusAI — Context Engineering Engine for AI Agents

Before / After

python

# ❌ Without NocturnusAI — replay everything, every turn
messages = system_prompt + full_history + tool_outputs   # ~1,259 tokens/turn
response = llm(messages)                                 # $13,600/mo at scale

# ✅ With NocturnusAI — send only what changed
ctx = nocturnus.process_turns(raw_turns)                 # extract → infer → delta
messages = system_prompt + ctx.briefing_delta             # ~221 tokens/turn
response = llm(messages)                                 # $2,400/mo. Same accuracy.

The Numbers

Measured on live APIs. 15-turn product support conversation. Real usage.input_tokens counts. Run it yourself.

	Naive replay	RAG-optimized	NocturnusAI
Tokens per turn	~1,259	~800	~221
Cost per month (1K req/hr, Opus 4, $15/1M)	$13,600	$12,000	$2,400
Latency	high	medium	low
Truth-preserving	no	no	yes

Claude Opus 4: 5.7× reduction. Gemini 2.0 Flash: 10.0×. Full calculations.

Install

Terminal

pip install nocturnusai          # Python
npm install nocturnusai-sdk      # TypeScript
docker run -p 9300:9300 ghcr.io/auctalis/nocturnusai:latest  # Docker

Or use the setup wizard:

Terminal

curl -fsSL https://raw.githubusercontent.com/Auctalis/nocturnusai/main/install.sh | bash

Why Developers Star This Repo

Reproducible token reduction — benchmark in the repo, methodology published, run it against your own workload
Deterministic inference — same query, same result, every time. No embedding drift, no cosine similarity lottery
Truth maintenance — retract a fact, all derived conclusions auto-retract. No stale context, no hallucination on operational state
Plugs into existing stacks — LangChain, LlamaIndex, CrewAI, AutoGen, MCP, Vercel AI SDK, OpenAI Agents SDK, Mastra
Benchmarkable against naive replay — numbers derived, not invented. Every claim traces to a notebook cell

Framework Quickstarts

Framework	Integration	Link
LangChain / LangGraph	Drop-in `NocturnusContextProvider`, LangSmith trace pass-through	Docs
CrewAI	Task-scoped context per agent role	Docs
AutoGen	Context server callable by any agent	Docs
MCP	Spec-compliant server for Claude Desktop, Cursor, Continue	Config
OpenAI Agents SDK	Context middleware, no tool modifications	Docs
Vercel AI SDK	Edge-compatible adapter for Next.js, Nuxt, SvelteKit	Docs
Python SDK	`pip install nocturnusai`	Docs
TypeScript SDK	`npm install nocturnusai-sdk`	Docs

How It Works

Three steps. Every turn.

Extract — raw conversation turns → structured facts via LLM extraction
Infer — backward-chaining logical inference finds only the facts reachable from the agent's current goal
Return the delta — a briefingDelta containing only what changed since the last turn

This is not vector search. It is not summarization. It is deterministic inference on a logic engine — Hexastore indexing, backward chaining, and truth maintenance.

The Working Loop

LLM required for natural-language turns. The examples below send raw text turns through an LLM to extract structured facts. If you start the server without an LLM provider, natural-language turns will return zero facts. See Quick Start for setup options, or use predicate syntax (e.g., "customer_tier(acme_corp, enterprise)") which works without any LLM.

1. First reduction: `POST /context`

Terminal

curl -X POST http://localhost:9300/context \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{
    "turns": [
      "user: Customer says they are enterprise and blocked on SLA credits.",
      "tool: CRM says account is Acme Corp with a 2M ARR contract.",
      "agent: Last week support promised to review SLA eligibility.",
      "tool: Billing note says renewal is due next month."
    ],
    "maxFacts": 12
  }'

2. Goal-driven pass: `POST /memory/context`

Terminal

curl -X POST http://localhost:9300/memory/context \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{
    "goals": [{"predicate":"eligible_for_sla","args":["acme_corp"]}],
    "maxFacts": 12,
    "sessionId": "ticket-42"
  }'

3. Later turns: `POST /context/diff`

Terminal

curl -X POST http://localhost:9300/context/diff \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{"sessionId": "ticket-42", "maxFacts": 12}'

Returns only added and removed entries between snapshots.

4. End of thread: `POST /context/session/clear`

Terminal

curl -X POST http://localhost:9300/context/session/clear \
  -H 'Content-Type: application/json' \
  -H 'X-Tenant-ID: default' \
  -d '{"sessionId":"ticket-42"}'

Choose Your Surface

Python SDK

server.ts

from nocturnusai import SyncNocturnusAIClient

with SyncNocturnusAIClient("http://localhost:9300") as client:
    ctx = client.process_turns(
        turns=[
            "user: Customer says they are enterprise and blocked on SLA credits.",
            "tool: CRM says account is Acme Corp with a 2M ARR contract.",
        ],
        scope="ticket-42",
        session_id="ticket-42",
    )

    diff = client.diff_context(session_id="ticket-42", max_facts=12)
    client.clear_context_session("ticket-42")

    print(ctx.briefing_delta)

TypeScript SDK

server.ts

import { NocturnusAIClient } from 'nocturnusai-sdk';

const client = new NocturnusAIClient({
  baseUrl: 'http://localhost:9300',
  tenantId: 'default',
});

const ctx = await client.processTurns({
  turns: [
    'user: Customer says they are enterprise and blocked on SLA credits.',
    'tool: CRM says account is Acme Corp with a 2M ARR contract.',
  ],
  scope: 'ticket-42',
  sessionId: 'ticket-42',
});

const diff = await client.diffContext({ sessionId: 'ticket-42', maxFacts: 12 });
await client.clearContextSession('ticket-42');
console.log(ctx.briefingDelta);

MCP

config.json

{
  "mcpServers": {
    "nocturnus": {
      "url": "http://localhost:9300/mcp/sse",
      "transport": "sse"
    }
  }
}

Use the context tool each turn for a salience-ranked working set. Pair MCP with the HTTP context endpoints when you need goal-driven assembly and diffs.

What Lives Behind The Workflow

When you do need backend mechanics, NocturnusAI provides them:

Deterministic fact and rule storage
Backward-chaining inference with proof chains
Truth maintenance and contradiction handling
Temporal facts with ttl, validFrom, and validUntil
Multi-tenancy via X-Database and X-Tenant-ID
MCP, REST, Python SDK, TypeScript SDK, and CLI surfaces over the same engine

Quick Start

Docker (fastest)

Terminal

docker run -d --name nocturnusai -p 9300:9300 \
  --restart unless-stopped \
  -v nocturnusai-data:/data \
  ghcr.io/auctalis/nocturnusai:latest

Terminal

curl http://localhost:9300/health   # Verify it's running

Docker with Ollama (enables natural-language extraction)

Terminal

docker run -d --name nocturnusai -p 9300:9300 \
  --add-host=host.docker.internal:host-gateway \
  -e LLM_PROVIDER=ollama \
  -e LLM_MODEL=granite3.3:8b \
  -e LLM_BASE_URL=http://host.docker.internal:11434/v1 \
  -e EXTRACTION_ENABLED=true \
  ghcr.io/auctalis/nocturnusai:latest

From this repo

bash

make up-ollama && make smoke

CLI

bash

nocturnusai                                # Interactive REPL
nocturnusai -e "context 10"               # Salience-ranked working set
nocturnusai -e "compress"                 # POST /memory/compress
nocturnusai -e "cleanup 0.05"             # POST /memory/cleanup

Documentation

Full docs: nocturnus.ai

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

28d ago

Most recent push to the default branch.

Install check

Inconclusive

Didn't respond in our test window — often a slow first install.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Nocturnusai

Yes, an LLM is required to extract structured facts from natural-language turns; without an LLM provider, natural-language input returns zero facts.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/auctalis-nocturnusai?style=directory)](https://allmcps.com/mcp/auctalis-nocturnusai)

HTML Embed

<a href="https://allmcps.com/mcp/auctalis-nocturnusai"><img src="https://allmcps.com/api/badge/auctalis-nocturnusai?style=directory" alt="Nocturnusai on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

PricingFree

10/10 checks healthy over the last 32d

52Quality signal: Good · 52/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools24/30

Adoption & activity5/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 23d ago via OSV.dev · nocturnusai (PyPI)

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Nocturnusai →Install in Claude Desktop Install in Cursor Install in VS Code

Nocturnusai

Quick Install

Client Config & Setup

Overview

Use cases

Key features

Capabilities & Tool Schemas

Documentation Overview

NocturnusAI

Before / After

The Numbers

Install

Why Developers Star This Repo

Framework Quickstarts

How It Works

The Working Loop

1. First reduction: POST /context

2. Goal-driven pass: POST /memory/context

3. Later turns: POST /context/diff

4. End of thread: POST /context/session/clear

Choose Your Surface

What Lives Behind The Workflow

Quick Start

Docker (fastest)

Docker with Ollama (enables natural-language extraction)

From this repo

CLI

Documentation

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Nocturnusai

AllMCPs Directory Badge

Technical Specs & Signals

Supply-chain signal

Feature Your MCP Server

Own this project?

Share & Embed

Explore more

Nocturnusai

Quick Install

Client Config & Setup

Overview

Use cases

Key features

Capabilities & Tool Schemas

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NocturnusAI

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1. First reduction: POST /context

2. Goal-driven pass: POST /memory/context

3. Later turns: POST /context/diff

4. End of thread: POST /context/session/clear

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1. First reduction: `POST /context`

2. Goal-driven pass: `POST /memory/context`

3. Later turns: `POST /context/diff`

4. End of thread: `POST /context/session/clear`

1. First reduction: `POST /context`

2. Goal-driven pass: `POST /memory/context`

3. Later turns: `POST /context/diff`

4. End of thread: `POST /context/session/clear`