march-ai 0.1.0

Most AI agents get dumber over time. March doesn't.

March

Most AI agents get dumber the longer you use them. March doesn't.

Agent frameworks love to dump everything into the LLM context window — your entire conversation history, vector DB search results from six months ago, every sub-agent's internal monologue. You pay for every token, and the LLM drowns in noise.

March takes a different approach: structured compression, isolated memory, and selective recall. The result is an agent that stays sharp after hundreds of turns, not one that slowly forgets what you told it ten minutes ago.

pip install march-ai
march start

Two commands. You're running.

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Why March?

🧠 Memory That Actually Works

Most frameworks either truncate history (losing critical decisions) or shove everything into a vector DB (retrieving outdated garbage). March uses rolling context compaction — a two-step process that compresses conversation history while preserving every decision, requirement, and code snippet that matters.

How it works:

1. When context fills up, March summarizes the conversation (keeping facts, dropping filler)
2. Then deduplicates against your static files — no redundant information in context
3. Session memory (facts.md, plan.md) is folded in and survives every compaction cycle

The result: after 100 turns, March still knows your project uses PostgreSQL, deploys to Lambda, and has a March 15 deadline. Other frameworks forgot that at turn 30.

🔒 Sub-Agent Isolation

When March spawns a sub-agent, it gets its own process, its own memory, its own LLM connection. A sub-agent can crash, OOM, or go rogue — the parent agent is unaffected.

	mtAgent (lightweight)	mpAgent (isolated)
Runs as	asyncio task	Separate OS process
Memory	Shared with parent	Fully independent
Best for	I/O-bound work, API calls	GPU compute, simulations, risky ops
Crash impact	Could affect parent	Contained — parent continues

Sub-agents communicate via IPC (Unix socketpair + msgpack). Results are pushed to the parent — no polling, no shared state corruption.

📝 Selective Memory (/rmb)

Vector databases remember everything. That sounds good until your agent retrieves a "relevant" decision from three months ago that was superseded last week.

March uses explicit, human-like memory:

• /rmb saves what you tell it to save — decisions, preferences, key facts
• Session memory auto-captures facts and plans during work
• Compaction deduplicates and keeps only the latest version of evolved facts
• Nothing stale sneaks back in through similarity search

🔌 Plugin Pipeline

Before/after hooks on every agent step. Write a plugin in 10 lines:

from march.plugins import hook

@hook("before_llm_call")
async def log_tokens(context):
    print(f"Sending {context.token_count} tokens")

📡 Multi-Channel, Single Codebase

One march start gives you:

• Terminal — interactive or one-shot mode
• Matrix — encrypted chat with E2EE support
• WebSocket — connect March Deck or any custom frontend
• ACP — IDE integration (VS Code, Cursor, any editor with agent protocol support)

All channels share the same agent, same memory, same session state.

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How Rolling Context Works

Turn 1-30: Full messages in context
           ↓ context window fills up
Turn 31:   Compaction triggers
           ├─ Step 1: Summarize (keep decisions, drop filler)
           ├─ Step 2: Dedup against static files
           └─ Session memory folded in
           ↓
Turn 31+:  [Compact rolling summary] + [Recent messages]
           ↓ context fills again
Turn 60:   Compaction triggers again
           └─ Builds on previous summary (accumulative)
           ↓
Turn 100:  Still knows your project constraints from Turn 3

Key properties:

• Accumulative — each compaction builds on the last, so early context is never fully lost
• Lossless for decisions — identifiers, code, URLs, action items always preserved
• Self-contained — after compaction, the rolling summary contains everything the LLM needs
• Configurable — tune compaction threshold, summary budget, and dedup ratio

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Architecture

┌──────────────────────────────────────────────────┐
│              Channels (Pure I/O)                  │
│  Terminal  ·  Matrix  ·  WebSocket  ·  ACP       │
└──────────────────┬───────────────────────────────┘
                   │
                   ▼
┌──────────────────────────────────────────────────┐
│              Orchestrator                         │
│  LLM calls · Tool dispatch · Cancel/redirect     │
│  Ephemeral turn state (never persisted)          │
└──────────┬────────────────────┬──────────────────┘
           │                    │
           ▼                    ▼
┌──────────────────┐  ┌────────────────────────────┐
│   Sub-Agents     │  │     Memory System          │
│  mtAgent (async) │  │  FileMemory (md files)     │
│  mpAgent (proc)  │  │  SessionMemory (per-task)  │
│  Nested (depth>1)│  │  Rolling Context           │
│  IPC + heartbeat │  │  SQLite persistence        │
└──────────────────┘  └────────────────────────────┘

23,000 lines of Python. No JavaScript. 6 native LLM providers.

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March Deck — PWA App Platform

Want a mobile-friendly UI? March Deck is a PWA platform that turns your agent into a collection of mini-apps you can open on any device.

App	What it does
🤖 March	Chat + dashboard (sessions, cost, providers, logs)
📰 Finviz	Financial news with 24h AI summaries
📄 ArXiv	Research paper semantic search
📊 System	Server monitoring (CPU, RAM, GPU, services)
📁 Files	File browser
📝 Notes	Markdown notes
📺 Cast	Chromecast streaming
🦞 OpenClaw	OpenClaw agent management

No app store. Add to home screen and go. Works with any WebSocket-compatible agent.

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Quick Start

# Install
pip install march-ai

# Or with extras
pip install "march-ai[anthropic]"     # Claude support
pip install "march-ai[bedrock]"       # AWS Bedrock
pip install "march-ai[matrix]"        # Matrix + E2EE
pip install "march-ai[browser]"       # Playwright browser tools
pip install "march-ai[voice]"         # Speech-to-text
pip install "march-ai[all]"           # Everything

# Run
march start                           # Start agent
march chat                            # Interactive terminal
march chat "summarize this repo"      # One-shot mode

Configuration

~/.march/config.yaml — created on first run:

llm:
  default: "openai"
  providers:
    openai:
      model: "${MARCH_MODEL:gpt-4o}"
      api_key: "${OPENAI_API_KEY:}"

channels:
  terminal:
    enabled: true

memory:
  compaction:
    threshold: 0.95
    summary_budget_ratio: 0.15

Supports ${VAR:default} environment variable interpolation.

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CLI

march start                  Start agent
march stop                   Stop all services
march restart                Restart
march enable / disable       Systemd service (auto-start on boot)

march chat                   Interactive terminal session
march chat "prompt"          One-shot mode
march status                 Health, version, model, plugins, channels
march log                    Follow log stream

march config show            Show config path
march agent list / show      Sub-agent management
march plugin list / enable   Plugin management

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Built-in Tools

March ships with 24 tools out of the box:

Category	Tools
Files	read, write, edit, glob, diff, apply_patch
Code	exec, process (background jobs)
Web	web_search, web_fetch, browser (Playwright)
Memory	session_memory (facts/plans/checkpoints)
Media	screenshot, pdf, voice-to-text, tts
Integration	github, huggingface, clipboard, translate
Agent	sessions (sub-agent spawn/manage), message

All tools are registered via @tool decorator. Add custom tools by dropping a Python file in your plugin directory.

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Comparison

March vs OpenClaw vs LangChain vs CrewAI

Feature	March	OpenClaw	LangChain	CrewAI
Rolling context compaction	✅ Structured 2-step	Basic	❌	❌
Session memory (survives compaction)	✅ facts/plans/checkpoints	❌	❌	❌
Process-isolated sub-agents	✅ mpAgent	❌ In-process	❌	❌
Selective memory (/rmb)	✅	❌	❌	❌
Native LLM providers	✅ 6 providers	1 (via LiteLLM proxy)	Via wrappers	Via wrappers
Multi-channel	4 (Matrix, Terminal, WS, ACP)	10+ (Telegram, WhatsApp, Discord, Signal…)	❌	❌
Plugin hooks	✅ Lifecycle hooks	Skill-based	Via callbacks	❌
Cost tracking	✅ Built-in per-turn	Via LiteLLM	❌	❌
Browser automation	✅ Playwright (headless)	✅ Playwright (multi-tab, profiles)	❌	❌
Mobile device integration	❌	✅ Node pairing	❌	❌
Community ecosystem	New	✅ Active + ClawHub	✅ Large	Growing
Codebase	~23K lines Python	~145K lines TypeScript	~300K+	~50K+

Where March wins

• Memory that doesn't degrade — Rolling context + session memory means your agent remembers decisions from turn 3 at turn 100. OpenClaw and LangChain lose this during compaction.
• Fault isolation — mpAgent sub-agents run in separate OS processes. A crash stays contained. Every other framework runs sub-agents in-process.
• No proxy dependency — March talks directly to Bedrock, Anthropic, OpenAI, Ollama, OpenRouter. OpenClaw requires a LiteLLM proxy for all LLM calls.
• Auditable — 23K lines. You can read the entire codebase in an afternoon.

Where OpenClaw wins

• Channel breadth — 10+ messaging platforms vs March's 4. If you need Telegram, WhatsApp, or Discord, OpenClaw has it.
• Browser & device integration — Multi-tab browser profiles, Chrome extension relay, mobile camera/location/notifications.
• Maturity — Production-tested with an active community and skill marketplace.

Benchmark details: March achieves identical goal retention (100%) to OpenClaw across all test scenarios with a 6.2× smaller codebase. See full benchmark report.

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Supported LLM Providers

Provider	Config key	Notes
OpenAI	openai	GPT-4o, GPT-4, etc.
Anthropic	anthropic	Claude Opus, Sonnet, Haiku
AWS Bedrock	bedrock	Claude, Llama, Mistral via AWS
Ollama	ollama	Local models, no API key
OpenRouter	openrouter	Multi-provider gateway
LiteLLM	litellm	Universal proxy

Switch providers per-session or per-sub-agent. No code changes needed.

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Development

git clone https://github.com/camopel/March.git
cd March
pip install -e ".[dev]"
pytest                        # 785 tests

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Design Philosophy

1. Memory is curation, not accumulation. The value of memory isn't how much you store — it's how well you filter. March compresses, deduplicates, and preserves only what matters.

2. Isolation prevents corruption. Sub-agents run in separate processes with their own memory. A rogue sub-agent can't pollute the parent's context or crash the main loop.

3. Explicit beats implicit. /rmb lets you decide what's worth remembering. No black-box embeddings, no stale vector search results sneaking into your context.

4. Small is fast. 23K lines means you can read the entire codebase in an afternoon. Every abstraction earns its place.

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License

MIT