Mwe-MCP vs Trajeckt

Mwe-MCP and Trajeckt are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Mwe-MCP

The store lives on your own server as human-readable Markdown files, which means you can open a file, spot a hallucinated fact, and correct it without touching any agent code. Per-fragment access controls let you scope which agents or users can read or write each memory entry — so a household assistant and a work agent can coexist without leaking context across boundaries. The docs describe an overnight self-organizing pass that restructures the wiki without agent intervention. Where this breaks: teams expecting a managed cloud endpoint will find none — the vendor states AGPL self-hosted only. Standing up and maintaining the server is your problem.

Trajeckt

The tool sits as a runtime gateway between your agent and its tools, evaluating the sequence of calls rather than any individual call in isolation. The vendor states enforcement runs deterministically in approximately 1.6ms, outside the model itself, so policy decisions cannot be overridden by prompt injection. It is open-source under Apache-2.0 and self-hostable via Docker or Cargo. Where it fits well is MCP-compatible setups that need sequence-level rules without building a custom policy engine. Where it breaks is anywhere you need probabilistic or context-aware rules — this is deterministic enforcement, and the constraints you do not define ahead of time do not get caught.

Attribute	Mwe-MCP	Trajeckt
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (self-hosted)	Rust, Docker
Pros	Human-readable Markdown storage, so you can audit, correct, or delete any memory fragment with a text editor — without writing agent code or issuing API calls to fix a hallucinated fact that would otherwise silently propagate. Per-fragment ACL at the memory level, which means a single server instance can serve agents with different trust levels or different users without leaking cross-context data — avoiding the need to run separate memory servers per tenant. Agent-agnostic MCP interface, so any framework that speaks Model Context Protocol can attach without a custom adapter — swapping or adding agent frameworks does not require migrating the memory backend. Self-hosted under AGPL with no external dependency, which means your memory store does not go down when a third-party API has an outage and your data does not leave your infrastructure. API available, so agents that prefer direct programmatic access over the MCP layer have a path without being forced through a protocol they may not support natively.	Trajectory-aware blocking catches multi-step exfiltration chains where every individual call looks clean, so attacks that evade per-action filters get stopped before data leaves the environment. Deterministic enforcement at approximately 1.6ms per decision, per the vendor, so adding this layer to an agent's tool-call path does not introduce meaningful latency at typical request volumes. Runs outside the model and is not reachable by prompt injection, which means a compromised prompt cannot talk the policy engine into allowing a blocked sequence. Apache-2.0 license with self-hosted Docker and Cargo deployment paths, so your traffic and trajectory data stay inside your own infrastructure without a vendor dependency. Python SDK included alongside the core gateway, which means integrating trajeckt into an existing Python-based agent stack does not require wrapping a foreign-language binary by hand.
Cons	No hosted option exists — the vendor states AGPL self-hosted only. Teams without server infrastructure or ops capacity hit this wall before writing a single agent integration, and they switch to a managed memory service that trades auditability for a working endpoint. The overnight self-organizing pass runs on a fixed schedule rather than on demand. Agents that write high volumes of facts during the day work against a wiki that may be structurally stale until the next reorganization cycle — teams with real-time coherence requirements add a manual trigger layer or accept the lag. AGPL licensing means any commercial product that ships with mwe-mcp linked in must open-source the combined work. Teams building proprietary software review the license, conclude they cannot comply, and move to a permissively licensed alternative regardless of the technical fit.	Policy coverage is limited to sequences you define in advance: if an attacker constructs an exfiltration chain you did not model in the config, no rule fires and the traffic passes. Teams with a broad or evolving threat surface end up writing and maintaining a growing config corpus, at which point the 'lightweight' framing stops holding. The deterministic rule engine has no probabilistic or semantic layer — it cannot flag 'this trajectory looks unusual even if no explicit rule matches.' Teams that need anomaly detection on top of hard rules typically add a separate monitoring layer, which means maintaining two systems. At the point where your agent's allowed call graph is genuinely complex — branching on prior results, conditional paths, dynamic tool registration — encoding that graph as causal constraints becomes its own engineering problem. Teams with agents at that complexity level often move to a full policy engine that supports programmatic rule generation rather than static config files.

Bottom line

Mwe-MCP and Trajeckt are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Mwe-MCP and Trajeckt?

Mwe-MCP is Free and open source, while Trajeckt is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Mwe-MCP better than Trajeckt?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.