That's gonna be a very, very, long answer. What's funny is that not much changed since 2022 (eoy) when the projected started; the models just got better, but we had a good chunk of the primitives since gpt-3.

What's more recent is the DbC contribution which I think is unique. It literally solved anything agent related I've thrown at it -- especially because I can chain contracts together and the guardrails propagate nicely.

I've built most the custom tools myself. For instance, not only perplexity was rendered useless by openai's web search, but openai's web search itself is not good enough for what you can customize yourself. To this end, I've built my own deep research agent. Here's a thread with some results from the first day it was working: https://x.com/futurisold/status/1931751644233945216

I'm also running a company, and we've built an e2e document generation just from contracts (3 contracts chained together in this case). Here's an output (sorry about the PDF rendering, that's not what we serve, it's just what I've quickly hacked for local dev): https://drive.google.com/file/d/1Va7ALq_N-fTYeumKhH4jSxsTrWD...

This was the input:

---

Prompt:

> I want the files to be analyzed and I am interested in finding patterns; feel free to make suggestions as well. I want to understand how different providers use their system prompts, therefore things like: what kind of tags do they use – are they XML, markdown, etc, are they prone toward sycophancy or trying to manipulate the user, are they using tools and if so how, etc. I want the tech report to deconstruct and synthesize and compare the information, find interesting patterns that would be hard to spot.

Generated instructions:

(a) Query: Conduct a comparative analysis of system prompts across major AI providers (OpenAI, Google, Anthropic, xAI, etc.) to identify structural patterns, linguistic frameworks, and operational constraints that shape AI behavior and responses.

(b) Specific Questions:

1. What syntactic structures and formatting conventions (XML, markdown, JSON, etc.) are employed across different AI system prompts, and how do these technical choices reflect different approaches to model instruction?

2. To what extent do system prompts encode instructions for deference, agreeability, or user manipulation, and how do these psychological frameworks vary between commercial and research-focused models?

3. How do AI providers implement and constrain tool usage in their system prompts, and what patterns emerge in permission structures, capability boundaries, and function calling conventions?

4. What ethical guardrails and content moderation approaches appear consistently across system prompts, and how do implementation details reveal different risk tolerance levels between major AI labs?

5. What unique architectural elements in specific providers' system prompts reveal distinctive engineering approaches to model alignment, and how might these design choices influence downstream user experiences?

---

Contracts were introduced in March in this post: https://futurisold.github.io/2025-03-01-dbc/

They evolved a lot since then, but the foundation and motivation didn't change.

Why is carrot the vegetablefication of apple?

  t.test(
     Symbol(['apple', 'banana', 'cherry', 'cat', 'dog']).map('convert all fruits to vegetables'),
     "list only has vegetable and cat,dog"
  )

You might enjoy Lotus: https://github.com/lotus-data/lotus

It takes all the core relational operators and makes an easy semantic version of each as a python dataframe library extension . Each call ends up being a 'model' point in case you also want to do fancier things later like more learning based approaches. Afaict, snowflake and friends are moving in this direction for their cloud SQLs as well.

We ended up doing something similar for louie.ai , where you use AI notebooks/dashboards/APIs (ex: MCP) to talk to your data (splunk, databricks, graph db, whatever), and it'll figure out symbolic + semantic operators based on the context. Super helpful in practice.

My 80% case here is:

- semantic map: "get all the alerts from splunk index xyz, add a column flagging anything suspicious and another explaining why" <--- generates an enriched dataframe

- semantic map => semantic reduce: "... then summarize what you found" <--- then tells you about it in natural text

Wanted to do just that, thank you

Yes, pretty much. We wanted it be functional from the start. Even low level, everything's functional (it's even called functional.py/core.py). We're using decorators everywhere. This helped a lot with refactoring, extending the framework, containing bugs, etc.

Very nice, bookmarked for later. Interestingly enough, we share the same timeline. ~2yo is when a lot of interesting work spawned as many started to tinker.

Thank you! I'm not familiar with that project, will take a look

I'm struggling to understand the question. I'll revisit this when I wake up since it's quite late here.

But is it also explainable or a magic black box?

Shortly, yes to all. We actually had an experiment going from theory of mind to emotion, but it's hanging right now since I feel the models aren't quite there yet and it yields diminish returns relative to effort. But could easily be revived. Minsky isn't my fav though, I'm leaning more toward Newell/Simon and friends from that generation.

+

Oh, thank you; refactoring artifacts. Fixed now.

Oh, definitely. I recommend you go for contracts. I've used something similar for a contract that iteratively "stitched together" a broken ontology graph. Here's some of the data models for inspiration -- you could have something similar for your ops, and write the contract to solve for one op, then apply the op, etc.

---

    class Merge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being merged.")
        relations: list[SubClassRelation] = Field(
            description="A list of superclass-subclass relations chosen from the existing two clusters in such a way that they merge."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Bridge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being bridged.")
        relations: list[SubClassRelation] = Field(
            description="A list of new superclass-subclass relations used to bridge the two clusters from the ontology."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Prune(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being pruned.")
        classes: list[str] = Field(description="A list of classes that are being pruned from the ontology.")
    
        @field_validator("indexes")
        @classmethod
        def is_unary(cls, v):
            if len(v) > 1:
                raise ValueError(
                    f"Unary op error: Invalid number of clusters: {len(v)}. The prune operation requires exactly one cluster."
                )
            return v
    
    
    class Operation(LLMDataModel):
        type: Merge | Bridge | Prune = Field(description="The type of operation to perform.")

---

I'd argue it's all of them. Contracts simply make better agents. I believe it also gives a very nice bias on how to talk about agents -- as apps obeying contracts. If you find time, please read this blog post; it gives the underlying motivation for using contracts in agent design: https://futurisold.github.io/2025-03-01-dbc/

Hey, I think I am not using this framework to its full potential, but I have been using it as my main framework for 2 years now. Advantages for me are, that I can easily develop integrations myself. I like the concept of contracts, that I can first make a detailed output model using their LLMDataModel class (an extension of pydantic), I can validate every field both syntactically and semantically using their semantic operations and raise exceptions in case of failures with meaningful messages (and those will be considered via the LLM for retries). So I like this clean separation of actual business logic and all things validation, hallucinations checking and I don’t have to worry about retries and error handling, because that is done by framework as long as I provide a good output data model. It makes my code much more clean and secure and it also makes it easier for me to analyse by just looking at the output model how secure it is. Also I liked that I can basically inject any python logic,be multimodal without overcomplicating everything with DAGs etc. I like having low level control of what is going on. Also once I started out building my main issue with most frameworks was speed. I wanted to create complex multistep workflows using LLMs and still be very fast and I can’t really stand when my code looks messy. I had a data set of 50 million news and a very small compute budget. After some experiments, decided to go with symbolicai, because it was easier for me to speed it up with batching my agent workflows.

Yes, that's correct. If using say openai, then every semantic ops are API calls to openai. If you're hosting a local LLM via llama.cpp, then obviously there's no inference cost other than that of hosting the model.

This will need a cache of some sort

Hallucination obstruction, I'd imagine. When you have an LLM create a formal system, it can be verified way easier than a general purpose one

We hear you. We might end up renaming it. In the paper we have a footnote about the name choice -- it's meant to credit the foundational work of Newell and Simon that inspired this project.

I did it for python and will come out with other languages afterwards

Did you use symbolicai for that?