Show HN: Agents Made Simple (github.com)

Install

  pip install flashlearn

Simply take user inputs, API responses, and calculations from other tools and feed them to FlashLearn.

  user_inputs = [{"query": "When was python launched?"}]

A skill is an LLM’s ability to perform a task, containing all the necessary information. You can create your own, use predefined samples, or generate them automatically from example data.

  ConvertToGoogleQueries = {
    "skill_class": "GeneralSkill",
    "system_prompt": "Exactly populate the provided function definition",
    "function_definition": {
      "type": "function",
      "function": {
        "name": "ConvertToGoogleQueries",
        "description": "Convert the given question into between 1 and n google queries to answer the given question.",
        "strict": True,
        "parameters": {
          "type": "object",
          "properties": {
            "google_queries": {
              "type": "array",
              "items": {"type": "string"}
            }
          },
          "required": ["google_queries"],
          "additionalProperties": False
        }
      }
    }
  }

Load the skill, create tasks (a list of dictionaries), and run them in parallel. Results are easy to parse in downstream steps.

  skill = GeneralSkill.load_skill(ConvertToGoogleQueries)
  tasks = skill.create_tasks([{"query": "User's query"}])
  results = skill.run_tasks_in_parallel(tasks)

The output is a dictionary, where each key corresponds to an index in the original list. This lets you keep track of results easily.

  flash_results = {'0': {'google_queries': ["QUERY_1", "QUERY_2"]}}

Use the structured JSON output in your next steps.

  queries = flash_results["0"]["google_queries"]
  results = SimpleGoogleSearch(GOOGLE_API_KEY, GOOGLE_CSE_ID).search(queries)
  msgs = [
    {"role": "system", "content": "insert links from search results in response to quote it"},
    {"role": "user", "content": str(results)},
    {"role": "user", "content": "When was python launched?"}
  ]
  print(client.chat.completions.create(model=MODEL_NAME, messages=msgs).choices[0].message.content)