imandra 2.5.0

A CLI and API client library for interfacing with Imandra's web APIs

Imandra CLI and API client library

Imandra is a cloud-native automated reasoning engine for analysis of algorithms and data.

This package contains the imandra Python library for interacting with Imandra's web APIs. It includes:

• imandra.core, which provides programmatic access to Imandra X, Imandra's core reasoning engine.
• imandra.u.agents.* and imandra.u.reasoners.*, bindings to Imandra Universe Agents and Reasoners.
• imandra.ipl, tools for analysing Imandra Protocol Language (IPL) files.

If you're interested in developing Imandra X or IPL models, you may also want to see the Imandra documentation.

The imandra python API reference documentation is available here.

Authentication

First obtain an API key from https://universe.imandra.ai.

The Python library will read the API key from the first of:

1. The api_key parameter passed when instantiating a Client.
2. The IMANDRA_API_KEY environment variable.
3. The file $HOME/.config/imandra/api_key (MacOS and Linux) or %USERPROFILE%\AppData\Local\imandra\api_key (Windows)

Example: Imandra Core

First, ensure dependencies for the core module are installed. Note that imandra.core requires Python >= 3.12.

$ pip install 'imandra[core]'

$ ipython
...
In [1]: from imandra.core import Client

In [2]: client = Client()

In [3]: client.eval_src('let f x = if x > 0 then if x * x < 0 then x else x + 1 else x')
Out[3]: success: true

In [4]: result = client.verify_src('fun x -> x > 0 ==> f x > 0')

In [5]: result
Out[5]:
proved {
  proof_pp: "..."
}

In [6]: print(result.proved.proof_pp)
{ id = 1; concl = `|- x > 0 ==> f x > 0`;
  view =
  T_deduction {
    premises =
    [("p",
      [{ id = 0; concl = `|- x > 0 ==> f x > 0`;
         view = T_deduction {premises = []} }
        ])
      ]}
  }

In [7]: result = client.instance_src('fun x -> f x = 43')

In [8]: result
Out[8]:
sat {
  model {
    m_type: Instance
    src: "module M = struct\n\n  let x = 42\n\n end\n"
    artifact {
      kind: "cir.model"
      data: "..."
      api_version: "v8"
    }
  }
}

In [9]: print(result.sat.model.src)
module M = struct

  let x = 42

 end

In [10]: result = client.decompose('f')

In [11]: result
Out[11]:
artifact {
  kind: "cir.fun_decomp"
  data: "..."
  api_version: "v8"
}
regions_str {
  constraints_str: "not (x > 0)"
  invariant_str: "x"
  model_str {
    k: "x"
    v: "0"
  }
}
regions_str {
  constraints_str: "not (x * x < 0)"
  constraints_str: "x > 0"
  invariant_str: "x + 1"
  model_str {
    k: "x"
    v: "1"
  }
}
task {
  id {
    id: "task:decomp:rE3VSX-t5kbrrAksQ4saBrMUs2uHTXfu-CqeZunV9aE="
  }
  kind: TASK_DECOMP
}

Example: Imandra Universe reasoners

$ pip install imandra

$ ipython

In [1]: from imandra.u.reasoners.prover9 import Client

In [2]: client = Client()

In [3]: input = "formulas(sos).\n\n  e * x = x.\n  x'\'' * x = e.\n  (x * y) * z = x * (y * z).\n\n  x * x = e.\n\nend_of_list.\n\nformulas(goals).\n\n  x * y = y * x.\n\nend_of_list ...: ."

In [4]: result = client.eval(input)

In [5]: print(result['results'][0])
============================== Prover9 ===============================
Prover9 (64) version 2009-11A, November 2009.
Process 18 was started by universe on localhost,
Mon Jan  6 14:52:26 2025
The command was "/imandra-universe/prover9/bin/prover9 -t 45".
============================== end of head ===========================

============================== INPUT =================================

formulas(sos).
e * x = x.
x''' * x = e.
(x * y) * z = x * (y * z).
x * x = e.
end_of_list.

formulas(goals).
x * y = y * x.
end_of_list.

============================== end of input ==========================

...

============================== PROOF =================================

% Proof 1 at 0.01 (+ 0.00) seconds.
% Length of proof is 16.
% Level of proof is 7.
% Maximum clause weight is 11.000.
% Given clauses 12.

1 x * y = y * x # label(non_clause) # label(goal).  [goal].
2 e * x = x.  [assumption].
3 x''' * x = e.  [assumption].
4 (x * y) * z = x * (y * z).  [assumption].
5 x * x = e.  [assumption].
6 c2 * c1 != c1 * c2.  [deny(1)].
7 x''' * (x * y) = y.  [para(3(a,1),4(a,1,1)),rewrite([2(2)]),flip(a)].
8 x * (x * y) = y.  [para(5(a,1),4(a,1,1)),rewrite([2(2)]),flip(a)].
9 x * (y * (x * y)) = e.  [para(5(a,1),4(a,1)),flip(a)].
11 x'''''' * e = x.  [para(3(a,1),7(a,1,2))].
13 x''' * e = x.  [para(5(a,1),7(a,1,2))].
15 x''' = x.  [back_rewrite(11),rewrite([13(8)])].
16 x * e = x.  [back_rewrite(13),rewrite([15(3)])].
19 x * (y * x) = y.  [para(9(a,1),8(a,1,2)),rewrite([16(2)]),flip(a)].
24 x * y = y * x.  [para(19(a,1),8(a,1,2))].
25 $F.  [resolve(24,a,6,a)].

============================== end of proof ==========================

============================== STATISTICS ============================

Given=12. Generated=122. Kept=23. proofs=1.
Usable=8. Sos=3. Demods=12. Limbo=2, Disabled=14. Hints=0.
Kept_by_rule=0, Deleted_by_rule=0.
Forward_subsumed=99. Back_subsumed=0.
Sos_limit_deleted=0. Sos_displaced=0. Sos_removed=0.
New_demodulators=21 (0 lex), Back_demodulated=9. Back_unit_deleted=0.
Demod_attempts=770. Demod_rewrites=156.
Res_instance_prunes=0. Para_instance_prunes=0. Basic_paramod_prunes=0.
Nonunit_fsub_feature_tests=0. Nonunit_bsub_feature_tests=0.
Megabytes=0.06.
User_CPU=0.01, System_CPU=0.00, Wall_clock=0.

============================== end of statistics =====================

============================== end of search =========================

THEOREM PROVED

Example: Imandra Universe agents

$ pip install imandra[universe]

$ ipython

In [1]: from imandra.u.agents.code_logician.graph import GraphState
   ...: from imandra.u.agents.code_logician.command import RootCommand
   ...: from imandra.u.agents import create_thread_sync, get_remote_graph

In [2]: graph = get_remote_graph("code_logician")
   ...: create_thread_sync(graph)

In [3]: gs = GraphState()
   ...: src_code = """def g(x: int) -> int:
   ...:     if x > 22:
   ...:         return 9
   ...:     else:
   ...:         return 100 + x
   ...: 
   ...: def f(x: int) -> int:
   ...:     if x > 99:
   ...:         return 100
   ...:     elif 70 > x > 23:
   ...:         return 89 + x
   ...:     elif x > 20:
   ...:         return g(x) + 20
   ...:     elif x > -2:
   ...:         return 103
   ...:     else:
   ...:         return 99"""
   ...: gs = GraphState()
   ...: gs = gs.add_commands([
   ...:     RootCommand(type="init_state", src_code=src_code, src_lang="python"),
   ...:     RootCommand(type="gen_formalization_data"),
   ...:     RootCommand(type="gen_model"),
   ...: ])
   ...: res = await gs.run(graph)
   ...: gs = res[0]

In [4]: fstate = gs.last_fstate
   ...: fstate.status
Out[4]: Transparent

In [5]: print(fstate.iml_code)
let g (x : int) : int =
  if x > 22 then
    9
  else
    100 + x

let f (x : int) : int =
  if x > 99 then
    100
  else if 70 > x && x > 23 then
    89 + x
  else if x > 20 then
    g x + 20
  else if x > -2 then
    103
  else
    99

In [6]: gs2 = gs.add_commands([
   ...:     RootCommand(type="gen_region_decomps", function_name="f"),
   ...:     RootCommand(type="gen_test_cases", decomp_idx=0),
   ...: ])
   ...: res2 = await gs2.run(graph)
   ...: gs2 = res2[0]
   ...: test_cases = gs2.last_fstate.region_decomps[0].test_cases
   ...: test_cases['src']
Out[6]: 
[{'args': {'x': '100'},
  'expected_output': '100',
  'docstr': 'Constraints:\n    - `x >= 100`\nInvariant:\n    - `100`\n'},
 {'args': {'x': '24'},
  'expected_output': '113',
  'docstr': 'Constraints:\n    - `x >= 24`\n    - `x <= 69`\nInvariant:\n    - `89 + x`\n'},
 {'args': {'x': '23'},
  'expected_output': '29',
  'docstr': 'Constraints:\n    - `x = 23`\nInvariant:\n    - `9 + 20`\n'},
 {'args': {'x': '21'},
  'expected_output': '141',
  'docstr': 'Constraints:\n    - `x >= 21`\n    - `x <= 22`\nInvariant:\n    - `100 + x + 20`\n'},
 {'args': {'x': '0'},
  'expected_output': '103',
  'docstr': 'Constraints:\n    - `x >= (-1)`\n    - `x <= 20`\nInvariant:\n    - `103`\n'},
 {'args': {'x': '-2'},
  'expected_output': '99',
  'docstr': 'Constraints:\n    - `x <= (-2)`\nInvariant:\n    - `99`\n'},
 {'args': {'x': '70'},
  'expected_output': '29',
  'docstr': 'Constraints:\n    - `x >= 70`\n    - `x <= 99`\nInvariant:\n    - `9 + 20`\n'}]

Example: IPL

$ pip install imandra

$ ipython

In [1]: from imandra.ipl import Client

In [2]: client = Client()

In [3]: job_id = client.unsat_analysis('/path/to/model.ipl')

In [4]: client.status(job_id)
Out[4]: 'processing'

In [5]: client.wait(job_id)
Out[5]: 'done'

In [6]: data = client.data(job_id)

In [7]: print(data['content'].decode('ascii'))
For message flow `simple_orders_one`, unsat cores: []

CLI

The imandra package also adds an entry point called imandra-cli which exposes the imandra library functionality in a more discoverable way:

$ python3 -m venv ./my/venv
...
$ ./my/venv/pip install imandra
...
$ ./my/venv/bin/imandra-cli --help
usage: imandra [-h] auth,ipl,core,rule-synth,cfb ...

Imandra CLI

positional arguments:
  {auth,ipl,core,rule-synth,cfb}

optional arguments:
  -h, --help            show this help message and exit

On Windows, the entry point can be found as .\my\venv\Scripts\imandra-cli.exe.

CLI Authentication

This is the first step to start using the Imandra CLI. Our cloud environment requires a user account, which you can setup like this:

$ ./my/venv/bin/imandra-cli auth login

and follow the prompts to authenticate. This will create the relevant credentials in ~/.imandra (or %APPDATA%\imandra on Windows).

You should now be able to invoke CLI commands that require authentication.