diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1.txt
new file mode 100644
index 0000000..8f17ade
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1.txt
@@ -0,0 +1,557 @@
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\ No newline at end of file
diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10.txt
new file mode 100644
index 0000000..e12c759
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10.txt
@@ -0,0 +1,166 @@
+ 15 
+ 10 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein100.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein100.txt
new file mode 100644
index 0000000..cec0c7b
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein100.txt
@@ -0,0 +1,1516 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1000.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1000.txt
new file mode 100644
index 0000000..2021148
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein1000.txt
@@ -0,0 +1,15016 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10000.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10000.txt
new file mode 100644
index 0000000..e1905dd
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein10000.txt
@@ -0,0 +1,10002 @@
+ 1 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein20.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein20.txt
new file mode 100644
index 0000000..5151bad
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein20.txt
@@ -0,0 +1,316 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein250.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein250.txt
new file mode 100644
index 0000000..6a0f5a3
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein250.txt
@@ -0,0 +1,3766 @@
+ 15 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein30.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein30.txt
new file mode 100644
index 0000000..5949352
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein30.txt
@@ -0,0 +1,466 @@
+ 15 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein40.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein40.txt
new file mode 100644
index 0000000..4599908
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein40.txt
@@ -0,0 +1,616 @@
+ 15 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein50.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein50.txt
new file mode 100644
index 0000000..827027a
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein50.txt
@@ -0,0 +1,766 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein500.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein500.txt
new file mode 100644
index 0000000..67a4470
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein500.txt
@@ -0,0 +1,7516 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein60.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein60.txt
new file mode 100644
index 0000000..edf67d8
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein60.txt
@@ -0,0 +1,916 @@
+ 15 
+ 60 
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein70.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein70.txt
new file mode 100644
index 0000000..2f362f0
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein70.txt
@@ -0,0 +1,1066 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein80.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein80.txt
new file mode 100644
index 0000000..959ce4d
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein80.txt
@@ -0,0 +1,1216 @@
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diff --git a/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein90.txt b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein90.txt
new file mode 100644
index 0000000..5f91c70
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Euclidian_Steiner_Benchmarks/estein90.txt
@@ -0,0 +1,1366 @@
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diff --git a/iohblade/benchmarks/combinatorics/Graph_Coloring_Benchmarks/gcol1.txt b/iohblade/benchmarks/combinatorics/Graph_Coloring_Benchmarks/gcol1.txt
new file mode 100644
index 0000000..97b2c48
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/Graph_Coloring_Benchmarks/gcol1.txt
@@ -0,0 +1,2488 @@
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+e 91 97
+e 91 99
+e 91 100
+e 92 93
+e 92 96
+e 92 98
+e 92 100
+e 93 94
+e 94 95
+e 94 96
+e 94 100
+e 95 96
+e 95 98
+e 95 99
+e 95 100
+e 96 97
+e 96 100
+e 97 98
+e 97 100
diff --git a/iohblade/benchmarks/combinatorics/__init__.py b/iohblade/benchmarks/combinatorics/__init__.py
index 293e2e1..67698f7 100644
--- a/iohblade/benchmarks/combinatorics/__init__.py
+++ b/iohblade/benchmarks/combinatorics/__init__.py
@@ -1,4 +1,11 @@
 from .erdos_min_overlap import ErdosMinOverlap
+from .euclidian_steiner_tree import EuclidianSteinerTree
+from .graph_coloring import GraphColoring
 from .get_erdos_problem import get_combinatorics_problems
 
-__all__ = ["ErdosMinOverlap", "get_combinatorics_problems"]
+__all__ = [
+    "ErdosMinOverlap",
+    "get_combinatorics_problems",
+    "EuclidianSteinerTree",
+    "GraphColoring",
+]
diff --git a/iohblade/benchmarks/combinatorics/euclidian_steiner_tree.py b/iohblade/benchmarks/combinatorics/euclidian_steiner_tree.py
new file mode 100644
index 0000000..9ce1451
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/euclidian_steiner_tree.py
@@ -0,0 +1,182 @@
+import math
+from pathlib import Path
+from statistics import mean
+from dataclasses import dataclass
+
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+from iohblade.misc.prepare_namespace import prepare_namespace
+
+
+@dataclass
+class Location:
+    id: int
+    x: float
+    y: float
+
+    def vectorise(self) -> list[float]:
+        return [self.x, self.y]
+
+    def __repr__(self) -> str:
+        return f"Point(id: {self.id}, x: {self.x}, y: {self.y})"
+
+    def distance_to(self, other: "Location"):
+        return math.hypot(self.x - other.x, self.y - other.y)
+
+
+class EuclidianSteinerTree(Problem):
+
+    def __init__(self, benchmark_id: int, tolerance=1e-6):
+        """
+        ## Euclidian Steiner Tree Benchmark:
+            Implements a Eucldian Steiner Tree Algorithm, which optimises the mimimum spanning tree, but with extra points.
+            Adding these points allows for shorts MST connecting each of the nodes. This benchmarks takes the set of points,
+            runs mst on it and on points + steiner_points, and return their ratio. Optimisation goal: min mst(steiner_points + points)/mst(points).
+
+        ## Args:
+        `benchmark_id: int` A benchmark id, selects a benchmark from the available instances in [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 250, 500, 1000, 10000]
+        `tolerance: float (10^-6)`: A tolerance to limit how close two points can be, stops algorithms from generating optimisers that may generate float-overflow.
+        """
+        self.benchmark = (
+            Path(__file__)
+            .resolve()
+            .parent.joinpath(f"Euclidian_Steiner_Benchmarks/estein{benchmark_id}.txt")
+        )
+        self.points = {}
+        self.tolerance = tolerance
+        self._read_file()
+        Problem.__init__(self, name=f"EucildianSteinerTree-n{20}")
+        self.best_so_far = [float("nan")] * len(self.points)
+        self.minimisation = True
+
+        self.task_prompt = f"""
+Write a python class with function `__call__`, that generates optimal Steiner Points for a given set of point, in order to optimise their Minimum Spanning Tree distance.
+- The class must initialise with 2 positional parameter:
+    1. `points: list[list[float][:2]]`: A list of locations, representing 2-D coordinates of nodes that needs to be connected using Minimum Spanning Tree using Steiner Points.
+    2. `tolerance: float (10^-6)`: A tolerance parameter that expects Steiner MST distance to be at a minimum `tolerance` units less than Normal MST.
+- The `__call__` method must return:
+    - `steiner_points : list[list[float][:2]]`: A list of steiner points.
+        - Each steiner point must be 2-D vector.
+- The optimisation goal is to minimise `steiner_mst/normal_mst` ratio where:
+    - `steiner_mst` is minimum spanning tree found using the provided points + returned `steiner_points`
+    - `normal_mst` is a mimumum spanning tree found using the only the provided points.
+- The returned fitness is going to be average fitness across {len(self.points)} distinct benchmarks.
+    """
+        self.example_prompt = """
+An example response can be
+---
+# Descripition:
+A novel algorithm for finding steiner points to optimise MST.
+# Code:
+```python
+import random
+
+class SteinerPointGenerator:
+    def __init__(self, points: list[list[float]], tolerance: float):
+        self.points = points[:]
+        self.steiner_points = []
+        self.tolerance = tolerance
+
+    def __call__(self):
+        # Find steiner points.
+        return self.steiner_points
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _read_file(self):
+        with open(self.benchmark) as f:
+            data = f.readlines()
+            count = int(data.pop(0))
+            for i in range(count):
+                points_count = int(data.pop(0))
+                points = []
+                for j in range(points_count):
+                    datum = data.pop(0).split()
+                    location = Location(j, float(datum[0]), float(datum[1]))
+                    points.append(location)
+                self.points[i] = points
+        return
+
+    def compute_mst_length(self, points: list[Location]):
+        n = len(points)
+        if n == 0:
+            return 0.0
+        in_mst = [False] * n
+        min_dist = [float("inf")] * n
+        min_dist[0] = 0.0
+        total = 0.0
+        for _ in range(n):
+            u = -1
+            best = float("inf")
+            for i in range(n):
+                if not in_mst[i] and min_dist[i] < best:
+                    best = min_dist[i]
+                    u = i
+            if u == -1:
+                break
+            in_mst[u] = True
+            total += best
+            for v in range(n):
+                if not in_mst[v]:
+                    d = points[u].distance_to(points[v])
+                    if d < min_dist[v]:
+                        min_dist[v] = d
+        return total
+
+    def evaluate(self, solution: Solution, logger=None):
+        code = solution.code
+        name = solution.name if solution.name else "EuclidianSteinerSolver"
+        try:
+            ns = prepare_namespace(code, self.dependencies)
+            compiled_code = compile(code, name, "exec")
+            exec(compiled_code, ns, ns)
+            cls = ns[name]
+            fitness = {}
+            for bench_id, points in self.points.items():
+                steiner_pts = cls(
+                    [point.vectorise() for point in points], self.tolerance
+                )()
+                steiner_points = []
+                for index, steiner_point in enumerate(steiner_pts):
+                    steiner_points.append(
+                        Location(
+                            len(self.points) + index + 1,
+                            steiner_point[0],
+                            steiner_point[1],
+                        )
+                    )
+                normal_mst = self.compute_mst_length(points)
+                steiner_mst = self.compute_mst_length(points + steiner_points)
+
+                assert (
+                    steiner_mst + self.tolerance >= normal_mst
+                ), f"Steiner's MST ({steiner_mst}) was close to or geater thn normal MST ({normal_mst})"
+                fitness[bench_id] = normal_mst / steiner_mst
+
+            solution = solution.set_scores(
+                mean(fitness.values()),
+                f"Got fitness {fitness} across {len(self.points)} benchmarks.",
+            )
+        except Exception as e:
+            solution = solution.set_scores(float("inf"), f"Got error {e}", e)
+        return solution
+
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    est = EuclidianSteinerTree(20)
+    print(est.get_prompt())
diff --git a/iohblade/benchmarks/combinatorics/graph_coloring.py b/iohblade/benchmarks/combinatorics/graph_coloring.py
new file mode 100644
index 0000000..fba50f9
--- /dev/null
+++ b/iohblade/benchmarks/combinatorics/graph_coloring.py
@@ -0,0 +1,112 @@
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+from iohblade.misc.prepare_namespace import prepare_namespace
+from pathlib import Path
+
+
+class GraphColoring(Problem):
+    def __init__(self, benchmark_id: str, logger=None):
+        self.nodes: list[int] = []
+        self.edges: list[tuple[int, int]] = []
+        self.benchmark = ""
+        self._load_data(benchmark_id)
+        Problem.__init__(self, name=self.benchmark, logger=logger)
+        self.minimisation = True
+
+        self.task_prompt = f"""
+Write a python class with function `__call__`, that generates optimal Graph Coloring for a given set of vertices and edges.
+- The class must initialise with 2 positional parameter:
+    1. `nodes: list[int]`: A list of nodes id'd 1...{self.nodes}.
+    2. `edges: list[tuple[int, int]]`: A list of edges in the undirected graph denotes as (u, v) where u and v are one of the `nodes`.
+- The `__call__` method must return:
+    - `color_mapping : dict[int, int]`: A node -> color mapping, i.e. `color_mapping[node_id] = color_id.
+        - Where color_id is `int`, in 1...n.
+- The optimisation goal is to minimise `n`, number of colours used, such that:
+    - The assertion of color_mapping[u] != color_mapping[v] for all (u,v) pairs in `edges` stands.
+- The returned fitness is going to be `len(set(color_mapping.values()))`.
+    """
+        self.example_prompt = """
+An example response can be
+---
+# Descripition:
+A novel algorithm for minimising the distinct colour count in graph colouring.
+# Code:
+```python
+import random
+
+class GraphColoring:
+    def __init__(self, nodes: list[int], edges: list[tuple[int, int]]):
+        self.nodes = nodes
+        self.edges = edges
+        self.color_mapping = {}
+        self.color_mapping[self.nodes[0]] = 1
+
+    def __call__(self):
+        # Find steiner points.
+        return self.color_mapping
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _load_data(self, benchmark_id: str):
+        path = (
+            Path(__file__)
+            .resolve()
+            .parent.joinpath(f"Graph_Coloring_Benchmarks/gcol{benchmark_id}.txt")
+        )
+        self.benchmark = f"Graph-Coloring-{benchmark_id}"
+        data = []
+        with open(path) as f:
+            data = f.readlines()
+        for datum in data:
+            datum = datum.split()
+            if len(datum) == 4:
+                self.nodes = list(range(1, int(datum[2]) + 1))
+            if len(datum) == 3:
+                assert datum[0] == "e"
+                u = int(datum[1])
+                v = int(datum[2])
+                self.edges.append((u, v))
+
+    def evaluate(self, solution: Solution, logger=None):
+        code = solution.code
+        name = solution.name if solution.name else "GraphColoring"
+        try:
+            compiled_code = compile(code, name, "exec")
+            ns = prepare_namespace(code, self.dependencies)
+            exec(compiled_code, ns, ns)
+            cls = ns[name]
+            coloring = cls(self.nodes, self.edges)()
+
+            for u, v in self.edges:
+                colorU = coloring[u]
+                colorV = coloring[v]
+                assert (
+                    colorU != colorV
+                ), f"Colours on nodes {u}, and {v} are same, while edge ({u}, {v}) exists."
+
+            score = float(len(set(coloring.values())))
+            solution = solution.set_scores(
+                score, f"Got score {score}, try minimising further."
+            )
+        except Exception as e:
+            solution = solution.set_scores(float("inf"), f"Encountered error {e}", e)
+        return solution
+
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    GraphColoring(benchmark_id=1)
diff --git a/iohblade/benchmarks/geometry/__init__.py b/iohblade/benchmarks/geometry/__init__.py
index d4ef9b5..73fed4f 100644
--- a/iohblade/benchmarks/geometry/__init__.py
+++ b/iohblade/benchmarks/geometry/__init__.py
@@ -2,6 +2,7 @@
 from .heilbronn_triangle import HeilbronnTriangle
 from .heilbronn_convex_region import HeilbronnConvexRegion
 from .kissing_number_11d import KissingNumber11D
+from .spherical_code import SphericalCode
 from .get_geometry_problems import (
     get_heilbronn_convex_region_problems,
     get_kissing_number_11D_problems,
@@ -18,4 +19,5 @@
     "get_heilbronn_triangle_problems",
     "get_heilbronn_convex_region_problems",
     "get_kissing_number_11D_problems",
+    "SphericalCode",
 ]
diff --git a/iohblade/benchmarks/geometry/spherical_code.py b/iohblade/benchmarks/geometry/spherical_code.py
new file mode 100644
index 0000000..18d3464
--- /dev/null
+++ b/iohblade/benchmarks/geometry/spherical_code.py
@@ -0,0 +1,177 @@
+import math
+from typing import Optional
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+from iohblade.misc.prepare_namespace import prepare_namespace
+
+
+class SphericalCode(Problem):
+    """
+    Sherical Code is a `maximisation` problem where 30 distinct points are projected on to a
+    unit sphere, and the optimisation goil is to maximise the minimum pairwise angle.
+    Best Known: n=30: 0.6736467551690225
+    """
+
+    def __init__(
+        self,
+        n_points: int = 30,
+        best_known: Optional[float] = 0.6736467551690225,
+        tolerance: float = 1e-12,
+        logger=None,
+    ):
+        self.n_points = n_points
+        self.best_known = best_known
+        self.tolerance = tolerance
+        self.minimisation = False
+        self.logger = logger
+        Problem.__init__(
+            self,
+            logger=logger,
+            name=f"spherical-code-n30",
+            dependencies=["scipy", "shapely"],
+        )
+        ## Set prompts:
+        self.task_prompt = (
+            f"""
+Write a python class with function `__call__`, that generate a solution for Spherical Code Problem on a unit sphere.
+- The class must initialise with 2 positional parameters:
+    1. n_points: Number of 3-D points that __call__ returns.
+    2. tolerance: The minimum allowed distance between any 2 points on sphere.
+- The `__call__` method must return:
+    - `points : list[tuple[float, float, float]]`: A list of {self.n_points} 3-D points as solution to the problem.
+- The optimisation goal is to maximize the minimum pairwise angle given by:
+
+"""
+            + "\\[\\theta_{\\min} = \\min_{i < j} \\cos^{-1}(\\braket{p_i, p_j})\\]"
+        )
+
+        self.example_prompt = f"""
+An example response can be
+---
+# Descripition: 
+A random selection algorithm for Spherical Code Solver.
+# Code:
+```python
+import random
+
+class SphericalCodeSolver:
+    def __init__(self, n_points, tolerance):
+        self.n_points = n_points
+        self.tolerance = tolerance
+
+    def __call__(self) -> list[tuple[float, float, float]]:
+        points = []
+        for _ in range(self.n_points):
+            points.append(
+                (random.random(), randm.random(), random.random())
+            )
+        return points
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _check_dimension(self, points: list[tuple[float, float, float]]):
+        if len(points) != self.n_points:
+            raise ValueError(f"Expeceted {self.n_points} points, got {len(points)}.")
+        for point in points:
+            if len(point) != 3:
+                raise ValueError(
+                    f"Expected each point to be 3-D, got {point}, which is {len(point)}D."
+                )
+
+    def _get_angle(
+        self, point1: tuple[float, float, float], point2: tuple[float, float, float]
+    ) -> float:
+        dot_product = 0
+        for i in range(3):
+            dot_product += point1[i] * point2[i]
+            dot_product = max(-1.0, min(1.0, dot_product))
+        try:
+            return math.acos(dot_product)
+        except:
+            raise ValueError(f"Got domain error for point {point1} . {point2}.")
+
+    def _get_min_angle(self, points: list[tuple[float, float, float]]):
+        min_angle = float("inf")
+        for i in range(len(points)):
+            for j in range(i + 1, len(points)):
+                theta = self._get_angle(points[i], points[j])
+                min_angle = min(min_angle, theta)
+        return min_angle
+
+    def _get_unit_vetor(
+        self, point: tuple[float, float, float]
+    ) -> tuple[float, float, float]:
+        magnitude = 0
+        for i in range(3):
+            magnitude += point[i] ** 2
+        magnitude = magnitude**0.5
+        return (
+            point[0] / magnitude,
+            point[1] / magnitude,
+            point[2] / magnitude,
+        )
+
+    def _check_tolerance(self, points: list[tuple[float, float, float]]):
+        for i in range(len(points)):
+            for j in range(i + 1, len(points)):
+                distance = (
+                    (points[i][0] - points[j][0]) ** 2
+                    + (points[i][1] - points[j][1]) ** 2
+                    + (points[i][2] - points[j][2]) ** 2
+                ) ** 0.5
+                if distance < self.tolerance:
+                    raise ValueError(
+                        f"Point {points[i]} and {points[j]} are closer than allowed tolerance on sphere."
+                    )
+
+    def evaluate(self, solution: Solution, logger=None):
+        name = solution.name if solution.name else "SphericalCode"
+        code = solution.code
+
+        try:
+            if code:
+                local_ns = {}
+                global_ns = prepare_namespace(code, allowed=self.dependencies)
+                compiled_code = compile(code, name, "exec")
+
+                exec(compiled_code, global_ns, local_ns)
+                cls = local_ns[name]
+                points = cls(self.n_points, self.tolerance)()
+
+                self._check_dimension(points)
+                points = [self._get_unit_vetor(point) for point in points]
+                self._check_tolerance(points)
+                min_angle = self._get_min_angle(points)
+                solution = solution.set_scores(
+                    min_angle,
+                    f"Got min angle {min_angle}, best known is {self.best_known}.",
+                )
+            else:
+                raise NotImplementedError(
+                    "Code not extractable, make sure code is encased in code block: ```."
+                )
+        except Exception as e:
+            solution = solution.set_scores(
+                -float("inf"), feedback=f"Got an error: {e}", error=e
+            )
+        return solution
+
+    def test(self, solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    sc = SphericalCode()
+    print(sc.get_prompt())
diff --git a/iohblade/benchmarks/logistics/A-n53-k7.json b/iohblade/benchmarks/logistics/A-n53-k7.json
new file mode 100644
index 0000000..7715749
--- /dev/null
+++ b/iohblade/benchmarks/logistics/A-n53-k7.json
@@ -0,0 +1 @@
+{"customers":[{"x":3,"y":77,"demand":30,"id":34},{"id":43,"demand":26,"x":85,"y":23},{"id":47,"y":73,"x":56,"demand":25},{"id":21,"y":87,"x":27,"demand":13},{"demand":23,"x":39,"id":18,"y":23},{"y":71,"demand":5,"id":52,"x":44},{"id":40,"demand":1,"y":44,"x":12},{"x":3,"y":45,"id":4,"demand":14},{"x":83,"demand":24,"y":86,"id":11},{"x":18,"demand":16,"y":8,"id":35},{"demand":18,"y":79,"x":50,"id":28},{"y":60,"x":35,"id":2,"demand":2},{"y":49,"demand":3,"id":23,"x":52},{"id":27,"x":82,"y":90,"demand":18},{"demand":18,"id":17,"x":75,"y":10},{"x":18,"y":1,"demand":13,"id":53},{"y":21,"id":24,"demand":6,"x":95},{"id":5,"x":42,"demand":2,"y":50},{"demand":2,"y":9,"x":96,"id":20},{"x":30,"demand":7,"y":6,"id":25},{"x":82,"id":30,"y":73,"demand":2},{"demand":20,"id":15,"y":19,"x":1},{"x":48,"demand":10,"y":49,"id":29},{"demand":20,"x":29,"id":7,"y":96},{"id":10,"demand":7,"y":30,"x":36},{"x":30,"y":6,"id":12,"demand":23},{"x":64,"y":62,"demand":9,"id":31},{"id":46,"x":71,"demand":15,"y":34},{"y":16,"x":14,"id":22,"demand":22},{"id":48,"x":37,"demand":22,"y":37},{"id":44,"x":93,"y":30,"demand":21},{"y":51,"demand":2,"id":37,"x":88},{"y":86,"id":36,"demand":23,"x":53},{"id":9,"y":77,"demand":26,"x":54},{"id":26,"demand":1,"x":18,"y":40},{"x":13,"y":2,"id":14,"demand":25},{"y":91,"x":62,"demand":6,"id":19},{"demand":2,"x":68,"y":67,"id":45},{"id":32,"y":78,"x":34,"demand":10},{"demand":8,"x":83,"id":33,"y":6},{"y":29,"x":55,"demand":13,"id":13},{"id":42,"x":36,"y":17,"demand":4},{"y":46,"id":3,"x":79,"demand":12},{"id":38,"demand":18,"y":51,"x":77},{"id":39,"y":89,"demand":22,"x":58},{"x":3,"id":6,"demand":17,"y":40},{"demand":2,"x":47,"y":30,"id":8},{"y":1,"demand":3,"id":16,"x":98},{"id":50,"x":88,"demand":3,"y":69},{"x":70,"y":88,"demand":8,"id":41},{"demand":3,"x":95,"id":51,"y":42},{"y":20,"id":49,"demand":19,"x":78},{"y":63,"demand":0,"x":24,"id":1}],"vehicleCapacity":100,"optimal":1010,"fleetSize":7,"benchmark":"A-n53-k7"}
\ No newline at end of file
diff --git a/iohblade/benchmarks/logistics/__init__.py b/iohblade/benchmarks/logistics/__init__.py
new file mode 100644
index 0000000..2626677
--- /dev/null
+++ b/iohblade/benchmarks/logistics/__init__.py
@@ -0,0 +1,4 @@
+from .vrp import VehicleRoutingProblem
+from .tsp import TravelingSalesmanProblem
+
+__all__ = ["VehicleRoutingProblem", "TravelingSalesmanProblem"]
diff --git a/iohblade/benchmarks/logistics/tsp.py b/iohblade/benchmarks/logistics/tsp.py
new file mode 100644
index 0000000..eb9a15d
--- /dev/null
+++ b/iohblade/benchmarks/logistics/tsp.py
@@ -0,0 +1,153 @@
+import json
+import math
+from pathlib import Path
+from dataclasses import dataclass
+
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+from iohblade.misc.prepare_namespace import prepare_namespace
+
+
+@dataclass
+class Location:
+    id: int
+    x: float
+    y: float
+
+    def vectorise(self):
+        return [self.id, self.x, self.y]
+
+    def distance_to(self, other: "Location"):
+        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5
+
+
+class TravelingSalesmanProblem(Problem):
+    def __init__(self, benchmark):
+        self.customers: list[Location] = []
+        self.best_known = float("inf")
+        self.benchmark = ""
+        self.minimisation = True
+        self._readfile(benchmark)
+
+        Problem.__init__(self, name=f"TSP-{self.benchmark}")
+
+        self.task_prompt = """
+Write a python class with function `__call__`, that generate a solution for Traveling Salesman Problem.
+- The class must initialise with 1 positional parameter:
+    1. `locations`: A list of locations, representing customers, that a traveling salesman must visit.
+- `Note`: Each of the `locations` are represented by list corresonding [id, x-coordinate, y-coordinate],
+- The `__call__` method must return:
+    - `paths : list[int]`: A list of customer `id`s, representing the path.
+        - Each customer must only be served once.
+        - No customer must be left un-served.
+- The optimisation goal is to minimise total distance travelled by the salesman."""
+
+        self.example_prompt = f"""
+An example response can be
+---
+# Descripition: 
+A random selection algorithm for Capacited Vehicle Routing Problem.
+# Code:
+```python
+import random
+
+class TravelingSalesmanSolver:
+    def __init__(self, customers):
+        self.customers = customers
+
+    def __call__(self):
+        ids = list(map(lambda customer: customer[0], self.customers))
+        random.shuffle(ids)
+        return ids
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _readfile(self, benchmark: str):
+        path = Path(__file__).resolve().parent.joinpath(f"{benchmark}.json")
+        with open(path, "r") as f:
+            data = "\n".join(f.readlines())
+            data = json.loads(data)
+            customers = data["customers"]
+
+            for customer in customers:
+                if customer["demand"] == 0:
+                    pass
+                else:
+                    self.customers.append(
+                        Location(
+                            customer["id"],
+                            customer["x"],
+                            customer["y"],
+                        )
+                    )
+            self.lookup_table = dict(
+                [(location.id, location) for location in self.customers]
+            )
+            self.benchmark = data["benchmark"]
+
+    def _check_accuracy(self, path: list[int]):
+        unknown = [item for item in path if item not in self.lookup_table]
+        if unknown:
+            raise ValueError(f'Location ids {", ".join(map(str, unknown))} unknown.')
+        missing = [item for item in self.lookup_table.keys() if item not in path]
+        if missing:
+            raise ValueError(f'Unserved customers {", ".join(map(str, missing))}.')
+        if len(path) != len(set(path)):
+            raise ValueError(f"Some customers were revisited.")
+
+    def _transform_to_location_list(self, paths: list[int]) -> list[Location]:
+        return list(map(lambda customer: self.lookup_table[customer], paths))
+
+    def _calculate_length(self, path: list[int]) -> float:
+        self._check_accuracy(path)
+        location_path = self._transform_to_location_list(path)
+        distance = 0
+        previous = location_path[0]
+        for customer in location_path[1:] + [previous]:
+            distance += previous.distance_to(customer)
+            previous = customer
+        return distance
+
+    def evaluate(self, solution: Solution, logger=None):
+        name = solution.name if solution.name else "TSPSolver"
+        code = solution.code
+        try:
+            local_ns = {}
+            global_ns = prepare_namespace(code, self.dependencies)
+            compiled_code = compile(code, name, "exec")
+            exec(compiled_code, global_ns, local_ns)
+
+            cls = local_ns[name]
+            paths = cls([customer.vectorise() for customer in self.customers])()
+            length = self._calculate_length(paths)
+            self.best_known = min(
+                self.best_known,
+                length if not math.isfinite(length) else self.best_known,
+            )
+            solution = solution.set_scores(
+                length,
+                f"Got distance {length}, best known distance is {self.best_known}.",
+            )
+        except Exception as e:
+            solution = solution.set_scores(float("inf"), f"Got error {e}", e)
+        return solution
+
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    tsp = TravelingSalesmanProblem()
+    print(tsp.get_prompt())
diff --git a/iohblade/benchmarks/logistics/vrp.py b/iohblade/benchmarks/logistics/vrp.py
new file mode 100644
index 0000000..acd6ba2
--- /dev/null
+++ b/iohblade/benchmarks/logistics/vrp.py
@@ -0,0 +1,215 @@
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+from iohblade.problem import Problem
+from iohblade.solution import Solution
+from iohblade.misc.prepare_namespace import prepare_namespace
+
+
+@dataclass
+class Location:
+    id: int
+    x: float
+    y: float
+    demand: float
+
+    def vectorise(self):
+        return [self.id, self.x, self.y, self.demand]
+
+    def distance_to(self, other: "Location"):
+        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5
+
+
+class VehicleRoutingProblem(Problem):
+    def __init__(self, benchmark_id):
+        self.customers: list[Location] = []
+        self.depot: Location = Location(0, 0, 0, 0)
+        self.vehicle_capacity = 0.0
+        self.fleet_size = 0
+        self.best_known = float("inf")
+        self.benchmark = ""
+        self.minimisation = True
+        self._readfile(benchmark_id)
+
+        Problem.__init__(self, name=f"CVRP-{self.benchmark}")
+
+        self.task_prompt = """
+Write a python class with function `__call__`, that generate a solution for Capacitated Vehicle Routing Problem.
+- The class must initialise with 4 positional parameters:
+    1. `depot`: A location from where all vehicles start and end their journey.
+    2. `customers`: A list of locations, representing customers.
+    3. `fleet_size`: Number of vehicles that has to be used during the routine.
+    4. `vehicle_capacity`: The total capacity of vehicles (each vehicle assumed to be same).
+- `Note`: Both depot and each of customers are represented by list corresonding [id, x-coordinate, y-coordinate, demand],
+    demand for depot is 0.
+- The `__call__` method must return:
+    - `paths : list[list[int]]`: A list of `fleet_size` paths, each path is a list of customer's id.
+        - The sum of demand of customers in any of the path must not exceed the `vehicle_capacity`.
+        - Each customer must only be served once.
+        - No customer must be left un-served.
+        - Depot must not exist in the `paths`.
+- The optimisation goal is to minimise total distance travelled by the fleet of vehicles."""
+
+        self.example_prompt = f"""
+An example response can be
+---
+# Descripition: 
+A random selection algorithm for Capacited Vehicle Routing Problem.
+# Code:
+```python
+import random
+import math
+
+class CapacitatedVehicleRoutingProblem:
+    def __init__(self, depot, customers, fleet_size, vehicle_capacity):
+        self.depot = depot
+        self.customers = customers
+        self.fleet_size = fleet_size
+        self.vehicle_capacity = vehicle_capacity
+
+    def __call__(self):
+        customers = self.customers[:]
+        random.shuffle(customers)
+
+        paths = [[] for _ in range(self.fleet_size)]
+        loads = [0] * self.fleet_size
+
+        for cid, x, y, demand in customers:
+            for i in range(self.fleet_size):
+                if loads[i] + demand <= self.vehicle_capacity:
+                    paths[i].append(cid)
+                    loads[i] += demand
+                    break
+            else:
+                raise ValueError("No feasible assignment found")
+
+        return paths
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _readfile(self, benchmark_id: str):
+        path = Path(__file__).resolve().parent.joinpath(f"{benchmark_id}.json")
+        with open(path, "r") as f:
+            data = "\n".join(f.readlines())
+            data = json.loads(data)
+            customers = data["customers"]
+            vehicle_capacity = data["vehicleCapacity"]
+            best_known = data["optimal"]
+            fleet_size = data["fleetSize"]
+
+            for customer in customers:
+                if customer["demand"] == 0:
+                    self.depot = Location(
+                        customer["id"], customer["x"], customer["y"], customer["demand"]
+                    )
+                else:
+                    self.customers.append(
+                        Location(
+                            customer["id"],
+                            customer["x"],
+                            customer["y"],
+                            customer["demand"],
+                        )
+                    )
+            self.vehicle_capacity = vehicle_capacity
+            self.best_known = best_known
+            self.fleet_size = fleet_size
+            self.lookup_table = dict(
+                [
+                    (location.id, location)
+                    for location in (self.customers + [self.depot])
+                ]
+            )
+            self.benchmark = data["benchmark"]
+
+    def _check_accuracy(self, paths: list[list[int]]):
+        if len(paths) != self.fleet_size:
+            raise ValueError(f"Expected {self.fleet_size} paths, got {len(paths)}.")
+        flatten_list = sum(paths, [])
+        unknown = [item for item in flatten_list if item not in self.lookup_table]
+        if unknown:
+            raise ValueError(f'Location ids {", ".join(map(str, unknown))} unknown.')
+        missing = [
+            item
+            for item in self.lookup_table.keys()
+            if item not in flatten_list and item != self.depot.id
+        ]
+        if missing:
+            raise ValueError(f'Unserved customers {", ".join(map(str, missing))}.')
+        if self.depot.id in flatten_list:
+            raise ValueError(f"Depot inexpectedly found in path.")
+        if len(flatten_list) != len(set(flatten_list)):
+            raise ValueError(f"Some customers were revisited.")
+
+    def _transform_to_location_list(
+        self, paths: list[list[int]]
+    ) -> list[list[Location]]:
+        routine = []
+        for path in paths:
+            location_path = list(
+                map(lambda customer: self.lookup_table[customer], path)
+            )
+            routine.append(location_path)
+            filled = sum(map(lambda x: x.demand, location_path))
+            if filled > self.vehicle_capacity:
+                raise ValueError(
+                    f'Path {", ".join(map(str, path))}, exceeds vehicle capacity {self.vehicle_capacity}'
+                )
+        return routine
+
+    def _calculate_length(self, paths: list[list[int]]) -> float:
+        self._check_accuracy(paths)
+        location_paths = self._transform_to_location_list(paths)
+        distance = 0
+        for path in location_paths:
+            previous = self.depot
+            for location in path + [self.depot]:
+                distance += previous.distance_to(location)
+                previous = location
+        return distance
+
+    def evaluate(self, solution: Solution, logger=None):
+        name = solution.name
+        code = solution.code
+        try:
+            local_ns = {}
+            global_ns = prepare_namespace(code, self.dependencies)
+            compiled_code = compile(code, name, "exec")
+            exec(compiled_code, global_ns, local_ns)
+
+            cls = local_ns[name]
+            paths = cls(
+                self.depot.vectorise(),
+                [customer.vectorise() for customer in self.customers],
+                self.fleet_size,
+                self.vehicle_capacity,
+            )()
+            length = self._calculate_length(paths)
+            solution = solution.set_scores(
+                length,
+                f"Got distance {length}, best known distance is {self.best_known}.",
+            )
+        except Exception as e:
+            solution = solution.set_scores(float("inf"), f"Got error {e}", e)
+        return solution
+
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    vrp = VehicleRoutingProblem()
+    print(vrp.get_prompt())
diff --git a/iohblade/benchmarks/packing/__init__.py b/iohblade/benchmarks/packing/__init__.py
index 3bf1ddf..eb48914 100644
--- a/iohblade/benchmarks/packing/__init__.py
+++ b/iohblade/benchmarks/packing/__init__.py
@@ -1,6 +1,7 @@
 from .unit_square_packing import UnitSquarePacking
 from .rectangle_packing import RectanglePacking
 from .hexagon_packing import HexagonPacking
+from .circles import CirclePacking
 from .get_packing_problems import (
     get_square_packing_problems,
     get_hexagon_packing_problems,
@@ -11,6 +12,7 @@
     "UnitSquarePacking",
     "RectanglePacking",
     "HexagonPacking",
+    "CirclePacking",
     "get_rectangle_packing_problems",
     "get_square_packing_problems",
     "get_hexagon_packing_problems",
diff --git a/iohblade/benchmarks/packing/circles.bench b/iohblade/benchmarks/packing/circles.bench
new file mode 100644
index 0000000..e3d8ea1
--- /dev/null
+++ b/iohblade/benchmarks/packing/circles.bench
@@ -0,0 +1,41 @@
+40 11.55 14.14 16.33	 
+1.00	   
+1.02	   
+1.09	   
+1.28	   
+1.38	   
+1.45	   
+1.53	   
+1.60	   
+1.63	   
+1.66	   
+1.68	   
+1.72	   
+1.85	   
+2.05	   
+2.07	   
+2.57	   
+2.69	   
+2.75	   
+2.92	   
+2.94	   
+2.97	   
+3.10	   
+3.16	   
+3.44	   
+3.65	   
+3.67	   
+3.76	   
+3.76	   
+3.94	   
+3.97	   
+4.09	   
+4.13	   
+4.20	   
+4.21	   
+4.24	   
+4.35	   
+4.63	   
+4.72	   
+4.85	   
+4.96
\ No newline at end of file
diff --git a/iohblade/benchmarks/packing/circles.py b/iohblade/benchmarks/packing/circles.py
new file mode 100644
index 0000000..b27889c
--- /dev/null
+++ b/iohblade/benchmarks/packing/circles.py
@@ -0,0 +1,164 @@
+import math
+from pathlib import Path
+from dataclasses import dataclass
+
+from iohblade.solution import Solution
+from iohblade.problem import Problem
+from iohblade.misc.prepare_namespace import prepare_namespace
+
+
+@dataclass
+class Container:
+    x: float
+    y: float
+    radius: float
+
+    def contains_circle(self, x: float, y: float, radius: float, tolerance):
+        dist = ((self.x - x) ** 2 + (self.y - y) ** 2) ** 0.5
+        dist -= tolerance
+        if dist <= self.radius - radius:
+            return True
+        return False
+
+    def vectorise(self):
+        return [self.x, self.y, self.radius]
+
+    def __repr__(self) -> str:
+        return f"Container(x: {self.x}, y: {self.y}, r={self.radius})"
+
+
+class CirclePacking(Problem):
+    def __init__(self, tolerance=1e-12):
+        self.container = Container(0, 0, 0)
+        self.candidate_radii: list[float] = []
+        self.n_circles = 0
+        self.tolerance = tolerance
+        self._read_file()
+        Problem.__init__(self, name="circle_packing_n40")
+        self.minimisation = False
+        ## Prompts;
+        self.task_prompt = f"""
+Write a python class with function `__call__`, that generate a solution for Circle Packing Area Problem.
+- The class must initialise with 3 positional parameters:
+    1. `container : list[float][:3]`: A list of float representing a container as [x, y, r].
+    2. `candidate_radii : list[float][:{self.n_circles}]`: A list of possible radius to be placed inside the container.
+    3. `tolerance: float`: A tolerance factor, for all calculations. The circles should be further than this value for feasibility.
+- The `__call__` method must return:
+    - `circles : list[list[float][:3]]`: A list of circles, that are packed inside the `container`.
+        - Each circle is represented as [x, y, r].
+        - Each circle's radius `r` must be in `candidate_radii`.
+        - Each circle must be container fully in the container.
+        - No two circle must intersect, witin the tolerance.
+- The optimisation goal is to maximize the area of the container filled by circles. Given by:
+    \\[\\omega = \\max \\sum_i^{self.n_circles} \\alpha_i \\pi R_i^2\\]
+"""
+
+        self.example_prompt = f"""
+An example response can be
+---
+# Descripition: 
+A random selection algorithm for Circle Packing Area Problem.
+# Code:
+```python
+import random
+import math
+
+class CirclePackingSolver:
+    def __init__(self, container, candidate_radii, tolerance):
+        self.container = container
+        self.candidate_radii = candidate_radii
+
+    def __call__(self):
+    cs=[]
+    for r in self.candidate_radii:
+        for _ in range(100):
+            x=random.random()*self.container
+            y=random.random()*self.container
+            if all((x-x2)**2+(y-y2)**2>=(r+r2)**2 for x2,y2,r2 in cs):
+                cs.append((x,y,r))
+                break
+    return cs
+```
+"""
+        self.format_prompt = """
+Give an excellent and novel algorithm to solve this task and also give it a
+one-line description, describing the main idea. Give the response in the format:
+# Description: <short-description>
+# Code:
+```python
+<code>
+```
+"""
+
+    def _read_file(self):
+        path = Path(__file__).resolve().parent.joinpath("circles.bench")
+        with open(path) as f:
+            for line in f.readlines():
+                data = list(map(float, line.strip().split()))
+                if len(data) == 4:
+                    n, cx, cy, r = data
+                    self.n_circles = int(n)
+                    self.container = Container(cx, cy, r)
+                else:
+                    self.candidate_radii.append(data[0])
+
+    def _check_validity(self, circles: list[list[float]]):
+        for circle in circles:
+            assert (
+                len(circle) == 3
+            ), f"Expected each circle to be represented by [x, y, r], got {circle}."
+            assert (
+                circle[2] in self.candidate_radii
+            ), f"Unknown candidate with radius {circle[2]}"
+            assert self.container.contains_circle(
+                circle[0], circle[1], circle[2], self.tolerance
+            ), f"Circle at ({circle[0]}, {circle[1]}) with radius {circle[2]} is not contained in {self.container}."
+
+        radii = list(map(lambda x: x[2], circles))
+        assert len(radii) == len(
+            set(radii)
+        ), f"Unexpected use of more than one circle with same radius."
+
+        for i in range(len(circles)):
+            for j in range(i + 1, len(circles)):
+                dist = (
+                    (circles[i][0] - circles[j][0]) ** 2
+                    + (circles[i][1] - circles[j][1]) ** 2
+                ) ** 0.5
+                assert (
+                    dist - circles[i][2] - circles[j][2] >= self.tolerance
+                ), f"Circles (x = {circles[i][0]}, y = {circles[i][1]}, r = {circles[i][2]}) and (x = {circles[j][0]}, y = {circles[j][1]}, r = {circles[j][2]}) overlap."
+
+    def _calc_area(self, circles):
+        return sum(list(map(lambda x: math.pi * x[2] ** 2, circles)))
+
+    def evaluate(self, solution: Solution, logger=None):
+        name = solution.name if solution.name else "CirclePacking"
+        code = solution.code
+
+        try:
+            local_ns = {}
+            global_ns = prepare_namespace(code, self.dependencies)
+            compiled_code = compile(code, name, "exec")
+            exec(compiled_code, global_ns, local_ns)
+            cls = local_ns[name]
+            circles = cls(
+                self.container.vectorise(), self.candidate_radii, self.tolerance
+            )()
+            self._check_validity(circles)
+            area = self._calc_area(circles)
+            solution = solution.set_scores(area, f"Got sum of area = {area}")
+        except Exception as e:
+            solution = solution.set_scores(float("-inf"), f"Got error {e}", e)
+        return solution
+
+    def test(self, solution: Solution):
+        return self.evaluate(solution)
+
+    def to_dict(self):
+        return self.__dict__
+
+
+if __name__ == "__main__":
+    cp = CirclePacking()
+    print(cp.get_prompt())
diff --git a/iohblade/llm.py b/iohblade/llm.py
index db768a8..9152b9c 100644
--- a/iohblade/llm.py
+++ b/iohblade/llm.py
@@ -755,16 +755,19 @@ def _query(self, session: list[dict[str, str]], max_tries: int = 5) -> str:
         request = session[-1]["content"]
         for _ in range(max_tries):
             try:
-                if self.config is not None:
-                    response = self.llm.respond(request, config=self.config)
-                else:
-                    response = self.llm.respond(request)
+                response = (
+                    self.llm.respond(request, config=self.config)
+                    if self.config is not None
+                    else self.llm.respond(request)
+                )
+
+                text = "".join(str(chunk) for chunk in response)
                 response = re.sub(  # Remove thinking section, if avaiable.
-                    r"<think>.*?</think>", "", str(response), flags=re.DOTALL
+                    r"<think>.*?</think>", "", str(text), flags=re.DOTALL
                 )
                 return response
             except:
-                pass
+                time.sleep(0.2)
         return ""
 
     def __getstate__(self):
diff --git a/iohblade/misc/prepare_namespace.py b/iohblade/misc/prepare_namespace.py
index 7566d85..07b29b2 100644
--- a/iohblade/misc/prepare_namespace.py
+++ b/iohblade/misc/prepare_namespace.py
@@ -28,7 +28,24 @@ def _collect_imports(code: str):
 
 
 def _add_builtins_into(allowed_list):
-    allowed_list += ["math", "random", "statistics", "itertools", "operator", "heapq"]
+    allowed_list += [
+        "copy",
+        "math",
+        "heapq",
+        "cmath",
+        "random",
+        "bisect",
+        "typing",
+        "pprint",
+        "decimal",
+        "operator",
+        "functools",
+        "fractions",
+        "itertools",
+        "statistics",
+        "collections",
+        "dataclasses",
+    ]
 
 
 def prepare_namespace(code: str, allowed: list[str]) -> dict[str, Any]:
diff --git a/run_benchmarks/circle_packing.py b/run_benchmarks/circle_packing.py
new file mode 100644
index 0000000..86689ee
--- /dev/null
+++ b/run_benchmarks/circle_packing.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.packing import CirclePacking
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    ollama_llm = Ollama_LLM('gpt-oss:20b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    # mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    circle_packing = CirclePacking()
+
+    methods = []
+    for llm in [ollama_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=circle_packing.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/CirclePacking")
+    experiment = Experiment(
+        methods,
+        [circle_packing],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/run_benchmarks/cvrp.py b/run_benchmarks/cvrp.py
new file mode 100644
index 0000000..bb934c2
--- /dev/null
+++ b/run_benchmarks/cvrp.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.logistics import VehicleRoutingProblem
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    ollama_llm = Ollama_LLM('gpt-oss:20b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    # mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    cvrp = VehicleRoutingProblem('A-n53-k7')
+
+    methods = []
+    for llm in [ollama_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=cvrp.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/CapacitatedVehicleRoutingProblem")
+    experiment = Experiment(
+        methods,
+        [cvrp],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/run_benchmarks/euclidian_steiner_tree.py b/run_benchmarks/euclidian_steiner_tree.py
new file mode 100644
index 0000000..80314fe
--- /dev/null
+++ b/run_benchmarks/euclidian_steiner_tree.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.combinatorics import EuclidianSteinerTree
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    ollama_llm = Ollama_LLM('gemma3:12b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    # mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    est = EuclidianSteinerTree(20)
+
+    methods = []
+    for llm in [ollama_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=est.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/EuclidianSteinerProblem")
+    experiment = Experiment(
+        methods,
+        [est],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/run_benchmarks/graph_coloring.py b/run_benchmarks/graph_coloring.py
new file mode 100644
index 0000000..7c7887e
--- /dev/null
+++ b/run_benchmarks/graph_coloring.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.combinatorics import GraphColoring
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    ollama_llm = Ollama_LLM('gemma3:12b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    # mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    gc = GraphColoring(1)
+
+    methods = []
+    for llm in [ollama_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=gc.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/GraphColoring")
+    experiment = Experiment(
+        methods,
+        [gc],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/run_benchmarks/spherical_code.py b/run_benchmarks/spherical_code.py
new file mode 100644
index 0000000..b968fd2
--- /dev/null
+++ b/run_benchmarks/spherical_code.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.geometry import SphericalCode
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    # ollama_llm = Ollama_LLM('qwen2.5-coder:14b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    spherical_code = SphericalCode()
+
+    methods = []
+    for llm in [mlx_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=spherical_code.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/Spherical-Code")
+    experiment = Experiment(
+        methods,
+        [spherical_code],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/run_benchmarks/tsp.py b/run_benchmarks/tsp.py
new file mode 100644
index 0000000..9bda5da
--- /dev/null
+++ b/run_benchmarks/tsp.py
@@ -0,0 +1,43 @@
+from os import environ
+
+from iohblade.experiment import Experiment
+from iohblade.llm import Gemini_LLM, Ollama_LLM, LMStudio_LLM
+from iohblade.methods import LLaMEA
+from iohblade.loggers import ExperimentLogger
+
+from iohblade.benchmarks.logistics import TravelingSalesmanProblem
+
+
+if __name__ == "__main__":
+    budget = 10
+
+    # api_key = environ.get("GOOGLE_API_KEY")
+
+
+    ollama_llm = Ollama_LLM('gpt-oss:20b')
+    # gemini_llm = Gemini_LLM(api_key=api_key)
+    # mlx_llm = LMStudio_LLM('google/gemma-3-12b')
+
+    tsp = TravelingSalesmanProblem('A-n53-k7')
+
+    methods = []
+    for llm in [ollama_llm]:
+        method = LLaMEA(
+            llm,
+            n_parents=1,
+            n_offspring=1,
+            budget=budget,
+            minimization=tsp.minimisation,
+        )
+        methods.append(method)
+    logger = ExperimentLogger(f"results/TravelingSalesmanProblem")
+    experiment = Experiment(
+        methods,
+        [tsp],
+        runs=1,
+        budget=budget,
+        show_stdout=True,
+        exp_logger=logger,
+    )
+
+    experiment()
diff --git a/uv.lock b/uv.lock
index 31944aa..1b5aba8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -152,16 +152,16 @@ wheels = [
 
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+    { url = "https://files.pythonhosted.org/packages/45/8e/d79281f323e7469b060f15bd229e48d7cdd219559e67e71c013720a88340/alembic-1.18.3-py3-none-any.whl", hash = "sha256:12a0359bfc068a4ecbb9b3b02cf77856033abfdb59e4a5aca08b7eacd7b74ddd", size = 262282, upload-time = "2026-01-29T20:24:17.488Z" },
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+version = "6.1.0.dev20260126"
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+sdist = { url = "https://files.pythonhosted.org/packages/ac/00/55594ad932d9bd5c0d59341c51657e8c39e988039f9b773727ab80a2e535/altair-6.1.0.dev20260126.tar.gz", hash = "sha256:c418897dddf4af7327486899c3914b57f4c1fcbcf970f943aeb58d7305a1c849", size = 764169, upload-time = "2026-01-26T03:39:06.933Z" }
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+    { url = "https://files.pythonhosted.org/packages/01/71/afa432c768bd0492e7650e4e2734073dfe5b45f16ca7a244bace0d97bb4c/altair-6.1.0.dev20260126-py3-none-any.whl", hash = "sha256:4361195f736575cf19fb1458ea477a66d7513dddb241bf383b16606fa4ad516f", size = 795683, upload-time = "2026-01-26T03:39:04.908Z" },
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@@ -231,16 +231,19 @@ dependencies = [
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+sdist = { url = "https://files.pythonhosted.org/packages/88/61/50aef0587acd9dd8bf1b8b7fd7fbb25ba4c6ec5387a6ffc195a697951fcc/anthropic-0.77.1.tar.gz", hash = "sha256:a19d78ff6fff9e05d211e3a936051cd5b9462f0eac043d2d45b2372f455d11cd", size = 504691, upload-time = "2026-02-03T17:44:22.667Z" }
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+    { url = "https://files.pythonhosted.org/packages/2b/54/e83babf9833547c5548b4e25230ef3d62492e45925b0d104a43e501918a0/anthropic-0.77.1-py3-none-any.whl", hash = "sha256:76fd6f2ab36033a5294d58182a5f712dab9573c3a54413a275ecdf29e727c1e0", size = 397856, upload-time = "2026-02-03T17:44:20.962Z" },
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/89/03/a851e84fcbb85214dc637b6378121ef9a0dd61b4c65264675d8a5c9b1ae7/antlr4_python3_runtime-4.13.2-py3-none-any.whl", hash = "sha256:fe3835eb8d33daece0e799090eda89719dbccee7aa39ef94eed3818cafa5a7e8", size = 144462, upload-time = "2024-08-03T19:00:11.134Z" },
+]
 
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